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.

Producer Prices in Fiji increased 6.70 percent in March of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Fiji Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

In 2024, the Mexico exhibited the highest inflation-adjusted increase in house prices among the countries under observation. In the fourth quarter of the year, house prices in Mexico grew by nearly **** percent in real terms, whereas globally, prices declined by *** percent. These figures are based on the development of the real house price index, with 2010 chosen as a baseline year. When looking at the long-term index development, Colombia observed the biggest increase in prices in the region.

Norway Consumer Price Index (CPI): YoY: Tax Changes Adjustment: excl Temporary Changes in Energy Prices data was reported at 2.000 % in Oct 2018. This records a decrease from the previous number of 2.200 % for Sep 2018. Norway Consumer Price Index (CPI): YoY: Tax Changes Adjustment: excl Temporary Changes in Energy Prices data is updated monthly, averaging 1.700 % from Jan 2002 (Median) to Oct 2018, with 202 observations. The data reached an all-time high of 3.700 % in Sep 2008 and a record low of 0.400 % in Feb 2004. Norway Consumer Price Index (CPI): YoY: Tax Changes Adjustment: excl Temporary Changes in Energy Prices data remains active status in CEIC and is reported by Norges Bank. The data is categorized under Global Database’s Norway – Table NO.I012: Consumer Price Index: Tax Changes Adjustment: Percentage Change.

Producer Prices in Netherlands decreased 0.90 percent in May of 2025 over the same month in the previous year. This dataset provides - Netherlands Producer Prices Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset is restricted, for more information please contact the author. Data were collected from multiple sources:The Electricity & Co-Generation Regulatory AuthoritySaudi Electricity companyWeb news article (2015, December 28). Increase of Fuel, Electricity and Water prices. Retrieved from https://akhbaar24.argaam.com/article/detail/255091accessed on March 22, 2018.In October 1984, the government adopted a Tariff that increased with increasing consumption. The changes of Tariffs started in November 1984.Tariff approved by Council of Ministries 170 and become effective in October 2000. This Tariff remained effective for approximately ten years The residential, agricultural, mosques, and charitable societies remained unchanged till 2018In 2010, a new tariff for government, commercial, and industrial consumption came into force, this was adopted by a decision of ECRA's board, to set tariffs for non-residential consumption with an upper limit of SR0.26/kWh.In 2015, the total value of electricity consumed by the residential sector was worth about 38 billion U.S. dollars.In 2018, the Council of Ministers has approved gradual revision of energy prices in the Kingdom including changes to electricity tariffs effective from Jan. 1. 2018, the Electricity and Cogeneration Regulatory Authority (ECRA) announced that new prices will take effect on January 1st, 2018.source: ECRACitation: Alghamdi, Abeer. 2018. “Changes in Saudi Arabia Electricity Prices.” [dataset]. https://datasource.kapsarc.org/explore/dataset/electricity-prices-in-saudi-arabia/information/.

Source ID: FL010000386.Q

For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx).

With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx).

In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL010000386&t=) provided by the source.

This table contains the annual rates of change of the CPI from the moment they were first published. The annual rate of change reflects changes in prices of consumer goods and services in a certain month compared with the same month in the previous year; it is the year on-year change of the consumer price index. This table also contains the derived series for the annual rate of change. This is based on the normal series but without the effect of changes in the rates of product-related taxes (for instance VAT and excise duty on alcohol and tobacco) and subsidies. The derived series answers the question: how would prices have changed if the tax rates remained the same? CPI figures are published every month. In addition, an annual figure is published at the end of the year. The CPI of a calendar year is calculated as the average of the indices of the twelve months of that year. Data available from: January 1963 Status of the figures: When first published, the figures are provisional. Their status becomes final simultaneously with the second publication about the same month. Differences between the provisional and final figures are caused by source material that has become available after the provisional publication. Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented. When will new figures be published? New figures will usually be published between the first and second Thursday of the month following on the reporting month. The figures of the previous reporting month then become final. All CPI publications are announced on the publication calendar.

Statistics Netherlands collects monthly data on imports and exports of goods. In this table on imports and exports of goods the change of ownership of the goods is decisive, not whether they crossed the Dutch border. The table comprises index figures and changes in terms of percentage of total imports and exports of goods, broken down by value, price and volume. The indices are based on 2021=100. The changes in terms of percentage are compared with the same period in the previous year.

Data available from: 1995 January

Status of the figures: Data from 1995 up to and including 2021 are final. Data over 2022, 2023 and 2024 are provisional.

Changes as of May 16th 2025: Data from April have been added. The data from January, February and March 2025 have been adjusted.

Statistics Netherlands has carried out a revision of the national accounts. The Dutch national accounts are recently revised. New statistical sources, methods and concepts are implemented in the national accounts, in order to align the picture of the Dutch economy with all underlying source data and international guidelines for the compilation of the national accounts. This table contains revised data. For further information see section 3.

Import and export figures may be adjusted as new or updated source information from the monthly international trade statistics and producer prices becomes available. In addition, the figures are adjusted retrospectively to fit those of imports and exports of goods in the quarterly National Accounts and the annual National Accounts. A complete revision of the National Accounts is carried out once every five years.

When will new figures be published? Six to seven weeks after the end of the month under review.

The dataset provides the price indices computed for the academic paper "Price and Global Inequality", available at https://www.xavierjaravel.com/papers. The data has been created as part of the project addressing two questions: (1) What are the implications of prices changes for inequality and standards of living? (2) To what extent do the price effects induced by policies alter the cost-benefit analysis of these policies? Despite extensive research, we currently lack detailed data as well as various empirical and theoretical tools to appropriately answer these questions. These questions are fundamental because it is well-known that individuals across the income distribution purchase different baskets of goods and services. Therefore, changes in prices or product availability over time can potentially have an important impact on inequality.

This project asks two questions:

(1) What are the implications of prices changes for inequality and standards of living?

(2) To what extent do the price effects induced by policies alter the cost-benefit analysis of these policies?

Despite extensive research, we currently lack detailed data as well as various empirical and theoretical tools to appropriately answer these questions.

These questions are fundamental because it is well-known that individuals across the income distribution purchase different baskets of goods and services. Therefore, changes in prices or product availability over time can potentially have an important impact on inequality. Likewise, differences in prices across countries can have a profound impact on standards of living across countries.

The few studies that have investigated these questions have used "macro" data (at a high level of aggregation), but I have shown in previous work (Jaravel 2017) that it is crucial to use "micro" data (i.e. very disaggregated data, at the product level) to accurately answer these questions.

We know that policies may have large price effects (see Jaravel 2018 on the price effects of food stamps). For instance, increasing import tariffs is likely to result in higher prices for domestic consumers (which I have started investigating in ongoing work: Borusyak and Jaravel 2017 and Jaravel and Sager 2018). But we do not have a good understanding of how large this effect might be. Likewise, other important policies like income redistribution schemes or monetary policy could have significant effects on prices, which are not well understood currently.

There are two main challenges to answer the two fundamental questions asked in this project. First, it is not easy to properly measure how prices change over time and across countries, because the set of available goods and services is always changing and detailed micro data is required. Second, it is challenging to understand the impact of policies on prices because of feedback loops. For instance, if a given policy makes a particular group of individuals richer, they might change their consumption patterns and start buying a different set of goods or services, which may have an impact on the income of other agents, who in turn will change their consumption patterns, etc.

In this project, I propose to proceed in two steps, tackling each of these two major challenges in turn to advance our understanding of the effects of price changes and of their implications for major policies. The first part of the project aims at addressing three fundamental limitations in the literature on the measurement of "quality-adjusted" price changes (building on Jaravel 2017): (i) limited availability of scanner data across countries; (ii) limited use of hedonic regressions; and (iii) limited understanding of the welfare impact of house prices changes. Using new models and new empirical tools, the second part of the project aims at shedding new light on the welfare impact of three important types of policies, given their price effects: (i) optimal income and commodity taxation; (ii) trade policy (building on Borusyak and Jaravel 2017 and Jaravel and Sager 2018); and (iii) monetary policy.

The various parts of this project constitute a cohesive whole. Taking a multi-faceted approach is the only way of making significant progress on understanding the effects of prices and their policy implications.

This project has a strong potential for impact. In particular, it could change the type of inflation statistics published by national government agencies, as well as the type of standards-of-living statistics across countries published by international organisations. To ensure that the new data and new findings from the project are easily accessible by other researchers, policymakers, think tanks, as well as by the general public, the results and data will be made available online on a dedicated, user-friendly website.

Annual percentages of constant price GDP are year-on-year changes; the base year is country-specific. Expenditure-based GDP is total final expenditures at purchasers' prices (including the f.o.b. value of exports of goods and services), less the f.o.b. value of imports of goods and services. [SNA 1993]

From ************* to *************, fruit prices grew by over ** percent, while vegetable prices grew by approximately *** percent. However, milling products such as flour and cereals dropped by *** percent in price and sugar by *** percent.

Producer Prices in Mexico increased 5.19 percent in June of 2025 over the same month in the previous year. This dataset provides - Mexico Producer Prices Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

An analysis of hospital prices (commercial negotiated rates) among over 200 hospitals in the 10 largest metropolitan areas found that there is evidence that prices are converging. The top 25 percent of prices showed, on average, a 6.3 percent decrease in annualized real rate change* between December 2021 and June 2024. On the other hand, the bottom 25 percent of the analyzed market saw an increase of 3.4 percent in annualized real rate change.

United States SBP: RE: Change in Prices Prior to Mar 13: Little to Number Change data was reported at 32.700 % in 10 Jan 2022. This records a decrease from the previous number of 33.500 % for 03 Jan 2022. United States SBP: RE: Change in Prices Prior to Mar 13: Little to Number Change data is updated weekly, averaging 39.050 % from Aug 2021 (Median) to 10 Jan 2022, with 18 observations. The data reached an all-time high of 44.300 % in 27 Sep 2021 and a record low of 31.100 % in 22 Nov 2021. United States SBP: RE: Change in Prices Prior to Mar 13: Little to Number Change data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S045: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

Abstract

The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

Geographic coverage

Palestine West Bank Gaza Strip Jerusalem

Analysis unit

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Universe

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Kind of data

Sample survey data [ssd]

Sampling procedure

A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

Sampling deviation

Not apply

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

Cleaning operations

The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

Response rate

Not apply

Sampling error estimates

The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. For example, for the CPI, the variation between its goods was very low, except in some cases such as banana, tomato, and cucumber goods that had a high coefficient of variation during 2019 due to the high oscillation in their prices. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

Data appraisal

Other technical procedures to improve data quality: Seasonal adjustment processes

The percentage change over the same quarter of the previous year of value added per worker, expressed in 2010 market prices. Estimates are compiled based on the best available data at the time of release. They may be subsequently revised when new data becomes available.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Measured in €

The annual percentage change over previous year of total demand and its components, expressed in 2010 market prices. Total demand is the sum of total domestic demand and external demand.