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 benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Australia was last recorded at 3.60 percent. This dataset provides - Australia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Interest is charged if payment is not received by the due date. Remember: if the due date falls on a weekend or holiday, your payment is due the next working day.

The Ministry of Finance also applies interest to amounts the ministry owes to individuals and corporations.

Tax interest is compounded daily and interest rates are reset every 3 months.

Note: Provincial land tax interest rates are not reset every three months. Provincial land tax interest rates are summarized on the "https://www.ontario.ca/document/provincial-land-tax">provincial land tax webpage.

Note: Interest rates do not apply to the Estate Administration Tax Act, 1998.

Current interest rates (July 1, 2025 to September 30, 2025):

• 8% on taxes you owe to the ministry
• 2% on taxes you overpaid
• 5% on taxes or refunds you are eligible for as a result of a successful appeal or objection
• 6% on late International Fuel Tax Agreement payments
• 6% on International Fuel Tax Agreement refunds the ministry has not paid you within 90 days

You can download the dataset to view the historical tax interest rates.

Non-Resident Speculation Tax (NRST)

(1) Interest on tax you overpaid begins to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid.

(2) On refunds you are eligible for as a result of a successful appeal or objection of a NRST refund/rebate disallowance, the interest rate is the same rate as though you had overpaid and will begin to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid. Refunds as a result of a successful appeal or objection of NRST that was paid pursuant to a Notice of Assessment, interest will accrue at the higher appeals/objection rate, beginning to accrue from the date of payment to the date the rebate or refund is paid.

The benchmark interest rate in Poland was last recorded at 5 percent. This dataset provides - Poland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar 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.

Zimbabwe ZW: Real Interest Rate data was reported at 5.728 % pa in 2016. This records a decrease from the previous number of 7.576 % pa for 2015. Zimbabwe ZW: Real Interest Rate data is updated yearly, averaging 34.675 % pa from Dec 1980 (Median) to 2016, with 32 observations. The data reached an all-time high of 572.936 % pa in 2007 and a record low of 4.257 % pa in 1980. Zimbabwe ZW: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.; ;

The benchmark interest rate in Azerbaijan was last recorded at 7 percent. This dataset provides the latest reported value for - Azerbaijan Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Germany was last recorded at 4.50 percent. This dataset provides - Germany Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Monetary policy is generally regarded as a central element in the attempts of policy makers to attenuate business-cycle fluctuations. According to the New Keynesian paradigm, central banks are able to stimulate or depress aggregate demand in the short run by adjusting their nominal interest rate targets. The effects of interest rate changes on aggregate consumption, the largest component of aggregate demand, are well understood in the context of this paradigm, on which the canonical "workhorse'' model used in monetary policy analysis is grounded. A key feature of the model is that aggregate consumption is fully described by the amount of goods consumed by a representative household. A decline in the policy rate for instance implies that the real interest rate declines, the representative household saves less and hence increase its demand for consumption. At the same time, general equilibrium effects let labour income grow causing consumption to increase further. However, the mechanism outlined above ignores a considerable amount of empirically-observed heterogeneity among households. For example, households with a higher earnings elasticity to interest rate changes benefit more from a rate cut than those with a lower elasticity; households with large debt positions are at a relative advantage over households with large bond holdings; and households with low exposure to inflation are relatively better off than those holding a sizeable amount of nominal assets. As a result, the contribution to the aggregate consumption response differs substantially across households, implying that monetary expansions and tightenings produce relative "winners'' and relative "losers''. The aim of the project laid out in this proposal is to give a disaggregated account of the heterogeneous effects of monetary-policy induced interest rate changes on household consumption and a detailed analysis of the channels underlying them. Additionally, it seeks to draw conclusions about the determinants of the strength of the transmission mechanism of monetary policy. To do so, it relies on a large panel comprising detailed data from the universe of all households residing in Norway between 1993 and 2015 supplemented with additional micro-data provided by the European Commission. I will be assisted by two project partners, Pascal Paul who is a member of the Research Department of the Federal Reserve Bank of San Francisco and Martin Holm who is affiliated with the Research Unit of Statistics Norway and the University of Oslo. In addition, I would like to collaborate with and help train a doctoral student based at the University of Lausanne on this project. Existing empirical studies of the consumption response to monetary policy at the micro level rely on survey data. Therefore, they are subject to a number of severe data limitations. The surveys employed typically have either no or only a short panel dimension, suffer from attrition, include only limited information on income and wealth, are top-coded, and contain a significant amount of measurement error. The administrative data set provided to us by Statistics Norway suffers from none of these issues, implying that we are in a unique position to evaluate the household-level effects of policy rate changes. In a first step, we use forecasts published by the Norwegian central bank to derive monetary policy shocks that are robust to the simultaneity problem inherent in the identification of the effects of monetary policy following Romer and Romer (2004). We then confront the micro-data with the estimated shocks to study the consumption response along different segments of the income and wealth distribution and to test the importance of heterogeneity in labour earnings, financial income, liquid assets, inflation exposure and interest rate exposure among others. The findings will be of high relevance as they will not only allow us to evaluate channels hypothesised in the analytical literature, improve our understanding of the monetary policy transmission mechanism and its distributional consequences but also serve as a benchmark for structural models built both by theorists and practitioners.

Data collected as part of the NERC funded Radioactivity and the Environment (RATE), Long-lived Radionuclides in the Surface Environment (Lo-RISE), research consortium.This data comes from the terrestrial workstream group based at the University of Manchester. The data consists of radionuclide measurements of environmental and biological samples including uranium (238), thorium (232) and radium (226), and soil subsurface and surface biota bioprospecting (plants and AM fungi). The data from this first dataset has been published in the following publication: Davies et al. (2018) Multiple environmental factors influence 238U, 232Th and 226Ra bioaccumulation in arbuscular mycorrhizal-associated plants. Science of the Total Environment 640-641:921-934.

This dataset is comprised of data submitted to HCAI by prescription drug manufacturers for wholesale acquisition cost (WAC) increases that exceed the statutorily-mandated WAC increase threshold of an increase of more than 16% above the WAC of the drug product on December 31 of the calendar year three years prior to the current calendar year. This threshold applies to prescription drug products with a WAC greater than $40 for a course of therapy. Required WAC increase reports are to be submitted to HCAI within a month after the end of the quarter in which the WAC increase went into effect. Please see the statute and regulations for additional information regarding reporting thresholds and report due dates.

Key data elements in this dataset include the National Drug Code (NDC) maintained by the FDA, narrative descriptions of the reasons for the increase in WAC, and the five-year history of WAC increases for the NDC. A WAC Increase Report consists of 27 data elements that have been divided into two separate Excel data sets: Prescription Drug WAC Increase and Prescription Drug WAC Increase – 5 Year History. The datasets include manufacturer WAC Increase Reports received since January 1, 2019. The Prescription Drugs WAC Increase dataset consists of the information submitted by prescription drug manufacturers that pertains to the current WAC increase of a given report, including the amount of the current increase, the WAC after increase, and the effective date of the increase. The Prescription Drugs WAC Increase – 5 Year History dataset consists of the information submitted by prescription drug manufacturers for the data elements that comprise the 5-year history of WAC increases of a given report, including the amount of each increase and their effective dates.

There are 2 types of WAC Increase datasets below: Monthly and Annual. The Monthly datasets include the data in completed reports submitted by manufacturers for calendar year 2025, as of April 7, 2025. The Annual datasets include data in completed reports submitted by manufacturers for the specified year. The datasets may include reports that do not meet the specified minimum thresholds for reporting.

The Quick Guide explaining how to link the information in each data set to form complete reports is here: https://hcai.ca.gov/wp-content/uploads/2024/03/QuickGuide_LinkingTheDatasets.pdf

The program regulations are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/CTRx-Regulations-Text.pdf

The data format and file specifications are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/Format-and-File-Specifications-version-2.0-ada.pdf

DATA NOTES: Due to recent changes in Excel, it is not recommended that you save these files to .csv format. If you do, when importing back into Excel the leading zeros in the NDC number column will be dropped. If you need to save it into a different format other than .xlsx it must be .txt

DATA UPDATES: Annual datasets of reports from the preceding year are reviewed in the second half of the current year to identify if any revisions or additions have been made since the original release of the datasets. If revisions or additions have been found, an update of the datasets will be released. Datasets will be clearly marked with 'Updated' in their titles for convenient identification. Not all datasets may require an updated release. The review of previously released datasets will only be conducted once to determine if an updated release is necessary. Datasets with revisions or additions that may have been made after the one-time review can be requested. These requests should be sent via email to ctrx@hcai.ca.gov. Due to regulatory changes that went into effect April 1, 2024, reports submitted prior to April 1, 2024, will include the data field "Unit Sales Volume in US" and reports submitted on or after April 1, 2024, will instead include "Total Volume of Gross Sales in US Dollars".

This dataset contains calculated rates of sea-level rise derived from the nearest NOAA National Water Level Observation Network (NWLON) stations relevant for each tidal wetland monitoring site. Calculated rates include the entire record for long-term, as well as more limited dataset for more recent 19-year rates. The 19-year rates were calculated to end at the most recent surface elevation table (SET) measurement. Rates are directly compared with rates from SET measurements of surface elevation change to provide estimates of vulnerability to sea level rise.

About this dataset

Context

The dataset tabulates the Rising Sun population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Rising Sun across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Rising Sun was 2,773, a 0.51% increase year-by-year from 2022. Previously, in 2022, Rising Sun population was 2,759, an increase of 0.25% compared to a population of 2,752 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Rising Sun increased by 983. In this period, the peak population was 2,819 in the year 2013. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Rising Sun is shown in this column.
• Year on Year Change: This column displays the change in Rising Sun population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Rising Sun Population by Year. You can refer the same here

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

Local and regional food supply chains are gaining increasing support from public and private sectors for their contributions to economic development and promoting sustainability. However, the impacts of regionalization are not well understood. We employ a spatial-temporal model of production and transportation to evaluate the supply chain outcomes of a decade-long process of food regionalization for fresh broccoli in the eastern United States (US). Our results indicate that eastern broccoli supply chains displaced products sourced from the western US and met over 15% of the annual demand in eastern markets in 2017. We find that total broccoli supply chain costs and food miles increased in the period 2007–2017. Nevertheless, eastern-grown broccoli has contributed to reducing regional food miles in the eastern region (from 365 miles in 2007 to 255 miles in 2017) and experienced only modest increases in supply chains costs (a 3.4% increase, compared to a 16.5% increase for broccoli shipped from western US) during the same period. Our results provide valuable information for policymakers and the fresh produce industry interested in promoting regional food supply chains.

Data produced by this study include 1) shape file of the processed road segments within the floodplain for all coastal counties, 2) python code for processing the raw HPMS data into processed segments and identifying the segments within the flood plain, 3) delays and costs by county, year, sea level rise scenario, and adaptation scenario, and 4) Matlab code for road data statistics, distillation of the processed road segment dataset, and estimation of delays and costs for all scenarios. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

The benchmark interest rate in Malaysia was last recorded at 2.75 percent. This dataset provides - Malaysia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

We used WARMER, a 1-D cohort model of wetland accretion (Swanson et al. 2014), which is based on Callaway et al. (1996), to examine SLR projections across each study site. Each cohort in the model represents the total organic and inorganic matter added to the soil column each year. WARMER calculates elevation changes relative to MSL based on projected changes in relative sea level, subsidence, inorganic sediment accumulation, aboveground and belowground organic matter productivity, compaction, and decay for a representative marsh area. Each cohort provides the mass of inorganic and organic matter accumulated at the surface in a single year as well as any subsequent belowground organic matter productivity (root growth) minus decay. Cohort density, a function of mineral, organic, and water content, is calculated at each time step to account for the decay of organic material and auto-compaction of the soil column. The change in relative elevation is then calculated as the difference between the change in modeled sea level and the change in height of the soil column, which was estimated as the sum of the volume of all cohorts over the unit area model domain. The total volume of an individual cohort is estimated as the sum of the mass of pore space water, sediment, and organic matter, divided by the cohort bulk density for each annual time step. Elevation is adjusted relative to sea level rise after each year of organic and inorganic input, compaction, and decomposition. We parameterized WARMER from the elevation, vegetation, and water level data collected at each site. We evaluated model outputs between 2010 and 2110 using marsh elevation zones defined above.Model inputs Sea-level rise scenariosIn WARMER, we incorporated a recent forecast for the Pacific coast which projects low, mid, and high SLR scenarios of 12, 64 and 142 cm by 2110, respectively (NRC 2012). We used the average annual SLR curve as the input function for the WARMER model. We assumed the difference between the maximum tidal height and minimum tidal height (tide range) remained constant through time, with only MSL changing annually.Inorganic matterThe annual sediment accretion rate is a function of inundation frequency and the mineral accumulation rates measured from 137Csdating of soil cores sampled across each site. For each site, we developed a continuous model of water level from the major harmonic constituents of a nearby NOAA tide gauge. This allowed a more accurate characterization of the full tidal regime as our water loggers were located above MLLW. Following Swanson et al. (2014), we assumed that inundation frequency was directly related to sediment mass accumulation; this simplifying assumption does not account for the potential feedback between biomass and sediment deposition and holds suspended sediment concentration and settling velocity constant. Sediment accretion, Ms,at a given elevation, z, is equal to, where f(z) is dimensionless inundation frequency as a function of elevation (z), and Sis the annual sediment accumulation rate in g cm-2 y-1.Organic matterWe used a unimodal functional shape to describe the relationship between elevation and organic matter (Morris et al. 2002), based on Atlantic coast work on Spartina alterniflora. Given that Pacific Northwest tidal marshes are dominated by other plant species, we developed site-specific, asymmetric unimodal relationships to characterize elevation-productivity relationships. We used Bezier curves to draw a unimodal parabola, anchored on the low elevation by MTL at the high elevation by the maximum observed water level from a nearby NOAA tide gauge. We determined the elevation of peak productivity by analyzing the Normalized Difference Vegetation Index (NDVI; (NIR - Red)/(NIR + Red)) from 2011 NAIP imagery (4 spectral bands, 1 m resolution; Tucker 1979) and our interpolated DEM. We then calibrated the amplitude of the unimodal function to the organic matter input rates (determined from sediment accumulation rates and the percent organic matter in the surface layer of the core) obtained from sediment cores across an elevation range at each site. The curves were truncated to zero below the lowest observed marsh elevation for each site from our vegetation surveys, reflecting the observed transition to unvegetated mudflat. The root-to-shoot ratio for each site was set to 1.95, the mean value from an inundation experiment conducted at Siletz in 2014 for Juncus balticusand Carex lyngbyei, two common high and low marsh species in the Pacific Northwest (C. Janousek et al., unpublished results). Compaction and decompositionCompaction and decomposition functions of WARMER followed Callaway et al. (1996). We determined sediment compaction by estimating a rate of decrease in porosity from the difference in measured porosity between the top 5 cm and the bottom 5 cm of each sediment core. We estimated the rate of decrease, r, in porosity of a given cohort as a function of the density of all of the material above that cohort.Following Swanson et al. (2014), we modeled decomposition as a three-tiered process where the youngest organic material, less than one year old, decomposed at the fastest rate; organic matter one to two years old decayed at a moderate rate; and organic matter greater than two years old decayed at the slowest rate. Decomposition also decreased exponentially with depth. We determined the percentage of refractory (insoluble) organic material from the organic content measured in the sediment cores. We used constants to parameterize the decomposition functions from Deverel et al. (2008). ImplementationFor each site, we ran WARMER at 37 initial elevations (every 10 cm from 0 to 360 cm, NAVD88). A two hundred year spin-up period for each model run was used to build an initial soil core. A constant rate of sea-level rise was chosen that the modeled elevation after 200 years was equal to the initial elevation. After the spin-up period, sea-level rose according to the scenario (+12, 63, or 142 cm by 2110). Linear interpolation was used to project model results every 10 years onto the continuous DEM developed from the RTK surveys. This raster contains data from Bandon marsh with the projection from the WARMER model for the year 2010 with a 63 cm sea-level rise rate.

A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise within Cape Hatteras National Seashore in North Carolina. The CVI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, historical shoreline change rates, mean tidal range and mean significant wave height. The rankings for each input variable were combined and an index value calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The CVI and the data contained within this dataset provide an objective technique for evaluation and long-term planning by scientists and park managers.