This dataset contains the textual data of Federal Reserve Federal Open Market Committee (FOMC) meeting statements and minutes. Its purpose is to provide a historical archive of communications from the US central bank, offering valuable context and insights into monetary policy decisions and economic outlooks over time. The dataset is regularly updated, ensuring access to the latest official communications.

Columns

• Date: This column specifies the date of the actual FOMC meeting.
• Release Date: This indicates when the statement or minutes were officially released to the public. It is important to note that minutes are typically released with approximately a three-week lag from the meeting date.
• Type: This column categorises the communication, distinguishing between a formal 'statement' and the more detailed 'minutes' of the meeting.
• Text: This contains the full textual content of each communication release, whether it is a statement or minutes.

Distribution

The dataset is typically provided in a CSV (Comma Separated Values) format. It includes communications from 2 February 2000 to 18 June 2025. The file is updated on a weekly basis with new data sourced directly from the Federal Reserve website. Based on available information, there are approximately 420 records within the specified date range. The dataset comprises roughly 52% minutes and 48% statements.

Usage

This dataset is ideal for various applications and use cases, particularly within finance, banking, and economics. It can be used for: * Natural Language Processing (NLP) tasks, such as sentiment analysis or topic modelling on central bank communications. * Economic research to analyse policy shifts, communication strategies, and their impact on financial markets. * Financial modelling and forecasting, by integrating insights from official monetary policy communications. * Academic studies on central banking, macroeconomic policy, and financial history.

Coverage

The dataset covers the period from 2 February 2000 to 18 June 2025, providing an extensive historical record of FOMC communications. While the content focuses on US monetary policy, which is inherently US-centric, the dataset's availability is global, making it accessible to users worldwide. There are no specific notes on data availability for certain demographic groups or years, as the data represents official public releases.

License

CC0

Who Can Use It

This dataset is designed for a wide range of users, including: * Financial analysts and economists seeking to understand and forecast monetary policy decisions. * Data scientists and machine learning engineers developing NLP models for financial text. * Academic researchers in economics, finance, and political science studying central bank behaviour and communication. * Government policy advisors interested in historical policy decisions and their effects. * Journalists and media professionals reporting on economic and financial news.

Dataset Name Suggestions

• Federal Reserve FOMC Communications Archive
• US Monetary Policy Statements and Minutes
• Central Bank Economic Communications
• FOMC Historical Document Collection

Attributes

Original Data Source: FOMC Meeting Statements & Minutes

Analysis of ‘USA Key Economic Indicators’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/calven22/usa-key-macroeconomic-indicators on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Domino’s Pizza, like many other restaurant chains, is getting pinched by higher food costs. The company’s chief executive, Richard Allison, anticipates “unprecedented increases” in the company’s food costs, which could jump by 8-10%. He said that is three to four times what the pizza chain would normally expect in a year.

This leads to the paramount issue of inflation which affects every aspects of the economy, from consumer spending, business investment and employment rates to government programs, tax policies, and interest rates. The recent release of consumer inflation data showed prices rose at the fastest pace since 1982. Inflation forecasting is key in the conduct of monetary policy and can be used in many other ways such as preserving asset values. This dataset is a consolidated macroeconomic official statistics from 1981 to 2021, containing data available in month and quarterly format.

Content

The Core Consumer Price Index (ccpi) measures the changes in the price of goods and services, excluding food and energy due to their volatility. It measures price change from the perspective of the consumer. It is a often used to measure changes in purchasing trends and inflation.

Do note there are some null values in the dataset.

Acknowledgements

All data belongs to the U.S. Bureau of Economic Analysis official release, and are retrieved from FRED, Federal Reserve Bank of St. Louis.

Inspiration

What are some noticeable patterns or seasonality of the economy? What are the current trends of the economy? Which indicators has an effect on Core CPI or vice-versa based on predictive power or influence?

Quarterly data and monthly data can be merged with forward-fill or interpolation methods.

What is the forecast of Core CPI in 2022?

--- Original source retains full ownership of the source dataset ---

An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.

View data of the Effective Federal Funds Rate, or the interest rate depository institutions charge each other for overnight loans of funds.

Money Supply M2 in the United States increased to 21942 USD Billion in May from 21862.40 USD Billion in April of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

US Adult COVID-19 Impact Survey Data

Regional, Socio-Economic, and Health Effects

By Meghan Hoyer [source]

About this dataset

The Associated Press is proud to present the COVID Impact Survey, a statistical survey providing data on how the coronavirus pandemic has affected people in the United States. Conducted by NORC at the University of Chicago with sponsorship from the Data Foundation and Federal Reserve Bank of Minneapolis, this probability-based survey offers valuable insight into three core areas related to physical health, economic and financial security, and social and mental health.

Through this vital survey data, we can gain a better understanding of how individuals are dealing with symptoms related to COVID-19, their financial situation during this time period as well as changes in employment or government assistance policies, food security ization (in both nationwide & regional scope), communication with friends and family members, anxiety levels & if people are volunteering more during pandemic restrictions; furthermore gaining an overall comprehensive snapshot into what factors are impacting public perception regarding COVID-19’s effect on US citizens.

Using these insights it's possible to track metrics over time - Observing which issues Americans face everyday but also long-term effects such as mental distress or self sacrificing volunteer activities that appear due to underlying stress factors. It’s imperative that we properly weight our analysis when using this data & never report raw numbers; instead we must apply queries using statistical software such R/SPSS - thus being able to find results nationally as well as within 10 states + metropolitan areas across America whilst utilising margin of error for detecting statistically significant differences between each researched segment!

Let’s open our minds today – digging beneath surface level information so data tells us stories about humanity & our social behavior patterns during these uncertain times!

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How to use the dataset

This dataset contains survey data related to the impact of COVID-19 on US adult residents. The survey covers physical health, mental health, economic security, and social dynamics that have been affected by the pandemic. It is important to remember that this is survey data and must be properly weighted when analyzing it. Raw or aggregated numbers should not be used to generate insights. In order to weight the data appropriately, we recommend using statistical software such as R or SPSS or our provided queries (linked in this guide).

To generate a table relating to a specific topic covered in the survey, use the survey questionnaire and code book to match a question (the variable label) with its corresponding variable name. For instance “How often have you felt lonely in the past 7 days?” is variable “soc5c”. After entering a variable name into one of our provided queries, a sentence summarizing national results can be written out such as “People in some states are less likely to report loneliness than others… nationally 60% of people said they hadn't felt lonely”

When making comparisons for numerical statistics between different regions it is important to consider the margin of error associated with each set of surveys for national and regional figures provided within this document; it will help determine if differences between groups are statistically significant. If differences are: at least twice as large as margin of error then there is clear difference; at least as large as margin then there is slight/apparent difference; less than/equal margin no real difference can be determined

Survey results are generally posted under embargo on Tuesday evenings with data release taking place at 1 pm ET Thursdays afterward under an appropriate title including month & year ie 01_April_30_covid_impact_survey). Data will come in comma-delimited & statistical formats containing necessary inferences regarding sample collection etc outlined within this guide

When citing survey results these should always attributed with qualification— The Covid Impact Survey conducted by NORC at University Chicago for The Data Foundation sponsored by Federal Reserve Bank Minneapolis & Packard Foundation .
Lastly more resources regarding AP’s data journalism& distributions capabilities can found via link here or contact kromanoap.org

Research Ideas

• Comparing mental health outcomes of the pandemic in different states and metropolitan areas, such as rates of anxiety or lonelines...

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.

Philadelphia Fed Manufacturing Index in the United States remained unchanged at -4 points in June. This dataset provides the latest reported value for - United States Philadelphia Fed Manufacturing Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dallas Fed Manufacturing Index in the United States increased to -12.70 points in June from -15.30 points in May of 2025. This dataset provides the latest reported value for - United States Dallas Fed Manufacturing Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Over 44.7 million Americans carry student loan debt, with the total amount valued at approximately $1.31 trillion (Quarterly Report, 2019). Ergo, consumer spending, a factor of GDP, is stifled and negatively impacts the economy (Frizell, 2014, p. 22). This study examined the relationship between student loan debt and the probability of a recession in the near future, as well as the effects of proposed student loan forgiveness policies through the use of a created model. The Federal Reserve Bank of St. Louis’s website (FRED) was used to extract data regarding total GDP per quarter and student loan debt per quarter ("Federal Reserve Economic Data," 2019). Through the combination of the student loan debt per quarter and total GDP per quarter datasets, the percentage of total GDP composed of student loan debt per quarter was calculated and fitted to a logistic curve. Future quarterly values for total GDP and the percentage of total GDP composed by student loan debt per quarter were found through Long Short Term Models and Euler’s Method, respectively. Through the creation of a probability of recession index, the probability of recession per quarter was compared to the percentage of total GDP composed by student loan debt per quarter to construct an exponential regression model. Utilizing a primarily quantitative method of analysis, the percentage of total GDP composed by student loan debt per quarter was found to be strongly associated[p < 1.26696* 10-8]with the probability of recession per quarter(p(R)), with the p(R) tending to peak as the percentage of total GDP composed of student loan debt per quarter strayed away from the carrying capacity of the logistic curve. Inputting the student loan debt forgiveness policies of potential congressional bills proposed by lawmakers found that eliminating 49.7 % and 36.7% of student loan debt would reduce the recession probabilities to be 1.73545*10-29% and 9.74474*10-25%, respectively.

Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Core PCE Price Index Annual Change in the United States increased to 2.70 percent in May from 2.60 percent in April of 2025. This dataset includes a chart with historical data for the United States Core Pce Price Index Annual Change.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

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