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.

Under the "Database(WORLDWIDE INFLATION SAYEEDIN.1.1.1.1.1)", there are seven data tables,and those data tables names are "Countries", "Inflation rate world", "Europe Inflation rate", "America Inflation Rate", "Asia Inflation Rate", "Australia", and "G-20".

However, under the "COUNTRIES" named data table, six fields, six columns, and fifty-four rows exist.

Moreover, under the "Inflation rate world" or "World Inflation Rate" named data-table, 4 fields, 4 columns, 186 rows exist.Also, on the basis of the selective data-ranges of the "World Inflation Rate" named data table, "P.Table Based on Global Rate-I" named pivot table has been made,and on the basis of the selective data ranges of the "P.Table Based on Global Rate-I" named pivot table, "Globally Inflation Rate Chart" named "Column Chart" has been generated.

Furthermore, under the "Inflation Rate| Europe"/ "Europe Inflation rate" named table, 4 fields, 4 columns, 46 rows exist.On the basis of the selective data-ranges("$A$3:$C$49) of the "Inflation Rate| Europe"/ "Europe Inflation rate" named table, "Pivot Tab on Europe Inflation" named pivot table has been made,and depending on this, "Europe Inflation Rate"/ "Pivot Chart on Europe Inflation" named bar chart has been made.

In addition,under the name of the table (“Inflation Rate |America” or “America Inflation Rate”), there are 4 columns, 4 fields, 33 rows. On the basis of the selective data ranges($A$5:$D$38) of the table,”P.Table on American Inflation” named pivot table has been made,and, on this pivot table, “P.Chart on American Inflation” or“P.CHART ON AMERICAN INFLATION RATE” named bar chart has been generated.

Besides, under “Inflation Rate|Asia”/ “Asia Inflation Rate” named data table, 4 columns, 4 fields, 47 rows exist.On the basis of the selective data ranges(“$A$5:$C$52”) of the existing table, “Chart Based on Asia Inflation R”/ “Line Chart on Asian Inflation Rate” named line chart has been made.

Also, under the name of the table “Australia”/ “Inflation Rate |Australia”, there are 4 columns,4 fields, and 7 rows. On the basis of the selective data ranges(“$E$6:$G$13”) of the existing table, “Australia Pivot Table” named pivot table has been made,and, relying on this existing pivot table, “Chart on Australia Inflation”/ “Australia Inflation Rate Chart” named column chart has been created.

And also, under the “Inflation Rate G-20”/ “G-20” named table, 4 fields, 4 columns , 24 rows exist.Depending on the chosen data ranges(“$A$6:$D$30”) of the existing table, “G-20 Inflation P.Table” named pivot table has been made,and so, relying on this pivot table “G-20 Inflation Rate Chart” named pivot column chart has been generated.

Be that as it may, on the “background Information” named sheet, the methodology of the data collection has been given.In data collection procedure’s case, which keyword/which search-term has been imputed in the search engine box,which web-browser has been used, from which website data has been collected, and website URL has been taken doing cut-copy-paste process, on the whole, all of these are given on the “Background Information” named existing sheet.

This dataset provides values for INFLATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

World vs. United States - Yearly data for GDP growth (annual %) vs. GNI growth (annual %) vs. Inflation, consumer prices (annual %) - filtered from 1996 to 2020. All data are from World Bank

CNBC Economy Articles Dataset is an invaluable collection of data extracted from CNBC’s economy section, offering deep insights into global and U.S. economic trends, market dynamics, financial policies, and industry developments.

This dataset encompasses a diverse array of economic articles on critical topics like GDP growth, inflation rates, employment statistics, central bank policies, and major global events influencing the market. Designed for researchers, analysts, and businesses, it serves as an essential resource for understanding economic patterns, conducting sentiment analysis, and developing financial forecasting models.

Dataset Highlights

Each record in the dataset is meticulously structured and includes:

• Article Titles
• Publication Dates
• Author Names
• Content Summaries
• URLs to Original Articles

This rich combination of fields ensures seamless integration into data science projects, research papers, and market analyses.

Key Features

• Number of Articles: Hundreds of articles sourced directly from CNBC.
• Data Fields: Includes title, publication date, author, article content, summary, URL, and relevant keywords.
• Topics Covered: U.S. and global economy, GDP trends, inflation, employment, financial markets, and monetary policies.
• Format: Delivered in CSV format for easy integration with research tools and analytical platforms.
• Source: Extracted directly from CNBC’s economy news section, ensuring accuracy and relevance.

Use Cases

• Economic Research: Gain insights into U.S. and global economic policies, market trends, and industry developments.
• Sentiment Analysis: Assess the sentiment of economic articles to gauge market perspectives and investor confidence.
• Financial Modeling: Build forecasting models leveraging key economic indicators discussed in the dataset.
• Content Creation: Develop research-backed reports, articles, and presentations on economic topics.

Who Benefits?

• Researchers & Academics studying macro-economics or financial policy.
  
• Data Scientists building AI models, trend analyzers, or economic forecasting tools.
  
• Economists & Analysts need real-world news data for policy analysis.
  
• Content Strategists who write data-backed articles about economic trends.

Why Choose This Dataset?

• No need to manually scrape CNBC — data is pre-extracted and clean.
  
• High-quality economy news metadata enables detailed filtering (by date, author, topic).
  
• Ready for machine learning, sentiment analysis, or building news-based economic models.
  
• Well-suited for trend tracking, policy analysis, and economic forecasting.

Explore More News Datasets

Interested in additional structured news datasets for your research or analytics needs? Check out our news dataset collection to find datasets tailored for diverse analytical applications.

Historical dataset showing U.S. inflation rate by year from 1960 to 2024.

Historical dataset showing Virgin Islands (U.S.) inflation rate by year from N/A to N/A.

United States Underlying Inflation Gauge: Full Data Set Measure data was reported at 2.874 % in Sep 2023. This records a decrease from the previous number of 3.032 % for Aug 2023. United States Underlying Inflation Gauge: Full Data Set Measure data is updated monthly, averaging 2.162 % from Jan 1995 (Median) to Sep 2023, with 345 observations. The data reached an all-time high of 6.318 % in Jun 2022 and a record low of -0.648 % in Sep 2009. United States Underlying Inflation Gauge: Full Data Set Measure data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.I: Underlying Inflation Gauge (Discontinued).

Harmonised Indices of Consumer Prices (HICPs) are designed for international comparisons of consumer price inflation. HICP is used for example by the European Central Bank for monitoring of inflation in the Economic and Monetary Union and for the assessment of inflation convergence as required under Article 121 of the Treaty of Amsterdam. For the U.S. and Japan national consumer price indices are used in the table.

Historical dataset showing North America inflation rate by year from 1960 to 2023.

Historical dataset showing Central America inflation rate by year from N/A to N/A.

United States US: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data was reported at 1.799 % in 2017. This records an increase from the previous number of 1.276 % for 2016. United States US: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data is updated yearly, averaging 1.978 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 3.699 % in 1990 and a record low of 0.759 % in 2009. United States US: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years.; ; World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.; ;

U.S. Public Debt vs. GDP

Trends and Comparisons

By Charlie Hutcheson [source]

About this dataset

This dataset contains quarterly data on the US Gross Domestic Product (GDP) and Total Public Debt from 1947 through 2020. It provides a comprehensive view into the development of debt versus GDP over the years, offering insights into how our economy has grown and changed since The Great Depression. Explore this valuable information to answer questions such as: How do debt and GDP relate to one another? Has US government spending been outpacing wealth throughout history? From what sources does our national debt originate? This dataset can be utilized by economists, governments, researchers, investors, financial institutions, journalists — anyone looking to gain a better understanding of where our economy stands today compared to past decades

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How to use the dataset

This dataset, U.S. GDP vs Debt Over Time, contains quarterly data on the Gross Domestic Product (GDP) and Total Public Debt of the United States between 1947 to 2020. This can be useful for conducting research into how the total public debt relates to economic growth in the US.

The dataset includes 4 columns: Quarter , Gross Domestic Product ($mil), Total Public Debt ($mil). The Quarter column consists of strings that represent each quarter from 1947-2020 with a corresponding number (e.g., “Q1-1947”). The Gross Domestic Product ($mil) and Total Public Debt ($mil) columns consist of numbers that indicate the respective amounts in millions for each quarter during this same time period.

By analyzing this dataset you can explore various trends over different periods as it relates to public debt versus economic growth in America and make informed decisions about how certain policies may affect future outcomes. Additionally, you could also compare these two values with other variables such as unemployment rate or inflation rate to gain deeper insights into America’s economy over time

Research Ideas

• Comparing the quarterly growth in GDP with public debt to show the correlation between economic growth and government spending.
• Creating a bar or line visualization that compares the US’s total public debt to comparable economic powers like China, Japan, and Europe over time.
• Examining how changes in government deficit have contributed towards an increase in public debt by analyzing which quarters saw significant leaps of growth from one year to the next

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: US GDP vs Debt.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------------------------------------------| | Quarter | The quarter of the year in which the data was collected. (String) | | Gross Domestic Product ($mil) | The total value of all goods and services produced by the US in a given quarter. (Integer) | | Total Public Debt ($mil) | The total amount owed by the federal government. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Charlie Hutcheson.

United States Breakeven Inflation: 5-Year data was reported at 2.300 % in 02 Dec 2025. This records a decrease from the previous number of 2.310 % for 01 Dec 2025. United States Breakeven Inflation: 5-Year data is updated daily, averaging 1.980 % from Jan 2003 (Median) to 02 Dec 2025, with 5733 observations. The data reached an all-time high of 3.590 % in 25 Mar 2022 and a record low of 0.140 % in 19 Mar 2020. United States Breakeven Inflation: 5-Year data remains active status in CEIC and is reported by Federal Reserve Bank of St. Louis. The data is categorized under Global Database’s United States – Table US.I: Breakeven Inflation Rate. [COVID-19-IMPACT]

Historical dataset showing Latin America & Caribbean inflation rate by year from 1967 to 2023.

We provide the data used for this research in both Excel (one file with one matrix per sheet, 'Allmatrices.xlsx'), and CSV (one file per matrix).

Patent applications (Patent_applications.csv) Patent applications from residents and no residents per million inhabitants. Data obtained from the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

High-tech exports (High-tech_exports.csv) The proportion of exports of high-level technology manufactures from total exports by technology intensity, obtained from the Trade Structure by Partner, Product or Service-Category database (Lall, 2000; UNCTAD, 2019)

Expenditure on education (Expenditure_on_education.csv) Per capita government expenditure on education, total (2010 US$). The data was obtained from the government expenditure on education (total % of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Scientific publications (Scientific_publications.csv) Scientific and technical journal articles per million inhabitants. The data were obtained from the scientific and technical journal articles and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Expenditure on R&D (Expenditure_on_R&D.csv) Expenditure on research and development. Data obtained from the research and development expenditure (% of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Two centuries of GDP (GDP_two_centuries.csv) GDP per capita that accounts for inflation. Data obtained from the Maddison Project Database, version 2018 (Inklaar et al. 2018), and available from the Open Numbers community (open-numbers.github.io).

Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing “Maddison”: new income comparisons and the shape of long-run economic development (GD-174; GGDC Research Memorandum). https://www.rug.nl/research/portal/files/53088705/gd174.pdf

Lall, S. (2000). The Technological Structure and Performance of Developing Country Manufactured Exports, 1985‐98. Oxford Development Studies, 28(3), 337–369. https://doi.org/10.1080/713688318

Unctad. 2019. “Trade Structure by Partner, Product or Service-Category.” 2019. https://unctadstat.unctad.org/EN/.

World Bank. (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

US Turkey Production

US Turkey Production: Value, Pounds, and Turkey Numbers

By Throwback Thursday [source]

About this dataset

The dataset titled Turkey Production in the US: 1984-2016 provides comprehensive information on the value of turkey production, pounds produced, and the number of turkeys raised in the United States. This dataset draws data from the United States Department of Agriculture Economic Research Service (USDA ERS) and covers a period spanning from 1984 to 2016.

This dataset includes multiple columns that offer crucial insights into turkey production trends over time. Firstly, there are columns dedicated to capturing the value of turkey production both in raw monetary terms and after adjusting for inflation. These values are reported in US dollars and serve as indicators of the economic significance and growth within this sector.

Additionally, this dataset presents data on pounds produced, which measures the total weight of turkeys produced within a given year. This information is essential for assessing production levels and fluctuations over time.

Moreover, another key column provides details on turkeys raised annually. This metric represents the total number of turkeys bred or developed during each specific year. By tracking changes in these figures across different years, it becomes possible to discern patterns related to turkey farming practices or industry demand.

In summary, this extensive dataset offers rich insights into various aspects relating to turkey production in the United States between 1984 and 2016. It covers significant variables such as value of production (adjusted for inflation), pounds produced, and number of turkeys raised throughout these years. With such detailed data available within this dataset, researchers can delve into analyzing historical trends while policymakers can make well-informed decisions based on an understanding of past developments in this crucial industry sector

How to use the dataset

This dataset provides information on the value of turkey production, pounds produced, and the number of turkeys raised in the United States from 1984 to 2016. It can be used for analysis or research purposes related to turkey production trends and patterns over this period.

Here's a guide on how to effectively utilize this dataset:

• Understanding the Columns:

  • Year: This column represents the year in which the data was recorded.
  • Value of Production: This column shows the total value of turkey production in dollars for a given year.
  • Value of Production - Inflation Adjusted: This column provides an adjusted value of turkey production, taking inflation into account.
  • Pounds Produced: This column displays the total weight of turkeys produced in pounds for a given year.
  • Turkeys Raised: This column indicates the total number of turkeys raised for a given year.

• Analyzing Turkey Production Trends: You can analyze how turkey production has changed over time by examining each variable individually or comparing them with each other. For example:

  • Plotting Year against Value of Production will give you an overview of how turkey production's value has evolved over the years.
  • Analyzing Pounds Produced and Turkeys Raised together could provide insights into productivity per bird.

• Identifying Factors Affecting Turkey Production: Use this dataset to investigate factors that may have influenced changes in turkey production from 1984-2016. Consider exploring these questions:

  • Are there any notable spikes or declines in value, pounds produced, or turkeys raised? What could be causing these patterns?
  • How does inflation-adjusted value differ from nominal value? Can you identify any trends related to economic conditions?

• Comparing Data Across Years: By grouping data by specific years or sets of years, you can make comparisons and identify trends. Some potential questions to explore include:

  • How has turkey production changed before and after significant events, such as economic recessions or disease outbreaks?
  • Have there been any notable shifts in turkey production methods or technology that may have affected the industry's performance?

• Potential Applications:

  • Researchers: This dataset can be valuable for researchers studying the economics and market dynamics of turkey production in the United States. You can use these data points to analyze long-term trends, identify influential factors, and develop predictive models.
  • Investors: Investors interested in the agriculture

Research Ideas

• Analyzing Trends: This dataset can be used to an...

Africa Inflation, Consumer Prices (annual %) Dataset

  Overview

This dataset contains inflation, consumer prices (annual %) data for African countries from the World Bank.

  Data Details

Indicator Code: FP.CPI.TOTL.ZG Description: Inflation, Consumer Prices (annual %) Geographic Coverage: 52 African countries Time Period: 1960-2024 Data Points: 2,397 observations Coverage: 68.29% of possible country-year combinations

  File Formats





  Main Dataset… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Africa-Inflation-Consumer-Prices-annual-percentage.

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.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Black Earth. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Black Earth, the median income for all workers aged 15 years and older, regardless of work hours, was $37,083 for males and $36,528 for females.

Based on these incomes, we observe a gender gap percentage of approximately 1%, indicating a significant disparity between the median incomes of males and females in Black Earth. Women, regardless of work hours, still earn 99 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Black Earth, among full-time, year-round workers aged 15 years and older, males earned a median income of $63,125, while females earned $60,729, resulting in a 4% gender pay gap among full-time workers. This illustrates that women earn 96 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the village of Black Earth.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Black Earth, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Black Earth median household income by race. You can refer the same here