Graph and download economic data for Leading Indicators OECD: Component Series: Interest Rate Spread: Original Series for United States (USALOCOSIORSTM) from Jan 1960 to Dec 2023 about leading indicator, origination, and spread.

Graph and download economic data for Leading Indicators OECD: Component series: Short-term interest rate: Normalised for New Zealand (NZLLOCOSTNOSTSAM) from Dec 1973 to Jan 2020 about short-term, New Zealand, and leading indicator.

Industrial Cost Indicator: Producer Price Index: Manufacturing: Bureau of Labor Statistics: BRL data was reported at 170.238 2018=100 in Mar 2024. This records an increase from the previous number of 169.523 2018=100 for Dec 2023. Industrial Cost Indicator: Producer Price Index: Manufacturing: Bureau of Labor Statistics: BRL data is updated quarterly, averaging 53.818 2018=100 from Mar 1994 (Median) to Mar 2024, with 121 observations. The data reached an all-time high of 27,778.078 2018=100 in Jun 1994 and a record low of 14.274 2018=100 in Dec 1994. Industrial Cost Indicator: Producer Price Index: Manufacturing: Bureau of Labor Statistics: BRL data remains active status in CEIC and is reported by National Confederation of Industry. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SF001: Industrial Cost Indicator.

Leading Indicators OECD: Component series: Interest rate spread: Normalised for the United States was 99.16215 Index in December of 2023, according to the United States Federal Reserve. Historically, Leading Indicators OECD: Component series: Interest rate spread: Normalised for the United States reached a record high of 102.61237 in February of 1976 and a record low of 95.12661 in July of 1974. Trading Economics provides the current actual value, an historical data chart and related indicators for Leading Indicators OECD: Component series: Interest rate spread: Normalised for the United States - last updated from the United States Federal Reserve on May of 2025.

244 Global import shipment records of Rate Indicator with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Series Name: Unemployment rate by sex and age (percent)Series Code: SL_TLF_UEMRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 8.5.2: Unemployment rate, by sex, age and persons with disabilitiesTarget 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal valueGoal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

United States - Delinquency Rate on Lease Financing Receivables, All Commercial Banks was 1.13% in January of 2025, according to the United States Federal Reserve. Historically, United States - Delinquency Rate on Lease Financing Receivables, All Commercial Banks reached a record high of 2.75 in January of 1991 and a record low of 0.68 in April of 2014. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Delinquency Rate on Lease Financing Receivables, All Commercial Banks - last updated from the United States Federal Reserve on June of 2025.

The economic environment represents the external conditions under which people are engaged in, and benefit from, economic activity. The indicators of the economic environment measure the ability of households to access goods and services important to quality of life.

This poverty rate data shows what percentage of the measured population* falls below the poverty line. Poverty is closely related to income: different “poverty thresholds” are in place for different sizes and types of household. A family or individual is considered to be below the poverty line if that family or individual’s income falls below their relevant poverty threshold. For more information on how poverty is measured by the U.S. Census Bureau (the source for this indicator’s data), visit the U.S. Census Bureau’s poverty webpage.

The poverty rate is an important piece of information when evaluating an area’s economic health and well-being. The poverty rate can also be illustrative when considered in the contexts of other indicators and categories. As a piece of data, it is too important and too useful to omit from any indicator set.

The poverty rate for all individuals in the measured population in Champaign County has hovered around roughly 20% since 2005. However, it reached its lowest rate in 2021 at 14.9%, and its second lowest rate in 2023 at 16.3%. Although the American Community Survey (ACS) data shows fluctuations between years, given their margins of error, none of the differences between consecutive years’ estimates are statistically significant, making it impossible to identify a trend.

Poverty rate data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Poverty Status in the Past 12 Months by Age.

*According to the U.S. Census Bureau document “How Poverty is Calculated in the ACS," poverty status is calculated for everyone but those in the following groups: “people living in institutional group quarters (such as prisons or nursing homes), people in military barracks, people in college dormitories, living situations without conventional housing, and unrelated individuals under 15 years old."

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (16 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).

This dataset present information on the age-sex specific incidence rate of multiple sclerosis for Alberta Health Service (AHS) and five AHS Continuum zones expressed as per 100,000 population and as a percentage.

Indicator 11.3.1Ratio of land consumption rate to population growth rate.The equation used to calculate the results is:Deciding on the analysis period/yearsDelimitation of the urban area or city which will act as the geographical scope for the analysisSpatial analysis and computation of the land consumption rateSpatial analysis and computation of the population growth rateComputation of the ratio of land consumption rate to population growth rateComputation of recommended secondary indicatorsData Source:National Planning Council, Ministry of Municipality.

Graph and download economic data for Leading Indicators OECD: Component Series: Interest Rate Spread: Normalised for Canada (CANLOCOSINOSTSAM) from Jan 1960 to Dec 2023 about leading indicator, spread, and Canada.

Industrial Cost Indicator: Tax Cost: IPI & PIS & COFINS data was reported at 132.809 2018=100 in Mar 2024. This records an increase from the previous number of 121.327 2018=100 for Dec 2023. Industrial Cost Indicator: Tax Cost: IPI & PIS & COFINS data is updated quarterly, averaging 98.629 2018=100 from Mar 2016 (Median) to Mar 2024, with 33 observations. The data reached an all-time high of 132.809 2018=100 in Mar 2024 and a record low of 46.827 2018=100 in Jun 2020. Industrial Cost Indicator: Tax Cost: IPI & PIS & COFINS data remains active status in CEIC and is reported by National Confederation of Industry. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SF001: Industrial Cost Indicator.

This map shows the incidence age-adjusted rate per 100,000 for all cancer types by county. Counties are shaded based on quartile distribution. The lighter shaded counties have a lower all cancer incidence age-adjusted rate. The darker shaded counties have a higher all cancer incidence age-adjusted rate. New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and are updated annually to consolidate and improve data linkages for the health indicators included in the County Health Assessment Indicators (CHAI) for all communities in New York. The CHIRS present data for more than 300 health indicators that are organized by 15 different health topics. Data if provided for all 62 New York State counties, 11 regions (including New York City), the State excluding New York City, and New York State. For more information, check out: http://www.health.ny.gov/statistics/chac/indicators/. The "About" tab contains additional details concerning this dataset..

Series Name: GDP real rates of growthPublication Year: 2018 The Statistical Yearbook provides in a single volume a comprehensive compilation of internationally available statistics on social and economic conditions and activities, at world, regional and national levels, for an appropriate historical period. It is prepared by the Statistics Division, Department of Economic and Social Affairs, of the United Nations Secretariat.Table: Gross domestic product and gross domestic product per capitaTopic: National accountsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/unsd/publications/statistical-yearbook/

Series Name: Neonatal mortality rate (deaths per 1 000 live births)Series Code: SH_DYN_NMRTRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.2.2: Neonatal mortality rateTarget 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live birthsGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Table 1: Grain Transport Cost Indicators

Macroeconomic Indicator 2007-2017 by Price.This data was extracted from economic survey 2016/17.

Series Name: Police reporting rate for physical assault by sex (percent)Series Code: VC_PRR_PHYVRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 16.3.1: Proportion of victims of violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanismsTarget 16.3: Promote the rule of law at the national and international levels and ensure equal access to justice for allGoal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levelsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

These indicators are designed to accompany the SHMI publication. Information on the main condition the patient is in hospital for (the primary diagnosis) is used to calculate the expected number of deaths used in the calculation of the SHMI. A high percentage of records with an invalid primary diagnosis may indicate a data quality problem. A high percentage of records with a primary diagnosis which is a symptom or sign may indicate problems with data quality or timely diagnosis of patients, but may also reflect the case-mix of patients or the service model of the trust (e.g. a high level of admissions to acute admissions wards for assessment and stabilisation). Contextual indicators on the percentage of provider spells with an invalid primary diagnosis and the percentage of provider spells with a primary diagnosis which is a symptom or sign are produced to support the interpretation of the SHMI. Notes: 1. On 1st January 2025, North Middlesex University Hospital NHS Trust (trust code RAP) was acquired by Royal Free London NHS Foundation Trust (trust code RAL). Due to processing issues, we are currently producing separate indicator values for these trusts in the SHMI data. Data for the merged organisation will be produced at a future date. 2. There is a shortfall in the number of records for North Middlesex University Hospital NHS Trust (trust code RAP), Northumbria Healthcare NHS Foundation Trust (trust code RTF), The Rotherham NHS Foundation Trust (trust code RFR), and The Shrewsbury and Telford Hospital NHS Trust (trust code RXW). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 3. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 4. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.