For a quick summary of the case study, please click "US Economy Powerpoint" and download the Powerpoint.

This dataset was inspired by rising prices for essential goods, the abnormally high inflation rate in March of 7.9 percent of this year, and the 30 trillion-dollar debt that we have. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

I labeled all of the datasets to be self-explanatory based off of the title of the datasets. The US Economy Notebook has most of the code that I used as well as the four of the six phases of data analysis. The last two phases are in the US Economy Powerpoint. The "US Historical Inflation Rates" dataset could have also been labeled "The Inflation Of The US Dollar Month By Month". Lastly, the Average Sales of Houses in Jan is just a filtered version of "Average Sales of Houses in the US" dataset.

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.

Monthly and long-term United States economic indicators data: historical series and analyst forecasts curated by FocusEconomics.

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The Gross Domestic Product (GDP) in the United States expanded 3.80 percent in the second quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

The Gross Domestic Product (GDP) in the United States expanded 2.10 percent in the second quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - United States GDP Annual Growth 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 tabulates the Economy population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Economy. The dataset can be utilized to understand the population distribution of Economy by age. For example, using this dataset, we can identify the largest age group in Economy.

Key observations

The largest age group in Economy, IN was for the group of age 40 to 44 years years with a population of 22 (14.67%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Economy, IN was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Economy is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Economy total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Economy Population by Age. You can refer the same here

US Industry Data by State, by Industry

Number of Establishments, Sales, Payroll, and Employees

By Gary Hoover [source]

About this dataset

This data set provides a detailed look into the US economy. It includes information on establishments and nonemployer businesses, as well as sales revenue, payrolls, and the number of employees. Gleaned from the Economic Census done every five years, this data is a valuable resource to anyone curious about where the nation was economically at the time. With columns including geographic area name, North American Industry Classification System (NAICS) codes for industries, descriptions of those codes meaning of operation or tax status, and annual payroll, this information-rich dataset contains all you need to track economic trends over time. Whether you’re a researcher studying industry patterns or an entrepreneur looking for market insight — this dataset has what you’re looking for!

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How to use the dataset

This dataset provides detailed US industry data by state, including the number of establishments, value of sales, payroll, and number of employees. All the data is based on the North American Industry Classification System (NAICS) code for each specific industry. This will allow you to easily analyze and compare industries across different states or regions.

Research Ideas

• Analyzing the economic impact of a new business or industry trends in different states: Comparing the change in the number of establishments, payroll, and employees over time can give insight into how a state is affected by a new industry trend or introduction of a new service or product.
• Estimating customer sales potential for businesses: This dataset can be used to estimate the potential customer base for businesses in different geographic areas. By analyzing total business done by non-employers in an area along with its estimated population can help estimate how much overall sales potential exists for a given region.
• Tracking competitor performance: By looking at shipments, receipts, and value of business done across industries in different regions or even cities, companies can track their competitors’ performance and compare it to their own to better assess their strategies going forward

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: 2012 Industry Data by Industry and State.csv | Column name | Description | |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | Geographic area name | The name of the geographic area the data is for. (String) | | NAICS code | The North American Industry Classification System (NAICS) code for the industry. (String) | | Meaning of NAICS code | The description of the NAICS code. (String) | | Meaning of Type of operation or tax status code | The description of the type of operation or tax status code. (String) ...

This dataset contains historical quarterly data for the U.S. Real Gross Domestic Product, from the first quarter of 1947 to the Q2 2023. Real GDP is an inflation-adjusted measure that reflects the value of all goods and services produced by an economy in a given year, expressed in base-year prices, and is often considered an indicator of a country's standard of living.

The dataset has two columns:

Date: The end of the respective quarter (in MM/DD/0YYYY format). Value: The Real GDP at the end of the respective quarter.

Inspiration: Real GDP is a comprehensive measure of U.S. economic activity and a key tool for economic decision-making and forecasting. Real GDP is used by economists, policy-makers, researchers, and investors to understand the growth and performance of the U.S. economy over time.

Usability: The Real GDP data can be used for a variety of purposes:

Economic Analysis: It can be used for macroeconomic analysis and forecasting. Policy Understanding: It can help understand the impact and effectiveness of economic policies implemented by the U.S. government. Investment Analysis: GDP growth impacts financial markets, and this data can help investors understand and forecast market trends. Education: It can be used in classrooms for teaching economics, finance, and related disciplines.

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

Unemployment Rate in the United States increased to 4.40 percent in September from 4.30 percent in August of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The U.S. Census Bureau.s economic indicator surveys provide monthly and quarterly data that are timely, reliable, and offer comprehensive measures of the U.S. economy. These surveys produce a variety of statistics covering construction, housing, international trade, retail trade, wholesale trade, services and manufacturing. The survey data provide measures of economic activity that allow analysis of economic performance and inform business investment and policy decisions. Other data included, which are not considered principal economic indicators, are the Quarterly Summary of State & Local Taxes, Quarterly Survey of Public Pensions, and the Manufactured Homes Survey. For information on the reliability and use of the data, including important notes on estimation and sampling variance, seasonal adjustment, measures of sampling variability, and other information pertinent to the economic indicators, visit the individual programs' webpages - http://www.census.gov/cgi-bin/briefroom/BriefRm.

US Covid-19 Cases, Deaths and Mobility by State/County

Analyzing the Impact of the Pandemic on Low-Income Populations

By Liz Friedman [source]

About this dataset

Welcome to the Opportunity Insights Economic Tracker! Our goal is to provide a comprehensive, real-time look into how COVID-19 and stabilization policies are affecting the US economy. To do this, we have compiled a wide array of data points on spending and employment, gathered from several sources.

This dataset includes daily/weekly/monthly information at the state/county/city level for eight types of data: Google Mobility; Low-Income Employment and Earnings; UI Claims; Womply Merchants and Revenue; as well as weekly Math Learning from Zearn. Additionally, three files- Accounting for Geoids-State/County/City provide crosswalks between geographic areas that can be merged with other files having shared geographical levels.

Our goal here is to enable data users around the world to follow economic conditions in the US during this tumultuous period with maximum clarity and precision. We make all our datasets freely available so if you use them we kindly ask you attribute our work by linking or citing both our accompanying paper as well as this Economic Tracker at https://tracktherecoveryorg By doing so you are also agreeing to uphold our privacy & integrity standards which commit us both to individual & business confidentiality without compromising on independent nonpartisan research & policy analysis!

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How to use the dataset

This dataset provides US COVID-19 case and death data, as well as Google Community Mobility Reports, on the state/county level. Here is how to use this dataset:

• Understand the file structure: This dataset consists of three main files: 1) US Cases & Deaths by State/County, 2) Google Community Mobility Reports, and 3) Data from third-parties providing small business openings & revenue information and unemployment insurance claim data (Low Inc Earnings & Employment, UI Claims and Womply Merchants & Revenue).
• Select your Subset: If you are interested in particular types of data (e.g., mobility or employment), select the corresponding files from within each section based on your geographic area of interest – national, state or county level – as indicated in each filename.
• Review metadata variables: Become familiar with the provided variables so that you can select which ones you need to explore further in your analysis. For example, if analyzing mobility trends at a city level look for columns such as ‘Retailer_and_recreation_percent_change’ or ‘Transit Stations Percent Change’; if focusing on employment decline look for columns such pay or emp figures that align with industries of interest to you such as low-income earners (emp_{inclow},pay_{inclow}).
• Unify dateformatting across row values : Convert date formats into one common unit so that all entries have consistent formatting if necessary; for exampe some entries may display dates using YYYY/MM/DD notation while others may use MM//DD//YY format depending on their source datasets; make sure to review column labels carefully before converting units where needed..

• Merge datasets where applicable : Utilize GeoID crosswalks to combine multiple sets with same geographical coverageregionally covering ; example might be combining low income earnings figures with specific county settings by reference geo codes found in related documents like GeoIDs-County .
  6 . Visualise Data : Now that all the different measures have been reviewed can begin generating charts visualize findings . This process may include cleaning up raw figures normalizing across currency formats , mapping geospatial locations others ; once ready create bar graphs line charts maps other visual according aggregate output desired Insightful representations at this stage will help inform concrete policy decisions during outbreak recovery period..

  Remember to cite

Research Ideas

• Estimating the Impact of the COVID-19 Pandemic on Small Businesses - By comparing county-level Womply revenue and employment data with pre-COVID data, policymakers can gain an understanding of the economic impact that COVID has had on local small businesses.
• Analyzing Effects of Mobility Restrictions - The Google Mobility data provides insight into geographic areas where...

The Anthropic Economic Index

  Overview

The Anthropic Economic Index provides insights into how AI is being incorporated into real-world tasks across the modern economy.

  Data Releases

This repository contains multiple data releases, each with its own documentation:

2025-09-15 Release: Updated analysis with geographic and first-party API data using Sonnet 4 2025-03-27 Release: Updated analysis with Claude 3.7 Sonnet data and cluster-level insights 2025-02-10… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/EconomicIndex.

Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The author argues that the economic benefits of low gasoline prices for the U.S. economy have fallen substantially since the reemergence of America as a major oil producer. The old rule-of thumb that a 10% fall in the oil price raises inflation-adjusted U.S. GDP by 0.2% is too large—the impact on economic activity should be closer to zero, and may even be negative if consumption grows slowly. The reasons for this change are straightforward, if underappreciated: (i) the value of oil production accounts for a larger share of the U.S. economy; and (ii) consumers are not spending the windfall like they used to because of higher debt levels, limited access to credit, slow wage rowth, and an older population.

The U.S. Counties With the Highest GDP

How Much They Contribute to the National Economy

By Charlie Hutcheson [source]

About this dataset

This dataset contains data on the GDP of counties in the United States. The data is from the BEA.gov website and is for the year 2019. The columns in the dataset are: County FIPS, Region, SUB_REGION, State, STATE_ABBR, County Full Name, and GDP (Chained $)

How to use the dataset

The U.S. Counties With the Highest Economic Output is a dataset that contains data on the GDP of counties in the United States. This data can be used to determine which counties have the highest economic output and to compare the economic output of different counties

Research Ideas

-To see how the GDP affects the economy -To see how different states are doing in terms of GDP -To compare counties in different states

Acknowledgements

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: GDP by County.csv | Column name | Description | |:---------------------|:---------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | County FIPS | The FIPS code for the county. (String) | | Region | The region the county is in. (String) | | SUB_REGION | The subregion the county is in. (String) | | State | The state the county is in. (String) | | STATE_ABBR | The two-letter abbreviation for the state the county is in. (String) | | County Full Name | The full name of the county. (String) | | GDP (Chained $) | The GDP of the county in chained dollars. (Float) |

Acknowledgements

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