View monthly updates and historical trends for US Inflation Rate. from United States. Source: Bureau of Labor Statistics. Track economic data with YCharts…

Inflation Rate in the United States increased to 2.90 percent in August from 2.70 percent in July of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The FAOSTAT monthly Food CPI and General CPI database was based on the ILO CPI data until December 2014. In 2014, IMF-ILO-FAO agreed to transfer global CPI data compilation from ILO to IMF. Upon agreement, CPIs for all items and its sub components originates from the International Monetary Fund (IMF), and the UN Statistics Division(UNSD) for countries not covered by the IMF. However, due to a limited time coverage from IMF and UNSD for a number of countries, the Organisation for Economic Co-operation and Development (OECD), Central Bank of Western African States (BCEAO), Eastern Caribbean Central Bank (ECCB), UNdata, United Nations Conference on Trade and Development (UNCTAD) and national statistical office website data are used for missing historical data from IMF and UNSD food CPI.

The FAO CPI dataset for all items(or general CPI) and the Food CPI, consists of a complete and consistent set of time series from January 2000 onwards. Data gaps on monthly Food CPI and General CPI are filled using statistical estimation procedures to have full data coverage for all countries for Food CPI and for General CPI. These indices measure the price change between the current and reference periods of the average basket of goods and services purchased by households. The General CPI is typically used to measure and monitor inflation, set monetary policy targets, index social benefits such as pensions and unemployment benefits, and to escalate thresholds and credits in the income tax systems and wages in public and private wage contracts. The FAOSTAT monthly Food CPI inflation rates are annual year-over-year inflation or percentage change over corresponding month of the previous year.

The data included in Data360 is a subset of the data available from the source. Please refer to the source for complete data and methodology details.

This collection includes only a subset of indicators from the source dataset.

Global media inflation rates are projected to vary significantly across different mediums in 2025, with online video leading at *** percent and radio at just *** percent. This reflects the ongoing shift in media consumption patterns and advertising spend. The data highlights the resilience of digital platforms and the challenges faced by traditional print media in an increasingly digital landscape. Digital dominance and traditional media's struggle The disparity in inflation rates across media types underscores the growing divide between digital and traditional platforms. In 2023, online media worldwide experienced an inflation rate of *** percent, more than double that of offline media at *** percent. This trend is expected to continue in 2024, with online video and display maintaining higher inflation rates compared to newspapers and magazines. The shift is further evidenced by global media consumption patterns, with users spending an average of ***** hours and ** minutes daily on mobile devices in 2024. Industry leaders and market dynamics The changing media landscape is reflected in the revenue rankings of top media companies. In 2023, tech giants Alphabet Inc. and Meta Platforms Inc. led the pack, followed by traditional media conglomerates like Comcast Corporation and Walt Disney. This hierarchy illustrates the growing influence of digital platforms in the media industry. The United States remains a crucial market for these companies, with American consumers spending an average of over ** hours daily consuming major media. As the global entertainment and media market continues to expand, and projections suggest it could reach a value of *** trillion U.S. dollars by 2027, driven largely by the continued growth of digital platforms.

Due to the recent hyperinflation crisis in Venezuela, the average inflation rate in Venezuela is estimated to be around 225 percent in 2026. However, this is well below the peak of 63,000 percent observed in 2018.What is hyperinflation?In short, hyperinflation is a very high inflation rate that accelerates quickly. It can be caused by a government printing huge amounts of new money to pay for its expenses. The subsequent rapid increase of prices causes the country’s currency to lose value and shortages in goods to occur. People then typically start hoarding goods, which become even more scarce and expensive, money becomes worthless, financial institutions go bankrupt, and eventually, the country’s economy collapses. The Venezuelan descent into hyperinflationIn Venezuela, the economic catastrophe began with government price controls and plummeting oil prices, which caused state-run oil companies to go bankrupt. The government then starting printing new money to cope, thus prices rose rapidly, unemployment increased, and GDP collapsed, all of which was exacerbated by international sanctions. Today, many Venezuelans are emigrating to find work and supplies elsewhere, and population growth is at a decade-low. Current president Nicolás Maduro does not seem inclined to steer away from his course of price controls and economic mismanagement, so the standard of living in the country is not expected to improve significantly anytime soon.

The source forecast that, in 2023, the cost of digital display advertising in Argentina will increase by ** percent, on average. The medium's average inflation rate in Turkey and Taiwan will reach ** and ** percent that year, respectively.

Brazil Market Expectation: Inflation: Accumulated Over Next 12 Months: Extended National Consumer Price Index (IPCA): Smoothed: Median data was reported at 3.680 % in 28 Jun 2019. This records an increase from the previous number of 3.670 % for 27 Jun 2019. Brazil Market Expectation: Inflation: Accumulated Over Next 12 Months: Extended National Consumer Price Index (IPCA): Smoothed: Median data is updated daily, averaging 5.240 % from Dec 2001 (Median) to 28 Jun 2019, with 4401 observations. The data reached an all-time high of 12.350 % in 19 Dec 2002 and a record low of 3.380 % in 23 May 2007. Brazil Market Expectation: Inflation: Accumulated Over Next 12 Months: Extended National Consumer Price Index (IPCA): Smoothed: Median data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SA032: Market Expectation: Inflation: Accumulated Over Next 12 Months: Extended National Consumer Price Index (IPCA): Smoothed. Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Monitors the variations in costs of people earning from one to forty minimum wages in the metropolitan areas of Belém, Belo Horizonte, Curitiba, Fortaleza, Porto Alegre, Recife, Rio de Janeiro, Salvador, São Paulo, municipality of Goiânia and Federal District. The IPCA measures the change in cost of expenses as described above in the period from the first to the last day of each reference month. In the period from the eleventh day to the twenty day of the following month the IBGE announces the variations.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

🏳️‍🌈 International Organization English The FAOSTAT monthly Food CPI and General CPI database was based on the ILO CPI data until December 2014. In 2014, IMF-ILO-FAO agreed to transfer global CPI data compilation from ILO to IMF. Upon agreement, CPIs for all items and its sub components originates from the International Monetary Fund (IMF), and the UN Statistics Division(UNSD) for countries not covered by the IMF. However, due to a limited time coverage from IMF and UNSD for a number of countries, the Organisation for Economic Co-operation and Development (OECD), Central Bank of Western African States (BCEAO), Eastern Caribbean Central Bank (ECCB), UNdata, United Nations Conference on Trade and Development (UNCTAD) and national statistical office website data are used for missing historical data from IMF and UNSD food CPI. The FAO CPI dataset for all items(or general CPI) and the Food CPI, consists of a complete and consistent set of time series from January 2000 onwards. Data gaps on monthly Food CPI and General CPI are filled using statistical estimation procedures to have full data coverage for all countries for Food CPI and for General CPI. These indices measure the price change between the current and reference periods of the average basket of goods and services purchased by households. The General CPI is typically used to measure and monitor inflation, set monetary policy targets, index social benefits such as pensions and unemployment benefits, and to escalate thresholds and credits in the income tax systems and wages in public and private wage contracts. The FAOSTAT monthly Food CPI inflation rates are annual year-over-year inflation or percentage change over corresponding month of the previous year. The data included in Data360 is a subset of the data available from the source. Please refer to the source for complete data and methodology details. This collection includes only a subset of indicators from the source dataset.

ABSTRACT This article shows that the theory that supports the inflation targeting regime does not have a relationship with reality. Moreover, this stresses that monetary policy should be used to full capacity because it not only controls inflation, but is also useful to achieve real goals, such as employment. The article shows that there is no evidence that the regime reduced inflation in the 1990’s. Developed countries which adopted the regime and developed countries which did not adopt inflation targeting regime have both reduced and kept inflation under control.

We study the impact of targeted price controls on supermarket products in Argentina between 2007 and 2015. Using web-scraping methods, we collected daily prices for controlled and non-controlled goods and examined the differential effects of the policy on inflation, product availability, entry and exit, and price dispersion. We first show that price controls have only a small and temporary effect on inflation that reverses itself as soon as the controls are lifted. Second, contrary to common beliefs, we find that controlled goods are consistently available for sale. Third, firms compensate for price controls by introducing new product varieties at higher prices, thereby increasing price dispersion within narrow categories. Overall, our results show that targeted price controls are just as ineffective as more traditional forms of price controls in reducing aggregate 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

Brazil Market Expectation: Inflation: Accumulated Over Next 12 Months: Extended National Consumer Price Index (IPCA): Smoothed: Average data was reported at 3.740 % in Jun 2019. This records an increase from the previous number of 3.590 % for May 2019. Brazil Market Expectation: Inflation: Accumulated Over Next 12 Months: Extended National Consumer Price Index (IPCA): Smoothed: Average data is updated monthly, averaging 5.240 % from Dec 2001 (Median) to Jun 2019, with 211 observations. The data reached an all-time high of 12.100 % in Dec 2002 and a record low of 3.440 % in May 2007. Brazil Market Expectation: Inflation: Accumulated Over Next 12 Months: Extended National Consumer Price Index (IPCA): Smoothed: Average data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SB032: Market Expectation: Inflation: Accumulated Over Next 12 Months: Extended National Consumer Price Index (IPCA): Smoothed. Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Monitors the variations in costs of people earning from one to forty minimum wages in the metropolitan areas of Belém, Belo Horizonte, Curitiba, Fortaleza, Porto Alegre, Recife, Rio de Janeiro, Salvador, São Paulo, municipality of Goiânia and Federal District. The IPCA measures the change in cost of expenses as described above in the period from the first to the last day of each reference month. In the period from the eleventh day to the twenty day of the following month the IBGE announces the variations.

Graph and download economic data for Producer Price Index by Industry: Relay and Industrial Control Manufacturing: Specific-Purpose Industrial Controls (DISCONTINUED) (PCU3353143353143Z) from Jun 1985 to Dec 2017 about manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Commodity: Miscellaneous Products: Signs and Advertising Displays (WPU159A04) from Dec 1985 to Aug 2025 about advertisement, miscellaneous, commodities, PPI, inflation, price index, indexes, price, and USA.

This scatter chart displays inflation (annual %) against central government debt (% of GDP) in Germany. The data is about countries per year.

Graph and download economic data for Producer Price Index by Industry: Internet Publishing and Web Search Portals: Internet Publishing and Web Search Portals - Display and Other Advertising Sales (PCU519130519130102) from Dec 2009 to Oct 2020 about advertisement, internet, printing, sales, PPI, industry, inflation, price index, indexes, price, and USA.

Russia Consumer Price Index (CPI): Same Mth PY=100: Computers: Monitor for Computer data was reported at 100.260 Same Mth PY=100 in Dec 2018. This records an increase from the previous number of 99.460 Same Mth PY=100 for Nov 2018. Russia Consumer Price Index (CPI): Same Mth PY=100: Computers: Monitor for Computer data is updated monthly, averaging 97.030 Same Mth PY=100 from Jan 2003 (Median) to Dec 2018, with 192 observations. The data reached an all-time high of 131.890 Same Mth PY=100 in Oct 2015 and a record low of 91.770 Same Mth PY=100 in Sep 2011. Russia Consumer Price Index (CPI): Same Mth PY=100: Computers: Monitor for Computer data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA015: Consumer Price Index: Same Month Previous Year=100: Non Food.

Russia Consumer Price Index (CPI): Prev Month=100: Computers: Monitor for Computer data was reported at 100.520 Prev Mth=100 in Dec 2018. This records an increase from the previous number of 99.950 Prev Mth=100 for Nov 2018. Russia Consumer Price Index (CPI): Prev Month=100: Computers: Monitor for Computer data is updated monthly, averaging 99.775 Prev Mth=100 from Jan 2002 to Dec 2018, with 204 observations. The data reached an all-time high of 112.250 Prev Mth=100 in Dec 2014 and a record low of 98.300 Prev Mth=100 in May 2003. Russia Consumer Price Index (CPI): Prev Month=100: Computers: Monitor for Computer data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA010: Consumer Price Index: Previous Month=100: Non Food.

This scatter chart displays inflation (annual %) against central government debt (% of GDP) in Japan. The data is about countries per year.