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This dataset contains annual measures of training and vocational schools in the United States from 1990 through 2022. The data include counts, per capita densities, area densities, and employment figures for twelve categories of training and vocational establishments: business and secretarial schools, data processing schools, general educational services, beauty schools and barber colleges, vocational schools, dance schools, instruction schools and camps, arts and crafts schools, music and drama schools, vehicle driving schools, reading and speaking schools, and personal development schools.

The unit of analysis is either the Census Tract or ZIP Code Tabulation Area (ZCTA), with separate files standardized to 2010 Census Tract boundaries, 2020 Census Tract boundaries, 2010 ZCTA boundaries, and 2020 ZCTA boundaries. Each file covers all census tracts or ZCTAs in the fifty United States, including Alaska, Hawaii, and US island territories.

Business establishment data were drawn from the National Establishment Time Series (NETS) database, which was cleaned and geocoded to Census Bureau TIGER/Line shapefiles. Population denominators came from the American Community Survey and Decennial Census. The data cleaning protocol addressed known NETS limitations by re-geocoding addresses, standardizing SIC codes across time, collapsing duplicate records, and removing businesses located at residential addresses using Zillow's ZTRAX data.

Key variables include count (e.g., count_businesscho), per capita density per 1000 population (e.g., den_datascho), area density per square mile (e.g., aden_vocationscho), and total employment (e.g., emps_beautyscho) for each establishment category. The Census Tract 2020 dataset includes both tract_fips20 and tract_fips22 variables to accommodate Connecticut's 2022 county boundary changes.

This dataset contains measures of the number and density of businesses and their employees deemed essential in the first year (2020) of the COVID-19 pandemic by the US Department of Homeland Security’s Cybersecurity & Infrastructure Security Agency (CISA) in versions 3.0 (April 17, 2020) and 4.0 (August 18, 2020) of their advisory guidance on the essential critical infrastructure workforce. Measures are provided for 2020 per United States Census Tract or ZIP Code Tabulation Area (ZCTA). This 2020 dataset includes four separate files for four different geographic areas (GIS shapefiles from the United States Census Bureau). The four geographies include:

• Census Tract 2010
• Census Tract 2020
• ZIP Code Tabulation Area (ZCTA) 2010
• ZIP Code Tabulation Area (ZCTA) 2020

Information about which dataset to use can be found in the Usage Notes section of the data documentation.

The U.S. Census Bureau's Business Trends and Outlook Survey (BTOS), a survey that measures business conditions, provides insight into the state of the economy with data for key economic measures every two weeks. The continuous, timely nature of BTOS measures captures the impact of events like natural disasters and economic crises and assists in monitoring recovery efforts.

The BTOS is the successor to the Small Business Pulse Survey (SBPS), a high-frequency survey that measured the effect of changing business conditions during the coronavirus pandemic, and other major events like hurricanes, on our nation's small businesses. BTOS increases the scope of the SBPS to include large single-location employer businesses (those with 500 or more employees).

Released biweekly and available by sector, state, and the 25 most populous metropolitan statistical areas, the BTOS offers real-time data to aid in policy and economic decision-making. BTOS data are representative of all single-location employer businesses in the U.S. economy, excluding farms, and include geographic and subsector detail. The sector and subsector commonly used to study arts-related businesses are Sector 71 (Arts and Entertainment) and Subsector 711 (Performing Arts, Spectator Sports, and Related Industries).

The BTOS sample consists of approximately 1.2 million businesses with biweekly data collection. Selected businesses are split into six panels (approximately 200,000 cases per panel) that will be asked to report every 12 weeks for a year.

The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley conducted the Kenya COVID-19 Rapid Response Phone Survey (RRPS) to track the socioeconomic impacts of the COVID-19 pandemic and the recovery from it to provide timely data to inform policy. This collection contains information from seven waves of the COVID-19 RRPS, which was part of a panel survey that targeted Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques. Sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. The "WAVE" variable represents in which wave the households were interviewed in. All waves of this survey included information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations.

The data contain information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority were randomly selected. The samples covered urban and rural areas and were designed to be representative of the population of Kenya using cell phones. The sample size for each completed wave was:

• Wave 1: 4,061 Kenyan households
• Wave 2: 4,492 Kenyan households
• Wave 3: 4,979 Kenyan households
• Wave 4: 4,892 Kenyan households
• Wave 5: 5,854 Kenyan households
• Wave 6: 5,765 Kenyan households
• Wave 7: 5,633 Kenyan households

The collection is organized into three levels. The first level is the Household Level Data, which contains household level information. The 'HHID' variable uniquely identifies all households. The second level is the Adult Level Data, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'ADULT_ID' variable. The third level is the Child Level Data, which contains information for every child in the household. Each child in a household is uniquely identified by the 'CHILD_ID' variable.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

The study includes data collected with the purpose of creating an integrated dataset that would allow researchers to address significant, policy-relevant gaps in the literature--those that are best answered with cross-jurisdictional data representing a wide array of economic and social factors. The research addressed five research questions:

1. What is the impact of gentrification and suburban diversification on crime within and across jurisdictional boundaries?
2. How does crime cluster along and around transportation networks and hubs in relation to other characteristics of the social and physical environment?
3. What is the distribution of criminal justice-supervised populations in relation to services they must access to fulfill their conditions of supervision?
4. What are the relationships among offenders, victims, and crimes across jurisdictional boundaries?
5. What is the increased predictive power of simulation models that employ cross-jurisdictional data?

In 2014, the United States Department of Agriculture's Economic Research Service (USDA ERS) conducted the Rural Establishment Innovation Survey (REIS). This survey provides a nationally representative sample of innovation processes in rural businesses. REIS defines innovation as the introduction of new goods, services, or ways of doing business that are valued by consumers. Traditional measures from secondary data sources, such as patents or research and development (R&D) expenditures, focus on science and engineering-based innovation, which usually depict rural innovation as rare or idiosyncratic. By focusing instead on a broader definition of innovation, the REIS provides a fuller assessment of rural innovative capacity.

The main research questions for this survey were:

• Are rural firms as innovative as urban firms?
• What constraints are impeding the innovative capacity of firms?
• What strategies are innovative firms using to mitigate these constraints?

The target population for the survey was Nonmetro and metro establishments with 5 or more employees in tradable sectors (mining, manufacturing, wholesale trade, transportation and warehousing, finance, information, professional/technical/scientific services, arts and management of businesses). REIS used the Bureau of Labor Statistics (BLS) Quarterly Census of Employment and Wages Business Register for its sampling frame. Responses from 11,600 businesses were usable.

These data include responses from businesses in the Arts & Museums industry category. These businesses were oversampled by a factor of 3.3 to ensure reliable statistics.

The REIS data are restricted and require users to apply for access to the data. For permission to access to these data, visit the ERS Rural Economy Population: Business Industry page and scroll to the bottom of the page for contact information. If permission to access the data is granted, the data can be viewed through the NORC data enclave.

The Economic Census is the United States 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. Every 5 years starting in 1977, forms are sent out to millions of 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.

The Economic Census provides data for several arts-related NAICS industries, 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
• Graphic Design 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

Some industries are not covered by the economic census. View a full list here.

Data from the Economic Census is important for industries, communities, and businesses. Trade associations, chambers of commerce, and businesses rely on this information for economic development, business decisions, and strategic planning. Government agencies, analysts, and business organizations nationwide also rely on census information for planning and key economic reports.

The Statistics of U.S. Businesses (SUSB) provides detailed annual data for all U.S. business establishments with paid employees by geography, industry, and enterprise size. This program covers all NAICS industries except crop and animal production; rail transportation; National Postal Service; pension, health, welfare, and vacation funds; trusts, estates, and agency accounts; private households; and public administration. The SUSB also excludes most government employees. Further, SUSB data for years 1988-1997 were tabulated based on the Standard Industrial Classification (SIC) system.

The SUSB features several arts-related NAICS industries, 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

• Architectural services
• Graphic Design Services
• Landscape architectural services
• Photographic services

• Sporting goods, hobby, and musical instrument stores
• Sewing, needlework, and piece goods stores
• Book stores
• Art dealers

Also, the SUSB features several arts related SIC industries, including the following:

• Commercial photography (SIC Code 7335)
• Commercial art and graphic design (SIC Code 7336)
• Museums and art galleries (SIC Code 8412)
• Dance studios, schools, and halls (SIC Code 7911)
• Theatrical producers and services (SIC Code 7922)
• Sports clubs, managers, & promoters (SIC Code 7941)
• Motion Picture Production & Services (SIC Code 7810)

Data compiled for the SUSB are extracted from the Business Register (BR). The BR contains continuously updated data from the Census Bureau's economic censuses and currently business surveys, quarterly and annual Federal tax records and other department and federal statistics. SUSB data are available approximately 24 months after each reference year and are available for the United States, each state, and Metropolitan Statistical Areas (MSA). The annual SUSB consist of number of firms, number of establishments, annual payroll, and employment during the week of March 12. In addition, estimated receipts data are included for years ending in 2 and 7. Dynamic data, which are created from the Business Information Tracking Series (BITS), consist of the number of establishments and corresponding employment change for births, deaths, expansions, and contractions.

The SUSB is important because it provides the only source of annual, complete, and consistent enterprise-level data for U.S. businesses, with industry detail. Private businesses use the data for market research, strategic business planning, and managing sales territories. State and local governments, as well as, budget, economic development, and planning offices use the data to assess business changes, develop fiscal policies, and plan future policies and programs. In addition, the data are the standard reference source for small business statistics.

Users can view the latest SUSB annual data and employment change data on the main SUSB page. For more detailed industry and employment size classes, users can download additional data in comma-delimited format. Annual data are tabulated back to 1988 and employment change data back to 1989-1990. Data users can find news and updates about the SUSB data via the News & Updates section.

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 data collection was a part of a larger research project designed to examine the role of public-private partnerships and local development organizations (LDO) in rural America. Most studies of local development policy have examined the activities of local governments, or, in a few cases, the effects of LDOs. There has been, however, little research on how local governments and development organizations interact, the effects of their activities on policies, and the outcomes of those policies on job and income growth. The purpose of this research project was to gain a better understanding of the organization of economic development in nonmetropolitan areas, specifically, what factors led to policy adoption and the creation of an LDO in a community.

In the fall of 1998, this survey was sent to local government officials in United States cities with a population between 2,500 and 50,000 (nonmetropolitan areas).

The survey included questions on what was being done to promote economic development and attract new businesses, whether new businesses were created or moved into the community as a result of the development efforts, funding for economic development, and sources of the funding (e.g., state grants-in-aid or local revenues). Additional topics included types of business incentives, performance agreements, labor surveys (identifying wages and benefits), job training programs, and types of barriers experienced. Each local government agency was also surveyed on their interaction with organizations like the Chamber of Commerce, private lending institutions, neighborhood associations, churches, and regional planning commissions, and whether any of these organizations helped in developing local economic development strategies and in what manner.

This data collection was a part of a larger research project designed to examine the role of public-private partnerships and local development organizations (LDO) in rural America. Most studies of local development policy have examined the activities of local governments, or, in a few cases, the effects of LDOs. There has been, however, little research on how local governments and development organizations interact, the effects of their activities on policies, and the outcomes of those policies on job and income growth. The purpose of this research project was to gain a better understanding of the organization of economic development in nonmetropolitan areas, specifically, what factors led to policy adoption and the creation of an LDO in a community.

In the fall of 1999, this survey was sent to the local development organizations listed on the NATIONAL SURVEY OF LOCAL GOVERNMENT ECONOMIC DEVELOPMENT, 1998 (ICPSR 4433) or found through a variety of Web sites that included lists of development organizations operating in the given community.

Each local economic development organization was surveyed on labor unions, business incentives, and economic development activities (small business development, business attraction, and business retention/expansion). A series of questions were asked about the board of directors, their primary professions/affiliations, race/ethnic composition, gender, and how they were selected. Respondents were also asked about their relationships with other organizations, like private lending institutions, Chamber of Commerce, real estate or property developers, and citizen advisory groups.

The data pertaining to this study was the result of an exhaustive investigation into the nature of the firms composing the Egyptian garment industry. The data capture various characteristics of the firms relating to each one's level and order of integration into the production of fabrics and garments and into retail. Part 1 of the study contains the data obtained from the initial screening interviews administered to each firm by phone to determine the prevalence and nature of integration present in its operations. This information was used to determine which one of the four study questionnaires would be administered to each firm during the final interview. Each questionnaire produced four datasets containing (in this order):

• general questions
• contracts
• lock in, switching costs, and temporal specificity
• product information.

Questionnaire 1 (Parts 2-5) was administered to the firms for which the following four scenarios was true: (1) garment production and retail occurred at the same time at the establishment, and both garment production and fabric production took place at the same time at the establishment, (2) garment production and retail occurred simultaneously at the establishment, but fabrics were not produced in-house, (3) garment production occurred before retail while garment and fabric production were simultaneous at the establishment, and (4) garment and fabric production that occurred simultaneously at the establishment but retail operations not performed in-house (i.e. did not own or rent its own retail stores). Questionnaire 2 (Parts 6-9) was completed by the firms for which the following two scenarios were true: (1) garment production was subsequent to fabric production, and garment production was started prior to retail, or (2) garment production was started prior to retail, and the firm did not produce any of its own fabrics. Questionnaire 3 (Parts 10-13) was given to the firms for which the following three scenarios were true: (1) garment production began simultaneously with fabric production but not at the onset, and for which retail started subsequent to both garment and fabric production, (2) both fabric production and retail had started subsequent to garment production, and (3) garment production started before fabric production, and the firm did not perform in-house retail operations. Questionnaire 4 (Parts 14-17) was administered to firms for which the following two scenarios were true: (1) garment production was subsequent to fabric production, but in-house retail operations were not performed, or (2) there was no fabric production or in-house retail operations. Each of the four questionnaires contained an identical screening section (in addition to the screening information found in Part 1) in order to ensure that the appropriate questionnaire was administered during the interview. Specific questions regarding each firm's management, sister companies, products, operations, and other firm-level characteristics varied depending on the questionnaire. However, sections eight and nine, dealing with fabrics and fabric suppliers, were identical across all questionnaires.