Graph and download economic data for Consumer Price Index for All Urban Consumers: Purchasing Power of the Consumer Dollar in U.S. City Average (CUUR0000SA0R) from Jan 1913 to May 2025 about urban, consumer, CPI, inflation, price index, indexes, price, and USA.

The Gross Domestic Product per capita in the United States was last recorded at 75491.61 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in the United States, when adjusted by Purchasing Power Parity is equivalent to 425 percent of the world's average. This dataset provides - United States GDP per capita PPP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

When inflation occurs in a country, the value of the currency decreases. That means that the purchasing power consumers have with a fixed amount of money decreases. Wages, especially lower and middle class wages, usually increase at a MUCH slower rate than prices of consumer goods; so consumers are likely to make the same wage, but are not able to buy the same amount of goods and services. Consumers in countries with hyperinflation suffer greatly because of this economic phenomenon.

Data was downloaded from: Link

For notes/metadata regarding the definition, measurement, or data collection for a certain country or group can be found by downloading the excel file from the linked webpage.

Original data provider: International Monetary Fund, World Development Indicators. License : CC BY-4.0.

INDICATOR_CODE: FP.CPI.TOTL.ZG

INDICATOR_NAME: Inflation, consumer prices (annual %)

SOURCE_NOTE: 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.

Years included: 1960-2016

The following countries have no values for any year:

• Somalia

• Puerto Rico

• Guam

• US Virgin Islands

The dataset also conains some records that refer to groups of countries, which may be useful for those with no recorded values. Some of those groups are:

• Fragile and conflict affected situations

• Heavily indebted poor countries (HIPC)

• Caribbean small states

• Latin America & Caribbean (excluding high income)

• Latin America & the Caribbean (IDA & IBRD countries)

• East Asia & Pacific (excluding high income)

• East Asia & Pacific (IDA & IBRD countries)

• Least developed countries: UN classification

• Middle East & North Africa (IDA & IBRD countries)

If this data is being used for the Kiva Crowdfunding Data Science for Good event; The following countries (as they are named in this dataset), are named slightly differently in the Kiva dataset (to the best of my knowledge). For example, West Bank in Gaza is referred to as Palestine in the Kiva Dataset.

• Congo, Dem. Rep.

• Congo, Rep.

• Kyrgyz Republic

• Lao PDR

• Myanmar

• West Bank and Gaza

• St. Vincent and the Grenadines

• Virgin Islands (U.S.)

• Yemen, Rep.

The Consumer Price Index (CPI) is a statistical measure that tracks the average change over time in the prices paid by consumers for a basket of goods and services. It serves as a key indicator of inflation, reflecting the cost of living and the purchasing power of a currency. Calculated periodically, the CPI is used by governments, economists, and policymakers to make informed decisions on monetary policy, wage negotiations, and economic forecasting. By comparing the CPI across different periods, one can gauge the health of an economy, understand inflationary pressures, and assess the impact of economic policies on everyday consumer expenses.

Annual Consumer Price Index (CPI) values for most countries in the world, measured relative to the reference year of 2005 (where the value of CPI for all countries is 100). The data, collected by The World Bank from 1960 to 2011, can be used to track inflation rates and analyze changes in purchasing power over time. However, it should be noted that there are some missing values in the dataset which may require users to make educated guesses. The data can be downloaded through The World Bank's API in CSV format, making it easily accessible for analysis and use in various applications.

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.

The Gross Domestic Product per capita in India was last recorded at 9817.07 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in India, when adjusted by Purchasing Power Parity is equivalent to 55 percent of the world's average. This dataset provides - India GDP per capita PPP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

US Recorded Music Revenues by Format

US Recorded Music Revenues by Format in 2019 (Inflation Adjusted)

By Throwback Thursday [source]

About this dataset

This dataset contains comprehensive information about the US recorded music industry in 2019 Week 10. It includes details on the various formats of recorded music, such as CDs, vinyl records, digital downloads, and more. The dataset also provides data on the respective years in which these records were made, allowing for accurate historical comparison and analysis.

Key metrics provided include the number of units sold for each format, as well as corresponding revenue generated from their sales. In addition to the raw revenue figures, this dataset offers an extra column that presents inflation-adjusted revenue values. These adjusted figures take into account changes in purchasing power over time and enable a fair comparison of different years' revenues.

Overall, this dataset offers valuable insights into the US recorded music industry's performance in terms of format popularity and economic gains throughout a specific week in 2019. Researchers, analysts, and music professionals can utilize this comprehensive dataset to explore trends within specific formats while considering both absolute revenue and inflation-adjusted figures

How to use the dataset

Introduction:

• Understanding the Columns: a) Format: This column categorizes the format of the recorded music, such as CD, vinyl, digital download, etc. b) Year: This column represents the year in which the data was recorded. c) Units: The number of units sold for a particular format of recorded music. d) Revenue: The revenue generated from sales for a specific format. e) Revenue (Inflation Adjusted): The column that shows revenue adjusted for inflation.

• Analyzing Formats: By exploring and analyzing the Format column in this dataset, you can gain insights into changing consumer preferences over time. You can identify which formats have gained popularity or declined over different years or periods.

• Understanding Revenue Generation: To understand revenue patterns in relation to various formats and years, analyze both Revenue and Revenue (Inflation Adjusted) columns separately. Comparing these two columns will help you assess changes due to inflation accurately.

• Exploring Units Sold: The column Units provides insight into how many units were sold for each format within a specific year or period. Analyzing this data helps understand consumer demand across various formats.

• Calculating Inflation-Adjusted Revenue: Utilize the Revenue (Inflation Adjusted) column when analyzing long-term trends or comparisons across different periods without worrying about how inflation affects purchasing power over time.

• Comparing Multiple Years or Periods: This dataset includes information specifically for 2019 Week 10. However, you can use this dataset in conjunction with other datasets covering different years to compare revenue, units sold, and format performance across multiple years.

• Creating Visualizations: Visualizations such as line charts or bar graphs can help represent patterns and trends more comprehensively. Consider creating visualizations based on formats over multiple years or comparing revenue generated by different formats.

• Deriving Insights: Make use of the information provided to identify trends, understand customer preferences, and make informed decisions related to marketing strategies or product offerings in the music industry.

Conclusion:

Research Ideas

• Analyzing the impact of different music formats on revenue: This dataset provides information on the revenue and units sold for different recorded music formats such as CDs, vinyl, and digital downloads. By analyzing this data, one can identify which format generates the highest revenue and understand how consumer preferences have shifted over time.
• Tracking changes in purchasing power over time: The dataset includes both revenue and inflation-adjusted revenue figures, allowing for a comparison of how purchasing power has changed over the years. This can be useful in understanding trends in consumer spending habits or evaluating the success of marketing campaigns.
• Assessing market performance by year: With data on both units sold and revenue by year, this dataset can be used to assess the overall performance of the US recorded music industry over time. By comparing different years, one can identify periods of growth or decline and gain insights into factors driving these changes, such as technological advancements or shifts in consumer behavior

Acknowledgements

&...

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

This dataset provides statistics on real gross domestic product (GDP) and real GDP per capita for subnational regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Regional gross domestic product data is collected at current prices, in millions of national currency from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites.

To allow comparability over time and between countries, data at current prices are transformed into constant prices and purchasing power parity measures. Regional GDP per capita is calculated by dividing regional GDP by the average annual population of the region.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

The Gross Domestic Product per capita in Germany was last recorded at 62829.80 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in Germany, when adjusted by Purchasing Power Parity is equivalent to 354 percent of the world's average. This dataset provides the latest reported value for - Germany GDP per capita PPP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Table 4: June 19 to Mar 21. Table 8: Jan 2018 to Dec 19

"The Classification of Individual Consumption According to Purpose (COICOP) is the international reference classification of household expenditure. The objective of COICOP is to provide a framework of homogeneous categories of goods and services, which are considered a function or purpose of household consumption expenditure. COICOP is an integral part of the System of National Accounts (SNA), but it is also used in several other statistical areas, such as: household expenditure statistics based on household budget surveys and the analysis of living standards; consumer price indi-ces; international comparisons of gross domestic product (GDP) and its component expenditures through purchasing power parities; and statistics relating to culture, sports, food, health, and tourism." Reference; Classification of Individual Consumption According to Purpose (COICOP) 2018, United Nations Department of Economic and Social Affairs, Statistics Division, Statistical Papers Series M No. 99

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

The OECD Main Science and Technology Indicators (MSTI) provide a set of indicators that compare the Science and Technology (S&T) performance of OECD member countries and selected non-member economies. The MSTI database focuses principally on tracking financial and human resources devoted to research and experimental development (R&D), as defined in the OECD Frascati Manual, complemented by additional indicators of outputs and potential outcomes of S&T activities, namely patent data and international trade in R&D-intensive industries. MSTI also comprises several OECD economic and demographic statistical series which are used to calculate relevant benchmarks that account for differences in the relative size of economies, purchasing power and the effect of inflation.

African Country data can be downloaded from the IMF for: Current account balance,- Employment,- General government gross debt,- General government net debt,- General government net lending/borrowing,- General government primary net lending/borrowing,- General government revenue,- General government structural balance,- General government total expenditure,- Gross domestic product based on purchasing-power-parity (PPP) share of world total,- Gross domestic product corresponding to fiscal year, current prices,- Gross domestic product per capita, constant prices,- Gross domestic product per capita, current prices,- Gross domestic product, constant prices,- Gross domestic product, current prices,- Gross domestic product, deflator,- Gross national savings,- Implied PPP conversion rate,- Inflation, average consumer prices,- Inflation, end of period consumer prices,- Output gap in percent of potential GDP,- Population,- Six-month London interbank offered rate (LIBOR),- Total investment,- Unemployment rate,- Volume of exports of goods,- Volume of exports of goods and services,- Volume of Imports of goods,- Volume of imports of goods and services,- IMF Copyright and Usage here https://www.imf.org/external/terms.htm

This paper investigates the long-run purchasing power parity hypothesis when exchange rate returns and inflation rates are assumed to be heavy-tailed stochastic processes. More specifically, residual-based and likelihood-ratio-based cointegration tests of PPP that explicitly allow for infinite-variance innovations are applied to monthly data (1973:1-1999:12) for Belgium, Canada, Denmark, France, Germany, Italy, Japan, the Netherlands, Norway, Spain, Sweden, and the United Kingdom. Our test results are marginally less supportive of PPP when the innovations are assumed to be infinite-variance, α-stable processes.

The Gross Domestic Product per capita in the United Kingdom was last recorded at 52517.98 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in the United Kingdom, when adjusted by Purchasing Power Parity is equivalent to 296 percent of the world's average. This dataset provides the latest reported value for - United Kingdom GDP per capita PPP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product per capita in New Zealand was last recorded at 48162.86 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in New Zealand, when adjusted by Purchasing Power Parity is equivalent to 271 percent of the world's average. This dataset provides - New Zealand GDP per capita PPP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The USD/RUB exchange rate fell to 78.7495 on July 4, 2025, down 0.41% from the previous session. Over the past month, the Russian Ruble has weakened 1.94%, but it's up by 10.51% over the last 12 months. Russian Ruble - values, historical data, forecasts and news - updated on July of 2025.

The USD/JPY exchange rate fell to 144.4120 on July 4, 2025, down 0.20% from the previous session. Over the past month, the Japanese Yen has weakened 0.60%, but it's up by 10.16% over the last 12 months. Japanese Yen - values, historical data, forecasts and news - updated on July of 2025.