With 56 Million Businesses in the United States of America, Techsalerator has access to the highest B2B count of Data/ Business Data in the country.

Thanks to our unique tools and large data specialist team, we are able to select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all states and cities in the country : Example covered.

All states :

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho IllinoisIndiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri MontanaNebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon PennsylvaniaRhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

A few cities : New York City NY Los Angeles CA Chicago IL Houston TX Phoenix AZ Philadelphia PA San Antonio TX San Diego CA Dallas TX Austin TX San Jose CA Fort Worth TX Jacksonville FL Columbus OH Charlotte NC Indianapolis IN San Francisco CA Seattle WA Denver CO Washington DC Boston MA El Paso TX Nashville TN Oklahoma City OK Las Vegas NV Detroit MI Portland OR Memphis TN Louisville KY Milwaukee WI Baltimore MD Albuquerque NM Tucson AZ Mesa AZ Fresno CA Sacramento CA Atlanta GA Kansas City MO Colorado Springs CO Raleigh NC Omaha NE Miami FL Long Beach CA Virginia Beach VA Oakland CA Minneapolis MN Tampa FL Tulsa OK Arlington TX Wichita KS Bakersfield CA Aurora CO New Orleans LA Cleveland OH Anaheim CA Henderson NV Honolulu HI Riverside CA Santa Ana CA Corpus Christi TX Lexington KY San Juan PR Stockton CA St. Paul MN Cincinnati OH Greensboro NC Pittsburgh PA Irvine CA St. Louis MO Lincoln NE Orlando FL Durham NC Plano TX Anchorage AK Newark NJ Chula Vista CA Fort Wayne IN Chandler AZ Toledo OH St. Petersburg FL Reno NV Laredo TX Scottsdale AZ North Las Vegas NV Lubbock TX Madison WI Gilbert AZ Jersey City NJ Glendale AZ Buffalo NY Winston-Salem NC Chesapeake VA Fremont CA Norfolk VA Irving TX Garland TX Paradise NV Arlington VA Richmond VA Hialeah FL Boise ID Spokane WA Frisco TX Moreno Valley CA Tacoma WA Fontana CA Modesto CA Baton Rouge LA Port St. Lucie FL San Bernardino CA McKinney TX Fayetteville NC Santa Clarita CA Des Moines IA Oxnard CA Birmingham AL Spring Valley NV Huntsville AL Rochester NY Cape Coral FL Tempe AZ Grand Rapids MI Yonkers NY Overland Park KS Salt Lake City UT Amarillo TX Augusta GA Columbus GA Tallahassee FL Montgomery AL Huntington Beach CA Akron OH Little Rock AR Glendale CA Grand Prairie TX Aurora IL Sunrise Manor NV Ontario CA Sioux Falls SD Knoxville TN Vancouver WA Mobile AL Worcester MA Chattanooga TN Brownsville TX Peoria AZ Fort Lauderdale FL Shreveport LA Newport News VA Providence RI Elk Grove CA Rancho Cucamonga CA Salem OR Pembroke Pines FL Santa Rosa CA Eugene OR Oceanside CA Cary NC Fort Collins CO Corona CA Enterprise NV Garden Grove CA Springfield MO Clarksville TN Bayamon PR Lakewood CO Alexandria VA Hayward CA Murfreesboro TN Killeen TX Hollywood FL Lancaster CA Salinas CA Jackson MS Midland TX Macon County GA Kansas City KS Palmdale CA Sunnyvale CA Springfield MA Escondido CA Pomona CA Bellevue WA Surprise AZ Naperville IL Pasadena TX Denton TX Roseville CA Joliet IL Thornton CO McAllen TX Paterson NJ Rockford IL Carrollton TX Bridgeport CT Miramar FL Round Rock TX Metairie LA Olathe KS Waco TX

Listing of all active businesses currently registered with the Office of Finance. An "active" business is defined as a registered business whose owner has not notified the Office of Finance of a cease of business operations. Update Interval: Monthly. NAICS Codes are from 2007 NAICS: https://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2007

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).

Dataset available only to University of Arizona affiliates. To obtain access, you must log in to ReDATA with your NetID. Data is for research use by each individual downloader only. Sharing and/or redistribution of any portion of this dataset is prohibited.The ReferenceUSA datasets from Data Axle (formerly Infogroup) contain establishment-level data about US businesses in annual snapshots from 1997-2021 and can help users create marketing plans and conduct competitive analyses. Each year has 53-94 indicators, with documentation for layouts found in Documentation.zip. Codebooks for 2015, 2017, and 2019 are missing, but all file layouts for 2014-2021 are identical.The University of Arizona University Libraries also subscribe to Data Axle Reference Solutions which provides this data in a searchable, online database with historical data available going back to 2003.NOTE: The uncompressed datasets are very large.Detailed file descriptions and MD5 hash values for each file can be found in the README.txt file.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Graph and download economic data for Total Business Sales (TOTBUSSMSA) from Jan 1992 to Mar 2025 about business, sales, and USA.

United States Employment: Management, Business & Financial Operations data was reported at 25,231.000 Person th in Jun 2018. This records a decrease from the previous number of 25,784.000 Person th for May 2018. United States Employment: Management, Business & Financial Operations data is updated monthly, averaging 19,605.000 Person th from Jan 1983 (Median) to Jun 2018, with 426 observations. The data reached an all-time high of 25,992.000 Person th in Mar 2018 and a record low of 11,609.000 Person th in Feb 1983. United States Employment: Management, Business & Financial Operations data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G013: Current Population Survey: Employment.

Overview:

This dataset provides a comprehensive look into the financial expenditures and profits of 50 startups based in the United States. It is an invaluable resource for analysts, economists, and business strategists seeking to understand the correlation between different types of spending and profitability in startup ventures.

Attributes: 1. R&D Spend: - Description: The amount of money each company has invested in Research and Development activities. - Data Type: Numeric (US dollars) - Importance: Indicates the company's commitment to innovation and technological advancement. 2. Administration: - Description: Expenditure on administrative functions and operations. - Data Type: Numeric (US dollars) - Relevance: Reflects the overhead costs associated with managing the company. 3. Marketing Spend: - Description: Investment in marketing and promotional activities. - Data Type: Numeric (US dollars) - Significance: A key factor in revenue generation and market penetration. 4. State: - Description: The U.S. state where the company is operating. - Data Type: Categorical (California, New York, or Florida) - Purpose: Provides geographical context and allows for regional analysis. 5. Profit: - Description: The net profit earned by the company. - Data Type: Numeric (US dollars) - Utility: A direct measure of the company’s financial success.

Potential Uses: - Business Analysis: Understanding how different types of spending (R&D, administration, marketing) affect profitability. - Regional Studies: Examining the impact of geographical location on business success. - Startup Growth: Insights into the financial practices of successful startups. - Economic Research: Data-driven study of the startup ecosystem in the U.S.

Target Audience: - Business Analysts and Economists - Marketing Strategists - Startup Consultants - Data Science Enthusiasts - Academic Researchers

Conclusion: This dataset is a rich resource for anyone looking to delve into the financial dynamics of startups in the U.S. It offers a unique perspective on how different types of investments correlate with company success across various states.

Please note that the data is anonymized and does not include any confidential information about the companies listed. The dataset is intended for educational and research purposes.

A survey conducted in April and May 2023 found that 60 percent of the companies that do business in the United States find it challenging to track the status of the data privacy legislation and the differences between state laws when preparing for changes in the data privacy laws. The challenge for around 50 percent of the respondents were increasing their budget because of the changes.

Only counties in nonmetropolitan areas may be eligible for HUBZone designated status. In order for a nonmetropolitan county to qualify: the median household income in the county must be less than 80% of the nonmetropolitan state median household income, or the unemployment rate in the county must be at least 140% of either the national or state unemployment rate, or the county is classified as a Difficult Development Area, as designated by HUD within Alaska, Hawaii, or any territory or possession of the United States, outside of the 48 contiguous states.

During an August 2023 survey, approximately 11 percent of surveyed small or medium business (SMB) owners used artificial intelligence (AI) for data analysis. 35 percent of respondents said they would consider using AI in the future, while another 35 percent stated they were not planning on using AI for this purpose.

Access B2B Contact Data for North American Small Business Owners with Success.ai—your go-to provider for verified, high-quality business datasets. This dataset is tailored for businesses, agencies, and professionals seeking direct access to decision-makers within the small business ecosystem across North America. With over 170 million professional profiles, it’s an unparalleled resource for powering your marketing, sales, and lead generation efforts.

Key Features of the Dataset:

Verified Contact Details

Includes accurate and up-to-date email addresses and phone numbers to ensure you reach your targets reliably.

AI-validated for 99% accuracy, eliminating errors and reducing wasted efforts.

Detailed Professional Insights

Comprehensive data points include job titles, skills, work experience, and education to enable precise segmentation and targeting.

Enriched with insights into decision-making roles, helping you connect directly with small business owners, CEOs, and other key stakeholders.

Business-Specific Information

Covers essential details such as industry, company size, location, and more, enabling you to tailor your campaigns effectively. Ideal for profiling and understanding the unique needs of small businesses.

Continuously Updated Data

Our dataset is maintained and updated regularly to ensure relevance and accuracy in fast-changing market conditions. New business contacts are added frequently, helping you stay ahead of the competition.

Why Choose Success.ai?

At Success.ai, we understand the critical importance of high-quality data for your business success. Here’s why our dataset stands out:

Tailored for Small Business Engagement Focused specifically on North American small business owners, this dataset is an invaluable resource for building relationships with SMEs (Small and Medium Enterprises). Whether you’re targeting startups, local businesses, or established small enterprises, our dataset has you covered.

Comprehensive Coverage Across North America Spanning the United States, Canada, and Mexico, our dataset ensures wide-reaching access to verified small business contacts in the region.

Categories Tailored to Your Needs Includes highly relevant categories such as Small Business Contact Data, CEO Contact Data, B2B Contact Data, and Email Address Data to match your marketing and sales strategies.

Customizable and Flexible Choose from a wide range of filtering options to create datasets that meet your exact specifications, including filtering by industry, company size, geographic location, and more.

Best Price Guaranteed We pride ourselves on offering the most competitive rates without compromising on quality. When you partner with Success.ai, you receive superior data at the best value.

Seamless Integration Delivered in formats that integrate effortlessly with your CRM, marketing automation, or sales platforms, so you can start acting on the data immediately.

Use Cases: This dataset empowers you to:

Drive Sales Growth: Build and refine your sales pipeline by connecting directly with decision-makers in small businesses. Optimize Marketing Campaigns: Launch highly targeted email and phone outreach campaigns with verified contact data. Expand Your Network: Leverage the dataset to build relationships with small business owners and other key figures within the B2B landscape. Improve Data Accuracy: Enhance your existing databases with verified, enriched contact information, reducing bounce rates and increasing ROI. Industries Served: Whether you're in B2B SaaS, digital marketing, consulting, or any field requiring accurate and targeted contact data, this dataset serves industries of all kinds. It is especially useful for professionals focused on:

Lead Generation Business Development Market Research Sales Outreach Customer Acquisition What’s Included in the Dataset: Each profile provides:

Full Name Verified Email Address Phone Number (where available) Job Title Company Name Industry Company Size Location Skills and Professional Experience Education Background With over 170 million profiles, you can tap into a wealth of opportunities to expand your reach and grow your business.

Why High-Quality Contact Data Matters: Accurate, verified contact data is the foundation of any successful B2B strategy. Reaching small business owners and decision-makers directly ensures your message lands where it matters most, reducing costs and improving the effectiveness of your campaigns. By choosing Success.ai, you ensure that every contact in your pipeline is a genuine opportunity.

Partner with Success.ai for Better Data, Better Results: Success.ai is committed to delivering premium-quality B2B data solutions at scale. With our small business owner dataset, you can unlock the potential of North America's dynamic small business market.

Get Started Today Request a sample or customize your dataset to fit your unique...

Business Inventories in the United States increased 0.10 percent in March of 2025 over the previous month. This dataset provides - United States Business Inventories - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In a 2024 survey of small business leaders and IT professionals in the United States, 58 percent of respondents stated that employee data was their most compromised company data in cyberattacks. Additionally, 53 percent of respondents reported customer or consumer data as the second most impacted company data.

The US government is a major producer of economic and financial data, statistics, analysis, and forecasts that are gathered, compiled, and published as public goods for use by citizens, government agencies, researchers, nonprofits, and the business community. There is no market transaction in the publication and dissemination of these government data and therefore no market-determined value. The purpose of this paper is to outline and augment our understanding of the value of government data for business decision-making. We provide an overview of the topic, including results from government reports and a private sector survey. We then provide concrete examples of how these government data are used to make business decisions focusing on three sectors: automotive, energy, and financial services. Examples of new initiatives by the federal government to open access to more data, exploiting technology advances associated with the internet, cloud storage, and software applications, are discussed. With the significant growth in the digital economy, we also include discussion and insights around how digital platform companies utilize government data in conjunction with their privately generated data (or "big data") to foster more informed business decisions.

Number of Businesses statistics on the Data Center Colocation Services industry in United States

Motivation

In the United States, city governments provide many services: they run public school districts, administer certain welfare and health programs, build roads and manage airports, provide police and fire protection, inspect buildings, and often run water and utility systems. Cities also get revenues through certain local taxes, various fees and permit costs, sale of property, and through the fees they charge for the utilities they run.

It would be interesting to compare all these expenses and revenues across cities and over time, but also quite difficult. Cities share many of these service responsibilities with other government agencies: in one particular city, some roads may be maintained by the state government, some law enforcement provided by the county sheriff, some schools run by independent school districts with their own tax revenue, and some utilities run by special independent utility districts. These governmental structures vary greatly by state and by individual city. It would be hard to make a fair comparison without taking into account all these differences.

This dataset takes into account all those differences. The Lincoln Institute of Land Policy produces what they call “Fiscally Standardized Cities” (FiSCs), aggregating all services provided to city residents regardless of how they may be divided up by different government agencies and jurisdictions. Using this, we can study city expenses and revenues, and how the proportions of different costs vary over time.

Data

The dataset tracks over 200 American cities between 1977 and 2020. Each row represents one city for one year. Revenue and expenditures are broken down into more than 120 categories.

Values are available for FiSCs and also for the entities that make it up: the city, the county, independent school districts, and any special districts, such as utility districts. There are hence five versions of each variable, with suffixes indicating the entity. For example, taxes gives the FiSC’s tax revenue, while taxes_city, taxes_cnty, taxes_schl, and taxes_spec break it down for the city, county, school districts, and special districts.

The values are organized hierarchically. For example, taxes is the sum of tax_property (property taxes), tax_sales_general (sales taxes), tax_income (income tax), and tax_other (other taxes). And tax_income is itself the sum of tax_income_indiv (individual income tax) and tax_income_corp (corporate income tax) subcategories.

Variable Description

• year Year for these values
• city_name Name of the city, such as “AK: Anchorage”, where “AK” is the standard two-letter abbreviation for Alaska
• city_population Estimated city population, based on Census data
• county_name Name of the county the city is in
• county_population Estimated county population, based on Census data
• cpi Consumer Price Index for this year, scaled so that 2020 is 1.
• relationship_city_school Type of school district. 1: City-wide independent school district that serves the entire city. 2: County-wide independent school district that serves the entire county. 3: One or more independent school districts whose boundaries extend beyond the city. 4: School district run by or dependent on the city. 5: School district run by or dependent on the county.
• enrollment Estimated number of public school students living in the city.
• districts_in_city Estimated number of school districts in the city.
• consolidated_govt Whether the city has a consolidated city-county government (1 = yes, 0 = no). For example, Philadelphia’s city and county government are the same entity; they are not separate governments.
• id2_city 12-digit city identifier, from the Annual Survey of State and Local Government Finances
• id2_county 12-digit county identifier
• city_types Two types: core and legacy. There are 150 core cities, “including the two largest cities in each state, plus all cities with populations of 150,000+ in 1980 and 200,000+ in 2010”. Legacy cities include “95 cities with population declines of at least 20 percent from their peak, poverty rates exceeding the national average, and a peak population of at least 50,000”. Some cities are both (denoted “core

The revenue and expenses variables are described in this detailed table. Further documentation is available on the FiSC Database website, linked in References below.

All monetary data is already adjusted for inflation, and is given in terms of 2020 US dollars per capita. The Consumer Price Index is provided for each year if you prefer to use numbers not adjusted for inflation, scaled so that 2020 is 1; simply divide each value by the CPI to get the value in that year’s nominal dollars. The total population is also provided if you want total values instead of per-capita values.

Questions

• Do some exploratory data analysis. Are there any outlying cities? Any interesting trends and rela...

The State Small Business Credit Initiative (SSBCI)Transactions Dataset is a set of files reporting transaction level data for all transactions conducted through the SSBCI program from inception in 2011 through December 31, 2016. This dataset categorizes transactions by program type, according to the five approved SSBCI programs: Capital Access Programs, Collateral Support Programs, Loan Guarantee Programs, Loan Participation Programs, and Venture Capital Programs. The transaction level data was reported to Treasury by Participating States on an annual basis, as required by the Allocation Agreements. Participating States included all 50 states, the District of Columbia, American Samoa, Guam, Northern Mariana Islands, Puerto Rico and the U.S. Virgin Islands. The data fields provided here include the total financing amount, the amount of federal dollars expended, the date of the transaction, and the industry, zip code, and FTEs of the business receiving financing at the point of transaction, among other fields. The data files are available for public use. This dataset provides quantitative information that can be used for analysis of federal expenditure in supporting small business and economic development in identifying how and where federal financing was used.

Techsalerator’s News Event Data in Latin America offers a detailed and extensive dataset designed to provide businesses, analysts, journalists, and researchers with an in-depth view of significant news events across the Latin American region. This dataset captures and categorizes key events reported from a wide array of news sources, including press releases, industry news sites, blogs, and PR platforms, offering valuable insights into regional developments, economic changes, political shifts, and cultural events.

Key Features of the Dataset: Comprehensive Coverage:

The dataset aggregates news events from numerous sources such as company press releases, industry news outlets, blogs, PR sites, and traditional news media. This broad coverage ensures a wide range of information from multiple reporting channels. Categorization of Events:

News events are categorized into various types including business and economic updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly locate and analyze information relevant to their interests or sectors. Real-Time Updates:

The dataset is updated regularly to include the most recent events, ensuring users have access to the latest news and can stay informed about current developments. Geographic Segmentation:

Events are tagged with their respective countries and regions within Latin America. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:

Each event entry includes comprehensive details such as the date of occurrence, source of the news, a description of the event, and relevant keywords. This thorough detailing helps in understanding the context and significance of each event. Historical Data:

The dataset includes historical news event data, enabling users to track trends and perform comparative analysis over time. This feature supports longitudinal studies and provides insights into how news events evolve. Advanced Search and Filter Options:

Users can search and filter news events based on criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. Latin American Countries Covered: South America: Argentina Bolivia Brazil Chile Colombia Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela Central America: Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama Caribbean: Cuba Dominican Republic Haiti (Note: Primarily French-speaking but included due to geographic and cultural ties) Jamaica Trinidad and Tobago Benefits of the Dataset: Strategic Insights: Businesses and analysts can use the dataset to gain insights into significant regional developments, economic conditions, and political changes, aiding in strategic decision-making and market analysis. Market and Industry Trends: The dataset provides valuable information on industry-specific trends and events, helping users understand market dynamics and emerging opportunities. Media and PR Monitoring: Journalists and PR professionals can track relevant news across Latin America, enabling them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Use: Researchers can utilize the dataset for longitudinal studies, trend analysis, and academic research on various topics related to Latin American news and events. Techsalerator’s News Event Data in Latin America is a crucial resource for accessing and analyzing significant news events across the region. By providing detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.

Graph and download economic data for Total Business Inventories (BUSINV) from Jan 1992 to Mar 2025 about inventories, headline figure, business, and USA.

Release Date: 2017-07-13.[NOTE: Includes firms with payroll at any time during 2015. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2015 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status for at least one owner and were not publicly held or not classifiable by gender, ethnicity, race, and veteran status. The 2015 Annual Survey of Entrepreneurs asked for information for up to four persons owning the largest percentage(s) of the business. Percentages are for owners of respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for Owners of Respondent Employer Firms by Whether the Owner Previously Owned a Business or Had Been Self-Employed by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2015. ..Release Schedule. . This file was released in July 2017.. ..Key Table Information. . These data are related to all other 2015 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2015 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2015 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. For Characteristics of Business Owners (CBO) data, all estimates are of owners of firms responding to the ASE. That is, estimates are based only on firms providing gender, ethnicity, race, or veteran status; or firms not classifiable by gender, ethnicity, race, and veteran status that returned an ASE online questionnaire with at least one question answered. The ASE online questionnaire provided space for up to four owners to report their characteristics.. CBO data are not representative of all owners of all firms operating in the United States. The data do not represent all business owners in the United States.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for Owners of Respondent Employer Firms by Whether the Owner Previously Owned a Business or Had Been Self-Employed by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2015 contains data on:. . Number of owners of respondent firms with paid employees. Percent of number of owners of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of owners of respondent firms. . All owners of respondent firms. Female. Male. Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Nonminority. Veteran. Nonveteran. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more years in business. . . Whether the owner owned anoth...