Consumer Price Index CPI in the United States increased to 321.47 points in May from 320.80 points in April of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

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.

Consumer Price Index: Shadow data was reported at 416.200 1980=100 in Mar 2025. This records a decrease from the previous number of 419.080 1980=100 for Feb 2025. Consumer Price Index: Shadow data is updated monthly, averaging 273.640 1980=100 from Jan 1980 (Median) to Mar 2025, with 543 observations. The data reached an all-time high of 419.080 1980=100 in Feb 2025 and a record low of 95.300 1980=100 in Jan 1980. Consumer Price Index: Shadow data remains active status in CEIC and is reported by Statistics Sweden. The data is categorized under Global Database’s Sweden – Table SE.I005: Consumer Price Index: Fixed and Shadow: 1980=100. Shadow Index is being used to calculate CPI. The shadow index numbers are adjusted to take into account certain inadequacies in the basic data on prices or in the methods used for calculating fixed index numbers. [COVID-19-IMPACT]

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

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

The Consumer Price Index in the United States increased 0.10 percent in May of 2025 over the previous month. This dataset provides - United States Inflation Rate MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

Graph and download economic data for Producer Price Index by Commodity: All Commodities (PPIACO) from Jan 1913 to May 2025 about commodities, PPI, inflation, price index, indexes, price, and USA.

Consumer Price Index CPI in Egypt increased to 239.20 points in January from 235.60 points in December of 2024. This dataset provides - Egypt Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Price Index CPI in Saudi Arabia increased to 113.46 points in April from 113.10 points in March of 2025. This dataset provides - Saudi Arabia Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate In the Euro Area decreased to 1.90 percent in May from 2.20 percent in April of 2025. This dataset provides the latest reported value for - Euro Area Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Consumer Price Index CPI in Vietnam increased to 118.93 points in May from 118.74 points in April of 2025. This dataset provides - Vietnam Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Price Index CPI in Pakistan decreased to 263.60 points in May from 264.06 points in April of 2025. This dataset provides - Pakistan Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

1. Temporal Coverage of Data: The data collection periods are 2010, 2030, and 2050.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope: The data pertains to the fields of Earth Sciences and Geography.4. Data Volume: The total data volume is approximately 31.5 MB.5. Data Type: Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene): · 7. Field (Feature) Name Explanation:a. Name Explanation: IND: Industrial Value Addedb. Unit of Measurement: Unit: US Dollars (USD)8. Data Source Description:a. Remote Sensing Data:2010 Global Vegetation Index data (Enhanced Vegetation Index, EVI, from MODIS monthly average data) and 2010 Nighttime Light Remote Sensing data (DMSP/OLS)b. Meteorological Data:From the CMCC-CM model in the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC)c. Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agenciesd. Gross Domestic Product Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencese. Other Data:Rivers, roads, settlements, and DEM, sourced from the National Oceanic and Atmospheric Administration (NOAA), Global Risk Data Platform, and Natural Earth9. Data Processing Methods(1) Spatialization of Baseline Industrial Value Added: Using 2010 global EVI vegetation index data and nighttime light remote sensing data, we addressed the oversaturation issue in nighttime light data by constructing an adjusted nighttime light index to obtain the optimal global light data. The EANTIL model was developed using NTL, NTLn, and EVI data, with the following formula:Here, EANTLI represents the adjusted nighttime light index, NTL represents the original nighttime light intensity value, and NTLn represents the normalized nighttime light intensity value. Based on the optimal light index EANTLI and the industrial value-added data from the World Bank, we constructed a regression allocation model to derive industrial value added (I), generating the global 2010 industrial value-added data with the formula:Here, I represents the industrial value added for each grid cell, and Ii represents the industrial value added for each country, EANTLi derived from ArcGIS statistical analysis and the regression allocation model.(2) Spatial Boundaries for Future Industrial Value Added: Using the Logistic-CA-Markov simulation principle and global land use data from 2010 and 2015 (from the European Space Agency), we simulated national land use changes for 2030 and 2050 and extracted urban land data as the spatial boundaries for future industrial value added. To comprehensively characterize the influence of different factors on land use and considering the research scale, we selected elevation, slope, population, GDP, distance to rivers, and distance to roads as land use driving factors. Accuracy validation using global 2015 land use data showed an average accuracy of 91.89%.(3) Estimation of Future Industrial Value Added: Based on machine learning and using the random forest model, we constructed spatialization models for industrial value added under different climate change scenarios: Here, tem represents temperature, prep represents precipitation, GDP represents national economic output, L represents urban land, D represents slope, and P represents population. The random forest model was constructed using factors such as 2010 industrial value added, urban land distribution, elevation, slope, distances to rivers, roads, railways (considering transportation), and settlements (considering noise and environmental pollution from industrial buildings), along with temperature and precipitation as climate scenario data. Except for varying temperature and precipitation values across scenarios, other variables remained constant. The model comprised 100 decision trees, with each iteration randomly selecting 90% of the samples for model construction and using the remaining 10% as test data, achieving a training sample accuracy of 0.94 and a test sample accuracy of 0.81.By analyzing the proportion of industrial value added to GDP (average from 2000 to 2020, data from the World Bank) and projected GDP under future Shared Socioeconomic Pathways (SSPs), we derived future industrial value added for each country under different SSP scenarios. Using these projections, we constructed regression models to allocate future industrial value added proportionally, resulting in spatial distribution data for 2030 and 2050 under different SSP scenarios.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports and Articles):Achievements: Developed a method for downscaling national-scale industrial value-added data by integrating DMSP/OLS nighttime light data, vegetation distribution, and other data. Published the global industrial value-added dataset.

This large longitudinal study is the result of professor Tatu Vanhanen's long-term research on democratization and power resources. International scientific community knows this data also by the name "Vanhanen's Index of Power Resources". The data have been collected from several written sources and have been published as appendices of five different books. The books are listed in the section Data sources below. The original sources of the numerical data published in these books have been collected to a separate document containing background information. Vanhanen divides the variables of his dataset into two main groups. The first group consists of Measures of Democracy and includes three variables. The second group is called Measures of Resource Distribution. The variables in the first group (Measures of Democracy) are Competition, Participation and Index of Democratization. The value of Competition is calculated by subtracting the percentage of votes/seats gained by the largest political party in parliamentary elections and/or in presidential (executive) elections from 100%. The Participation variable is an aggregate of the turnout in elections (percentage of the total population who voted in the same election) and the number of referendums. Each national referendum raises the value of Participation by five percentage points and each state referendum by one percentage point for the year of the referendum. The upper limit for both variables is 70%. Index of Democratization is derived by first multiplying the above mentioned variables Competition and Participation and then dividing this product by 100. Six variables are used to measure resource distribution: 1) Urban Population (%) (as a percentage of total population). 2) Non-Agricultural Population (%) (derived by subtracting the percentage of agricultural population from 100%). 3) Number of students: the variable denotes how many students there are in universities and other higher education institutions per 100.000 inhabitants of the country. Two ways are used to calculate the percentage of Students (%): before the year 1988 the value 1000 of the variable Number of students is equivalent to 100% and between the years 1988-1998 the value 5000 of the same variable is equivalent to 100%. 4) Literates (%) (as a percentage of adult population). 5) Family Farms Are (%) (as a percentage of total cultivated area or of total area of holdings). 6) Degree of Decentralization of Non-Agricultural Economic Resources. This variable has been calculated from the 1970s. Three new variables have been derived from the above mentioned six variables. 1) Index of Occupational Diversification is derived by calculating the arithmetic mean of Urban Population and Non-Agricultural Population. 2) Index of Knowledge Distribution is derived by calculating the arithmetic mean of Students and Literates. 3) Index of Distribution of Economic Power Resources is derived by first multiplying the value of Family Farm Area with the percentage of agricultural population. Then the value of Degree of Decentralization of Non-Agricultural Economic Resources is multiplied with the percentage of Non-Agricultural Population. After this these two products are simply added up. Finally two new variables have derived from the above mentioned variables. First derived variable is Index of Power Resources, calculated by multiplying the values of Index of Occupational Diversification, Index of Knowledge Distribution and Index of the Distribution of Economic Power Resources and then dividing the product by 10 000. The second derived variable Mean is the arithmetic mean of the five (from the 1970s six) explanatory variables. This differs from Index of Power Resources in that a low value of any single variable does not reduce the value of Mean to any great extent.

Equations to calculate Important value index (IVI) of species.

Inflation Rate in Mexico increased to 4.42 percent in May from 3.93 percent in April of 2025. This dataset provides - Mexico Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Cost of food in India increased 0.99 percent in May of 2025 over the same month in the previous year. This dataset provides - India Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Pakistan increased to 3.50 percent in May from 0.30 percent in April of 2025. This dataset provides the latest reported value for - Pakistan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.