TikTok, a social video app launched in 2016 used for sharing and creating video content, continues to grow in popularity. As of 2022, approximately 41 percent of users in the U.S. engaged with the popular social video app. Around seven in 10 respondents aged between 18 and 19 years were using TikTok, while usage among respondents aged between 20 and 29 years was approximately 56 percent.

TikTok global usage In 2021, TikTok had 1 billion active monthly users worldwide, a growth of 45 percent on the previous year. As of April 2021, the majority of global TikTok users spent less than one hour per week on the platform. Eleven percent of users spent five to ten hours a week on the app, and six percent were using it for ten hours or more per week.

Users' opinion: safety concerns and positive influenceOnline TikTok challenges and hoaxes have been a topic of discussion in recent years, with parents raising concerns about safety regarding content posted on the platform. As for U.S. adults’ opinions of TikTok, favorable attitudes towards the platform decrease with age. Overall, as of November 2021, 33 percent of users aged 18 to 34 years had a favorable opinion of the app, but just one in 10 users aged between 45 and 64 years felt the same. Around 20 percent of all adults had a very unfavorable view of TikTok. Despite the criticism, the platform hosts a large number of positive influencers, focusing on themes such as mental health awareness and body positivity. In 2021, one of the most followed TikTok educators and activists was Nicole Lilly Christou, with her profile @nikkililly reporting approximately 7.7 million followers in the United Kingdom alone.

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Legal Form of Organization Statistics for Nonemployer Firms by Industry, Sex, Ethnicity, Race, and Veteran Status for the U.S., States, Metro Areas, and Counties: 2022.Table ID.ABSNESD2022.AB2200NESD03.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys..Methodology.Data Items and Other Identifying Records.Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)These data are aggregated by sex, ethnicity, race, and veteran status when classifiable.The data are also shown by the following legal form of organization (LFO) categories: S-Corporations C-Corporations Individual proprietorships Partnerships Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS is shown for:Metropolitan Statistical AreasMicropolitan Statistical AreasCountiesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The NES-D adds demographic characteristics to the NES data and produces the total firm counts and the total receipts by those demographic characteristics. The NES-D utilizes various administrative records (AR) and the Census Bureau data sources that include the Business Register (BR), Internal Revenue Service (IRS) tax Form 1040 data, tax Schedule K-1 data, Decennial Census and American Community Survey (ACS) data, Social Security Administration's database (Numident), and AR from the Department of Veterans Affairs (VA).For more information, see Nonemployer Statistics by Demographics Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7504866, Disclosure Review Board (DRB) approval number: CBDRB-FY25-0195).This dataset contains both nonemployer and employer data.For the nonemployer data, the NES-D uses noise infusion as the primary method of disclosure avoidance for receipts, and In certain circumstances, some individual cells may be suppressed for additional disclosure avoidance. More information on nonemployer firm disclosure avoidance is available in the Nonemployer Statistics by Demographics Methodology.For the employer data, data rows with high relative standard errors (RSE) are not presented. Additionally, firm counts are suppressed when other select statistics in the same row are suppressed. More information on employer firm disclosure avoidance is available in the Annual Business Survey Methodology..Te...

Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data AttributesWard Data Overview: These municipal wards were created by grouping Census 2010 population collection blocks into municipal wards. This project started with the release of Census 2010 geography and population totals to all 72 Wisconsin counties on March 21, 2011, and were made available via the Legislative Technology Services Bureau (LTSB) GIS website and the WISE-LR web application. The 180 day statutory timeline for local redistricting ended on September 19, 2011. Wisconsin Legislative and Congressional redistricting plans were enacted in 2011 by Wisconsin Act 43 and Act 44. These new districts were created using Census 2010 block geography. Some municipal wards, created before the passing of Act 43 and 44, were required to be split between assembly, senate and congressional district boundaries. 2011 Wisconsin Act 39 allowed communities to divide wards, along census block boundaries, if they were divided by newly enacted boundaries. A number of wards created under Wisconsin Act 39 were named using alpha-numeric labels. An example would be where ward 1 divided by an assembly district would become ward 1A and ward 1B, and in other municipalities the next sequential ward number was used: ward 1 and ward 2. The process of dividing wards under Act 39 ended on April 10, 2012. On April 11, 2012, the United States Eastern District Federal Court ordered Assembly Districts 8 and 9 (both in the City of Milwaukee) be changed to follow the court’s description. On September 19, 2012, LTSB divided the few remaining municipal wards that were split by a 2011 Wisconsin Act 43 or 44 district line.Election Data Overview: Election data that is included in this file was collected by LTSB from the Government Accountability Board (GAB)/Wisconsin Elections Commission (WEC) after each general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. The ward data that is collected after each decennial census is made up of collections of whole and split census blocks. (Note: Split census blocks occur during local redistricting when municipalities include recently annexed property in their ward submissions to the legislature).Disaggregation of Election Data: Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward.The data then is distributed down to the block level, again based on total population.When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the GAB/WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the GAB at ward level may not match the ward totals in the disaggregated election data file.Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2010 historical population limits.Other things of note… We use a static, official ward layer (in this case created in 2011) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if "Cityville" has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward five was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards one and four according to population percentage. Outline Ward-by-Ward Election Results: The process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from GAB/WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards spring 2017 (Census 2010 totals used for disaggregation)Blocks 2011 -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined aboveIn the event that a ward exists now in which no block exists (Occurred with spring 2017) due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks 2011This yields a block centroid layer that contains all elections from 1990 to 2016Blocks 2011 [with all election data] -> Wards 2011 (then MCD 2011, and County 2011) All election data (including later elections such as 2016) is aggregated to the Wards 2011 assignment of the blocksNotes:Population of municipal wards 1991, 2001 and 2011 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though municipal and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same. Therefore, data totals within a county should be the same between 2011 wards and 2018 wards.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2018. This is due to (a) boundary corrections in the data from 2011 to 2018, and (b) annexations, where a block may have been reassigned.