Wisconsin

The SAHIE program updated its methodology to incorporate the estimates of health insurance coverage from the American Community Survey (ACS). The incorporation of the ACS into the modeling procedures has allowed for more detailed income group data as well as a higher level of precision in the estimates.

New Privately Owned Housing Units Authorized, Unadjusted Units for Regions, Divisions, and States County data collecting from the link https://www2.census.gov/econ/bps/County/

This dataset is updated with data obtained from Statistics Canada and the U.S. Census Bureau. Trade Data is updated on a monthly and annual basis, with revisions in March, April, May, August and November to previous year's data. Trade Data is available on both product and industry-based versions. The product Trade Data is classified by Harmonized System (HS) codes while the industry data is based on North American Industry Classification System(NAICS) classification codes. Source: Statistics Canada and the U.S.Census Bureau

Data cited at:Federal Reserve Bank of Chicago Notes: A zero value for the MEI has been associated with the Midwest economy expanding at its historical trend (average) rate of growth; positive values with above-average growth (in standard deviation units); and negative values with below-average growth. A zero value for the relative MEI has been associated with the Midwest economy growing at a rate historically consistent with the growth of the national economy; positive values with above-average relative growth (in standard deviation units); and negative values with below-average relative growth.

Climate Change Knowledge Portal: CMIP6 Mean Projections

Note: Effective September 27, 2023, this dataset will no longer be updated.  This dataset shows health conditions and contributing causes mentioned in conjunction with deaths involving coronavirus disease 2019 (COVID-19).   Number of conditions reported in this table are tabulated from deaths received and coded as of the date of analysis and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more. Conditions contributing to the death were identified using the International Classification of Diseases, Tenth Revision (ICD-10). Deaths involving more than one condition (e.g., deaths involving both diabetes and respiratory arrest) were counted in both totals. To avoid counting the same death multiple times, the numbers for different conditions should not be summated. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1  

Data cited at: Homebase https://joinhomebase.com/data/covid-19/. This dataset is based on Homebase data covering 60,000 businesses and 1 million hourly employees active in these metropolitan areas in January 2020.   All the rates compare that day vs. the median for that day of the week for the period Jan 4, 2020 – Jan 31, 2020. In other words, they show the extent to which Covid-19 has impacted Main St. as compared to pre-Covid levels. 

Deaths involving coronavirus disease 2019 (COVID-19), pneumonia, and influenza reported to NCHS by race, age, and state.   Number of deaths reported in this dataset are the total number of deaths received and coded as of the date of analysis, and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more.

The Veteran Population Projection Model 2020 (VetPop2020) provides the latest official Veteran population projection from the Department of Veterans Affairs (VA).  VetPop2020 is a deterministic projection model developed by the office of Predictive Analytics to estimate and project the Veteran Population from Fiscal Year (FY) 2020 to FY2050. Using the best available Veteran data at the end of FY2020 as the base population. VetPop2020 projects living and deceased Veteran counts by key demographic characteristics such as age, gender, period of service, and race/ethnicity at various geographic levels for the next 30 years

The dataset is about reported electric emergency incidents and disturbances. Note: This dataset is collected from "https://knoema.com/eqdpivf" using aggregation method present in knoema.

Indicator Details are as Below:Advanced Meters – This file, compiled from data collected on both Forms EIA-861 and EIA-861S, contains information on Automated Meter Readings (AMR) and Advanced Metering Infrastructure (AMI). (Formerly File 8)Balancing Authority – This file, contains the list of Balancing Authorities and the states they operate in, for the EIA-861 and EIA-861S. (Formerly File 1_cao)Demand Response (2013 forward) – This file, compiled from data collected on Form EIA-861 only, contains the number of customers enrolled, energy savings, potential and actual peak savings, and associated costs.Distribution Systems (2013 forward) – This file, compiled from data collected on Form EIA-861 only, contains the number of distribution circuits and circuits with voltage optimization.Dynamic Pricing (2013 forward) – This file, compiled from data collected on Form EIA-861 only, contains the number of customers enrolled in various programs, i.e. time of use, real time, variable peak and critical peak pricing, and critical peak rebate programs.Energy Efficiency (2013 forward) – This file, compiled from data collected on Form EIA-861 only, contains incremental and life cycle data on energy savings, peak demand savings, weighted average life, and associated costs.Mergers (2007 forward) – This file, compiled from data collected on Form EIA-861 only, contains information on mergers and acquisitions. (Formerly File 7)Net Metering (2007 forward) – This file, compiled from data collected on Form EIA-861 only, contains number of customers and displaced energy, by sector and state. For 2010 forward, it contains capacity, customer count, and energy sold back (an optional question on the survey) by sector and state and by technology type, i.e. photovoltaic, wind, and/or other.Non Net Metering Distributed – This file, compiled from data collected on Form EIA-861 only, contains information on utility or customer-owned distributed generators such as the number, capacity, and technology type of generators. Capacities by photovoltaic and storage generation types were added in 2010, fuel cells were added in 2016. Starting in 2016, this data is now collected at the sector level. (Formerly Distributed Generation, Formerly File 6)Operational Data – This file, compiled from data collected on Form EIA-861 only, contains aggregate operational data for the source and disposition of energy and revenue information from each electric utility in the country, including power marketers and federal power marketing administrations. (Formerly File 1)Reliability (2013 forward) – This file, compiled from data collected on Form EIA-861 only, contains SAIDI and SAIFI data.Sales to Ultimate Customers – This file, compiled from data collected on the Form EIA-861 and an estimate from Form EIA-861S for data by customer sector, contains information on retail revenue, sales, and customer counts by state, balancing authority, and class of service (including the transportation sector which was added in 2003) for each electric distribution utility or energy service provider. (Formerly File 2)Sales to Ultimate Customers, Customer Sited – This file, compiled from data collected on the Form EIA-923 only, contains information on retail revenue, sales, and customer counts by state and balancing authority. This includes retail sales from any units located at a customer site.Utility Data (2007 forward) – This file, compiled from data collected on Form EIA-861 only, contains information on the types of activities each utility engages in, the NERC regions of operation, whether the utility generates power, whether it operates alternative-fueled vehicles, and, beginning in 2010, the ISO or RTO region in which the entity conducts operations. (Formerly File 1_a)Demand-Side Management (discontinued after 2012) – This file, compiled from data collected on both Form EIA-861 and, for time-based rate programs, Form EIA-861S, contains information on electric utility demand-side management programs, including energy efficiency and load management effects and expenditures. Beginning in 2007, it also contains the number of customers in time-based rate programs. (Formerly File 3)Green Pricing (discontinued after 2012) – This file, compiled from data collected on Form EIA-861 only, contains number of customers, sales, and revenue, by sector and state. (Formerly File 5)  

Estimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19. Excess deaths are typically defined as the difference between observed numbers of deaths and expected numbers. This visualization provides weekly data on excess deaths by jurisdiction of occurrence. Counts of deaths in more recent weeks are compared with historical trends to determine whether the number of deaths is significantly higher than expected. Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this web page were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential under reporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction.

The FMHPI provides a measure of typical price inflation for houses within the United States. Values are calculated monthly and released at the end of the following month. For example, the FMHPI for March is published in late April. Seasonally and non-seasonally adjusted series are available at three levels of geographical aggregation: Metropolitan Statistical Area (MSA), state, and national. All series begin in January 1975. The national index is defined as a weighted average of the 50 states and Washington, D.C. indices. The FMHPI is based on an ever-expanding database of loans purchased by either Freddie Mac or Fannie Mae. Data cited at: Freddie Mac-http://www.freddiemac.com/research/indices/house-price-index.page#:~:text=Freddie%20Mac%20House%20Price%20Index,is%20published%20in%20late%20April

Health Insurance in the United States

In December 2022, IHME paused its COVID-19 modeling. IHME has developed projections for total and daily deaths, daily infections and testing, hospital resource use, and social distancing due to COVID-19 for a number of countries. Forecasts at the subnational level are included for select countries. The projections for total deaths, daily deaths, and daily infections and testing each include a reference scenario: Current projection, which assumes social distancing mandates are re-imposed for 6 weeks whenever daily deaths reach 8 per million (0.8 per 100k). They also include two additional scenarios: Mandates easing, which reflects continued easing of social distancing mandates, and mandates are not re-imposed; and Universal Masks, which reflects 95% mask usage in public in every location. Hospital resource use forecasts are based on the Current projection scenario. Social distancing forecasts are based on the Mandates easing scenario. These projections are produced with a model that incorporates data on observed COVID-19 deaths, hospitalizations, and cases, information about social distancing and other protective measures, mobility, and other factors. They include uncertainty intervals and are being updated daily with new data. These forecasts were developed in order to provide hospitals, policy makers, and the public with crucial information about how expected need aligns with existing resources, so that cities and countries can best prepare. Dataset contains Observed and Projected data

These annual national indexes should be considered developmental. As with the standard FHFA HPIs, revisions to these indexes may reflect the impact of new data or technical adjustments. Indexes are calibrated using appraisal values and sales prices for mortgages bought or guaranteed by Fannie Mae and Freddie Mac. Index values always reflect data from that year. Three HPI values are provided and, since the indexes reflect cumulative appreciation since a certain period, the values reflect the base year being used (annual appreciations are the same). 

Housing Vacancies and Homeownership in US https://www.census.gov/housing/hvs/data/ann19ind.html 

Data cited at: Prof.Prof. Lauren Gardner; Center for Systems Science and Engineering at John Hopkins University, blog Post -  https://systems.jhu.edu/research/public-health/ncov/   On December 31, 2019, the World Health Organization (WHO) was informed of an outbreak of “pneumonia of unknown cause” detected in Wuhan City, Hubei Province, China – the seventh-largest city in China with 11 million residents. As of February 04, 2020, there are over 24,502 cases confirmed globally, including cases in at least 30 regions in China and 30 countries.  Interests: In-Market Segments Knoema All Users   Knoema modified the original dataset to include calculations per million.   https://knoema.com/WBPEP2018Oct https://knoema.com/USICUBDS2020 https://knoema.com/NBSCN_P_A_A0301 https://knoema.com/IMFIFSS2017Nov https://knoema.com/AUDSS2019 https://knoema.com/UNAIDSS2017 https://knoema.com/UNCTADPOPOCT2019Nov https://knoema.com/WHOWSS2018 https://knoema.com/KPMGDHC2019

Note: For 2022 data is as of July 1 2022 This dataset provides provides eligibility levels in each state for key coverage groups that use Modified Adjusted Gross Income (MAGI), as of July 1, 2022. The data represent the principal, but not all, MAGI coverage groups in Medicaid, the Children’s Health Insurance Program (CHIP), and the Basic Health Program (BHP). All income standards are expressed as a percentage of the federal poverty level (FPL). The MAGI-based rules generally include adjusting an individual’s income by an amount equivalent to a 5% FPL disregard. Other eligibility criteria also apply, such as citizenship, immigration status, and state residency.

Note: The source has discontinued this dataset with note-"With the discontinuation of the database that is the input for the MEI, we will no longer update the index after March 31, 2021. For questions, please contact Tyler Atkinson ( [email protected] < [email protected]>;)"   The Dallas Fed Mobility and Engagement Index (formerly the “Social Distancing Index”) summarizes the information in seven different variables based on geolocation data collected from a large sample of mobile devices to gain insight into the economic impact of the pandemic. The Mobility and Engagement Index measures the deviation from normal mobility behaviors induced by COVID-19. The updated name recognizes that social distancing, or the limiting of close contact with others outside your household, can be practiced while mobility and engagement improve. Along with revising the index’s name, we also changed the sign of the index to make it more intuitive as a measure of mobility and engagement. The underlying data is provided by SafeGraph. The national series is aggregated from county-level data with device counts as weights. Similar for the states. In the county files, the county name is in the first row, with FIPS code in the variable name. MSA data are for metro statistical areas (MSA), aggregated from county data using the March 2020. MSA names are in the first row, and CBSA codes in the variable name. The index is scaled so that the average of January-February is zero, and the lowest weekly value (week ended April 11) is -100. File names including 'weekly' are averages of the daily data. The data corresponds to the last day of the calendar week.

Data cited at: AllClear.App-https://home.allclear.app/  

Occupational Injuries and Illnesses Industry Data (IS)DimensionsData TypeAreaSupersectorIndustryCase TypeSeasonal

Index Period: January 4th - January 31st Indexing Type: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 1 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Deaths involving coronavirus disease 2019 (COVID-19), pneumonia and influenza reported to NCHS by place of death and state, United States.

Deaths involving coronavirus disease 2019 (COVID-19), pneumonia, and influenza reported to NCHS by sex and age group and state.   Number of deaths reported in this table are the total number of deaths received and coded as of the date of analysis, and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more.

Provisional count of deaths involving coronavirus disease 2019 (COVID-19) by United States county.   Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Counties included in this table have 10 or more COVID-19 deaths at the time of analysis. Number of deaths reported in this table are the total number of deaths received and coded as of the date of analysis and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes.

This data file contains the following indicators that can be used to illustrate potential differences in the burden of deaths due to COVID-19 according to race and ethnicity: •Count of COVID-19 deaths: Number of deaths due to COVID-19 reported for each race and Hispanic origin group •Distribution of COVID-19 deaths (%): Deaths for each group as a percent of the total number of COVID-19 deaths reported •Unweighted distribution of population (%): Population of each group as a percent of the total population •Weighted distribution of population (%): Population of each group as percent of the total population after accounting for how the race and Hispanic origin population is distributed in relation to the geographic areas impacted by COVID-19

 Change in seated diners by month / day, 2024 vs. 2023 This table measures the volume of seated diners on a daily/monthly basis in 2024 vs. 2023. For example, in Berlin on January 8, 2024, seated diners were up 8% compared to 2023. In the monthly view, data for the current month shows the YoY change in seated diners for the month-to-date (up until one day prior to the current date). For example, if the date is January 10, 2024, the data compares January 1 – 9, 2024 to the same range in 2023.

Reporting of new Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. According to the source, this dataset will receive a final update on June 1, 2023, to reconcile historical data.

1). U.S. Gross Output: Gross output is the value of gross domestic product (GDP) plus the value of intermediate inputs used to produce GDP 2). Implicit Price Deflator: The gross domestic product implicit price deflator is used to convert nominal dollars to chained (2009) dollars.

US Trade By Commodity (HS), All commodities from chapter 73 Note:- For commodity "7323930080 - Household Articles, Stainless Steel, Nesoi & Parts" unit is different for exports and imports. The unit for exports and Imports are  Kilogram and Number respectively.For commodity " 7319901000 - Sewing, Darning Or Embroidery Needles, Iron Or Stl" we have data only for imports and unit is thousand and for Export Quantity Unit is "X".If a commodity has unit as “X” and “Blank” then there is no quantity data provided for the commodity. This happens when US government does not want to disclose the quantity to the Exporter or Importer. Since there is no quantity available, unit price calculations can not be provided.  

The HPI is a broad measure of the movement of single-family house prices. The HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancing's on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975.

Coronavirus in the United States

The US venture capital (VC) industry had a record year in 2020. Despite headwinds from the COVID-19 pandemic and the worst recession since the Great Depression, the VC industry posted records for fundraising, investments, and exits. For the third consecutive year, high-growth startups raised more than $130 billion, and 2020 represented the fourth straight year where more than 10,000 venture-backed companies received an investment. At the end of 2020, 1,965 VC firms managed 3,680 venture funds and had approximately $548 billion in US venture capital assets under management (AUM), as well as a record $151 billion in dry powder heading into 2021. In 2020, the US remained the destination for about half of global VC investment dollars, attracting 51% of global capital invested. This year’s share of global investment is up slightly from the 49% reported for 2019 and is eight percentage points higher than the most recent low of 43% in 2018. However, these percentages stand in stark contrast to the 83% global share the US garnered in 2004, when the US held dominant sway over investors, and serves as a good reminder that capital and talent are everywhere.

Note: Effective September 27, 2023, this dataset will no longer be updated. Provisional counts of deaths by the week the deaths occurred, by state of occurrence, and by select underlying causes of death. The dataset also includes weekly provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.  

  Zillow Home Value Index (ZHVI) for Metro & U.S and Statewise data. A smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. Zillow publishes top-tier ZHVI ($, typical value for homes within the 65th to 95th percentile range for a given region) and bottom-tier ZHVI ($, typical value for homes that fall within the 5th to 35th percentile range for a given region). Zillow also publishes ZHVI for all single-family residences ($, typical value for all single-family homes in a given region), for condo/coops ($), for all homes with 1, 2, 3, 4 and 5+ bedrooms ($), and the ZHVI per square foot ($, typical value of all homes per square foot calculated by taking the estimated home value for each home in a given region and dividing it by the home’s square footage).

Zillow Observed Rent Index (ZORI): A smoothed measure of the typical observed market rate rent across a given region. ZORI is a repeat-rent index that is weighted to the rental housing stock to ensure representativeness across the entire market, not just those homes currently listed for-rent. The index is dollar-denominated by computing the mean of listed rents that fall into the 40th to 60th percentile range for all homes and apartments in a given region, which is once again weighted to reflect the rental housing stock.