The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dataset Description

This dataset contains the actual and predicted federal funds target rate for the United States from 1990 to 2023. The federal funds target rate is the interest rate at which depository institutions lend their excess reserves to each other overnight. It is set by the Federal Open Market Committee (FOMC) and is a key tool used by the Federal Reserve to influence the economy.

The dataset includes the following five columns:

Release Date: The date on which the data was released by the Federal Reserve. Time: The time of day at which the data was released. Actual: The actual federal funds target rate. Predicted: The predicted federal funds target rate. Forecast: The forecast federal funds target rate.

Data Usage

This dataset can be used for a variety of purposes, including: - Analyzing trends in the federal funds target rate over time. - Forecasting the future path of the federal funds target rate. - Assessing the effectiveness of monetary policy. - Data Quality

The data for this dataset is of high quality. The Federal Reserve is a reputable source of data and the data is updated regularly.

Data Limitations

The data for this dataset is limited to the United States. Additionally, the data does not include information on the factors that influenced the Federal Open Market Committee's decision to set the federal funds target rate.

The U.S. federal funds effective rate underwent a dramatic reduction in early 2020 in response to the COVID-19 pandemic. The rate plummeted from 1.58 percent in February 2020 to 0.65 percent in March, and further decreased to 0.05 percent in April. This sharp reduction, accompanied by the Federal Reserve's quantitative easing program, was implemented to stabilize the economy during the global health crisis. After maintaining historically low rates for nearly two years, the Federal Reserve began a series of rate hikes in early 2022, with the rate moving from 0.33 percent in April 2022 to 5.33 percent in August 2023. The rate remained unchanged for over a year, before the Federal Reserve initiated its first rate cut in nearly three years in September 2024, bringing the rate to 5.13 percent. By December 2024, the rate was cut to 4.48 percent, signaling a shift in monetary policy in the second half of 2024. In January 2025, the Federal Reserve implemented another cut, setting the rate at 4.33 percent, which remained unchanged throughout the following months. What is the federal funds effective rate? The U.S. federal funds effective rate determines the interest rate paid by depository institutions, such as banks and credit unions, that lend reserve balances to other depository institutions overnight. Changing the effective rate in times of crisis is a common way to stimulate the economy, as it has a significant impact on the whole economy, such as economic growth, employment, and inflation. Central bank policy rates The adjustment of interest rates in response to the COVID-19 pandemic was a coordinated global effort. In early 2020, central banks worldwide implemented aggressive monetary easing policies to combat the economic crisis. The U.S. Federal Reserve's dramatic reduction of its federal funds rate - from 1.58 percent in February 2020 to 0.05 percent by April - mirrored similar actions taken by central banks globally. While these low rates remained in place throughout 2021, mounting inflationary pressures led to a synchronized tightening cycle beginning in 2022, with central banks pushing rates to multi-year highs. By mid-2024, as inflation moderated across major economies, central banks began implementing their first rate cuts in several years, with the U.S. Federal Reserve, Bank of England, and European Central Bank all easing monetary policy.

Effective Federal Funds Rate in the United States remained unchanged at 4.33 percent on Wednesday July 2. This dataset includes a chart with historical data for the United States Effective Federal Funds Rate.

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

Note: Updates to this data product are discontinued. The underlying data are no longer available. The data are from the Census Bureau's Consolidated Federal Funds Reports on Federal expenditures and obligations for grants, salaries and wages, procurements, direct payments, direct loans, guaranteed loans, and insurance obtained from Federal Government agencies. ERS screens the data for each Federal program for accuracy at the county level and then presents the data by function and type of program for each county and State. See the complete data documentation for details.

Each file contains Federal outlays by program for each county in the State. County population is also included so that per capita spending can be calculated. Summary of the data for the United States and a ZIP file for all States are also available.

Graph and download economic data for FOMC Summary of Economic Projections for the Fed Funds Rate, Median (FEDTARMD) from 2025 to 2027 about projection, federal, median, rate, and USA.

description: ---CURRENTLY IN TESTING-NOT FOR OFFICIAL USE--- This is a dataset of checkbook level expenditures of federal funds. When used in conjunction with the SID to CFDA crosswalk dataset users can determine expenditures associated with the federal program: https://data.ct.gov/dataset/Catalog-of-Federal-Domestic-Assictance-CFDA-Number/cm2u-6v8f; abstract: ---CURRENTLY IN TESTING-NOT FOR OFFICIAL USE--- This is a dataset of checkbook level expenditures of federal funds. When used in conjunction with the SID to CFDA crosswalk dataset users can determine expenditures associated with the federal program: https://data.ct.gov/dataset/Catalog-of-Federal-Domestic-Assictance-CFDA-Number/cm2u-6v8f

Content

Source ID: LA214104005.Q

For more information about the Flow of Funds tables, see: https://www.federalreserve.gov/apps/fof/Default.aspx

For a detailed description, including how this series is constructed, see: https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LA214104005&t=

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1945-10-01

• Observation End : 2019-07-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Ryan Moreno on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This table is a subsidiary table for Means of Financing the Deficit or Disposition of Surplus by the U.S. Government providing a detailed view of federal funds and trust funds that are invested in Government Account Series (GAS) securities. Federal funds include general funds, special funds, and revolving funds (public enterprise revolving funds, intragovernmental revolving funds, and credit financing accounts). A trust fund is a type of account, designated by law, for receipts or offsetting receipts dedicated to specific purposes and the expenditure of these receipts. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

Money Supply M0 in the United States decreased to 5648600 USD Million in May from 5732900 USD Million in April of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

📜 Title 31 CFR (Money and Finance: Treasury)

https://img.shields.io/badge/license-public%20domain-brightgreen.svg" alt="License: Public Domain"> - Maintainer: Terry Eppler
- Ownership: US Federal Government | US Government Publishing Office - Reference Standard: Title 31 CFR Money & Finance - Source Availability: The datasets and source files also available here Hugging Face

📋 Overview

Title 31 of the Code of Federal Regulations (CFR) governs “Money and Finance: Treasury” and is issued by the Department of the Treasury. It contains rules and procedures for fiscal operations, including disclosure of records, depositaries, payment processes, and Treasury‐administered programs. This repository provides the Title 31 CFR content in plain‐text format for easy reference, parsing, and integration into compliance or regulatory workflows.

This dataset consists of two files:

• 📄 Title 31 CFR Part 1.txt
  Contains Parts 0–199 of Title 31 CFR (effective July 1, 2024). Covers general Treasury regulations, including:

  • Disclosure of Records (FOIA/Privacy Act procedures)
  • Employee conduct, ethics, and privacy rules
  • Treasury reporting requirements

• 📄 Title 31 CFR Part 2.txt
  Contains Parts 200–499 of Title 31 CFR (effective July 1, 2024). Covers Bureau of the Fiscal Service and other Treasury‐related regulations, including:

  • Deposit and payment procedures (depositories, electronic fund transfers)
  • U.S. Savings Bonds and Treasury securities rules
  • Secret Service regulations and protective functions

🗂️ File Description

💡 Tips & Tricks

• ⚡ Quick Section Searches

  • To find FOIA procedures under Part 1:
    bash grep -i "FOIA" "Title 31 CFR Part 1.txt"
  • To locate EFT payment rules in Part 2:
    bash grep -i "Electronic Funds Transfer" "Title 31 CFR Part 2.txt"

• 🔢 Extracting All Section Headings

  • List all section numbers in Part 1:
    bash grep -oE "§ [0-9]+.[0-9]+" "Title 31 CFR Part 1.txt" | sort | uniq
  • Likewise, for Part 2:
    bash grep -oE "§ [0-9]+.[0-9]+" "Title 31 CFR Part 2.txt" | sort | uniq

• 📅 Staying Updated

  • Title 31 CFR is updated annually. Confirm you have the latest (July 1, 2024) edition by checking the Federal Register or eCFR for any interim changes.

📜 License & Attribution

• Title 31 CFR is a U.S. Government work in the public domain. You may reproduce, redistribute, or adapt any portion as needed.
• When citing, reference:
  > Title 31 Code of Federal Regulations (Money and Finance: Treasury), current edition (effective July 1, 2024).

🧭 Navigation

1. 🔍 Locating a Section in Part 1

   • Open Title 31 CFR Part 1.txt.
   • Search for “§ 1.210” to find “Disclosure of Records.”
   • Section headings include part and section numbers at the start of each entry.

2. 📝 Finding a Regulation in Part 2

   • Open Title 31 CFR Part 2.txt.
   • Search for “§ 202.2” to view “Applicability and Definitions” under Depositories.
   • Each section’s title follows the section number for clarity.

3. 📂 Browsing by Topic

   • For FOIA procedures, se...

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.

description: The Financial Processing Centers layer consists of the following> Defense Finance and Accounting Services (DFAS) > DFAS like services associated with non DOD Federal Agencies > State Government Payment Centers > Internal Revenue Service payment centers (the IRS calls these 'Taxpayers Assistance Centers' > Check clearing houses including Federal Reserve locations that act as check clearing houses > Credit card payment processing centers > Defense Finance and Accounting Services provide accounting and finance services for military departments and defense agencies. Non-Defense Federal Government Equivalent agencies provide accounting and finance services for non-military government agencies. Internal Revenue Service Taxpayers Assistance Centers provided payment arrangements, account inquiries, adjustments, tax forms and preparation, and accepts payments. State Government Payment Centers provide payroll services for state government employees. Credit card clearinghouses participate in the transfer of funds for a credit card. And check clearinghouses participate in the transfer of funds for a check transaction. The basis of this dataset was information gathered from official internet websites for the agencies represented in this dataset, as well as other public domain and open source research. The name, address, phone number and geospatial location for 90% of the entities were completely verified by TGS. The locations for the balance of the entities were assigned using automated methods. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g. the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute the oldest record dates from 09/25/2006 and the newest record dates from 10/02/2006.; abstract: The Financial Processing Centers layer consists of the following> Defense Finance and Accounting Services (DFAS) > DFAS like services associated with non DOD Federal Agencies > State Government Payment Centers > Internal Revenue Service payment centers (the IRS calls these 'Taxpayers Assistance Centers' > Check clearing houses including Federal Reserve locations that act as check clearing houses > Credit card payment processing centers > Defense Finance and Accounting Services provide accounting and finance services for military departments and defense agencies. Non-Defense Federal Government Equivalent agencies provide accounting and finance services for non-military government agencies. Internal Revenue Service Taxpayers Assistance Centers provided payment arrangements, account inquiries, adjustments, tax forms and preparation, and accepts payments. State Government Payment Centers provide payroll services for state government employees. Credit card clearinghouses participate in the transfer of funds for a credit card. And check clearinghouses participate in the transfer of funds for a check transaction. The basis of this dataset was information gathered from official internet websites for the agencies represented in this dataset, as well as other public domain and open source research. The name, address, phone number and geospatial location for 90% of the entities were completely verified by TGS. The locations for the balance of the entities were assigned using automated methods. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g. the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute the oldest record dates from 09/25/2006 and the newest record dates from 10/02/2006.

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.

This data set is a subset of the "Records of foreign capital" (Registros de capitais estrangeiros", RCE) published by the Central Bank of Brazil (CBB) on their website.The data set consists of three data files and three corresponding metadata files. All files are in openly accessible .csv or .txt formats. See detailed outline below for data contained in each. Data files contain transaction-specific data such as unique identifier, currency, cancelled status and amount. Metadata files outline variables in the corresponding data file.RCE_Unclean_full_dataset.csv - all transactions published to the Central Bank website from the four main categories outlined belowMetadata_Unclean_full_dataset.csvRCE_Unclean_cancelled_dataset.csv - data extracted from the RCE_Unclean_full_dataset.csv where transactions were registered then cancelledMetadata_Unclean_cancelled_dataset.csvRCE_Clean_selection_dataset.csv - transaction data extracted from RCE_Unclean_full_dataset.csv and RCE_Unclean_cancelled_dataset.csv for the nine companies and criteria identified belowMetadata_Clean_selection_dataset.csvThe data include the period between October 2000 and July 2011. This is the only time span for the data provided by the Central Bank of Brazil at this stage. The records were published monthly by the Central Bank of Brazil as required by Art. 66 in Decree nº 55.762 of 17 February 1965, modified by Decree nº 4.842 of 17 September 2003. The records were published on the bank’s website starting October 2000, as per communique nº 011489 of 7 October 2003. This remained the case until August 2011, after which the amount of each transaction was no longer disclosed (and publication of these stopped altogether after October 2011). The disclosure of the records was suspended in order to review their legal and technical aspects, and ensure their suitability to the requirements of the rules governing the confidentiality of the information (Law nº 12.527 of 18 November 2011 and Decree nº 7724 of May 2012) (pers. comm. Central Bank of Brazil, 2016. Name of contact available upon request to Authors).The records track transfers of foreign capital made from abroad to companies domiciled in Brazil, with information on the foreign company (name and country) transferring the money, and on the company receiving the capital (name and federative unit). For the purpose of this study, we consider the four categories of foreign capital transactions which are published with their amount and currency in the Central Bank’s data, and which are all part of the “Register of financial transactions” (abbreviated RDE-ROF): loans, leasing, financed import and cash in advance (see below for a detailed description). Additional categories exist, such as foreign direct investment (RDE-IED) and External Investment in Portfolio (RDE-Portfólio), for which no amount is published and which are therefore not included.We used the data posted online as PDFs on the bank’s website, and created a script to extract the data automatically from these four categories into the RCE_Unclean_full_dataset.csv file. This data set has not been double-checked manually and may contain errors. We used a similar script to extract rows from the "cancelled transactions" sections of the PDFs into the RCE_Unclean_cancelled_dataset.csv file. This is useful to identify transactions that have been registered to the Central Bank but later cancelled. This data set has not been double-checked manually and may contain errors.From these raw data sets, we conducted the following selections and calculations in order to create the RCE_Clean_selection_dataset.csv file. This data set has been double-checked manually to secure that no errors have been made in the extraction process.We selected all transactions whose recipient company name corresponds to one of these nine companies, or to one of their known subsidiaries in Brazil, according to the list of subsidiaries recorded in the Orbis database, maintained by Bureau Van Dijk. Transactions are included if the recipient company name matches one of the following:- the current or former name of one of the nine companies in our sample (former names are identified using Orbis, Bloomberg’s company profiles or the company website);- the name of a known subsidiary of one of the nine companies, if and only if we find evidence (in Orbis, Bloomberg’s company profiles or on the company website) that this subsidiary was owned at some point during the period 2000-2011, and that it operated in a sector related to the soy or beef industry (including fertilizers and trading activities).For each transaction, we extracted the name of the company sending capital and when possible, attributed the transaction to the known ultimate owner.The name of the countries of origin sometimes comes with typos or different denominations: we harmonized them.A manual check of all the selected data unveiled that a few transactions (n=14), appear twice in the database while bearing the same unique identification number. According to the Central Bank of Brazil (pers. comm., November 2016), this is due to errors in their routine of data extraction. We therefore deleted duplicates in our database, keeping only the latest occurrence of each unique transaction. Six (6) transactions recorded with an amount of zero were also deleted. Two (2) transactions registered in August 2003 with incoherent currencies (Deutsche Mark and Dutch guilder, which were demonetised in early 2002) were also deleted.To secure that the import of data from PDF to the database did not contain any systematic errors, for instance due to mistakes in coding, data were checked in two ways. First, because the script identifies the end of the row in the PDF using the amount of the transaction, which can sometimes fail if the amount is not entered correctly, we went through the extracted raw data (2798 rows) and cleaned all rows whose end had not been correctly identified by the script. Next, we manually double-checked the 486 largest transactions representing 90% of the total amount of capital inflows, as well as 140 randomly selected additional rows representing 5% of the total rows, compared the extracted data to the original PDFs, and found no mistakes.Transfers recorded in the database have been made in different currencies, including US dollars, Euros, Japanese Yens, Brazilian Reais, and more. The conversion to US dollars of all amounts denominated in other currencies was done using the average monthly exchange rate as published by the International Monetary Fund (International Financial Statistics: Exchange rates, national currency per US dollar, period average). Due to the limited time period, we have not corrected for inflation but aggregated nominal amounts in USD over the period 2000-2011.The categories loans, cash in advance (anticipated payment for exports), financed import, and leasing/rental, are those used by the Central Bank of Brazil in their published data. They are denominated respectively: “Loans” (“emprestimos” in original source) - : includes all loans, either contracted directly with creditors or indirectly through the issuance of securities, brokered by foreign agents. “Anticipated payment for exports” (“pagamento/renovacao pagamento antecipado de exportacao” in original source): defined as a type of loan (used in trade finance)“Financed import” (“importacao financiada” in original source): comprises all import financing transactions either direct (contracted by the importer with a foreign bank or with a foreign supplier), or indirect (contracted by Brazilian banks with foreign banks on behalf of Brazilian importers). They must be declared to the Central Bank if their term of payment is superior to 360 days.“Leasing/rental” (“arrendamento mercantil, leasing e aluguel” in original source) : concerns all types of external leasing operations consented by a Brazilian entity to a foreign one. They must be declared if the term of payment is superior to 360 days.More information about the different categories can be found through the Central Bank online.(Research Data Support provided by Springer Nature)

This submission includes a presentation and paper for the Geothermal Resources Council (GRC) that discuss: - Federal agency revenues received for geothermal projects, including: (1) lease revenues, (2) fixed fees, and (3) cost recovery fees for the Bureau of Land Management (BLM) and U.S. Forest Service (USFS); and - Potential federal agency budget sources for processing geothermal applications, including: (1) cost-recovery fees for services rendered, (2) set-aside funds (such as those employed by the Energy Policy Act of 2005), and (3) the appropriations process. The paper then analyzes the three budget sources as mechanisms for increasing funds available to federal agencies for processing geothermal permits and approvals. This paper concludes that both set-asides and cost-recovery fees for services rendered were effective ways of ensuring sufficient funding for processing geothermal authorizations on federally-managed public lands in a timely manner.

This table shows the total receipts and outlays and the resulting surplus or deficit (shown on the table as excess) for the current month and the current fiscal year-to-date for all federal trust funds. The table also shows the totals for securities held as investments by the federal trust funds for the beginning of the fiscal year and the beginning and ending of the current accounting month. A trust fund is a type of account, designated by law, for receipts or offsetting receipts dedicated to specific purposes and the expenditure of these receipts. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

Concept: Credit operations outstanding by source of funds - small-sized enterprise - earmarked credit - federal government specil funds Source: Credit Information System 25781-credit-operations-outstanding-by-source-of-funds---small-sized-enterprise---earmarked-credit- 25781-credit-operations-outstanding-by-source-of-funds---small-sized-enterprise---earmarked-credit- 0 0 Feedback Thank you! Close Feedback. Sorry. Tell us what happen. The data is out of date. I was unable to access the dataset (specify the resource). Insufficient documentation to understand the data set. The data contains error or inconsistency. Describe Your assessment will be sent to the e-Ouv system as a complaint. Click here if you want to track your progress. Name Email Your statement was sent to the e-Ouv system. Click here for details. Protocol number: Access code: Send to e-Ouv system Close

This table represents the breakdown of tax refunds by recipient (individual vs business) and type (check vs electronic funds transfer). Tax refunds are also represented as withdrawals in the Deposits and Withdrawals of Operating Cash table. All figures are rounded to the nearest million. As of February 14, 2023, Table VI Income Tax Refunds Issued was renamed to Table V Income Tax Refunds Issued within the published report.