The number of small and medium-sized enterprises in the United States was forecast to continuously decrease between 2024 and 2029 by in total 6.7 thousand enterprises (-2.24 percent). After the fourteenth consecutive decreasing year, the number is estimated to reach 291.94 thousand enterprises and therefore a new minimum in 2029. According to the OECD an enterprise is defined as the smallest combination of legal units, which is an organisational unit producing services or goods, that benefits from a degree of autonomy with regards to the allocation of resources and decision making. Shown here are small and medium-sized enterprises, which are defined as companies with 1-249 employees.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

The dataset exists to observe the entrepreneurial activity of Austin over a long time period. The data comes from the U.S. Census County Business Pattern table and is capturing data at the Travis County level. It contains the cumulative count of firms by employee size and count of firms by employee size by industry. This data can be used to see changes of employer growth by industry; to project where workforce growth could be occurring; or to simply see how many small businesses there are in Austin. View more details and insights related to this data set on the story page: data.austintexas.gov/stories/s/ndb5-si22

In March 2020, Mayor Carter announced the Saint Paul Bridge Fund to provide emergency relief for families and small businesses most vulnerable to the economic impacts of the COVID-19 pandemic. The program was funded through $3.25 million dollars from the Saint Paul Housing and Redevelopment Authority along with contributions from philanthropic, corporate and individual donors. Through these additional contributions, the fund provided $4.1 million to families and small businesses in Saint Paul.Data previously shared in this space included only the 380 recipients funded through "Phase 1". This dataset includes all three phases that were ultimately rolled out through the Bridge Fund for Small Business program.Nearly 2,000 unique applications applied for a small business grant of $7,50036% were from ACP50 areas (Areas of Concentrated Poverty where 50% or more of the residents are people of color)The applications were reviewed in order of a random number assigned at application close. Of these applications:633 small businesses were awarded a $7,500 grant36% of applications in the city were from ACP50 areas86% of applicants in the city cited they were ordered closed under one of the Governor’s Executive OrdersThis is a dataset of the small businesses that applied for the Bridge Fund and includes:Self-reported survey responsesAward informationGeographic information Additional information about the Saint Paul Bridge Fund may be found at stpaul.gov/bridge-fund.

The Small Business Administration maintains the Dynamic Small Business Search (DSBS) database. As a small business registers in the System for Award Management, there is an opportunity to fill out the small business profile. The information provided populates DSBS. DSBS is another tool contracting officers use to identify potential small business contractors for upcoming contracting opportunities. Small businesses can also use DSBS to identify other small businesses for teaming and joint venturing.

This data set tracks the number of established businesses, non-profits, and startups that the City of Austin Economic Development and Small and Minority Business Resources departments supported each year. The data set lists the programs each business was served through, the race or ethnicity of the CEO or Executive supported, and zipcode, if available. This data can be used to distinguish areas of Austin and around the globe that have received small business services as well as the racial makeup of the executives of these established businesses, non-profits, and startups.

View more details and insights related to this data set on the story page: data.austintexas.gov/stories/s/hgbb-jkth

The Business Dynamics Statistics (BDS) includes measures of establishment openings and closings, firm startups, job creation and destruction by firm size, age, and industrial sector, and several other statistics on business dynamics. The U.S. economy is comprised of over 6 million establishments with paid employees. The population of these businesses is constantly churning -- some businesses grow, others decline and yet others close. New businesses are constantly replenishing this pool. The BDS series provide annual statistics on gross job gains and losses for the entire economy and by industrial sector, state, and MSA. These data track changes in employment at the establishment level, and thus provide a picture of the dynamics underlying aggregate net employment growth.

There is a longstanding interest in the contribution of small businesses to job and productivity growth in the U.S. Some recent research suggests that it is business age rather than size that is the critical factor. The BDS permits exploring the respective contributions of both firm age and size.

BDS is based on data going back through 1976. This allows business dynamics to be tracked, measured and analyzed for young firms in their first critical years as well as for more mature firms including those that are in the process of reinventing themselves in an ever changing economic environment.

If you need help understanding the terms used, check out these definitions.

Data Dictionary

Key	List of...	Comment	Example Value
State	String	The state that this report was made for (full name, not the two letter abbreviation).	"Alabama"
Year	Integer	The year that this report was made for.	1978
Data.DHS Denominator	Integer	The Davis-Haltiwanger-Schuh (DHS) denominator is the two-period trailing moving average of employment, intended to prevent transitory shocks from distorting net growth. In other words, this value roughly represents the employment for the area, but is resistant to sudden, spiking growth.	972627
Data.Number of Firms	Integer	The number of firms in this state during this year.	54597
Data.Calculated.Net Job Creation	Integer	The sum of the Job Creation Rate minus the Job Destruction Rate.	74178
Data.Calculated.Net Job Creation Rate	Float	The sum of the Job Creation Rate and the Job Destruction Rate, minus the Net Job Creation Rate.	7.627
Data.Calculated.Reallocation Rate	Float	The sum of the Job Creation Rate and the Job Destruction Rate, minus the absolute Net Job Creation Rate.	29.183
Data.Establishments.Entered	Integer	The number of establishments that entered during this time. Entering occurs when an establishment did not exist in the previous year.	10457
Data.Establishments.Entered Rate	Float	The number of establishments that entered during this time divided by the number of establishments. Entering occurs when an establishment did not exist in the previous year.	16.375
Data.Establishments.Exited	Integer	The number of establishments that exited during this time. Exiting occurs when an establishment has positive employment in the previous year and zero this year.	7749
Data.Establishments.Exited Rate	Float	The number of establishments that exited during this time divided by the number of establishments. Exiting occurs when an establishment has positive employment in the previous year and zero this year.	12.135
Data.Establishments.Physical Locations	Integer	The number of establishments in this region during this time.	65213
Data.Firm Exits.Count	Integer	The number of firms that exited this year.	5248
Data.Firm Exits.Establishment Exit	Integer	The number of establishments exited because of firm deaths.	5329
Data...

The below reference files provide current and historical small business size standards effective during the provided reference periods. Size standards for reference periods prior to August 2019 are provided based on the size standards in effect on January 1 of each CFR reference year, and thus, may not provide the entire history of SBA’s changes to size standards that occurred after January 1 of the CFR reference year. As such, changes that occur after January 1 of the CFR reference year are reflected in the Table of Small Business Size Standards for the next CFR reference year. Legal requirements related to size standards are governed by SBA’s size regulations actually in effect during the applicable period. Size standards for reference periods after August 2019 are provided based on the actual effective dates of the size standards. The Small Business Size Standards APIs, also provided below, power the following application: https://fanyv88.com:443/https/www.sba.gov/size-standards/, which can be used to determine if a business qualifies as small for purposes of Federal government contracting.

The SME data warehouse is based on existing administrative data sources from Statistics Canada and Canada Revenue Agency. Data covers tax year 2001 to tax year 2006. The SME Data Warehouse contains a complete, up to date and unduplicated list of all businesses in Canada based on Statistics Canada's Business Register for tax years 2001-2006. This product currently produces data for Small and Medium Sized Enterprises (SMEs). SMEs are defined as enterprises with less than 250 employees and less than $50 million in total revenue.

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.

The Small Business Survey (SBS) is a large scale telephone survey commissioned by the Department for Business, Innovation and Skills (BIS) as a follow up to the Annual Survey of Small Businesses 2007/8. The main aims of the first SBS survey in 2010 were to:

• monitor key enterprise indicators (e.g. employment, turnover, finance, business growth) and how these have changed in comparison to previous surveys. In particular the survey measures characteristics of Small and Medium-sized Enterprises (SME) such as the proportions which are women and minority-ethnic led, and the proportions applying for and obtaining external finance


• gauge SME intentions, needs, concerns and the obstacles to fulfilling their potential
• provide evidence for possible Government interventions to assist SMEs
• create a database which can be used for follow-up studies among the general SME population, or sub-groups within it

The main aims of the next survey in 2012 were to:

• monitor key enterprise indicators and how these have changed in comparison to previous surveys, the 2010 SBS in particular
• create a database that can be used for follow-up studies among the general SME population, and sub-groups within it.

From 2015, the survey methodology has changed to include a longitudinal tracking element. The Longitudinal Small Business Survey: Secure Access is available under SN 8261.

Linking to other business studies
These data contain Inter-Departmental Business Register 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.

The second edition (August 2014) includes data from 2012.

Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2023. It shows number of jobs and number of business establishments by business size, classified by their CLUE industry, ANZSIC1 and CLUE small area allocation.Business size is determined by the total number of jobs at ech business establishment and is categorised as follows:Non employing, no jobs allocated to the establishment.Small business, 1 to 19 jobs employed at a business establishment.Medium business, 20 to 199 jobs employed at a business establishment.Larger business, 200 or more jobs employed at a business establishment.This dataset has been confidentialised to protect the commercially sensitive information of individual businesses. Data in cells which pertain to two or fewer businesses have been suppressed and are shown as a blank cell. The 'City of Melbourne' row totals refer to the true total, including those suppressed cells.Non-confidentialised data may be made available subject to a data supply agreement. For more information contact cityfacts@melbourne.vic.gov.auFor CLUE small area spatial files see https://data.melbourne.vic.gov.au/explore/dataset/small-areas-for-census-of-land-use-and-employment-clue/mapFor more information about CLUE see http://www.melbourne.vic.gov.au/clueFor more information about the ANZSIC industry classification system see http://www.abs.gov.au/ausstats/abs@.nsf/mf/1292.0

Bankruptcies in the United States increased to 23309 Companies in the first quarter of 2025 from 23107 Companies in the fourth quarter of 2024. This dataset provides - United States Bankruptcies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

** 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?)

Context

E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.

Content

This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.

The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.

There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.

Inspiration

Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?

Photo by CardMapr on Unsplash

Should This Loan be Approved or Denied?

If you like the data set and download it, an upvote would be appreciated.

The Small Business Administration (SBA) was founded in 1953 to assist small businesses in obtaining loans. Small businesses have been the primary source of employment in the United States. Helping small businesses help with job creation, which reduces unemployment. Small business growth also promotes economic growth. One of the ways the SBA helps small businesses is by guaranteeing bank loans. This guarantee reduces the risk to banks and encourages them to lend to small businesses. If the loan defaults, the SBA covers the amount guaranteed, and the bank suffers a loss for the remaining balance.

There have been several small business success stories like FedEx and Apple. However, the rate of default is very high. Many economists believe the banking market works better without the assistance of the SBA. Supporter claim that the social benefits and job creation outweigh any financial costs to the government in defaulted loans.

The Data Set

The original data set is from the U.S.SBA loan database, which includes historical data from 1987 through 2014 (899,164 observations) with 27 variables. The data set includes information on whether the loan was paid off in full or if the SMA had to charge off any amount and how much that amount was. The data set used is a subset of the original set. It contains loans about the Real Estate and Rental and Leasing industry in California. This file has 2,102 observations and 35 variables. The column Default is an integer of 1 or zero, and I had to change this column to a factor.

For more information on this data set go to https://amstat.tandfonline.com/doi/full/10.1080/10691898.2018.1434342

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This dataset contains non-personally identifiable (non-PII) data from the U.S. Small Business Administration (SBA) Disaster Loan Program. Following a declared disaster, the SBA provides disaster assistance in the form of low-interest, long-term disaster loans for damages not covered by insurance or other recoveries to businesses of all sizes, private nonprofit organizations, as well as homeowners and renters. For more information about the SBA Disaster Loan Program, please visit www.sba.gov/disasterassistance. This dataset includes raw, unedited data from SBA’s Disaster Credit Management System (DCMS) which may have been entered directly by disaster survivors and as such is subject to human error. Additionally, the dollar values in the data set may not reflect subsequent changes to verified losses or approved loan amounts. SBA adjusts damages and loan amounts as needed based on the availability of new or corrected information. For example, verified loss and approved loan amounts may be increased later if new damages are discovered or the cost of repairs increase during the rebuilding project. Similarly, loan amounts may be decreased if the disaster survivor receives additional recoveries from insurance or grant assistance which duplicate SBA’s assistance, thereby decreasing the overall loan eligibility. This dataset is not intended to be an official Federal report, and should not be considered as such. If you have media inquiries about the SBA Disaster Loan Program, please email SBA’s Office of Communications and Public Liaison at press_office@sba.gov. For inquiries about how to submit a Freedom of Information Act (FOIA) or a Privacy Act request, please contact SBA’s Freedom of Information/Privacy Acts Office by email at foia@sba.gov. For all other inquiries about this data set, including requests from States and local governments for more detailed loan data, please email the SBA’s Office of Disaster Assistance’s Data Steward, Alejandro Contreras, at alejandro.contreras@sba.gov. Information for State and local governments on how to request a data sharing agreement with SBA to help identify and prevent duplications of benefits.

To search for a business - click here

To view a glossary of terms for the directory - click here

What is this directory for?

Mayor Michelle Wu is committed to creating equal opportunities for businesses of all kinds in Boston. Through the business certification process, the City identifies businesses that are owned by women, minorities, veterans as well as those that are small or local. Once a business is certified with our office, they are included in any vendor outreach efforts for City contracting opportunities and are also connected to resources offered inside and outside of the City.

In order to provide access to more minority-owned and woman-owned businesses, small and small local businesses, and veteran and service disabled veteran-owned small businesses, the City of Boston Directory of certified businesses is now available on Analyze Boston.

If you think you might be eligible for certification, visit our website and apply today

If you have questions about obtaining certification, please contact stacey.williams@boston.gov

What does each certification category mean?

Minority Business Enterprise (MBE) - means a business organization which is beneficially owned or substantially invested in by one or more minority group members as follows:

• The business must be at least 51% beneficially owned by minority group members.
• The minority owners must demonstrate that they have control over management.

• The firm has not been solely established for the purpose of taking advantage of a special program which has been developed to assist minority-owned businesses.

  Woman Business Enterprise (WBE) - means a business organization which is beneficially owned or substantially invested in by one or more women meeting the following criteria:

• The business must be at least 51% beneficially owned by a woman.

• The woman owner must demonstrate that she has control over management.

• The firm has not been solely established for the purpose of taking advantage of a special program which has been developed to assist woman-owned businesses.

Small Business Enterprise (SBE) - means a business with gross receipts, that when averaged over a three-year period do not exceed gross income limitations for that particular industry as defined by the Small Local Business Enterprise Office.

Small Local Business Enterprise (SLBE) - means a business which is a Small Business Enterprise, as defined above, and whose principal office is physically located in the City of Boston, as defined by the SLBE certification regulations.

A Veteran Owned Small Business (VOSB) and a Service Disabled Veteran Owned Small Business (SDVOSB) is a business that has already been verified as such by the U.S. Department of Veteran Affairs.

Can a business hold multiple certifications at the same time?

Yes, businesses may qualify for more than one certification.

How long are these certifications valid for?

Businesses are required to renew their certification _ every three years_.

Abstract copyright UK Data Service and data collection copyright owner.

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 Business Structure Database Longitudinal, 1997-2013 was compiled by Michael Anyadike-Danes, Aston Business School, with support from Economic and Social Research Council funding.

Researchers are advised to read the documentation accompanying the main BSD collection held by the UK Data Archive under SN 6697 before applying for or using the longitudinal 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.

For the second edition (April 2019), the full postcodes have been replaced with only the first part of the postcode (e.g., SW1V rather than SW1V 2QQ) in the two geography data files. A look up file that includes postcode districts has been added so that users can still aggregate to higher geographies.

Main Topics:

The Business Structure Database Longitudinal, 1997-2013 includes the following files:

• enterprise birth and death date file
• enterprise employees file, giving the total number of employees (excluding owners) of each enterprise
• two geography files, including Government Office Regions and postcodes, both at birth and throughout 1997-2013
• two Standard Industrial Classification code files both at birth and throughout 1997-2013

All files include the Enterprise reference number, which allows linking of the longitudinal dataset to other business surveys.

An aggregated dataset of PPP (Paycheck Protection Program) SBA (Small Business Administration) loans involving 3 million businesses would be a comprehensive collection of financial information, aimed at analyzing the distribution and impact of these loans. This dataset would include key details such as the names of the businesses, loan amounts, loan disbursement dates, and the terms of the loans. Additionally, the dataset would contain information on board members of these businesses, providing insights into the governance structures and potential networks influencing the flow of SBA funds. This aspect of the dataset can be crucial for understanding the distribution patterns of PPP loans, identifying trends in funding allocation among different types of businesses, and examining any correlations between board composition and loan receipt. Such a dataset would be valuable for various analyses, including: Financial Analysis: Assessing the financial health and stability of businesses that received PPP loans, and understanding how these loans have impacted their operations during challenging economic times. Governance Analysis: Evaluating the role of board members in acquiring PPP loans, and whether certain types of governance structures were more successful in securing funds. Economic Impact Assessment: Measuring the broader economic impact of the PPP loans, such as job retention, business survival rates, and sector-wise distribution of funds. Network Analysis: Mapping the connections between different businesses and their board members to identify any potential networks or clusters that may have influenced the flow of funds. Policy Evaluation: Providing data-driven insights to policymakers for assessing the effectiveness of the PPP program and for planning future economic relief measures.