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.

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.

Graph and download economic data for Federal Funds Target Range - Upper Limit (DFEDTARU) from 2008-12-16 to 2025-07-14 about federal, interest rate, interest, rate, and USA.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

In May 2025, global inflation rates and central bank interest rates showed significant variation across major economies. Most economies initiated interest rate cuts from mid-2024 due to declining inflationary pressures. The U.S., UK, and EU central banks followed a consistent pattern of regular rate reductions throughout late 2024. In early 2025, Russia maintained the highest interest rate at 20 percent, while Japan retained the lowest at 0.5 percent. Varied inflation rates across major economies The inflation landscape varies considerably among major economies. China had the lowest inflation rate at -0.1 percent in May 2025. In contrast, Russia maintained a high inflation rate of 9.9 percent. These figures align with broader trends observed in early 2025, where China had the lowest inflation rate among major developed and emerging economies, while Russia's rate remained the highest. Central bank responses and economic indicators Central banks globally implemented aggressive rate hikes throughout 2022-23 to combat inflation. The European Central Bank exemplified this trend, raising rates from 0 percent in January 2022 to 4.5 percent by September 2023. A coordinated shift among major central banks began in mid-2024, with the ECB, Bank of England, and Federal Reserve initiating rate cuts, with forecasts suggesting further cuts through 2025 and 2026.

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.

United States SCE: Interest Rate Expectation: Probability of Higher Average Interest Rate on Savings Accounts 1 Year from Now data was reported at 26.500 % in Apr 2025. This records an increase from the previous number of 26.133 % for Mar 2025. United States SCE: Interest Rate Expectation: Probability of Higher Average Interest Rate on Savings Accounts 1 Year from Now data is updated monthly, averaging 29.753 % from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 41.800 % in Mar 2017 and a record low of 24.225 % in Mar 2024. United States SCE: Interest Rate Expectation: Probability of Higher Average Interest Rate on Savings Accounts 1 Year from Now data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H085: Survey of Consumer Expectations: Financial.

The benchmark interest rate in the United Kingdom was last recorded at 4.25 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The central bank policy rate in Japan stood at *** percent in June 2025. In March 2024, the Bank of Japan raised short-term interest rates for the first time in 17 years, ending its negative interest rate policy. From August 2024 onwards, the central bank encouraged the uncollaterized overnight call rate to remain at **** percent. A third rate hike to *** percent was implemented in January 2025. In 2016, the Bank of Japan had introduced a policy of quantitative and qualitative monetary easing (QQE) with yield curve control, one component of which included controlling short-term and long-term interest rates through market operations.

United States SCE: Stock Price: Probability That US Stock Prices will be Higher 1 Year from Now data was reported at 35.662 % in Apr 2025. This records an increase from the previous number of 33.832 % for Mar 2025. United States SCE: Stock Price: Probability That US Stock Prices will be Higher 1 Year from Now data is updated monthly, averaging 39.618 % from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 51.840 % in Apr 2020 and a record low of 33.767 % in Jun 2022. United States SCE: Stock Price: Probability That US Stock Prices will be Higher 1 Year from Now data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H085: Survey of Consumer Expectations: Financial.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

During the 21st century, sea-level rise will have a wide range of effects on coastal environments, human development and infrastructure in coastal areas. Consequently there is a need to develop modeling or other analytical approaches that can be used to evaluate potential impacts to inform coastal management. This dataset provides the data that were used to develop and evaluate the performance of a Bayesian network (BN) that was developed to predict long-term shoreline change associated with sea-level rise along the Hawaii. The data consist of information compiled as part of the U.S. Geological Survey Coastal Vulnerability Index for the United States. In this work, the Bayesian network is used to define relationships between driving forces, geologic constraints, and coastal response which are represented by observations of local rates of relative sea-level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. Using this information, the BN is used to make probabilistic predictions of shoreline retreat in response to different future sea-level rise rates. The resulting probabilities were divided into five possible outcomes: Probability of shoreline change < -2 m/yr (erosion); Probability of shoreline change between -1 and -2 m/yr (erosion); Probability of shoreline change between -1 and +1 m/yr (stable); Probability of shoreline change between +1 and +2 m/yr (accretion); Probability of shoreline change > 2 m/yr (accretion).

This paper examines the association between the Great Recession and real assets among families with young children. Real assets such as homes and cars are key indicators of economic well-being that may be especially valuable to low-income families. Using longitudinal data from the Fragile Families and Child Wellbeing Study (N = 4,898), we investigate the association between the city unemployment rate and home and car ownership and how the relationship varies by family structure (married, cohabiting, and single parents) and by race/ethnicity (White, Black, and Hispanic mothers). Using mother fixed-effects models, we find that a one percentage point increase in the unemployment rate is associated with a -0.5 percentage point decline in the probability of home ownership and a -0.7 percentage point decline in the probability of car ownership. We also find that the recession was associated with lower levels of home ownership for cohabiting families and for Hispanic families, as well as lower car ownership among single mothers and among Black mothers, whereas no change was observed among married families or White households. Considering that homes and cars are the most important assets among middle and low-income households in the U.S., these results suggest that the rise in the unemployment rate during the Great Recession may have increased household asset inequality across family structures and race/ethnicities, limiting economic mobility, and exacerbating the cycle of poverty.

United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: 50% data was reported at 18.000 % in May 2018. This records an increase from the previous number of 16.000 % for Apr 2018. United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: 50% data is updated monthly, averaging 17.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 26.000 % in Feb 1998 and a record low of 11.000 % in Jul 2014. United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: 50% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance. The question was: What do you think the chances are that your (family) income will increase by more than the rate of inflation in the next five years or so?

Sea-level rise is an ongoing phenomenon that is expected to continue and is projected to have a wide range of effects on coastal environments and infrastructure during the 21st century and beyond. Consequently, there is a need to assemble relevant datasets and to develop modeling or other analytical approaches to evaluate the likelihood of particular sea-level rise impacts, such as coastal erosion, and to inform coastal management decisions with this information. This report builds on previous work that compiled oceanographic and geomorphic data as part of the U.S. Geological Survey's Coastal Vulnerability Index (CVI) for the U.S. Atlantic Coast, and developed a Bayesian Network to predict shoreline-change rates based on sea-level rise plus variables that describe the hydrodynamic and geologic setting. This report extends the previous analysis to include the Gulf and Pacific coasts of the continental United States and Alaska and Hawaii, which required using methods applied to the USGS CVI dataset to extract data for these regions. The Bayesian Network converts inputs that include observations of local rates of relative sea-level change, mean wave height, mean tide range, a geomorphic classification, coastal slope, and observed shoreline-change rates to calculate the probability of the shoreline-erosion rate exceeding a threshold level of 1 meter per year for the coasts of the United States. The calculated probabilities were compared to the historical observations of shoreline change to evaluate the hindcast success rate of the most likely probability of shoreline change.

Sea-level rise is an ongoing phenomenon that is expected to continue and is projected to have a wide range of effects on coastal environments and infrastructure during the 21st century and beyond. Consequently, there is a need to assemble relevant datasets and to develop modeling or other analytical approaches to evaluate the likelihood of particular sea-level rise impacts, such as coastal erosion, and to inform coastal management decisions with this information. This report builds on previous work that compiled oceanographic and geomorphic data as part of the U.S. Geological Survey's Coastal Vulnerability Index (CVI) for the U.S. Atlantic Coast, and developed a Bayesian Network to predict shoreline-change rates based on sea-level rise plus variables that describe the hydrodynamic and geologic setting. This report extends the previous analysis to include the Gulf and Pacific coasts of the continental United States and Alaska and Hawaii, which required using methods applied to the USGS CVI dataset to extract data for these regions. The Bayesian Network converts inputs that include observations of local rates of relative sea-level change, mean wave height, mean tide range, a geomorphic classification, coastal slope, and observed shoreline-change rates to calculate the probability of the shoreline-erosion rate exceeding a threshold level of 1 meter per year for the coasts of the United States. The calculated probabilities were compared to the historical observations of shoreline change to evaluate the hindcast success rate of the most likely probability of shoreline change.

Camera trapping has become an increasingly widespread tool for wildlife ecologists, with large numbers of studies relying on photo capture rates or presence/absence information. It is increasingly clear that camera placement can directly impact this kind of data, yet these biases are poorly understood. We used a paired camera design to investigate the effect of small-scale habitat features on species richness estimates, and capture rate and detection probability of several mammal species in the Shenandoah Valley of Virginia, USA. Cameras were deployed at either log features or on game trails with a paired camera at a nearby random location. Overall capture rates were significantly higher at trail and log cameras compared to their paired random cameras, and some species showed capture rates as much as 9.7 times greater at feature-based cameras. We recorded more species at both log (17) and trail features (15) than at their paired control cameras (13 and 12 species, respectively), yet richness estimates were indistinguishable after 659 and 385 camera nights of survey effort, respectively. We detected significant increases (ranging from 11–33%) in detection probability for five species resulting from the presence of game trails. For six species detection probability was also influenced by the presence of a log feature. This bias was most pronounced for the three rodents investigated, where in all cases detection probability was substantially higher (24.9–38.2%) at log cameras. Our results indicate that small-scale factors, including the presence of game trails and other features, can have significant impacts on species detection when camera traps are employed. Significant biases may result if the presence and quality of these features are not documented and either incorporated into analytical procedures, or controlled for in study design.

Sea-level rise is an ongoing phenomenon that is expected to continue and is projected to have a wide range of effects on coastal environments and infrastructure during the 21st century and beyond. Consequently, there is a need to assemble relevant datasets and to develop modeling or other analytical approaches to evaluate the likelihood of particular sea-level rise impacts, such as coastal erosion, and to inform coastal management decisions with this information. This report builds on previous work that compiled oceanographic and geomorphic data as part of the U.S. Geological Survey's Coastal Vulnerability Index (CVI) for the U.S. Atlantic Coast, and developed a Bayesian Network to predict shoreline-change rates based on sea-level rise plus variables that describe the hydrodynamic and geologic setting. This report extends the previous analysis to include the Gulf and Pacific coasts of the continental United States and Alaska and Hawaii, which required using methods applied to the USGS CVI dataset to extract data for these regions. The Bayesian Network converts inputs that include observations of local rates of relative sea-level change, mean wave height, mean tide range, a geomorphic classification, coastal slope, and observed shoreline-change rates to calculate the probability of the shoreline-erosion rate exceeding a threshold level of 1 meter per year for the coasts of the United States. The calculated probabilities were compared to the historical observations of shoreline change to evaluate the hindcast success rate of the most likely probability of shoreline change.

United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: Mean data was reported at 41.600 % in May 2018. This records an increase from the previous number of 39.900 % for Apr 2018. United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: Mean data is updated monthly, averaging 38.600 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 46.000 % in Feb 2000 and a record low of 27.200 % in Sep 2011. United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: Mean data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance. The question was: What do you think the chances are that your (family) income will increase by more than the rate of inflation in the next five years or so?