Annual Business Survey: 2019 (Data Year 2018)
This report provides data from the 2019 Annual Business Survey (ABS) (data year 2018). The ABS is the primary source of data on research and development of for-profit, nonfarm businesses with one to nine employees operating in the 50 U.S. states and the District of Columbia. The ABS also collects data on innovation, technology, intellectual property, and business owner characteristics of business of all sizes. The ABS is designed to incorporate new content each survey year based on topics of relevance. The 2019 ABS is the second year of the ABS. The survey is conducted annually by the Census Bureau for the National Center for Science and Engineering Statistics within the National Science Foundation.
Survey aggregate estimates: 2018
|1||View Table 1||Download Table 1 XLSX||Download Table 1 PDF|
|2||View Table 2||Download Table 2 XLSX||Download Table 2 PDF|
Total R&D cost, by industry, employment size, sex, and race and ethnicity: 2018
R&D employees: 2018
R&D tax credit: 2018
|18-1||View Table 18-1||Download Table 18-1 XLSX||Download Table 18-1 PDF|
|18-2||View Table 18-2||Download Table 18-2 XLSX||Download Table 18-2 PDF|
|18-3||View Table 18-3||Download Table 18-3 XLSX||Download Table 18-3 PDF|
Product innovation: 2016–18
Business process innovation: 2016–18
New or improved business processes: 2016–18
Product or business process innovation: 2016–18
Innovation and R&D activity: 2016–18
Product or business process innovation: 2016–18
|62||View Table 62||Download Table 62 XLSX||Download Table 62 PDF|
New to market product innovation: 2016–18
|63||View Table 63||Download Table 63 XLSX||Download Table 63 PDF|
|64||View Table 64||Download Table 64 XLSX||Download Table 64 PDF|
|65||View Table 65||Download Table 65 XLSX||Download Table 65 PDF|
New to market business process innovation: 2016–18
|66||View Table 66||Download Table 66 XLSX||Download Table 66 PDF|
|67||View Table 67||Download Table 67 XLSX||Download Table 67 PDF|
|68||View Table 68||Download Table 68 XLSX||Download Table 68 PDF|
Innovation activity costs: 2016–18
|69||View Table 69||Download Table 69 XLSX||Download Table 69 PDF|
|70||View Table 70||Download Table 70 XLSX||Download Table 70 PDF|
Importance of intellectual property: 2018
Artificial intelligence as a production technology: 2016–18
|84||View Table 84||Download Table 84 XLSX||Download Table 84 PDF|
|85||View Table 85||Download Table 85 XLSX||Download Table 85 PDF|
Cloud-based computing systems and applications as a production technology: 2016–18
|86||View Table 86||Download Table 86 XLSX||Download Table 86 PDF|
|87||View Table 87||Download Table 87 XLSX||Download Table 87 PDF|
Specialized software as a production technology: 2016–18
|88||View Table 88||Download Table 88 XLSX||Download Table 88 PDF|
|89||View Table 89||Download Table 89 XLSX||Download Table 89 PDF|
Robotics as a production technology: 2016–18
|90||View Table 90||Download Table 90 XLSX||Download Table 90 PDF|
|91||View Table 91||Download Table 91 XLSX||Download Table 91 PDF|
Specialized equipment as a production technology: 2016–18
|92||View Table 92||Download Table 92 XLSX||Download Table 92 PDF|
|93||View Table 93||Download Table 93 XLSX||Download Table 93 PDF|
Artificial intelligence - motivation and impact: 2016–18
|94||View Table 94||Download Table 94 XLSX||Download Table 94 PDF|
|95||View Table 95||Download Table 95 XLSX||Download Table 95 PDF|
|96||View Table 96||Download Table 96 XLSX||Download Table 96 PDF|
Cloud-based computing systems and applications - motivation and impact: 2016–18
|97||View Table 97||Download Table 97 XLSX||Download Table 97 PDF|
|98||View Table 98||Download Table 98 XLSX||Download Table 98 PDF|
|99||View Table 99||Download Table 99 XLSX||Download Table 99 PDF|
Specialized software - motivation and impact: 2016–18
|100||View Table 100||Download Table 100 XLSX||Download Table 100 PDF|
|101||View Table 101||Download Table 101 XLSX||Download Table 101 PDF|
|102||View Table 102||Download Table 102 XLSX||Download Table 102 PDF|
Robotics - motivation and impact: 2016–18
|103||View Table 103||Download Table 103 XLSX||Download Table 103 PDF|
|104||View Table 104||Download Table 104 XLSX||Download Table 104 PDF|
|105||View Table 105||Download Table 105 XLSX||Download Table 105 PDF|
Specialized equipment - motivation and impact: 2016–18
|106||View Table 106||Download Table 106 XLSX||Download Table 106 PDF|
|107||View Table 107||Download Table 107 XLSX||Download Table 107 PDF|
|108||View Table 108||Download Table 108 XLSX||Download Table 108 PDF|
Factors adversely affecting technology adoption and utilization: 2016–18
Companies that reported selling artificial intelligence: 2016–18
|114||View Table 114||Download Table 114 XLSX||Download Table 114 PDF|
|115||View Table 115||Download Table 115 XLSX||Download Table 115 PDF|
Companies that reported selling cloud-based computing systems and applications: 2016–18
|116||View Table 116||Download Table 116 XLSX||Download Table 116 PDF|
|117||View Table 117||Download Table 117 XLSX||Download Table 117 PDF|
Companies that reported selling specialized software: 2016–18
|118||View Table 118||Download Table 118 XLSX||Download Table 118 PDF|
|119||View Table 119||Download Table 119 XLSX||Download Table 119 PDF|
Companies that reported selling robotics: 2016–18
|120||View Table 120||Download Table 120 XLSX||Download Table 120 PDF|
|121||View Table 121||Download Table 121 XLSX||Download Table 121 PDF|
Companies that reported selling specialized equipment: 2016–18
|122||View Table 122||Download Table 122 XLSX||Download Table 122 PDF|
|123||View Table 123||Download Table 123 XLSX||Download Table 123 PDF|
Artificial intelligence production- motivation and impact: 2016–18
|124||View Table 124||Download Table 124 XLSX||Download Table 124 PDF|
|125||View Table 125||Download Table 125 XLSX||Download Table 125 PDF|
|126||View Table 126||Download Table 126 XLSX||Download Table 126 PDF|
Cloud-based computing systems and applications production- motivation and impact: 2016–18
|127||View Table 127||Download Table 127 XLSX||Download Table 127 PDF|
|128||View Table 128||Download Table 128 XLSX||Download Table 128 PDF|
|129||View Table 129||Download Table 129 XLSX||Download Table 129 PDF|
Specialized software production- motivation and impact: 2016–18
|130||View Table 130||Download Table 130 XLSX||Download Table 130 PDF|
|131||View Table 131||Download Table 131 XLSX||Download Table 131 PDF|
|132||View Table 132||Download Table 132 XLSX||Download Table 132 PDF|
Robotics production- motivation and impact: 2016–18
|133||View Table 133||Download Table 133 XLSX||Download Table 133 PDF|
|134||View Table 134||Download Table 134 XLSX||Download Table 134 PDF|
|135||View Table 135||Download Table 135 XLSX||Download Table 135 PDF|
Specialized equipment production- motivation and impact: 2016–18
|136||View Table 136||Download Table 136 XLSX||Download Table 136 PDF|
|137||View Table 137||Download Table 137 XLSX||Download Table 137 PDF|
|138||View Table 138||Download Table 138 XLSX||Download Table 138 PDF|
Factors adversely affecting production of artificial intelligence: 2016–18
|139||View Table 139||Download Table 139 XLSX||Download Table 139 PDF|
|140||View Table 140||Download Table 140 XLSX||Download Table 140 PDF|
Factors adversely affecting production of cloud-based computing systems and applications: 2016–18
|141||View Table 141||Download Table 141 XLSX||Download Table 141 PDF|
|142||View Table 142||Download Table 142 XLSX||Download Table 142 PDF|
Factors adversely affecting production of specialized software: 2016–18
|143||View Table 143||Download Table 143 XLSX||Download Table 143 PDF|
|144||View Table 144||Download Table 144 XLSX||Download Table 144 PDF|
Factors adversely affecting production of robotics: 2016–18
|145||View Table 145||Download Table 145 XLSX||Download Table 145 PDF|
|146||View Table 146||Download Table 146 XLSX||Download Table 146 PDF|
Factors adversely affecting production of specialized equipment: 2016–18
|147||View Table 147||Download Table 147 XLSX||Download Table 147 PDF|
|148||View Table 148||Download Table 148 XLSX||Download Table 148 PDF|
Survey Overview (2019 survey cycle: data year 2018)
Purpose. The Annual Business Survey (ABS) provides information on selected economic and demographic characteristics for businesses and for business owners, by sex, ethnicity, race, and veteran status. In addition, the survey measures research and development for microbusinesses, business topics such as innovation and technology, and other business characteristics. The ABS is conducted jointly by the Census Bureau and the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation (NSF). The ABS replaces the quinquennial Survey of Business Owners for employer businesses, the Annual Survey of Entrepreneurs, the Business R&D and Innovation for Microbusinesses survey (BRDI-M), and the Innovation section of the Business R&D and Innovation Survey.
The ABS is designed to incorporate new content each survey year based on topics of relevance. For the 2019 ABS (data year 2018), content includes innovation, R&D, and technology and intellectual property. The ABS also collects various data regarding business owners—such as sex, ethnicity, race, veteran status—and data on other business characteristics. R&D data are collected on the ABS for businesses with a W-2 employment range between one to nine employees.
Data collection authority. Title 13, United States Code, Sections 8(b), 131, and 182; Title 42, United States Code, Section 1861-76 (NSF Act of 1950, as amended); and Section 505 within the America COMPETES Reauthorization Act of 2010, authorize this collection. Sections 224 and 225 of Title 13 require mandatory response. Office of Management and Budget (OMB) No. 0607-1004.
Survey sponsor. NCSES.
Survey collection and tabulation agent. The survey is conducted annually by the Census Bureau in accordance with an interagency agreement with NCSES.
Key Survey Information
Initial survey year. 2017; for innovation, the reference period was 2015 to 2017.
Reference period. 2018; for innovation, the reference period is 2016 to 2018.
Response unit. Firm.
Sample or census. Sample.
Population size. A total of 5.3 million employer firms were in scope for sampling; additional out-of-scope firms were identified before tabulation based on either response or updated administrative data not available at the time of sampling, which resulted in 4.9 million employer firms in-scope for innovation and technology modules. Approximately 500,000 firms with one to nine employees and in North American Industry Classification System (NAICS) industries 31–33 (manufacturing), 42 (wholesale trade), 51 (information), 5413 (architectural, engineering, and related services), 5415 (computer systems design and related services), or 5417 (scientific research and development services) were in-scope for the R&D module.
Sample size. A total of 299,976 employer businesses.
Target population. Included are all nonfarm businesses filing Internal Revenue Service (IRS) tax forms as individual proprietorships, partnerships, or any type of corporation and with receipts of $1,000 or more. The ABS covers firms with paid employees only. The ABS is conducted on a company or firm basis rather than an establishment basis.
Sampling frame. The sampling universe was constructed from the final 2017 Business Register. The Business Register is the Census Bureau’s comprehensive database of U.S. businesses. Business Register data are compiled from a combination of business tax returns, data collected from the Economic Census, and data from other Census Bureau surveys. The 2017 Business Register includes sole proprietorships, partnerships, and corporations reporting business activity to the IRS on any one of the following 2017 IRS tax forms: 1040 (Schedule C), “Profit or Loss from Business” (Sole Proprietorship); 1065, “U.S. Return of Partnership Income”; 941, “Employer’s Quarterly Federal Tax Return”; 944, “Employer’s Annual Federal Tax Return”; or any one of the 1120 corporate tax forms.
The Business Register contains establishments that are out of scope for the ABS sample. These establishments are removed from the sampling universe. They include the following:
- Establishments engaged in the following NAICS industries:
- 110000, 111, or 112 - Agriculture production
- 482 - Railroads
- 491 - U.S. Postal Service
- 521 - Monetary authorities—central bank
- 525 - Funds, trusts, and other financial vehicles
- 813 - Religious grant operations and religious organizations
- 814 - Private households
- 92 - Public administration
- Unclassified with legal form of organization as tax-exempt or unknown
- Establishments located in American Samoa, Northern Mariana Islands, Guam, Puerto Rico, or the Virgin Islands
- Establishments belonging to foreign entities
- Government establishments
- Establishments with zero payroll and zero employment
- Establishments identified as out of business during prior year data review
- Single-unit establishments modeled as likely to be out of business using administrative data from the most recent monthly business register data sets.
Information on industry classification, receipts, payroll, and employment was extracted from the Business Register during the frame construction.
The sample is also designed to estimate demographic characteristics of the business owners. To efficiently sample demographic characteristics, a variety of sources of information is used to estimate the likelihood that a business is women- or minority-owned. Administrative sources include the Decennial Census, the American Community Survey (ACS), and the Numident file, which is the Social Security Administration’s comprehensive database of information from Social Security applications. Individual business owners are identified through IRS K-1 filings for partnerships and corporations and from the Business Register for sole proprietorships. The owners are matched to the 2000 and 2010 Decennial Censuses in order to get race, sex, and ethnicity data; the 2000–17 ACS; and the Numident, (in that order) through a Protected Identification Key. Country of birth is also identified through the linkages to the ACS or Numident data. Each firm is then placed in one of the following nine strata for sampling: American Indian or Alaska Native, Asian, Black or African American, Hispanic, White men who are not Hispanic, Native Hawaiian and Other Pacific Islander, other race or ethnicity, publicly owned, and women. Businesses are assigned to only one stratum with priority given to the less common stratum.
Sample design. The ABS frame is stratified by geographic area defined by state, race and ethnicity stratum, and industry, and is systematically sampled within each stratum. A standard type of estimation for stratified systematic sampling is used. Large companies were selected with certainty based on volume of sales, payroll, or number of paid employees. Certainty cases have a selection probability of one and a sampling weight of one and represent only themselves. Specifically for the 2019 ABS, firms were selected with certainty based on the following criteria:
- Firms with more than 500 employees
- Firms that had more than $1 million in R&D expenses in the 2016 BRDI-M survey
- Firms larger than stratum-specific payroll and receipt cut-offs
The certainty cutoffs vary by sampling stratum, and each stratum is sampled at varying rates, depending on the number and size of firms in a particular stratum.
The remaining frame is subjected to stratified systematic random sampling. Sampling rates vary by strata.
The coefficient of variation (CV) is initially set to 0.14, then it is adjusted to achieve the desired sample size. The final CV values were 0.15 for cases in the minority or female frames and 0.5 for cases in the public or not Hispanic, white men frames.
Each firm selected into the sample was asked to provide information on the business’s innovations and innovation activities and technology (patents, intellectual property, cloud service purchases, and business technologies). Additionally, firms that were not part of the 2017 data year sample were asked to provide the percentage of ownership, sex, ethnicity, race, age, education level, and veteran status for up to four persons owning the largest percentages in the business. Firms that were included in the 2017 ABS (data year 2017) were not asked the ownership questions, and prior year response data were used for data year 2018. Firms in NAICS industries 31–33 (manufacturing), 42 (wholesale trade), 51 (information), 5413 (architectural, engineering, and related services), 5415 (computer systems design and related services), or 5417 (scientific research and development services) with one to nine employees were asked to provide information regarding R&D.
The ABS sample consisted of 299,976 businesses. There were 41,793 selected with certainty. The certainty portion of the sample consisted of the following:
- 16,930 with employment ≥ 500
- 705 with R&D expenses greater than $1 million in 2016 BRDI-M
- 16,219 with payroll above the stratum cutoff
- 7,939 with receipts above the stratum cutoff (note payroll cutoffs are applied first so cases above both cutoffs appear in payroll count)
The remaining 258,183 noncertainty cases were selected using the systematic stratified random sample selection. The maximum sample weight was 35.
Data Collection and Processing Methods
Data collection. Prior to mailing the survey, certain businesses selected were determined to be out of scope and were not contacted. The survey was mailed to 299,976 employer businesses in July 2019. Businesses were sent a letter informing them of their requirement to report. The letter also provided instructions on how to access the survey and submit online. There were three mail follow-ups conducted to increase response. The third mail follow-up included a paper questionnaire for select nonrespondents. Additionally, the Census Bureau conducted e-mail follow-ups to respondents who entered the electronic system but did not submit the questionnaire. The collection period closed in January 2020.
Mode. The 2019 ABS (data year 2018) was collected using both a paper form and the electronic instrument.
Check-in rate. The check-in rate is defined as the unweighted number of surveys that were submitted online by in-scope companies, divided by the unweighted total number of all in-scope companies in the sample. Response to individual questions did not factor into this metric. At the close of the collection period in January 2020, there were 211,198 responses submitted. Of those, 205,375 reported online (68.5% of the total sample) and 5,823 reported using the paper form (1.9% of the total sample).
There were an additional 3,566 businesses that contacted the Census Bureau via the call center to indicate the business was no longer in operation or had been sold during the reference year.
Businesses selected to report R&D represented 37.6% of those mailed. Of the businesses mailed and selected to report R&D, 82,044 businesses submitted responses, or 72.7%.
Unit response rate (URR). The URR is the unweighted number of responding companies for the survey. For the ABS, response is defined as a company providing the number of owners, number of paid owners, and number of employees or as a company responding that they ceased operations prior to 2018.
For the ABS, the URR was 71.8%. The URR for businesses eligible to report the R&D module was 73.4%
Item response rates. The ABS collects data on approximately 650 variables, and the distribution of values reported by sample companies is highly skewed. Thus, rather than report unweighted item response rates, total quantity response rates are calculated, which are based on weighted data. The survey skip patterns vary for respondents, and therefore, it can be impossible to know an exact denominator for item response calculations.
Total quantity response rate (TQRR). For a given published estimate other than count or ratio estimates, TQRR is the percentage of the weighted estimate based on data that were reported by units in the sample or on data that were obtained from other sources and were determined to be equivalent in quality to reported data and weighted only by sampling but not nonresponse weights. The TQRR for total sales in the United States in 2018 was 64.0%.
Total quantity nonresponse rate (TQNR). For a given published estimate, TQNR, defined as 100% minus TQRR, is calculated for each tabulation cell from the ABS, except for cells that contain count or ratio estimates. TQNR measures the combined effect of the procedures used to handle unit and item nonresponse on the weighted ABS estimate. Detailed imputation rates are available upon request.
Data editing. Prior to tabulating the data, response data were reviewed and edited to correct reporting errors. R&D data were tabulated for records reporting $50,000 or more in R&D expenditures.
Additionally, R&D data were only tabbed for records classified in the following NAICS industries:
- 31–33 – Manufacturing
- 42 – Wholesale trade
- 51 – Information
- 5413 – Architectural, engineering, and related services
- 5415 – Computer systems design and related services
- 5417 – Scientific research and development services
Survey analysts reviewed the R&D reported by the survey respondents. Research was done by evaluating the reported business descriptions, reported R&D-to-sales ratio, and company website information. The majority of corrections involved false positive reports or data reported using incorrect units (such as dollars instead of thousands of dollars). For NAICS industries 5415 and 5417, it is difficult to differentiate R&D from other technical work based solely on company website information. Due to this difficulty and the large number of companies sampled in these industries, it was not feasible to review each case individually and relatively few corrections were made for false positive reports.
Additional data errors were detected and corrected through mass corrections, and an automated data edit system was designed to review the data for reasonableness and consistency. The editing process interactively performed corrections by using standard procedures to fix detectable errors. Quality control techniques were used to verify that operating procedures were carried out as specified.
Techniques for handling unit nonresponse. Weighted estimates produced from the ABS include adjustments to account for companies that did not respond to the survey (unit nonresponse). Unit nonresponse is handled by adjusting weighted reported data as follows. Each company’s sampling weight is multiplied by a nonresponse adjustment factor. To calculate the adjustment factors, each company in the sample that is eligible for tabulation is assigned to one (and only one) adjustment cell. The adjustment cells are based on employment size and NAICS sector. For employment size, there are five categories: 1 to 4 employees, 5 to 9 employees, 10 to 49 employees, 50 to 249 employees, and 250 or more employees. For a given adjustment cell, the nonresponse adjustment factor is the ratio of the sum of the sampling weights for all companies in the cell to the sum of the sampling weights for all companies in the cell with reported data. For the nonresponse adjustment, a business is considered a respondent if it responded to either the R&D module, innovation module, or technology module of the survey.
Item nonresponse. Item nonresponse for certain key items was handled by item imputation. Missing employment and demographic characteristics were imputed using administrative data where available. If no administrative data were available for demographic characteristics, data were imputed using donor imputation. For all other items, no item imputation was performed, and missing items are considered zeros for numeric totals and included in the No/NA tabulations for categorical items.
Weighting. The survey data are weighted for sampling as designed for the ABS. The weights were adjusted for complete nonresponse based on the ratio of the weights for the entire sample to the responding sample.
Tabulation. Although as many firms as possible were identified as out of scope during sampling, additional out-of-scope firms were identified with either response or updated administrative data not available at the time of sampling. These approximately 33,000 firms were removed for tabulations and include the following:
- Firms reporting zero employment
- Firms that responded as out of business before 2018
- Nonprofit organizations
- Firms that responded as being owned by a domestic parent company
- Firms with unclassified industry NAICS
Statistical disclosure avoidance. Statistical disclosure avoidance is accomplished using noise infusion. In this method, random noise factors are independently assigned for each respondent and incorporated in the tabulations as multiplicative factors applied to respondents' microdata. The 2019 ABS estimates have all been subjected to noise infusion. Because of the large sample size used for the ABS, the Census Bureau's Disclosure Review Board required an additional random rounding adjustment be applied to published firm counts to ensure adequate perturbation for these estimates. The adjustment for firm counts was applied independently for all cells. In some cases, the sum of detailed estimates did not equal the total. In such cases, the detailed estimates were raked to the total. The use of these techniques introduced uncertainty into the ABS estimates that is sufficient to protect the confidentiality of the respondents and their data.
Industry classification. The industry classifications of firms are based on the 2017 NAICS. Firms with more than one domestic establishment are assigned a single industry classification using a hierarchal system based on the largest payroll sector, largest payroll 3-digit NAICS (within the largest sector), largest payroll 4-digit NAICS (within the largest 3-digit), and largest payroll 6-digit NAICS (within the largest 4-digit). For tabulation, industry classification was based on administrative data for 2017.
Geography. Firms with establishments operating in more than one state are tabulated as unclassified and counted only once in state and national totals.
Variance estimation. The ABS uses the delete-a-group jackknife variance estimator. Note that certainty cases do not contribute to the sampling variance. The delete-a-group jackknife variance estimator requires that every sampling stratum contains at least two sampled firms. Sampling strata that do not meet this requirement are collapsed as needed to create a new set of variance estimation strata that satisfies this requirement.
Survey Quality Measures
The estimates produced from the ABS are subject to both sampling and nonsampling errors.
Sampling error. Detailed relative standard errors may be found in the accompanying tables that are available upon request.
Coverage error. The ABS uses the prior year Business Register to construct the frame so any changes in businesses that would change the inclusion or exclusion of the business to the survey scope could be sources of coverage error. Prior to tabulation, information for survey units is updated with the most recent available Business Register data to mitigate this source of error.
Nonsampling error. Although explicit measures of the effects of these nonsampling errors are not available, adjustments are made to the published relative standard errors to account for errors associated with imputation of missing data. It is believed that most of the important operational and data errors were detected and corrected through an automated data edit designed to review the data for reasonableness and consistency. Quality control techniques were used to verify that operating procedures were carried out as specified.
Measurement error. The most common source of measurement error was reporting in different units (for example, reporting whole dollars rather than thousands of dollars).
Some estimates from the 2019 ABS (data year 2018) may not be comparable to similar estimates from the 2017 ABS (data year 2017) due to changes in survey methodology. Sources of possible incomparability include changes to questionnaire wording and instructions, changes to data editing and tabulation, and changes to imputation and nonresponse adjustments.
Changes to questionnaire wording and instructions. The survey section that collected information on R&D from businesses (Section D) had nearly identical question wording and instructions for both the ABS 2019 (data year 2018) and the ABS 2017 (data year 2017) questionnaires. The 2019 ABS collected additional detail from businesses on the location of R&D performance and explicitly highlighted the concept of “domestic R&D performance.” For the 2019 ABS, detailed questions about R&D expenditures (types of costs, funding sources, and R&D categories) were tied to domestic R&D performance (a subset of total R&D costs), whereas the 2017 ABS asked similar questions tied to total R&D costs.
The survey section that collected information that was used to produce statistics on innovation (Section C of the 2019 ABS questionnaire) had several wording, definitional, and instruction changes that differ from the 2017 ABS:
- The section title was changed from “Innovation” on the 2017 ABS to “Products and Processes” on the 2019 ABS.
- The term “innovation” was used frequently in instructions, question headings, and question wording on the 2017 ABS. The term is used less frequently on the 2019 ABS and is not highlighted in a question until after the questions used to indicate product innovation and business process innovation.
- The 2019 ABS asks questions on product innovation and business process innovation before asking any other questions. The 2017 ABS preceded these questions with questions on innovation business strategies and whether the business sold goods or services. It is possible that the preceding questions on the 2017 ABS influenced or conditioned respondents to respond differently than had they not been presented in that order. See the related item below on changes to data editing and tabulation.
- The 2017 ABS questions used to indicate process innovation differ from the 2019 ABS questions used to indicate business process innovation in number, wording, and intent. The terms appear similar, but the underlying questions and concepts are substantially different.
- The 2017 ABS asked two questions (in-house R&D and external R&D) to assess whether companies were R&D-active. The 2019 ABS asks one question (R&D) to make the same determination.
Changes to data editing and tabulation. For data collected in the R&D section of the ABS, the largest difference in editing between data years 2017 and 2018 was that every response with $50,000 or more in R&D costs was reviewed for reasonableness in 2018 data year. The large sample size in the 2017 data year made this level of review impractical. For the 2017 ABS, all positive R&D cases were reviewed in industries with high rates of false-positive reports, but only cases reporting $1,000,000 or more of R&D were reviewed in NAICS 5417 and 5415—two industries where false-positive reports are less common. Outside of the review for potential false-positive R&D cases, the editing of R&D data was performed similarly in data years 2017 and 2018.
Data collected in the innovation section of the ABS were edited similarly in data years 2017 and 2018, but the estimates for product innovation were tabulated differently. For data year 2017, a business must have responded positively to both the product innovation question and the subsequent business product innovation question (new to market or new to business) to be considered a product innovator. For data year 2018, only a positive response to the “New or Improved Goods or Services” question was required to be tabulated as a product innovator.
Changes in imputation methods. In the 2019 ABS (data year 2018), a change was made in how item nonresponse is treated. Item nonresponse occurs when a company responds to the survey but leaves some items blank. In the ABS 2017 (data year 2017), item nonresponse was treated using a combination of mode and donor imputation. In the ABS 2019, no imputation was performed to treat item nonresponse, and missing items were considered the same as a reported no or zero in the estimates. For the majority of the estimates (91%), this change in methods did not yield a change in the estimates. For an additional 9% of estimates, there was a difference in the estimate, but the difference was not statistically significant.
Changes in collection methods. In an attempt to reduce respondent burden, respondents were not asked to complete the owner section on the 2019 ABS (data year 2018) if they were in the sample for the 2017 ABS (data year 2017).
Domestic or United States. Refers to a location in any of the U.S. 50 states and the District of Columbia.
Employment. Paid employment consists of full- and part-time employees, including salaried officers and executives of corporations who were on the payroll in the pay period including March 12. Included are employees on sick leave, holidays, and vacations; not included are proprietors and partners of unincorporated businesses.
Ethnicity. Based on OMB guidance, there are two minimum categories for ethnicity: Hispanic or Latino and not Hispanic or Latino. OMB considers race and Hispanic origin to be two separate and distinct concepts. Hispanics and Latinos may be of any race.
Firm. A business organization or entity consisting of one or more domestic establishment locations under common ownership or control.
Foreign parent (of a U.S.-located business). The first entity outside the United States in an affiliate’s ownership chain that has a direct or indirect investment interest of more than 50% of the affiliate’s voting securities.
Innovation. A business innovation is a new or improved product or business process (or combination thereof) that differs significantly from the firm's previous products or business processes and that has been introduced on the market or brought into use by the firm.
Intellectual property. Includes patents, processes, and trade secrets; books and music; trademarks; recorded performances and events, such as radio and television programs and motion pictures; broadcast and recorded live performances and events and their content; general use computer software; franchise fees; and other (for example, digital media).
Noise infusion. A method of disclosure avoidance in which values are perturbed prior to tabulation by applying a random noise multiplier to the magnitude data, such as the sales and receipts for all firms. Disclosure protection is accomplished in a manner that causes the vast majority of cell values to be perturbed by, at most, a few percentage points.
North American Industry Classification System (NAICS). NAICS is the standard used by federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy. This system is used by the United States, Canada, and Mexico.
Race. The data on race were derived from answers to the race question. Race data are collected in accordance with the guidelines provided by OMB, and these data are based on self-identification. The racial categories included in the questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. In addition, it is recognized that the categories of the race question include race and national origin or sociocultural groups. OMB requires that race data be collected for a minimum of five groups: White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or other Pacific Islander. Respondents may report more than one race.
Receipts. Includes the total sales, shipments, receipts, revenue, or grants for domestic operations, excluding foreign subsidiaries.
Research and development (R&D). R&D is planned, creative work aimed at discovering new knowledge or devising new applications of available knowledge. This includes (1) activities aimed at acquiring new knowledge or understanding without specific immediate commercial applications or uses (basic research); (2) activities aimed at solving a specific problem or meeting a specific commercial objective (applied research); and (3) systematic use of research and practical experience and resulting in additional knowledge, which is directed to producing new or improved goods, services, or processes (development). R&D includes both direct costs, such as salaries of researchers and administrative and overhead costs clearly associated with the company’s R&D. However, R&D does not include expenditures for routine product testing, quality control, and technical services unless they are an integral part of an R&D project. R&D also does not include market research; efficiency surveys or management studies; literary, artistic, or historical projects, such as films, music, or books and other publications; and prospecting or exploration for natural resources.
Sex. For the purposes of the ABS, sex refers to a person’s biological sex. The sex question wording very specifically intends to capture a person's biological sex and not gender.
Worldwide sales. Worldwide and domestic sales and operating revenues, including grants.
Acknowledgments and Suggested Citation
Audrey E. Kindlon of the National Center for Science and Engineering Statistics (NCSES) developed and coordinated this report under the guidance of John Jankowski, NCSES Program Director, and under the leadership of Emilda B. Rivers, NCSES Director; Vipin Arora, NCSES Deputy Director; and Matt Williams, Acting NCSES Chief Statistician. In partnership with NCSES, the Census Bureau conducted the survey and prepared the tables. NCSES staff members who made significant contributions include Gary Anderson, Jock Black, Jennifer Beck, Rebecca Morrison, and Timothy Wojan.
The Census Bureau, under National Science Foundation interagency agreement number NCSE-1748418, collected, processed, evaluated, and tabulated the data for this report. The Annual Business Survey is conducted within the Economic Directorate of the Census Bureau under the direction of Nick Orsini, Associate Director for Economic Programs, and Samuel Jones, Assistant Director for Economic Programs.
The data were prepared in the Economic Reimbursable Surveys Division under the direction of Kevin Deardorff, Division Chief, and Aneta Erdie, Assistant Division Chief. This work was performed under the supervision of Patrice Hall, assisted by John Clark, Lakitquana Leal, and Gail White, with staff assistance from Ahmad Bakhshi, Elaine Emanuel, Mary Frauenfelder, Aaron Finkle, Samantha Hernandez, James Jarzabkowski, Jessica Welch, and Tesfay Weldu. Additional support, including table creation and subject matter expertise was provided by Brandon Shackelford.
Mathematical and statistical techniques were provided by the Economic Statistical Methods Division under the direction of Carol Caldwell, Division Chief, and James Hunt, Assistant Division Chief. This work was performed under the supervision of Roberta Kurec, assisted by Sandra Peterson, with staff support from Taylor Beebe, Alexandra Abzun Cadenas, Charles Champion, and Daniel Cordes.
Data collection procedures and operations were provided by the Economic Management Division under the direction of Lisa Donaldson, Division Chief, and Michelle Karlsson, Assistant Division Chief. The staff of the National Processing Center performed mailout preparation, respondent assistance, and correspondence processing. Project management support was provided by Laura Hardesty.
Development and coordination of the computer processing system was provided by the Economic Application Division under the direction of Sumit Khaneja, Division Chief. This work was performed by Marilyn Balogh, Michael Feldman, David Gonzalez, Chakravarthy Sharad, and Joseph Talbot.
Publication processing support was provided by Devi Mishra, Catherine Corlies, and Tanya Gore (NCSES).
National Center for Science and Engineering Statistics (NCSES). 2022. Annual Business Survey: 2019 (Data Year 2018). NSF 22-315. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf22315/.
National Center for Science and Engineering Statistics
Directorate for Social, Behavioral and Economic Sciences
National Science Foundation
2415 Eisenhower Avenue, Suite W14200
Alexandria, VA 22314
Tel: (703) 292-8780
FIRS: (800) 877-8339
TDD: (800) 281-8749