1-month change in the Adjusted price index based on monthly adjusted consumer expenditure basket weights created by Statistics Canada, in partnership with the Bank of Canada. The Adjusted price index has been updated to incorporate the 2020 basket weights and is now based on a Similarity-linked Fisher price index formula. The expenditure data covers all goods and services in the Consumer Price Index.

Price index of consumer goods and services is calculated on the basis of the results of:
- surveys on prices of consumer goods and services on the retail market,
- surveys on household budgets, providing data on average expenditures on consumer goods and services; these data are then used for compilation of a weight system.

Calculating price index of consumer goods and services is done on the basis of the Classification of Individual Consumption by Purpose (COICOP) adapted for the use of Harmonized Indices of Consumer Prices (HICP).

The price index of a representative in the region included in the price survey results from relating its average monthly price to an average annual price from the previous yea The all-Polish price index of a representative included in the survey is calculated as geometric mean of price indices from all regions. Calculating price indices of groups of consumer goods and services at the lowest level of weight system aggregation is done on the basis of price indices of the representatives included in price survey in a given group by using geometric mean. They are then used by applying weight system to calculate indices of higher level of aggregation up to the price index of total consumer goods and services. price index is calculated in line with the Laspeyress’s formula by applying weights from the year preceding the reference year.

United States - Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds was 258.48200 Index 1982=100 in March of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds reached a record high of 301.55000 in September of 2022 and a record low of 44.20000 in February of 1962. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds - last updated from the United States Federal Reserve on July of 2025.

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

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

Graph and download economic data for Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds (WPS029301) from Jan 2005 to May 2025 about processed, food, commodities, PPI, inflation, price index, indexes, price, and USA.

El Salvador SV: Wholesale Price Index data was reported at 108.446 2010=100 in 2016. This records a decrease from the previous number of 111.345 2010=100 for 2015. El Salvador SV: Wholesale Price Index data is updated yearly, averaging 42.663 2010=100 from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 114.438 2010=100 in 2011 and a record low of 5.573 2010=100 in 1962. El Salvador SV: Wholesale Price Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s El Salvador – Table SV.World Bank.WDI: Inflation. Wholesale price index refers to a mix of agricultural and industrial goods at various stages of production and distribution, including import duties. The Laspeyres formula is generally used.; ; International Monetary Fund, International Financial Statistics and data files.; ;

United States PPI: Weights: PO: Formula Feeds data was reported at 0.413 % in 2024. This records a decrease from the previous number of 0.432 % for 2023. United States PPI: Weights: PO: Formula Feeds data is updated yearly, averaging 0.381 % from Dec 2007 (Median) to 2024, with 18 observations. The data reached an all-time high of 0.469 % in 2022 and a record low of 0.266 % in 2007. United States PPI: Weights: PO: Formula Feeds data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I068: Producer Price Index: by Commodities: Weights.

Price index of consumer goods and services is calculated on the basis of the results of: - surveys on prices of consumer goods and services on the retail market, - surveys on household budgets, providing data on average expenditures on consumer goods and services; these data are then used for compilation of a weight system. Calculating price index of consumer goods and services is done on the basis of the Classification of Individual Consumption by Purpose (COICOP) adapted for the use of Harmonized Indices of Consumer Prices (HICP). The price index of a representative in the region included in the price survey results from relating its average monthly price to an average annual price from the previous yea The all-Polish price index of a representative included in the survey is calculated as geometric mean of price indices from all regions. Calculating price indices of groups of consumer goods and services at the lowest level of weight system aggregation is done on the basis of price indices of the representatives included in price survey in a given group by using geometric mean. They are then used by applying weight system to calculate indices of higher level of aggregation up to the price index of total consumer goods and services. price index is calculated in line with the Laspeyress’s formula by applying weights from the year preceding the reference year.

United States PPI: Processed Foods: Animal Feeds: Formula Feeds data was reported at 203.500 1982=100 in Sep 2018. This records a decrease from the previous number of 206.800 1982=100 for Aug 2018. United States PPI: Processed Foods: Animal Feeds: Formula Feeds data is updated monthly, averaging 109.100 1982=100 from Jan 1962 (Median) to Sep 2018, with 681 observations. The data reached an all-time high of 272.900 1982=100 in Sep 2012 and a record low of 44.200 1982=100 in Feb 1962. United States PPI: Processed Foods: Animal Feeds: Formula Feeds data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I017: Producer Price Index: By Commodities.

🇱🇹 리투아니아 English The purpose of the calculation of the Service Prices Index (CPI) is to determine the general change in prices of services provided to economic operators over a period of time. The CPI is needed to calculate macroeconomic indicators at constant prices, analyse inflationary processes in the services sector, prepare forecasts. Geographical coverage - The entire economic territory of the country. Reporting period - Quarter. The classifications used are: Classification of economic activities (EERC Rev. 2); Classification of products by type of activity (CPA 2.1). For more information: https://osp.stat.gov.lt/documents/10180/5118910/%C5%AAkio+subjektams+suteikt%C5%B3+paslaug%C5%B3+kain%C5%B3+indeksai+%28PKI%29+ir+kain%C5%B3+poky%C4%8Diai+%5BLT%5D+604.html

Investment banking services price index (IBSPI) measures the change in price of investment banking services. Annual data are available from 2010. The table presents the year-over-year percentage changes. The calculation of the index is available in a Fisher, Paasche and Laspeyres price index. The base period for the index is 2017=10.

Abstract

Transition to free economic structure and, as a consequence, processes of privatization of large agricultural and industrial organizations and birth of numerous new economic entities led to significant changes in quantitative and qualitative characteristics of industrial organizations and peasant farms in RA. During the last decade and especially the last 4-5 years, the structural changes, in their turn, caused also certain complications in the mentioned fields in terms of ensuring collection, comprehensiveness and reliability of statistical data on prices and pricing.

In particular, in case of radical structural changes, international recommendations require the weights upon which price indexes are based to be periodically updated. In order to have a real picture and dynamics of the present situation on creation of indicators for new base year, i.e. collection of information on set of goods-representatives, their weights, average annual prices, prices and price changes, it would be necessary to periodically conduct sample surveys for further improvement of the methodology for price index calculation.

The objectives of the survey were: • to improve the sample, develop a new sample, • to revise the base year and weights, • to receive additional information on prices of sales of industrial, agricultural product and purchase (acquisition of production means) in RA, • to improve methodology for price observation and calculation of price indexes (survey technology, price and other necessary data collection, processing, analyzing), • to revise the base year for producer price indexes, components structure, shares, calculation mechanism, etc., • to derive price indexes that would be in line with the international definitions, standards and classifications, • to complement the NSS RA price indexes database and create preconditions for its regular updating, • to update the information on economic units covered by price indexes calculation, • to ensure use of international standards and classifications in statistics, • to form preconditions for extension of sample observation mechanisms in the state statistics.

Besides the above mentioned, the need of the given survey was also stipulated by the following reasons: - a great mobility of micro-sized, small and medium-sized organizations mainly caused by increased speed of their births, activity and produced commodity changes or deaths that decreases the opportunity to create long-term fixed-base time series of prices and price indexes, - According to the CPA classification coding and recoding activities related to the introduction of Armenian classification of economic activities - NACE (based on the European Communities’ NACE classification).

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN

Agriculture The sample of the survey was desighned in the conditions of lack of farm register. The number of peasant farms was calculated and derived by database analysis. The number of villages (quotas) selected from each marz was determined taking into account the percent of rural population of marzes. The villages from marz were selected randomly. The peasant farms covered by the survey were selected based on number of privatized plots. The survey was carried out in 200 rural communities selected from 10 marzes, in 5-20 households from each community. Pilot survey was conducted with 1 901 farms in the sample.

Industry The sample frame for the survey was designed as follows: 1. The industrial organizations with share 5 and more percent have been selected by reduction method from fifth level (each subsection) of NACE for whole RA industry. 476 out of 2231 industrial organizations covered by statistical observation were selected for pilot survey.

1. 70 organizations suggested by Industry statistics division of NSS RA and 70 organizations included in state observations on prices conducted previously by the NSS RA (in all 140 organizations), which are considered important and representative for price observation and excluded from the above-mentioned sample, were separated from the general population. These organizations have also been included in sample population of the pilot survey. As it became obvious from further work, the sample covered both the large and medium-sized and the small and micro-sized organizations, which ensured the representativeness of separate branches of industry and organizations by size. As a result, given by the objective of the survey, as well as available financial constraints, the sample population of the pilot survey comprised 616 industrial organizations, the volumes of produced production of whichaccording to the data for January-October of 2006 comprised more than 86% of total volume of RA industrial production. 165 (92.7%) out of 178 classes of NACE were covered by the sample.

Mode of data collection

Face-to-face [f2f]

1-month change in the Adjusted price index based on monthly adjusted consumer expenditure basket weights created by Statistics Canada, in partnership with the Bank of Canada. The Adjusted price index has been updated to incorporate the 2020 basket weights and is now based on a Similarity-linked Fisher price index formula. The expenditure data covers all goods and services in the Consumer Price Index.

We provide evidence on the effect of elementary index choice on inflation measurement in the euro area. Using scanner data for 15,844 individual items from 42 product categories and 10 euro area countries, we compute product category level elementary price indexes using eight different elementary index formulas. Measured inflation outcomes of the different index formulas are compared with the Fisher ideal index to quantify elementary index bias. We have three main findings. First, elementary index bias is quite variable across product categories, countries and index formulas. Second, a comparison of elementary index formulas with and without expenditure weights shows that a shift from price only indexes to expenditure weighted indexes would entail at the product level multiple percentage points differences in measured price changes. And finally, we show that elementary index bias is quantitatively more important than upper level substitution bias.

This table shows the price-indices, the quarterly and the yearly price changes of services that companies provided. There is a breakdown by type of services according to the Coordinated European goods and services classification (CPA). The prices of services are observed in the sectors for which the supply of the service is the main activity.

Included in the producer price indices are: Section I, transport, storage and communication services; Section K, real estate, renting and business services

Not included in producer price indices are: Section G, wholesale and retail trade, repair of motor vehicles and motorcycles; Section H, hotels and restaurants; Section J, financial services.

The index reference year of all producer price indices is 2006. The year average, the quarterly and the yearly changes are calculated with unrounded figures.

Data available form: 2002 4th quarter Frequency: quarterly

Status of the figures: the figures for the most recent period are final.

When will new figures be published: This table is put a stop on 30-6-2012 and continued as the table Price indices services; index 2010 = 100'.

Changes in comparison with last versions From the third quarter of 2010 onwards, a new method is used to calculate Total renting services of automobiles, which falls under the aggregated Renting services of machinery and equipment without operator and of personal and household goods. This method corresponds to the current calculation method of the services price index.

This article describes the new RPIJ measure of Consumer Price Inflation. RPIJ is a Retail Prices Index (RPI) based measure that will use a geometric (Jevons) formula in place of one type of arithmetic formula (Carli). It is being launched in response to the National Statistician's conclusion that the RPI does not meet international standards due to the use of the Carli formula in its calculation. The accompanying Excel file includes a back series for RPIJ from 1997 to 2012.

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: New RPIJ measure of Consumer Price Inflation

There are a number of differences between the Consumer Prices Index (CPI) and Retail Prices Index (RPI), including their coverage, population base, commodity measurement and methods of construction. Combined, these differences have meant that, for most of its history, the CPI has been lower than the RPI. One of the main reasons to this difference is the method of construction at the lowest level, where different formulae are used in the CPI and RPI to combine individual prices. This difference is usually referred to as the formula effect. This article will investigate similar formula effects present in the inflation measures of other countries, and where necessary will attempt to explain why the magnitude of the formula effect experienced by other countries differs from that of the UK. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: International Comparison

(CDID: CRFT) Month - Consumer price inflation time series Time series data for public sector finances and important fiscal aggregates, based on the new European System of Accounts 2010: ESA10 framework.

This table contains 7 series, with data starting from 1972 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Commodity (7 items: Total; all commodities; Metals and Minerals; Energy; Total excluding energy ...).