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

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.

This data package includes the underlying data to replicate the charts, tables, and calculations presented in Modernizing price measurement and evaluating recent critiques of the consumer price index, PIIE Working Paper 25-3.

If you use the data, please cite as:

Sichel, Daniel E., and Christopher Mackie. 2025. Modernizing price measurement and evaluating recent critiques of the consumer price index. PIIE Working Paper 25-3. Washington: Peterson Institute for International Economics.

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.

This data is a portion of a larger dataset, composed by over 120 million data points, collected by Starsift LLC for the Grocerybear Project (www.grocerybear.com) every day between January 2017 and May 2018 for over 50,000 unique items in about 750 commercial categories for eleven US cities: Boise, Honolulu, Houston, Las Vegas, Los Angeles, Orlando, Phoenix, Portland, San Francisco, Seattle, and Washington DC. This dataset is composed by 5 csv files, one for each CPI-U Entry Level Item disclosed: Apples, Bread, Butter, Cigarettes, and Coffee. Each file presents the following columns: Year, Month, Product name, Product code, City, Store Chain, Average price in the month. Store chains have been anonymized. This project also includes an R file to calculate sub-national consumer price indexes using the Time-interaction-Region Product Dummy (TiRPD) model.

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.

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.

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

This table shows the evolution of the house price index since 2016. The index makes it possible to calculate the evolution of the prices of dwellings, subdivided into apartments, houses and villas. Historically, the General Administration of Heritage Documentation (AGDP) performed its calculations based on the first quarter of 2003 as the reference period. Since then, AGDP has had complete electronic data for these calculations. Price indices could not be calculated for 2015. Indeed, the mathematical model of the AGDP was based on the output of the Cadnet/Loco application. Cadnet/Loco was replaced by STIPAD in 2015. Therefore, the sales of which data were available were insufficient to calculate an index for the whole year. In view of the improvements allowed by the new STIPAD application, we took over the publication of the index from 2016 as the reference year (=100 %). Since 2022, we have also calculated the evolution of this index by Region.

Inflation rates experienced by different groups of consumers within a country vary. This is because the prices of goods and services and the expenditure patterns of consumers differ. The published inflation rate is used for important decisions regarding the preservation of consumer purchasing power. These include the adjustment of social grants and minimum wages by government and the benchmarking of returns by investors when making investment decisions. It is thus vital that inflation is measured accurately to ensure the purchasing power of consumers is preserved. Current measures of inflation published by Stats SA are applicable to typical consumers and are not relevant to each individual. This resource supplements a study that seeks to provide a publicly available model that can be used by consumers to calculate their personal rate of inflation.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

The file "dataset_regression_income.csv" contains a dataset developed in the analysis of inflation heterogeneity for Italian Households in the period 2015-2023.The dataset is the outcome of merging the yearly Household Budget Surveys (HBS) conducted by the Italian National Institute of Statistics (Istat), the Harmonised Index of Consumer Prices (HICP) which is calculated monthly by Istat, according to EU regulations, and the Survey on Households Income and Wealth (SHIW) conducted by Bank of Italy.Mapping price information into consumption decisions and aggregating an individual price index for each household according to a Laspeyres Formula leads to the computation of household-level inflation rates.Furthermore, we compute non-durable equivalent expenditure for each household as a proxy of living standards. The variable is obtained by subtracting durable expenditure from total aggregate expenditure and scaling down by an household equivalent scale (in the benchmark specification, the square root of the household size). The decile distribution of the variable is also computed.Finally, we apply a statistical matching procedure to integrate income information from SHIW data sources. The output is a synthetic dataset containing both expenditure and income information that preserves the joint distribution and correlation structures of the original datasets.The file "ISTAT_MFR_HBS_EUR.csv" is a conversion table that maps ECOICOP items to HBS expenditure voices.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

This dataset shows Qatar’s score and ranking in the Quality of Life Index. The index uses an empirical formula that incorporates the following factors: purchasing power (higher is better), pollution (lower is better), house price-to-income ratio (lower is better), cost of living (lower is better), safety (higher is better), healthcare (higher is better), traffic commute time (lower is better), and climate (higher is better).

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.