Percentage of total businesses that are classified as small, meaning businesses with between one and 49 employees. Excludes businesses that cannot be classified into an industry. Statistics Canada does not recommend expressing this in a time series, as major methodological changes occur over time -- please keep this in mind when interpreting changes in this dataset.

Percentage of VAT registered businesses showing year-on-year employment growth. This indicator will include those businesses registered for VAT with less than 50 employment (around 98% of all VAT registered enterprises). It will measure the proportion of those businesses showing year on year employment growth, where employment is measured as the number of employees (full and part-time) plus the number of self-employed people that run the business.

The annual Small Business Procurement Scorecard is an assessment tool to (1) measure how well federal agencies reach their small business and socio-economic prime contracting and subcontracting goals, (2) provide accurate and transparent contracting data and (3) report agency-specific progress. The prime and subcontracting component goals include goals for small businesses, small businesses owned by women (WOSB), small disadvantaged businesses (SDB), service-disabled veteran-owned small businesses (SDVOSB), and small businesses located in Historically Underutilized Business Zones (HUBZones). Each federal agency has a different small business contracting goal, negotiated annually in consultation with SBA. SBA ensures that the sum total of all of the goals meets the 23 percent target established by law.

The Economic Census is the U.S. Government's official five-year measure of American business and the economy. It is conducted by the U.S. Census Bureau, and response is required by law. In October through December of the census year, forms are sent out to nearly 4 million businesses, including large, medium and small companies representing all U.S. locations and industries. Respondents were asked to provide a range of operational and performance data for their companies. This dataset presents company, establishments, value of shipments, value of product shipments, percentage of product shipments of the total value of shipments, and percentage of distribution of value of product shipments.

This dataset represents the percent change in net revenue of open small businesses in North Carolina counties calculated as a seven-day moving average, which is seasonally adjusted and indexed to January 4-31 2020. The data obtained from the Opportunity Insights data repository in Github includes the daily percentage change in net revenue of open small businesses compared to January 2020 levels. The data is then aggregated as a rolling 7-day average. Twenty-two of North Carolina’s 100 counties are represented in this dataset, none of which are non-CBSA (outside of both metropolitan and micropolitan areas). Additionally, only one county is identified as having high pre-existing unemployment, and two as having lower median income. As a result, the dataset disproportionately represents relatively prosperous metropolitan centers and does not represent other regions of the state.

Nonemployer Statistics is an annual series that provides statistics on U.S. businesses with no paid employees or payroll, are subject to federal income taxes, and have receipts of $1,000 or more ($1 or more for the Construction sector). This program is authorized by the United States Code, Titles 13 and 26. Also, the collection provides data for approximately 450 North American Industry Classification System (NAICS) industries at the national, state, county, metropolitan statistical area, and combined statistical area geography levels. The majority of NAICS industries are included with some exceptions as follows: crop and animal production; investment funds, trusts, and other financial vehicles; management of companies and enterprises; and public administration. Data are also presented by Legal Form of Organization (LFO) (U.S. and state only) as filed with the Internal Revenue Service (IRS). Most nonemployers are self-employed individuals operating unincorporated businesses (known as sole proprietorships), which may or may not be the owner's principal source of income. Nonemployers Statistics features nonemployers in several arts-related industries and occupations, including the following: Arts, entertainment, and recreation (NAICS Code 71) Performing arts companies Spectator sports Promoters of performing arts, sports, and similar events Independent artists, writers, and performers Museums, historical sites, and similar institutions Amusement parks and arcades Professional, scientific, and technical services (NAICS Code 54) Architectural services Landscape architectural services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, and musical instrument stores Sewing, needlework, and piece goods stores Book stores Art dealers Nonemployer Statistics data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The data are processed through various automated and analytical review to eliminate employers from the tabulation, correct and complete data items, remove anomalies, and validate geography coding and industry classification. Prior to publication, the noise infusion method is applied to protect individual businesses from disclosure. Noise infusion was first applied to Nonemployer Statistics in 2005. Prior to 2005, data were suppressed using the complementary cell suppression method. For more information on the coverage and methods used in Nonemployer Statistics, refer to NES Methodology. The majority of all business establishments in the United States are nonemployers, yet these firms average less than 4 percent of all sales and receipts nationally. Due to their small economic impact, these firms are excluded from most other Census Bureau business statistics (the primary exception being the Survey of Business Owners). The Nonemployers Statistics series is the primary resource available to study the scope and activities of nonemployers at a detailed geographic level. For complementary statistics on the firms that do have paid employees, refer to the County Business Patterns. Additional sources of data on small businesses include the Economic Census, and the Statistics of U.S. Businesses. The annual Nonemployer Statistics data are available approximately 18 months after each reference year. Data for years since 2002 are published via comma-delimited format (csv) for spreadsheet or database use, and in the American FactFinder (AFF). For help accessing the data, please refer to the Data User Guide.

This table includes total expenses, cost of sales (direct expenses), wages and benefits, purchases, materials and sub-contracts, opening inventory, closing inventory, operating expenses (indirect expenses), labour and commissions, amortization and depletion, repairs and maintenance, utilities and telephone and telecommunication, rent, interest and bank charges, advertising and promotion, delivery and shipping and warehouse, insurance, other indirect expenses, net profit or loss. All incorporation statuses. Values are averages in current dollars unless otherwise stated.

Dataset contains micro-business activity across the United States, measured by the density of micro-businesses in US counties. Microbusinesses are often too small or too new to show up in traditional economic data sources, but microbusiness activity may be correlated with other economic indicators of general interest.

Feature Description

cfips - A unique identifier for each county using the Federal Information Processing System. The first two digits correspond to the state FIPS code, while the following 3 represent the county. county - The written name of the county. state - The name of the state. first_day_of_month - The date of the first day of the month. microbusiness_density - Microbusinesses per 100 people over the age of 18 in the given county. This is the target variable. The population figures used to calculate the density are on a two-year lag due to the pace of update provided by the U.S. Census Bureau, which provides the underlying population data annually. 2021 density figures are calculated using 2019 population figures, etc. active - The raw count of micro-businesses in the county. year - Year in which the record is published (YYYY) month - Month in which the record is published (MM) pct_broadband - The percentage of households in the county with access to broadband of any type. Derived from ACS table B28002: PRESENCE AND TYPES OF INTERNET SUBSCRIPTIONS IN HOUSEHOLD. pct_college - The percent of the population in the county over age 25 with a 4-year college degree. Derived from ACS table S1501: EDUCATIONAL ATTAINMENT. pct_foreign_born - The percent of the population in the county born outside of the United States. Derived from ACS table DP02: SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES. pct_IT_workers - The percent of the workforce in the county employed in information related industries. Derived from ACS table S2405: INDUSTRY BY OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER. median_income - The median household income in the county. Derived from ACS table S1901: INCOME IN THE PAST 12 MONTHS (IN 2021 INFLATION-ADJUSTED DOLLARS).

This table includes percent of profitable businesses; total revenue, total expenses, and net profit (profitable businesses); total revenue, total expenses, and net loss (non-profitable businesses). All businesses only. Values are averages in current dollars unless otherwise stated.

During an early 2023 survey carried out among among people who run their own business or side hustle in the United Kingdom, ** percent stated they used paid social media posts to market their business. ost used channel amogn the *** presented in the data set was organic/non-paid social media, named by ** percent of respondents.

An aggregated, multi-agency data set with information on college degrees, statewide degree attainment, average school facility age, school facility maintenance evaluation, and Minority Business Enterprise (MBE) and Small-Business Reserve (SBR) program participation. The data set contains data from the Maryland Higher Education Commission (MHEC), the US Census Bureau, the Inter-Agency Council on School Construction (IAC), and the Governor's Office of Minority Affairs (GOMA)

The Business Structure Database (BSD) contains a small number of variables for almost all business organisations in the UK. The BSD is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. The IDBR data are complimented with data from ONS business surveys. If a business is liable for VAT (turnover exceeds the VAT threshold) and/or has at least one member of staff registered for the PAYE tax collection system, then the business will appear on the IDBR (and hence in the BSD). In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK. Only very small businesses, such as the self-employed were not found on the IDBR.

The IDBR is frequently updated, and contains confidential information that cannot be accessed by non-civil servants without special permission. However, the ONS Virtual Micro-data Laboratory (VML) created and developed the BSD, which is a 'snapshot' in time of the IDBR, in order to provide a version of the IDBR for research use, taking full account of changes in ownership and restructuring of businesses. The 'snapshot' is taken around April, and the captured point-in-time data are supplied to the VML by the following September. The reporting period is generally the financial year. For example, the 2000 BSD file is produced in September 2000, using data captured from the IDBR in April 2000. The data will reflect the financial year of April 1999 to March 2000. However, the ONS may, during this time, update the IDBR with data on companies from its own business surveys, such as the Annual Business Survey (SN 7451).

The data are divided into 'enterprises' and 'local units'. An enterprise is the overall business organisation. A local unit is a 'plant', such as a factory, shop, branch, etc. In some cases, an enterprise will only have one local unit, and in other cases (such as a bank or supermarket), an enterprise will own many local units.

For each company, data are available on employment, turnover, foreign ownership, and industrial activity based on Standard Industrial Classification (SIC)92, SIC 2003 or SIC 2007. Year of 'birth' (company start-up date) and 'death' (termination date) are also included, as well as postcodes for both enterprises and their local units. Previously only pseudo-anonymised postcodes were available but now all postcodes are real.

The ONS is continually developing the BSD, and so researchers are strongly recommended to read all documentation pertaining to this dataset before using the data.

Linking to Other Business Studies
These data contain IDBR reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest Edition Information
For the sixteenth edition (March 2024), data files and a variable catalogue document for 2023 have been added.

** Startup Failure Prediction Dataset

This dataset helps understand why some startups succeed while others fail. It contains 5,000 startups from different industries and includes important details like funding, revenue, team size, and market conditions. **

** What’s Inside?**

This dataset has key information about startups, including:

Industry– Type of business (Tech, Healthcare, E-commerce, etc.)

Startup Age – How many years the startup has been running

Funding Amount – Total investment received

Number of Founders – How many people started the company

Founder Experience – Work experience of the founders

Employees Count – Number of employees in the startup

Revenue – How much money the startup makes

Burn Rate – How much money the startup spends per month

Market Size – Size of the industry (Small, Medium, Large)

Business Model – Does the startup sell to businesses (B2B) or customers (B2C)?

Product Uniqueness Score – How unique the startup’s product is (Scale: 1-10)

Customer Retention Rate – Percentage of customers who return

Marketing Expense – How much money is spent on marketing

Startup Status – 1 = Successful, 0 = Failed (Did the startup succeed or fail?)

The total number and percentage of private enterprises owned by men or women, by age group of primary owner and enterprise size.

Quarterly Employment and Wage Data (Timeseries)

Detailed Insights into Local Industries

By State of New York [source]

About this dataset

The Quarterly Census of Employment and Wages (QCEW) program is the most comprehensive labor market data source out there, collecting vital information on employment and wage trends across New York State. It provides a virtual census of 97 percent of the state's nonfarm employees and employers who are covered by the Unemployment Insurance (UI) Law, making it incredibly precise in measuring total wages, establishments, unemployment insurance reports, as well as crucial geographical labor information by state region and county.

At its core, this program seeks to give users a precise quantitative view comparative data that takes into account differences in employee coverage regulatory policy across bureaus or federal laws. All this while taking into consideration factors like agricultural workers, private households employments students or unpaid family workers that are excluded from UI considerations but still count towards Current Employment Statistics totals. This dataset offers an eye-opening look into employment dynamics in New York State; one that you won't find anywhere else! Before using any found data though make sure to review and read through the Terms of Service license requirements first!

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How to use the dataset

The New York Quarterly Employment and Wage Data is an invaluable resource for researchers, students, professionals and policymakers. The data offers a wealth of information on employment status and wages in New York State across all industries. This guide will provide you with a step-by-step introduction to using this dataset.

Research Ideas

• Analyzing small business trends over time to understand hiring trends in the localization and industry level.
• Creating predictive models to forecast future employment levels and wage demands for New York State's departments, businesses, and regions in the upcoming quarters or fiscal years.
• Tracking changes in average wages and employment by industry, region or area type over time to identify potential labor shortages or job losses due to automation or other factors that could lead to policy recommendations at a state level

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: quarterly-census-of-employment-and-wages-quarterly-data-beginning-2000-1.csv | Column name | Description | |:-----------------------|:-----------------------------------------------------------------| | Area Type | The type of area the data is for (String) | | Year | The year the data is for (Integer) | | Quarter | The quarter the data is for (Integer) | | NAICS | The North American Industry Classification System code (Integer) | | NAICS Title | The title of the NAICS code (String) | | Establishments | The number of establishments in the area (Integer) | | Month 1 Employment | The number of employees in the first month (Integer) | | Month 2 Employment | The number of employees in the second month (Integer) | | Month 3 Employment | The number of employees in the third month (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit State of New York.

The dataset contains year- and company-wise percentage of input material sources from Micro, Small, and Medium Enterprises (MSMEs), and from within district and neighbouring districts of the location of the company by the top 1000 Companies listed by market capitalization in Business Responsibility and Sustainability Reporting (BRSR).

Note: We have observed certain discrepancies in the units mentioned by a few companies in their BRSR submissions between the PDF files and the corresponding XML files. The datasets on Dataful contain the units provided in the XML submissions. We recommend that you verify the information directly with SEBI and/or the respective companies in case you observe any such discrepancy.

Small and medium-sized enterprises (SMEs) comprise the majority of Canadian companies and make a substantial contribution to the economy. As of December 2021, there were about 1.2 million SMEs in Canada, representing 99.8 percent of all employer businesses (ISED, 2022). SMEs were responsible for 88.2% of all private sector jobs in 2021 and accounted for about 53.2% of GDP on average between 2015 and 2019. The importance of SMEs to the overall economy is not particular to Canada. Across Organization for Economic Cooperation and Development (OECD) economies, SMEs account for over 99% of firms and 60% of business sector employment and the majority of value-added (OECD, 2023).

The Survey on Financing of Small and Medium Enterprises - 2004, surveyed enterprises between September 2004 and March 2005. The estimates were generated from the responses obtained from 13 042 SMEs (enterprises that had a gross revenue of less than $50 million, had less than 500 full-time-equivalent employees and were operational even for a very short time during the reference year), rendering a response rate (percentage of in-scope businesses) of 45 percent. The estimates related to Part 2 of the survey were generated from responses to a paper questionnaire obtained from 3500 SMEs.

The survey was commissioned by the Ministry of Economy and Innovation of the Republic of Lithuania in 2022. from October 14 to November 18. The general population of the survey was all small and medium-sized enterprises registered in the Republic of Lithuania (SMEs, enterprises with a number of employees not exceeding 250 and an annual income of EUR 50 million). A total of 1004 respondents were interviewed. The sample of the survey was formed according to the general proportions of SME companies in the research population. The proportions of the activity sector, company size (number of employees) and place of registration (county level) were taken into account. The sample quotas are based on the actual proportions of SMEs in the population. The selection of specific companies was carried out in a random-probabilistic manner. The maximum statistical error of the entire sample is ±3.1 percent.

This table presents information about developments in retail turnover (SIC 2008 code 47). The data is broken down in two ways. The first breakdown refers to the sales channel: shops that predominantly sell goods online and those that predominantly sell goods through other sales channels (physical shops, markets, etc.). The second breakdown refers to the main economic activity: shops that predominantly sell food and drugstore items, consumer electronics, clothes and fashion items or other non-food. Developments are presented as percentage changes compared to a previous year and by means of indices. In this table, the base year is updated to 2015, in previous publications the base year was 2013. The survey used to measure turnover change for online sales covers retail trade companies with 10 or more employees; these represent 65-70 percent of total online sales. Small businesses are not covered.