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.

Inflation Rate in Japan decreased to 2.70 percent in August from 3.10 percent in July of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Consumer price inflation statistics are important indicators of how the UK economy is performing. They are used in many ways by individuals, government, businesses and academics. Inflation statistics impact on everyone in some way as they affect interest rates, tax allowances, benefits, pensions, savings rates, maintenance contracts and many other payments. This article provides information about the users and uses of consumer price inflation statistics, and user experiences of these statistics, including the new CPIH and RPIJ measures. In addition, it also provides information on the characteristics of the different measures of consumer price inflation in relation to their potential use. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: consumer price inflation statistics

Core consumer prices in the United States increased 3.10 percent in August of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - 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 Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

The Harmonised Index of Consumer Prices (HICP) gives comparable measures of inflation for the countries and country groups for which it is produced. It is an economic indicator that measures the change over time of the prices of consumer goods and services acquired by households. In other words, it is a set of consumer price indices (CPIs) calculated according to a harmonised approach and a set of definitions as laid down in Regulations and Recommendations.

In addition, the HICP provides the official measure of consumer price inflation in the euro area for the purposes of monetary policy and the assessment of inflation convergence as required under the Maastricht criteria for accession to the euro.

The HICP is available for all EU Member States, Iceland, Norway and Switzerland. In addition to the individual country series there are three country groups: the euro area (EA), the European Union (EU), and the European Economic Area (EEA), the latter covering Iceland and Norway, in addition to the EU. Liechtenstein does not produce HICP and is therefore not included in the EEA HICP aggregate.

The official indices for the country-groups reflect the changing country composition of the EA, the EU and the EEA. The HICP for new Member States is chained into the aggregate indices at the time of accession. For analytical purposes Eurostat also computes country-group indices with stable country composition over time.

HICP for Albania, Montenegro, North Macedonia, Serbia, Türkiye (candidate countries), as well as Kosovo (^*) are also published. Their data is flagged with 'd' ('definition differs'), given that its conformity with the methodological HICP requirements has not been evaluated by Eurostat.

A proxy-HICP for the all-items index and main headings is also available for the USA.

National HICPs are produced by National Statistical Institutes (NSIs), while country-group indices (EU, EA and EEA) are produced by Eurostat.

The data are released monthly in Eurostat's database and include price indices and rates of change (monthly, annual and 12-month moving average changes). In addition to the headline 'all-items HICP', over 400 sub-indices for different goods and services and over 30 special aggregates are available, including the HICP at administered prices (HICP-AP).

Every year, with the release of the January data, the relative weights for the indices and the special aggregates (item weights) as well as the individual countries' weight within the country groups (country weights) are published.

The composition of the HICP for administered prices (HICP-AP), i.e. which sub-indices are classified as mainly or fully administered by each Member State, is updated at the same time (more information on HICP-AP can be found under the Specific topics on the web page: Information on data - Harmonised Indices of Consumer Prices (HICP) - Eurostat (europa.eu) (#HICP - administered prices).

Eurostat publishes early estimates, called 'flash estimate', of the euro area overall inflation rate and selected components. These are published monthly, usually on the last working day of the reference month.

The HICP at constant tax rates (HICP-CT) is also published every month and follows the same computation principles as the HICP, but is based on prices at constant tax rates. The comparison with the standard HICP can show the potential impact of changes in indirect taxes, such as value-added tax (VAT) and excise duties, on the overall inflation (more information can be found in the 'HICP-CT Reference methodology document').

Flags

Flags used in the Eurostat online database provide information about the status of the data or a specific data value. The list of used flags can be found in the web page Database - Eurostat (europa.eu), above the tree, through the 'i' box 'information on the database' and then 'Flags and special values' topic.

(*) Under United Nations Security Council Resolution 1244/99.

This table contains 13 series, with data from 1949 (not all combinations necessarily have data for all years). Data are presented for the current month and previous four months. Users can select other time periods that are of interest to them.

Inflation Rate in India increased to 2.07 percent in August from 1.61 percent in July of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

The International Macroeconomic Data Set provides data from 1969 through 2030 for real (adjusted for inflation) gross domestic product (GDP), population, real exchange rates, and other variables for the 190 countries and 34 regions that are most important for U.S. agricultural trade. The data presented here are a key component of the USDA Baseline projections process, and can be used as a benchmark for analyzing the impacts of U.S. and global macroeconomic shocks.

Real interest rates refer to the nominal interest rate adjusted for inflation, and are an important economic indicator that can have significant impacts on investment, savings, and overall economic growth. Real interest rates can affect the demand for goods and services, investment decisions, and borrowing costs, among other things.

The real interest rates per country dataset provides a comprehensive overview of the real interest rates of each country. The dataset includes information on the real interest rates, covering all countries in the world. It is compiled from various sources, including national central banks, international financial institutions such as the International Monetary Fund (IMF), and other relevant data sources.

The real interest rates per country dataset can be used by researchers, policymakers, and investors to gain insight into the economic conditions of different countries and to compare the relative levels of real interest rates across the world. It can also be used to monitor changes in real interest rates over time and to evaluate the effectiveness of monetary policies and strategies.

Overall, the real interest rates per country dataset is an important resource for understanding the economic conditions of different countries and for developing policies and strategies that promote sustainable economic growth and stability.

Techsalerator's News Events Data for Senegal: A Comprehensive Overview

Techsalerator's News Events Data for Senegal offers a valuable resource for businesses, researchers, and media organizations. This dataset compiles information on significant news events across Senegal, sourced from a wide array of media outlets, online publications, and social platforms. It provides insights for tracking trends, analyzing public sentiment, or monitoring developments within specific industries.

Key Data Fields - Event Date: Records the exact date of the news event. This is essential for analysts tracking trends over time or businesses responding to market shifts. - Event Title: A concise headline describing the event. This allows users to quickly categorize and assess news content based on their interests. - Source: Indicates the news outlet or platform where the event was reported. This helps users evaluate the credibility and reach of the event. - Location: Provides geographic information about where the event occurred within Senegal. This is particularly useful for regional analysis or targeted marketing efforts. - Event Description: A detailed summary of the event, outlining key developments, participants, and potential impact. This helps researchers and businesses understand the context and implications of the event.

Top 5 News Categories in Senegal - Politics: Coverage of government decisions, political movements, elections, and policy changes affecting the national landscape. - Economy: Focuses on Senegal’s economic indicators, inflation rates, international trade, and corporate activities impacting the business and finance sectors. - Social Issues: News on protests, public health, education, and other societal concerns driving public discourse. - Sports: Highlights events in football, athletics, and other popular sports, often drawing significant attention and engagement nationwide. - Technology and Innovation: Reports on tech developments, startups, and advancements in Senegal’s growing tech sector, featuring emerging companies and technological progress.

Top 5 News Sources in Senegal - Le Soleil: A major news outlet providing comprehensive coverage of national politics, economy, and social issues. - Seneweb: A popular online platform known for timely updates on breaking news, politics, and current affairs. - Walfadjri: A widely-read newspaper offering insights into local politics, economic developments, and societal trends. - RFI (Radio France Internationale): Provides in-depth reports on various topics, including politics, economy, and social issues in Senegal. - Sud Quotidien: Covers a broad spectrum of topics, including politics, economy, and social issues, with a focus on local developments.

Accessing Techsalerator’s News Events Data for Senegal To access Techsalerator’s News Events Data for Senegal, please contact info@techsalerator.com with your specific requirements. We will provide a customized quote based on the data fields and records you need, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields - Event Date - Event Title - Source - Location - Event Description - Event Category (Politics, Economy, Sports, etc.) - Participants (if applicable) - Event Impact (Social, Economic, etc.)

Techsalerator’s dataset is an essential tool for tracking significant events in Senegal. It supports informed decision-making, whether for business strategy, market analysis, or academic research, providing a comprehensive view of the country's news landscape.

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Harrison Town, Waupaca County, Wisconsin, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Harrison Town, Waupaca County, Wisconsin reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Harrison town households based on income levels.

Key observations

• For Family Households: In Harrison town, the majority of family households, representing 33.78%, earn $75,000 to $99,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.0%, have incomes falling $100,000 to $124,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Harrison town, the majority of non-family households, accounting for 18.67%, have income $75,000 to $99,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 0.0%, earn $100,000 to $124,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Harrison Town, Waupaca County, Wisconsin (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Harrison Town, Waupaca County, Wisconsin
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Harrison Town, Waupaca County, Wisconsin
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Harrison Town, Waupaca County, Wisconsin

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Harrison town median household income. You can refer the same here

Inflation Rate in Vietnam increased to 3.24 percent in August from 3.19 percent in July of 2025. This dataset provides the latest reported value for - Vietnam Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Phoenix, AZ, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Phoenix, AZ reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Phoenix households based on income levels.

Key observations

• For Family Households: In Phoenix, the majority of family households, representing 14.15%, earn $75,000 to $99,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 2.02%, have incomes falling $150,000 to $199,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Phoenix, the majority of non-family households, accounting for 11.06%, have income $75,000 to $99,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 2.97%, earn $150,000 to $199,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Phoenix, AZ (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Phoenix, AZ
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Phoenix, AZ
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Phoenix, AZ

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Phoenix median household income. You can refer the same here

ABSTRACT This paper develops a model in which the distributive conflict between capital and labor is the driving force which generates inflationary pressures in a market economy. In the model the rate of inflation is a function of the capacity of firms to pass increases in costs to prices and of the relative power of workers and employees associations in the process of collective bargaining. One of the main results of this analytical framework is that the structure of the capital/labor relations in a country, the process of collective bargaining and the structure of unions organizations are important determinants of inflationary pressures. As a result, institutional reforms which promote cooperation on capital/labor relations are of great importance in stabilization policies, if the social costs of stabilization are to be minimized.

Dataset Description Title: Electricity Market Dataset for Long-Term Forecasting (2018–2024)

Overview: This dataset provides a comprehensive collection of electricity market data, focusing on long-term forecasting and strategic planning in the energy sector. The data is derived from real-world electricity market records and policy reports from Germany, specifically the Frankfurt region, a major European energy hub. It includes hourly observations spanning from January 1, 2018, to December 31, 2024, covering key economic, environmental, and operational factors that influence electricity market dynamics. This dataset is ideal for predictive modeling tasks such as electricity price forecasting, renewable energy integration planning, and market risk assessment.

Features Description Feature Name Description Type Timestamp The timestamp for each hourly observation. Datetime Historical_Electricity_Prices Hourly historical electricity prices in the Frankfurt market. Continuous (Float) Projected_Electricity_Prices Forecasted electricity prices (short, medium, long term). Continuous (Float) Inflation_Rates Hourly inflation rate trends impacting energy markets. Continuous (Float) GDP_Growth_Rate Hourly GDP growth rate trends for Germany. Continuous (Float) Energy_Market_Demand Hourly electricity demand across all sectors. Continuous (Float) Renewable_Investment_Costs Investment costs (capital and operational) for renewable energy projects. Continuous (Float) Fossil_Fuel_Costs Costs for fossil fuels like coal, oil, and natural gas. Continuous (Float) Electricity_Export_Prices Prices for electricity exports from Germany to neighboring regions. Continuous (Float) Market_Elasticity Sensitivity of electricity demand to price changes. Continuous (Float) Energy_Production_By_Solar Hourly solar energy production. Continuous (Float) Energy_Production_By_Wind Hourly wind energy production. Continuous (Float) Energy_Production_By_Coal Hourly coal-based energy production. Continuous (Float) Energy_Storage_Capacity Available storage capacity (e.g., batteries, pumped hydro). Continuous (Float) GHG_Emissions Hourly greenhouse gas emissions from energy production. Continuous (Float) Renewable_Penetration_Rate Percentage of renewable energy in total energy production. Continuous (Float) Regulatory_Policies Categorical representation of regulatory impact on electricity markets (e.g., Low, Medium, High). Categorical Energy_Access_Data Categorization of energy accessibility (Urban or Rural). Categorical LCOE Levelized Cost of Energy by source. Continuous (Float) ROI Return on investment for energy projects. Continuous (Float) Net_Present_Value Net present value of proposed energy projects. Continuous (Float) Population_Growth Population growth rate trends impacting energy demand. Continuous (Float) Optimal_Energy_Mix Suggested optimal mix of renewable, non-renewable, and nuclear energy. Continuous (Float) Electricity_Price_Forecast Predicted electricity prices based on various factors. Continuous (Float) Project_Risk_Analysis Categorical analysis of project risks (Low, Medium, High). Categorical Investment_Feasibility Indicator of the feasibility of energy investments. Continuous (Float) Use Cases Electricity Price Forecasting: Utilize historical and projected price trends to predict future electricity prices. Project Risk Classification: Categorize projects into risk levels for better decision-making. Optimal Energy Mix Analysis: Analyze the balance between renewable, non-renewable, and nuclear energy sources. Policy Impact Assessment: Study the effect of regulatory and market policies on energy planning. Long-Term Strategic Planning: Provide insights into investment feasibility, GHG emission reduction, and energy market dynamics. Acknowledgment This dataset is based on publicly available records and market data specific to the Frankfurt region, Germany. The dataset is designed for research and educational purposes in energy informatics, computational intelligence, and long-term forecasting.

Rice is a crucial crop in Sri Lanka, influencing both its agricultural and economic landscapes. This study delves into the complex interplay between economic indicators and rice production, aiming to uncover correlations and build prediction models using machine learning techniques. The dataset, spanning from 1960 to 2020, includes key economic variables such as GDP, inflation rate, manufacturing output, population, population growth rate, imports, arable land area, military expenditure, and rice production. The study’s findings reveal the significant influence of economic factors on rice production in Sri Lanka. Machine learning models, including Linear Regression, Support Vector Machines, Ensemble methods, and Gaussian Process Regression, demonstrate strong predictive accuracy in forecasting rice production based on economic indicators. These results underscore the importance of economic indicators in shaping rice production outcomes and highlight the potential of machine learning in predicting agricultural trends. The study suggests avenues for future research, such as exploring regional variations and refining models based on ongoing data collection.

Inflation Rate in Brazil decreased to 5.13 percent in August from 5.23 percent in July of 2025. This dataset provides - Brazil Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.