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.

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

Consumer Price Index CPI in India increased to 193 points in May from 192.60 points in April of 2025. This dataset provides - India Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Consumer Price Index (CPI) is a measure that examines the weighted average of prices of a basket of consumer goods and services, such as transportation, food, and medical care. It is calculated by taking price changes for each item in the predetermined basket and averaging them. Prices are collected periodically, and the CPI is often used to measure inflation, reflecting the cost of living. The CPI is typically set against a base year. The index is set to 100 in the base year, and changes in the CPI indicate price changes compared to that year. A typical household might purchase a wide range of products and services. Items in the basket are weighted according to their importance or share in total household spending. The Inflation Rate is the percentage increase in the general level of prices for goods and services over a period of time. It indicates how much prices have risen over a specific period, typically a year. Higher inflation decreases the purchasing power of money, meaning consumers can buy less with the same amount of money.It reflects the overall health of an economy. Moderate inflation is expected in a growing economy, but hyperinflation can indicate economic instability. The Inflation Rate is calculated using the following formula: Inflation Rate (%) = ((CPI in Current Year−CPI in Previous Year)/ (CPI in Previous Year))×100

United States PCE: PI: Qtr: Less Formula Effect data was reported at -0.050 Point in Mar 2013. This records an increase from the previous number of -0.160 Point for Dec 2012. United States PCE: PI: Qtr: Less Formula Effect data is updated quarterly, averaging -0.160 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of 0.710 Point in Dec 2008 and a record low of -0.450 Point in Sep 2005. United States PCE: PI: Qtr: Less Formula Effect data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A139: NIPA 2009: Personal Consumption Expenditure Price Index and CPI Reconciliation: Quarterly.

Austria AT: Consumer Price Index (CPI): Local Source Base Year: All Items data was reported at 120.267 2020=100 in 2023. This records an increase from the previous number of 111.550 2020=100 for 2022. Austria AT: Consumer Price Index (CPI): Local Source Base Year: All Items data is updated yearly, averaging 55.823 2020=100 from Dec 1958 (Median) to 2023, with 66 observations. The data reached an all-time high of 120.267 2020=100 in 2023 and a record low of 14.452 2020=100 in 1958. Austria AT: Consumer Price Index (CPI): Local Source Base Year: All Items data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Austria – Table AT.OECD.MEI: Consumer Price Index: COICOP 1999: OECD Member: Annual. The Austrian CPI measure price changes in a fixed basket of goods and services bought in Austria for the purpose of consumption by all Austrian households, foreign visitors and residents in institutional households. The prices used in the CPI calculation are the transaction prices actually paid by consumers including taxes less any general discounts, rebates or subsidies. Method of collection: Personal visits and mail questionnaire, paper collection forms, centrally collected prices by mail and telephone. Treatment of rentals for housing: Apartments rent abroad are included. Treatment of Owner-Occupied Housing: Regular payments for Owner occupied flats are included (payment approach), initial payments are excluded. House construction goods and services and major repairs are included. The purchase of a house and other real estate (land prices, housing agents) are not included. Treatment of missing prices: Prices are adjusted by the rate of change of the other price observations of the same product. If product offers are not available any more a new product offer is selected as replacement immediately after three months at latest. Treatment of quality changes: Quantity adjustment for food, Expert Judgment adjustment method e.g. for clothing, Option pricing method for durables and cars, Hedonic method for notebooks. Introduction of new products: New products are selected with respect to demand (turnover) and availability and they are introduced every December. New models and varieties are implemented by replacement as soon as they become relevant. Treatment of seasonal items: When a product offer disappears for seasonal non-availability, it is not replaced but its price relative is excluded from calculation. The index is then calculated with the rest of available prices. If the seasonal variety becomes available again the respective price relative is included in the calculation again (after potential quality adjustment). For a minority of products which would not at all be available in whole Austria the last prices are carried forward (e.g. schools and theatres in summer or public baths in winter).; Index series starts in January 1958

Comoros KM: Consumer Price Index (CPI): % Change data was reported at -4.295 % in 2013. This records a decrease from the previous number of 6.315 % for 2012. Comoros KM: Consumer Price Index (CPI): % Change data is updated yearly, averaging 3.533 % from Dec 2001 (Median) to 2013, with 13 observations. The data reached an all-time high of 6.315 % in 2012 and a record low of -4.295 % in 2013. Comoros KM: Consumer Price Index (CPI): % Change data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Comoros – Table KM.World Bank.WDI: Inflation. Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.;International Monetary Fund, International Financial Statistics and data files.;Median;

The Chinese economy has undergone a long-term transition reform, but there is still a planned economy characteristic in the financial sector, which is financial repression. Due to the existence of financial repression, China’s actual interest rate level should be lower than the Consumer Price Index (CPI). However, based on official China’s interest rates and CPI, over half of the years China’s actual interest rate remained higher than CPI by our calculation from 1999 to 2022. This is inconsistent with the financial repression that exists in China, and the main reason is the calculation methods of China’s CPI. China’s CPI measurement system originated from the planned economy era, which did not fully consider the rise in housing purchase prices, so the current CPI measurement system can be more realistically presented by taking the rise in housing prices into consider. The core idea of this study is to mining relevant official statistical data and calculate the proportion of Chinese residents’ expenditure on purchasing houses to their total expenditure. By taking the proportion of house purchases as the weight of house price factor, and taking the proportion of other consumption as the weight of official CPI, the Generalized CPI (GCPI) is formulated. The GCPI is then compared with market interest rates to determine the actual interest rate situation in China over the past 20 years. This study has found that if GCPI is used as a measure, China’s real interest rates have been negative for most years since 1999. Chinese residents have suffered the negative effects of financial repression over the past 20 years, and their property income cannot keep up with the actual losses caused by inflation. Therefore, it is believed that China’s CPI calculation method should be adjusted to take into account the rise in housing prices, so China’s actual inflation level could be more accurately reflected. In view of the above, deepening interest rate marketization reform and expand channels for financial investment are the future development goals of China’s financial system.

United States PCE: PI: saar: Less Formula Effect (LFE) data was reported at -0.100 % Point in Mar 2025. This records a decrease from the previous number of -0.060 % Point for Dec 2024. United States PCE: PI: saar: Less Formula Effect (LFE) data is updated quarterly, averaging -0.160 % Point from Mar 2002 (Median) to Mar 2025, with 93 observations. The data reached an all-time high of 0.550 % Point in Jun 2020 and a record low of -0.590 % Point in Sep 2005. United States PCE: PI: saar: Less Formula Effect (LFE) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.I048: PCE Price Index and CPI Reconciliation: NIPA 2023: Quarterly.

Inflation is an important measure of any country’s economy, and the Retail Price Index (RPI) is one of the most widely used indicators in the United Kingdom, with the rate expected to be 4.1 percent in 2025, compared with 3.6 percent in 2024. This followed 2022, when RPI inflation reached a rate of 11.6 percent, by far the highest annual rate during this provided time period. CPI vs RPI Although the Retail Price Index is a commonly utilized inflation indicator, the UK also uses a newer method of calculating inflation, the Consumer Price Index. The CPI, along with the CPIH (Consumer Price Index including owner occupiers' housing costs) are usually preferred by the UK government, but the RPI is still used in certain instances. Increases in rail fares for example, are calculated using the RPI, while increases in pension payments are calculated using CPI, when this is used as the uprating factor. The use of one inflation measure over the other can therefore have a significant impact on people’s lives in the UK. High inflation falls to more typical levels by 2024 Like the Retail Price Index, the Consumer Price Index inflation rate also reached a recent peak in October 2022. In that month, prices were rising by 11.1 percent and did not fall below double figures until April 2023. This fall was largely due to slower price increases in key sectors such as energy, which drove a significant amount of the 2022 wave of inflation. Inflation nevertheless remains elevated, fueled not only by high food inflation, but also by underlying core inflation. As of February 2025, the overall CPI inflation rate was 2.8 percent, although an uptick in inflation is expected later in the year, with a rate of 3.7 percent forecast for the third quarter of the year.

United States PCE: PI: Less Formula Effect: Housing data was reported at 0.000 Point in May 2013. This stayed constant from the previous number of 0.000 Point for Apr 2013. United States PCE: PI: Less Formula Effect: Housing data is updated monthly, averaging 0.000 Point from Jan 2002 (Median) to May 2013, with 137 observations. United States PCE: PI: Less Formula Effect: Housing data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A273: NIPA 2009: PCE Price Index and CPI Reconciliation.

San Marino Consumer Price Index (CPI): % Change data was reported at 1.046 % in 2017. This records an increase from the previous number of 0.574 % for 2016. San Marino Consumer Price Index (CPI): % Change data is updated yearly, averaging 1.890 % from Dec 2004 (Median) to 2017, with 14 observations. The data reached an all-time high of 4.293 % in 2008 and a record low of 0.146 % in 2015. San Marino Consumer Price Index (CPI): % Change data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s San Marino – Table SM.World Bank.WDI: Inflation. Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.; ; International Monetary Fund, International Financial Statistics and data files.; Median;

United States PCE: PI: Qtr: Less Formula Eff: Tobacco data was reported at 0.000 Point in Mar 2013. This stayed constant from the previous number of 0.000 Point for Dec 2012. United States PCE: PI: Qtr: Less Formula Eff: Tobacco data is updated quarterly, averaging 0.000 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of 0.010 Point in Jun 2003 and a record low of -0.090 Point in Jun 2009. United States PCE: PI: Qtr: Less Formula Eff: Tobacco data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A139: NIPA 2009: Personal Consumption Expenditure Price Index and CPI Reconciliation: Quarterly.

Abstract

The fourth expenditure and consumption survey (LECS 4) in Lao PDR is a survey in terms of socio-economy at the household echelon. This survey is conducted in every 5 years. The present round of surveys started from 1992 and the main statistical collection unit is the household. This survey is a sample survey which is carried out in every province and district over the whole country. The survey was undertaken from April 2007 to March 2008 (for a period of 12 months), in order to be able to provide data on expenditure and consumption covering all seasons and relating to aspects of every area and region in the Lao PDR.

The purpose of the expenditure and consumption survey (LECS) is to estimate the expenditure and consumption of household as well as production, investment, accumulation and other socio-economic aspects of the households in the formal and informal sector of the economy.

The results of expenditure and consumption survey in Lao PDR will provide necessary data to be used for calculation of various indicators and are intended for socio-economic planning. It will also provide data for calculation of GDP, definition of poverty line, data on nutrition and other important information. The LECS surveys are the most important surveys in the statistical data collection system of Lao PDR.

The main objectives of this survey are: - Estimation at macro level for national accounts, including private consumption, household investment, production and income from agriculture and household business; - Structure of household consumption (weight system) for consumption price index calculation (CPI); - Estimation on labor force; - Nutrition statistic; - Poverty statistics and statistics of income distribution.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Villages/ Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design and Selection

First Step: Description of Sample Village

The survey design for the LECS 4 uses the same methodology and sampling technique as used in the LECS 3. The sample selection is conducted in two steps. The first step is selection of sample villages using the zoom selection methodology according to the proportion of the population (PPS). Village unit is distributed according to the following echelon: village classified by province, district, rural area with access to road and rural area without access to road. The number of sample villages in each province is in between 17 to 48 villages depending on the number of villages, and the number of households in every survey area.

Comparing the last two surveys, LECS 3 and LECS 4, the number of sample villages is decreased from 540 to 518 villages. This is due to the situation of allocation and unification of small villages into larger villages, which in past years has appeared in every province in the whole country. In order to assure normal rule of distribution of sample, the number of sample households has been from 15 to 16 per village.

Each month the number of sample villages is almost the same, because the sample has been selected as zoom for every month.

Second Step: Selection of Sample Household

In the present expenditure and consumption survey half of the number of households are the same as households that were surveyed in the LECS 3, and the other half are new households that previously were not surveyed. The selection of households in the sample uses the zoom methodology on arbitrary and systematic basis. Selection of the 8 sample households from the survey of LECS 3 uses the zoom methodology on arbitrary basis by taking part in a lottery among LECS 3 households. New 8 sample household are selected among the other households in the village using the same methodology. Together the number of sample households in one village is 16. The selection of sample household is based on the number of existing households in the village at the time of the conduction of the survey. If the village has 16 or less households all households are covered by the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used to collect the 2007-2008 LECS: - Household Questionnaire - Dairy Sheet - Time USe Questionnaire - Village Questionnaire - Price Questionnaire

Cleaning operations

The survey data has been edited and data editing process include: - Structure checking and completeness - Checking and coding

Sampling error estimates

Sampling errors have been calculated for some important variables based on the confidence of 95% ("margin of errors").

United States PCE: PI: Less Formula Effect: Other data was reported at 0.000 Point in May 2013. This stayed constant from the previous number of 0.000 Point for Apr 2013. United States PCE: PI: Less Formula Effect: Other data is updated monthly, averaging 0.000 Point from Jan 2002 (Median) to May 2013, with 137 observations. The data reached an all-time high of 0.020 Point in Nov 2008 and a record low of -0.030 Point in Jan 2009. United States PCE: PI: Less Formula Effect: Other data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A273: NIPA 2009: PCE Price Index and CPI Reconciliation.

Singapore Consumer Price Index (CPI): Weights: FFSS: Milk, Cheese & Eggs: Formula Milk Powder data was reported at 0.270 % in Dec 2024. This stayed constant from the previous number of 0.270 % for Nov 2024. Singapore Consumer Price Index (CPI): Weights: FFSS: Milk, Cheese & Eggs: Formula Milk Powder data is updated monthly, averaging 0.270 % from Jan 2014 (Median) to Dec 2024, with 132 observations. The data reached an all-time high of 0.270 % in Dec 2024 and a record low of 0.270 % in Dec 2024. Singapore Consumer Price Index (CPI): Weights: FFSS: Milk, Cheese & Eggs: Formula Milk Powder data remains active status in CEIC and is reported by Singapore Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.I007: Consumer Price Index: 2019=100: Weights.

United States PCE: PI: Less Formula Effect data was reported at 0.000 Point in May 2013. This records a decrease from the previous number of 0.030 Point for Apr 2013. United States PCE: PI: Less Formula Effect data is updated monthly, averaging -0.010 Point from Jan 2002 (Median) to May 2013, with 137 observations. The data reached an all-time high of 0.110 Point in Nov 2008 and a record low of -0.080 Point in Sep 2005. United States PCE: PI: Less Formula Effect data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A273: NIPA 2009: PCE Price Index and CPI Reconciliation.

United States PCE: PI: Qtr: Less Formula Eff: Fd & Bev Off-Premises Consumption data was reported at 0.000 Point in Mar 2013. This records an increase from the previous number of -0.010 Point for Dec 2012. United States PCE: PI: Qtr: Less Formula Eff: Fd & Bev Off-Premises Consumption data is updated quarterly, averaging 0.000 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of 0.010 Point in Jun 2010 and a record low of -0.060 Point in Dec 2003. United States PCE: PI: Qtr: Less Formula Eff: Fd & Bev Off-Premises Consumption data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A274: NIPA 2009: PCE Price Index and CPI Reconciliation: Quarterly.

United States PCE: PI: Qtr: Less Formula Eff: Video & Audio Equipment data was reported at -0.030 Point in Mar 2013. This stayed constant from the previous number of -0.030 Point for Dec 2012. United States PCE: PI: Qtr: Less Formula Eff: Video & Audio Equipment data is updated quarterly, averaging -0.040 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of -0.010 Point in Mar 2002 and a record low of -0.110 Point in Dec 2009. United States PCE: PI: Qtr: Less Formula Eff: Video & Audio Equipment data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A139: NIPA 2009: Personal Consumption Expenditure Price Index and CPI Reconciliation: Quarterly.

United States PCE: PI: Less Formula Effect: Fd & Bev Off-Premises Consumption data was reported at 0.000 Point in May 2013. This stayed constant from the previous number of 0.000 Point for Apr 2013. United States PCE: PI: Less Formula Effect: Fd & Bev Off-Premises Consumption data is updated monthly, averaging 0.000 Point from Jan 2002 (Median) to May 2013, with 137 observations. The data reached an all-time high of 0.000 Point in May 2013 and a record low of -0.010 Point in Nov 2003. United States PCE: PI: Less Formula Effect: Fd & Bev Off-Premises Consumption data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A273: NIPA 2009: PCE Price Index and CPI Reconciliation.