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.

The Federal Reserve Banks provide the Fedwire Funds Service, a real-time gross settlement system that enables participants to initiate funds transfer that are immediate, final, and irrevocable once processed. Depository institutions and certain other financial institutions that hold an account with a Federal Reserve Bank are eligible to participate in the Fedwire Funds Services. In 2008, approximately 7,300 participants made Fedwire funds transfers. The Fedwire Funds Service is generally used to make large-value, time-critical payments.The Fedwire Funds Service is a credit transfer service. Participants originate funds transfers by instructing a Federal Reserve Bank to debit funds from its own account and credit funds to the account of another participant. Participants may originate funds transfers online, by initiating a secure electronic message, or off line, via telephone procedures.

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.

The FED dataset is constructed by annotating a set of human-system and human-human conversations with eighteen fine-grained dialog qualities.

Chicago Fed National Activity Index in the United States increased to -0.28 points in May from -0.36 points in April of 2025. This dataset provides the latest reported value for - United States Chicago Fed National Activity Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

These data show the geographic representation of Federal and State Waters for the purpose of display in the MarineCadastre.gov OceanReports application. The boundary between state and federal waters was determined by consulting The Submerged Lands Act (43 U.S.C. §§ 1301 et seq.), 48 U.S.C. §§ 1705 and The Abandoned Shipwreck Act (43 U.S.C. §§ 2101). Some boundary delineations based on the SLA were approximated in this data set, including areas in Hawaii, Alaska, and Washington State. Although state boarders do not extend over water, it was necessary to approximate these borders to produce this data set. The boundaries depicted in this data set are for visual purposes only. The placement of these boundaries was extrapolated from the Federal Outer Continental Shelf (OCS) Administrative Boundaries as described here http://edocket.access.gpo.gov/2006/pdf/05-24659.pdf. The delineation between waters under US sovereign territory jurisdiction and that of federal governance is also approximate. Although based upon legislation, these data do not represent legal boundaries, especially in the case of Navassa Island, The Northern Mariana Islands, Baker Island, Howland Island, Johnston Atoll, Kingman Reef, Palmyra Atoll, Wake Islands and Jarvis Island.The seaward limit of this data set is the boundary of the 200nm US Exclusive Economic Zone. The EEZ is measured from the US baseline, recognized as the low-water line along the coast as marked on NOAA's nautical charts in accordance with articles of the Laws of the Sea. These limits are ambulatory and subject to revision based on changes in coastline geometry. This dataset was produced based on an update to the Maritime Limits published in September, 2013. To view the most up-to-date Maritime Limits, please see http://www.nauticalcharts.noaa.gov/csdl/mbound.htm. Navassa Island does not have an EEZ around it, so the seaward extent of the federal waters surrounding it were based on the 12 mile offshore boundary of the USFWS National Wildlife Refuge established on the island. All data is displayed in WGS_1984_World_Mercator. Area calculations for all states except Alaska were completed in the same projection. Area calculations for Alaska were completed in Alaska Albers Equal Area Conic.

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.

About this dataset

Context

The dataset tabulates the Federal Heights 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 Federal Heights. The dataset can be utilized to understand the population distribution of Federal Heights by age. For example, using this dataset, we can identify the largest age group in Federal Heights.

Key observations

The largest age group in Federal Heights, CO was for the group of age 0-4 years with a population of 1,478 (10.47%), according to the 2021 American Community Survey. At the same time, the smallest age group in Federal Heights, CO was the 75-79 years with a population of 120 (0.85%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Federal Heights is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Federal Heights 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 Federal Heights Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Federal Heights, CO population pyramid, which represents the Federal Heights population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Federal Heights, CO, is 40.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Federal Heights, CO, is 19.8.
• Total dependency ratio for Federal Heights, CO is 60.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Federal Heights, CO is 5.1.

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 for the Federal Heights population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Federal Heights for the selected age group is shown in the following column.
• Population (Female): The female population in the Federal Heights for the selected age group is shown in the following column.
• Total Population: The total population of the Federal Heights for the selected age group is shown in the following column.

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 Federal Heights Population by Age. You can refer the same here

A XLSX export of summarized data for all DoD and specified civilian agency assets contained within the FRPP, a government-wide database of executive branch agency federal real property assets, that cannot be made public at a detailed level. Data for these records is summarized at the Installation Level.

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.

This paper evaluates inflation forecasts made by Norges Bank which is recognized as a successful forecast targeting central bank. It is reasonable to expect that Norges Bank produces inflation forecasts that are on average better than other forecasts, both ‘naïve’ forecasts, and forecasts from econometric models outside the central bank. The authors find that the superiority of the Bank’s forecast cannot be asserted, when compared with genuine ex-ante real time forecasts from an independent econometric model. The 1-step Monetary Policy Report forecasts are preferable to the 1-step forecasts from the outside model, but for the policy relevant horizons (4 to 9 quarters ahead), the forecasts from the outsider model are preferred with a wider margin. An explanation in terms of too high speed of adjustment to the inflation target is supported by the evidence. Norges Bank’s forecasts are convincingly better than ‘naïve’ forecasts over the second half of our sample, but not over the whole sample, which includes a change in the mean of inflation.

A list of Significant Guidance documents, which include guidance document disseminated to regulated entities or the general public that may reasonably be anticipated to lead to an annual effect on the economy of $100 million or more or adversely affect in a material way the economy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or State, local, or tribal governments or communities; create a serious inconsistency or otherwise interfere with an action taken or planned by another agency; materially alter the budgetary impact of entitlements, grants, user fees, or loan programs or the rights and obligations of recipients thereof; or raise novel legal or policy issues arising out of legal mandates, the President's priorities, or the principles set forth in Executive Order 12866, as further amended.

Central Bank Balance Sheet In the Euro Area decreased to 6141314 EUR Million in July 4 from 6232169 EUR Million in the previous week. This dataset provides - Euro Area Central Bank Balance Sheet - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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)

The Federal Transit Administration (FTA) Regions dataset was created on October 12, 2022 from the Federal Transit Administration (FTA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Federal Transit Administration (FTA) has 10 different regions across the United States. The regions cover all 50 states, the District of Columbia, and territories. Staff within the FTA regions provide support in cities/regions with greater transit activities. All these officials develop and manage grants. FTA also overseas safety measures.

This ownership dataset utilizes a methodology that results in a federal ownership extent that matches the Federal Responsibility Areas (FRA) footprint from CAL FIRE's State Responsibility Areas for Fire Protection (SRA) data. FRA lands are snapped to county parcel data, thus federal ownership areas will also be snapped. Since SRA Fees were first implemented in 2011, CAL FIRE has devoted significant resources to improve the quality of SRA data. This includes comparing SRA data to data from other federal, state, and local agencies, an annual comparison to county assessor roll files, and a formal SRA review process that includes input from CAL FIRE Units. As a result, FRA lands provide a solid basis as the footprint for federal lands in California (except in the southeastern desert area). The methodology for federal lands involves:

The Federal Emergency Management Agency (FEMA) produces Flood Insurance Rate maps and identifies Special Flood Hazard Areas as part of the National Flood Insurance Program's floodplain management. Special Flood Hazard Areas have regulations that include the mandatory purchase of flood insurance.Dataset SummaryPhenomenon Mapped: Flood Hazard AreasCoordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, the Northern Mariana Islands and American SamoaVisible Scale: The layer is limited to scales of 1:1,000,000 and larger. Use the USA Flood Hazard Areas imagery layer for smaller scales.Source: Federal Emergency Management AgencyPublication Date: April 1, 2019This layer is derived from the April 1, 2019 version of the National Flood Hazard Layer feature class S_Fld_Haz_Ar. The data were aggregated into eight classes to produce the Esri Symbology field based on symbology provided by FEMA. All other layer attributes are derived from the National Flood Hazard Layer. The layer was projected to Web Mercator Auxiliary Sphere and the resolution set to 1 meter.To improve performance Flood Zone values "Area Not Included", "Open Water", "D", "NP", and No Data were removed from the layer. Areas with Flood Zone value "X" subtype "Area of Minimal Flood Hazard" were also removed. An imagery layer created from this dataset provides access to the full set of records in the National Flood Hazard Layer.A web map featuring this layer is available for you to use.What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but an imagery layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could change the symbology field to Special Flood Hazard Area and set a filter for = “T” to create a map of only the special flood hazard areas. Add labels and set their propertiesCustomize the pop-upUse in analysis tools to discover patterns in the dataArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Areas up to 1,000-2,000 features can be exported successfully.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

The Subsurface Mineral Estate (SSME) dataset depicts Federal mineral interest in the State of Wyoming and classifies these lands by subsurface mineral ownership, Federal or Indian trust, types of minerals owned, and percent ownership within the State of Wyoming. Federal Mineral Estate refers to the mineral estate reserved to the Federal government or a Federally recognized tribe. The SSME data depicts which minerals are reserved, whether they are reserved to the Federal government or held in trust for a tribe, and the percentage of said minerals reserved. The SSME data does not depict private mineral ownership patterns. This data set is a result of compiling differing source materials of various vintages. Source material examples used to create and maintain this dataset include BLM 100k Subsurface Maps, Oil and Gas Plats, Coal Plats, Public Land Survey GIS Data (CadNSDI v.2.0), Field Office GIS Data, Compiled 24k USGS Maps, and Land Records.

Shapefile Format –This Federal Mineral Estate (Subsurface) dataset is a result of combining data sets that were collected at each BLM Colorado Field Office and using other source material of various ventages from the BLM Colorado State Office. In most cases the data was referenced from BLM's 100K Sub-Surface Published Maps (the most recent publications). In other cases data the source data was compiled to 7.5 minute USGS Quads and mosaic together to create a seamless field office layer. BLM State Office GIS Staff obtained these datasets and created a statewide mosaic. In cases where data was out dated or missing in the final mosaic, BLM Colorado GIS Staff filled in the gaps and updated the data layer by heads up digitizing using the most recent scanned and geo-refernced digital 100K Surface/Sub-Surface in a GIS system. Once created, this dataset has been continually updated.