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.

Fixed 30-year mortgage rates in the United States averaged 6.69 percent in the week ending August 22 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage 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 Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

30 Year Mortgage Rate in the United States decreased to 6.56 percent in August 28 from 6.58 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This indicator includes the high school graduation rates by district for the following districts with high schools within Champaign County: Champaign Community Unit School District #4, Fisher Community Unit School District #1, Mahomet-Seymour Community Unit School District #3, Rantoul Township High School District #193, St. Joseph-Ogden Community High School District #305, Tolono Community Unit School District #7, and Urbana School District #116.

Between 2010 and 2024, the graduation rates of the different districts fluctuated independently of each other, with no trend prevalent across the board. The Illinois Report Card states that there is a possible data impact on the 2020 and 2021 graduation rates due to the COVID-19 pandemic. This could explain the uncharacteristically low graduation rate in Tolono District #7 in 2021 compared to previous years. However, the graduation rate in Champaign Unit #4 and Urbana District #116 increased from 2019 to 2021, and the graduation rate in St. Joseph-Ogden District #305 was the same in 2019 and 2021.

The average graduation rate across all Champaign County high schools increased from 87.7% in 2019 before the COVID-19 pandemic to 88.1% in 2023 when the pandemic emergency ended. This rate increased again in 2024 to 89.2%. High school graduation rates are an apt measure of pre-college academic achievement in the county, and provide context for the other indicators in the education category.

This data, along with a variety of other school district data, is available on the Illinois Report Card, an Illinois State Board of Education and Northern Illinois University website.

Sources: Illinois Report Card. (2023-2024). Champaign CUSD 4. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). Fisher CUSD 1. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). Mahomet-Seymour CUSD 3. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). Rantoul Township HSD 193. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). St. Joseph Ogden CHSD 305. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). Tolono CUSD 7. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). Urbana SD 116. Illinois State Board of Education. (Accessed 6 December 2024).

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

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

Inflation Rate in the United States remained unchanged at 2.70 percent in July. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in South Africa was last recorded at 7 percent. This dataset provides - South Africa Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The violent crime rate indicator includes both the total number of violent crime incidents per year in Champaign County, and the number of violent crime incidents per 100,000 people per year in Champaign County. “Violent crimes” are those counted in the following categories in the Illinois State Police’s annual Crime in Illinois report: Criminal Homicide, Criminal Sexual Assault (Rape), Robbery, Aggravated Assault, and Aggravated Battery. The incidence of violent crime is an integral part of understanding the safety of a given community.

Both the total number of offenses in Champaign County and the rate per 100,000 population were significantly lower in 2021 than at the start of the measured time period, 1996. The most recent rise in both of these figures was in 2019-2020, before falling again in 2021. The year with the lowest number of total offenses and the rate per 100,000 population in the study period was 2015; both measures are slightly higher since then.

This data is sourced from the Illinois State Police’s annually released Crime in Illinois: Annual Uniform Crime Report, available on the Uniform Crime Report Index Offense Explorer.

Sources: Illinois State Police. (2021). Crime in Illinois: Annual Uniform Crime Report 2021. Illinois State Police. (2020). Crime in Illinois: Annual Uniform Crime Report 2020. Illinois State Police. (2019). Crime in Illinois: Annual Uniform Crime Report 2019. Illinois State Police. (2018). Crime in Illinois: Annual Uniform Crime Report 2018. Illinois State Police. (2017). Crime in Illinois: Annual Uniform Crime Report 2017.Illinois State Police. (2016). Crime in Illinois: Annual Uniform Crime Report 2016. Illinois State Police. (2015). Crime in Illinois: Annual Uniform Crime Report 2015. Illinois State Police. (2014). Crime in Illinois: Annual Uniform Crime Report 2014.; Illinois State Police. (2012). Crime in Illinois: Annual Uniform Crime Report 2012.; Illinois State Police. (2011). Crime in Illinois: Annual Uniform Crime Report 2010-2011.; Illinois State Police. (2009). Crime in Illinois: Annual Uniform Crime Report 2009.; Illinois State Police. (2007). Crime in Illinois: Annual Uniform Crime Report 2007.; Illinois State Police. (2005). Crime in Illinois: Annual Uniform Crime Report 2005.; Illinois State Police. (2003). Crime in Illinois: Annual Uniform Crime Report 2003.; Illinois State Police. (2001). Crime in Illinois: Annual Uniform Crime Report 2001.; Illinois State Police. (1999). Crime in Illinois: Annual Uniform Crime Report 1999.; Illinois State Police. (1997). Crime in Illinois: Annual Uniform Crime Report 1997.

A composite strontium isotopic seawater curve was constructed for the Miocene between 24 and 6 Ma by combining 87Sr/86Sr measurements of planktonic foraminifera from Deep Sea Drilling Project sites 289 and 588. Site 289, with its virtually continuous sedimentary record and high sedimentation rates (26 m/m.y.), was used for constructing the Oligocene to mid-Miocene part of the record, which included the calibration of 63 biostratigraphic datums to the Sr seawater curve using the timescale of Cande and Kent (1992 doi:10.1029/92JB01202). Across the Oligocene/Miocene boundary, a brief plateau occurred in the Sr seawater curve (87Sr/86Sr values averaged 0.70824) which is coincident with a carbon isotopic maximum (CM-O/M) from 24.3 to 22.6 Ma. During the early Miocene, the strontium isotopic curve was marked by a steep rise in 87Sr/86Sr that included a break in slope near 19 Ma. The rate of growth was about 60 ppm/m.y. between 22.5 and 19.0 Ma and increased to over 80 ppm/m.y. between 19.0 and 16 Ma. Beginning at ~16 Ma (between carbon isotopic maxima CM3 and CM4 of Woodruff and Savin (1991 doi:10.1029/91PA02561)), the rate of 87Sr/86Sr growth slowed and 87Sr/86Sr values were near constant from 15 to 13 Ma. After 13 Ma, growth in 87Sr/86Sr resumed and continued until ~9 Ma, when the rate of 87Sr/86Sr growth decreased to zero once again. The entire Miocene seawater curve can be described by a high-order function, and the first derivative (d87Sr/86Sr/dt) of this function reveals two periods of increased slope. The greatest rate of 87Sr/86Sr change occurred during the early Miocene between ~20 and 16 Ma, and a smaller, but distinct, period of increased slope also occurred during the late Miocene between ~12 and 9 Ma. These periods of steepened slope coincide with major phases of uplift and denudation of the Himalayan-Tibetan Plateau region, supporting previous interpretations that the primary control on seawater 87Sr/86Sr during the Miocene was related to the collision of India and Asia. The rapid increase in 87Sr/86Sr values during the early Miocene from 20 to 16 Ma imply high rates of chemical weathering and dissolved riverine fluxes to the oceans. In the absence of another source of CO2, these high rates of chemical weathering should have quickly resulted in a drawdown of atmospheric CO2 and climatic cooling through a reversed greenhouse effect. The paleoclimatic record, however, indicates a warming trend during the early Miocene, culminating in a climatic optimum between 17 and 14.5 Ma. We suggest that the high rates of chemical erosion and warm temperatures during the climatic optimum were caused by an increase in the contribution of volcanic CO2 from the eruption of the Columbia River Flood Basalts (CRFB) between 17 and 15 Ma. The decrease in the rate of CRFB eruptions at 15 Ma and the removal of atmospheric carbon dioxide by increased organic carbon burial in Monterey deposits eventually led to cooling and increased glaciation between ~14.5 and 13 Ma. The CRFB hypothesis helps to explain the significant time lag between the onset of increased rates of organic carbon burial in the Monterey at 17.5 Ma (as marked by increased delta13C values) and the climatic cooling and glaciation during the middle Miocene (as marked by the increase in delta18O values), which did not begin until ~14.5 Ma.

FocusEconomics' economic data is provided by official state statistical reporting agencies as well as our global network of leading banks, think tanks and consultancies. Our datasets provide not only historical data, but also Consensus Forecasts and individual forecasts from the aformentioned global network of economic analysts. This includes the latest forecasts as well as historical forecasts going back to 2010. Our global network consists of over 1000 world-renowned economic analysts from which we calculate our Consensus Forecasts. In this specific dataset you will find economic data for South Africa Interest Rate.

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.

The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.

This dataset is the GPM Level 3 IMERG Final Daily 10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH. The derived result represents the Final estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before "07", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared and rain gauge dataset, variable "precipitation", and appears in higher latitudes. Thus, in most cases users of global "precipitation" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable "MWprecipitation", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version "07", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so.

The latency of the derived Final Daily product depends on the delivery of the IMERG Final Half-Hourly product GPM_IMERGHH. Since the latter are delivered in a batch, once per month for the entire month, with up to 4 months latency, so will be the latency for the Final Daily, plus about 24 hours. Thus, e.g. the Dailies for January can be expected to appear no earlier than April 2.

The daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have

Pdaily_mean = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]

Where: Pdaily_cnt = SUM{1[Pi valid]}

Pi - half-hourly input, in (mm/hr) Nf - Number of half-hourly files per day, Nf=48 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day.

Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that Pi=0 is a valid value.

Pdaily_cnt are provided in the data files as variables "precipitation_cnt" and "MWprecipitation_cnt", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so.

There are various ways the daily error could be estimated from the source half-hourly random error (variable "randomError"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly "randomError" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):

Perr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] )

where: Perr_i - half-hourly input, "randomError", (mm/hr) Perr_daily - Magnitude of the daily error, (mm/day) Ncnt_err - Number of valid half-hour error estimates

Again, the sum of squared "randomError" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.

The benchmark interest rate in Switzerland was last recorded at 0 percent. This dataset provides - Switzerland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Homeownership Rate in the United States (RHORUSQ156N) from Q1 1965 to Q2 2025 about homeownership, housing, rate, and USA.

The benchmark interest rate in Norway was last recorded at 4.25 percent. This dataset provides the latest reported value for - Norway 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 Indonesia was last recorded at 5 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.