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.

Content

More details about each file are in the individual file descriptions.

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.

Acknowledgements

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

Cover photo by Pepi Stojanovski on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Content

More details about each file are in the individual file descriptions.

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.

Acknowledgements

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

Cover photo by Clay Banks on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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.

This data set is a digitized version of “All-Bank Statistics, United States, 1896-1955,” (ABS) which the Board of Governors of the Federal Reserve System published in 1959. That volume contained annual aggregate balance sheet aggregates for all depository institutions by state and class of institution for the years 1896 to 1955. The depository institutions include nationally chartered commercial banks, state chartered commercial banks, and private banks as well as mutual savings bank and building and loan societies. The data comes from the last business day of the year or the closest available data. This digital version of ABS contains all data in the original source and only data from the original source.This data set is similar to ICPSR 2393, “U.S. Historical Data on Bank Market Structure, ICPSR 2393” by Mark Flood. ICPSR 2393 reports data from ABS but excludes subcategories of data useful for analyzing the liquidity of bank balance sheets, the operation of financial markets, the functioning of the financial network, and depository institutions’ contribution to monetary aggregates. ICPSR 2393, for example, reports total cash assets from ABS but does not report the subcomponents of that total: bankers balances, cash in banks’ own vaults, and items in the process of collection. Those data are needed to understand how much liquidity banks kept on hand, how much liquidity banks stored in or hoped to draw from reserve depositories, and how much of the apparent cash in the financial system was double-counted checks in the process of collection, commonly called float. Those data are also needed to understand the contribution of commercial banks to the aggregate money supply since cash in banks’ vaults counts within monetary aggregates while interbank deposits and float do not. While this dataset provides comprehensive and complete data from ABS, ICPSR 2393 contains information from other sources that researchers may find valuable including data from the aggregate income statements of nationally chartered banks and regulatory variables. To facilitate the use of that information, the naming conventions in this data set are consistent with those in ICPSR 2393.

The H.6 release, published weekly, provides measures of the monetary aggregates (M1 and M2) and their components.M1 and M2 are progressively more inclusive measures of money: M1 is included in M2.M1, the more narrowly defined measure, consists of the most liquid forms of money, namely currency and checkable deposits.The non-M1 components of M2 are primarily household holdings of savings deposits, small time deposits, and retail money market mutual funds.Monthly data are available back to January 1959; for most series, weekly data are available back to January 1975.

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.

Content

This dataset has no description from FRED.

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: 1975-01-06

• Observation End : 2019-11-25

Acknowledgements

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

Central Bank Balance Sheet in the United States increased to 6661912 USD Million in July 9 from 6659598 USD Million in the previous week. This dataset provides - United States Central Bank Balance Sheet - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Content

This series deflates M1 money stock (https://fred.stlouisfed.org/series/M1SL) with CPI (https://fred.stlouisfed.org/series/CPIAUCSL).

Context

This is a dataset from the Federal Reserve Bank of St. Louis 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 Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1959-01-01

• Observation End : 2019-10-01

Acknowledgements

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

Cover photo by Precondo CA on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This table represents the breakdown of taxes that are received by the federal government. Federal taxes received are represented as deposits in the Deposits and Withdrawals of Operating Cash table. All figures are rounded to the nearest million.

Brazil Broad Money Supply: M3: Operation Committed with Federal Securities data was reported at 113,074.640 BRL mn in Jun 2018. This records an increase from the previous number of 103,266.242 BRL mn for May 2018. Brazil Broad Money Supply: M3: Operation Committed with Federal Securities data is updated monthly, averaging 29,866.033 BRL mn from Jul 1994 (Median) to Jun 2018, with 288 observations. The data reached an all-time high of 218,686.067 BRL mn in Mar 2016 and a record low of 0.000 BRL mn in Jul 1999. Brazil Broad Money Supply: M3: Operation Committed with Federal Securities data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Global Database’s Brazil – Table BR.KAA018: Money Supply. Brazilian Central Bank has made changes in methodology of Financial System Credit Data in February of 2013 after 13 years following the same methodology. These changes are necessary face the expansion of credit, favored by the improvement of the indicators of employment and income, continuous and sharp reduction of the interest rates and by important institutional advances. It is essential the availability of new information, in particular, which allows more detailed monitoring of credit arrangements with targeted resources, especially real estate financing, whose dynamism has contributed to reducing the housing deficit in the country. The main change includes coverage of data on concessions, interest rates, terms and default rates that were extended to the segment of directed credit and also became necessary to further detailing the statistical framework, to enable identification of the terms most relevant as well as reduce the relative share of loans not classified - embedded in 'other receivables'. The Money Supply statistics were revised in August 2018, incorporating methodological updates to increase compliance with international standards and consistency with other sets of macroeconomic statistics. The revision consists the inclusion of cooperatives among the institutions that meke up the money issuing system, resulting in M1 expansion, and the exclusion of non-residents assets, impacting mainly on M4. Replacement series ID: 408100927

The FR 2420 is a transaction-based report that collects daily liability data on federal funds, borrowings from non-exempt entities, Eurodollars, and certificates of deposits (CDs) and time deposits (TDs) from (1) domestically chartered commercial banks and thrifts that have $18 billion or more in total assets, or $5 billion or more in assets and meet certain unsecured borrowing activity thresholds, and (2) U.S. branches and agencies of foreign banks with total third-party assets of $2.5 billion or more.

The Interest Expense on Debt Outstanding dataset provides monthly and fiscal year-to-date values for interest expenses on federal government debt, that is, the cost to the U.S. for borrowing money (calculated at a specified rate and period of time). U.S. debt includes Treasury notes and bonds, foreign and domestic series certificates of indebtedness, savings bonds, Government Account Series (GAS), State and Local Government Series (SLGS) and other special purpose securities. While interest expenses are what the government pays to investors who loan money to the government, how much the government pays in interest depends on both the total federal debt and the interest rate investors charged when they loaned the money. This dataset is useful for those who wish to track the cost of maintaining federal debt.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to May 2025 about savings, personal, rate, and USA.

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 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 transactions and account balances for agency programs that borrow from the United States Treasury or from the Federal Financing Bank. 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 M1 in the United States increased to 18712.60 USD Billion in May from 18667.60 USD Billion in April of 2025. This dataset provides - United States Money Supply M1 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Total outstanding debt of the U.S. government reported daily. Includes a breakout of intragovernmental holdings (federal debt held by U.S. government) and debt held by the public (federal debt held by entities outside the U.S. government).

This dataset includes revenue from natural resources by fiscal year. Natural resources are extracted from federal lands, federal waters, and Native American lands. The data is further broken down by _location, lease type, revenue type (e.g., bonuses, rents, and royalties), and commodity/product information. It does not include privately-owned lands or U.S. state lands. Federal revenue data is available by _location. Native American data is only available at the national level to protect private and personally identifiable information. The dataset is tracked and managed by the Department of the Interior’s Office of Natural Resources Revenue. It contains revenue data from fiscal years 2004-present.