• Introduction
• Coverage
  • Nonemployers
• Dollar Values
• Confidentiality
  • Disclosure Limitation
  • Unpublished Estimates
• Sample Design and Estimation Procedures
  • Sampling Frame
  • Stratification, Sampling Rates, and Allocation
  • Sample Selection
  • Method of Assigning Tax Status
  • Sample Maintenance
  • Estimation and Sampling Variance
• Benchmarking
  • Linking Samples
• Reliability of the Estimates
  • Sampling Error
  • Nonsampling Error
  • Response Rates
  • Two Types of Response Rates
  • Estimates Suppressed from Publication

The U.S. Census Bureau conducts the Service Annual Survey (SAS) to provide national estimates of annual revenues and expenses of establishments classified in select service sectors. See the Coverage section below for more information on the industries included in the 2022 Service Annual Survey.

The estimates are developed using data from a probability sample of firms located in the United States that have paid employees (i.e., employer firms). Consequently, published estimates only include data for employer firms. The sample is regularly updated to reflect the universe of employer service businesses and covers both taxable and tax-exempt firms. For more information about the design and selection of the sample, see the Sample Design and Estimation Procedures section below.

For some industries, firms without paid employees (i.e., nonemployers) may comprise a relatively large part of an industry. Because of the potential contribution to the industry totals from nonemployer firms, a separate table that provides total revenue estimates for employers plus nonemployers is provided. The nonemployer data included in this table are obtained from administrative data provided by other Federal agencies and through imputation. The Census Bureau's Nonemployer Statistics program tabulates the administrative data to provide annual statistics on the universe of nonemployer firms. For more information, see the Nonemployer section below and the Nonemployer Statistics program website, <https://www.census.gov/programs-surveys/nonemployer-statistics.html>.

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The estimates are summarized by industry classification based on the 2012 North American Industry Classification System (NAICS). NAICS groups establishments into industries based on the activities in which they are primarily engaged. This system, developed jointly by the statistical agencies of Canada, Mexico, and the United States, allows for comparisons of business activity across North America. Detailed information can be found on the Census Bureau’s NAICS website, <https://www.census.gov/naics/>.

Estimates are presented for select industries in the following NAICS sectors:

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Estimates for employers plus nonemployers are only published for total revenue. All other estimates are based only on employer firms. Firms without paid employees (nonemployers) are included in the total revenue estimates through administrative data provided by other Federal agencies and through imputation. Nonemployer revenue totals for reference years 2021 and 2022 are based on imputed values because values from the Nonemployer Statistics program are not yet available.

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All dollar values presented in this report are expressed in current dollars; that is, the estimates are not adjusted to a constant dollar series. Consequently, when comparing estimates to prior years, users also should consider price level changes.

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Title 13 of the United States Code authorizes the Census Bureau to conduct censuses and surveys. Section 9 of the same Title requires that any information collected from the public under the authority of Title 13 be maintained as confidential. Section 214 of Title 13 and Sections 3559 and 3571 of Title 18 of the United States Code provide for the imposition of penalties of up to five years in prison and up to $250,000 in fines for wrongful disclosure of confidential census information. In accordance with Title 13, no estimates are published that would disclose the operations of an individual firm.

The Census Bureau's internal Disclosure Review Board sets the confidentiality rules for all data releases. A checklist approach is used to ensure that all potential risks to the confidentiality of the data are considered and addressed. Per the Federal Cybersecurity Enhancement Act of 2015, data from respondents are protected from cybersecurity risks through screening of the systems that transmit their data.

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Disclosure is the release of data that reveals information or permits deduction of information about a particular survey unit through the release of either tables or microdata. Disclosure avoidance is the process used to protect each survey unit’s identity and data from disclosure. Using disclosure avoidance procedures, the Census Bureau modifies or removes the characteristics that put information at risk of disclosure. Although it may appear that a table shows information about a specific survey unit, the Census Bureau has taken steps to disguise or suppress a unit’s data that may be “at risk” of disclosure while making sure the results are still useful.

Cell suppression is a disclosure avoidance technique that protects the confidentiality of individual survey units by withholding cell values from release and replacing the cell value with a symbol, usually a “D”.  If the suppressed cell value were known, it would allow one to estimate an individual survey unit’s value too closely.

The cells that must be protected are called primary suppressions.

To make sure the cell values of the primary suppressions cannot be closely estimated by using other published cell values, additional cells may also be suppressed. These additional suppressed cells are called complementary suppressions.

The process of suppression does not usually change the higher-level totals. Values for cells that are not suppressed remain unchanged. Before the Census Bureau releases data, computer programs and analysts ensure primary and complementary suppressions have been correctly applied.

The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7502871, Disclosure Review Board (DRB) approval number:  CBDRB-FY23-0393, CBDRB-FY24-0054).

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Some unpublished estimates can be derived directly from this report by subtracting published estimates from their respective totals. However, the figures obtained by such subtraction are subject to poor response rates, high sampling variability, or other factors that result in their failure to meet Census Bureau standards for publication.

Individuals who use Service Annual Survey estimates to create new estimates should cite the Census Bureau as the source of only the original estimates.

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A new sample was introduced with the 2016 Service Annual Survey. The new sample was designed to produce estimates based on the 2012 North American Industry Classification System (NAICS). This section describes the design, selection, and estimation procedures for the new sample. For descriptions of prior samples, see the SAS historical publications.

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The sampling frame used for the Service Annual Survey has two types of sampling units represented: single establishment firms and multiple-establishment firms. The information used to create these sampling units was extracted from data collected as part of the 2012 Economic Census and from establishment records contained on the Census Bureau's Business Register as updated to December 2015. The next few paragraphs give details about the Business Register and the construction of the sampling units. Though important, they are not essential to understanding the basic sample design and readers may continue to the Stratification, Sampling Rates, and Allocation section.

The Business Register is a multi-relational database that contains a record for each known establishment that is located in the United States or one of its territories and has paid employees. An establishment is a single physical location where business transactions take place and for which payroll and employment records are kept. Groups of one or more establishments under common ownership or control are firms. A single-unit firm owns or operates only one establishment. A multiunit firm owns or operates two or more establishments. The structure of a firm’s primary identifier on the sampling frame differs depending on whether it is a single-unit firm or a multiunit firm.

A single-unit firm's primary identifier is its Employer Identification Number (EIN). The Internal Revenue Service (IRS) issues the EIN, and the firm uses it as an identifier to report social security payments for its employees under the Federal Insurance Contributions Act (FICA). The same act requires all employer firms to use EINs. Each employer firm is associated with at least one EIN and only one firm can use a given EIN. Because a single-unit firm has only one establishment, there is a one-to-one relationship between the firm and the EIN. Thus the firm, the EIN, and the establishment all reference the same physical location and all three terms can be used interchangeably and unambiguously when referring to a single-unit firm.

The primary identifier of a multiunit firm is a unique alpha number. The Census Bureau assigns the alpha number to the multiunit firm, and assigns a unique establishment identification number to each establishment within a multiunit firm. All establishments owned or controlled by the same multiunit firm have the same alpha number. Different multiunit firms have different alpha numbers, and different establishments within the same multiunit firm have different establishment identification numbers. The Census Bureau assigns both the alpha number to the multiunit firm and the establishment identification number to the corresponding establishments based on the results of the quinquennial Economic Census and the annual Company Organization Survey.

To create the sampling frame, we extract the records for all establishments located in the United States and classified in select service sectors as defined by the 2012 NAICS. For these establishments, we extract revenue, payroll, employment, inventory, name and address information, as well as primary identifiers and other classification and identification information from the Business Register.

To create the sampling units for multiunit firms, we aggregate the extracted economic data to a multiunit firm level by tabulating the establishment data for all service establishments associated with the same alpha number. No aggregation is necessary to put single-unit establishment information on a firm basis. Thus, the sampling units created for single-unit firms simultaneously represent establishment, EIN, and firm information. The sampling frame is an amalgam of establishments/EINs and firms/alphas.

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The primary stratification of the sampling frame is by industry group based on the detail required for publication. We further stratify the sampling units within industry group by a measure of size (substratify) related to their annual revenue. Sampling units expected to have a large effect on the precision of the estimates are selected "with certainty." This means they are sure to be selected and will represent only themselves (i.e., have a selection probability of 1 and a sampling weight of 1). Within each industry stratum, we determine a substratum boundary (or cutoff) that divides the certainty units from the noncertainty units. We base these cutoffs on a statistical analysis of data from the 2012 Economic Census. Accordingly, these values are on a 2012 revenue basis. We also used this analysis to determine the number of size substrata and substratum bounds for each industry stratum and to set preliminary sampling rates needed to achieve specified sampling variability constraints on revenue estimates for different industry groups. The size substrata, substratum bounds, and sampling rates are later updated through analysis of the sampling frame.

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First, if a firm's annual revenue is greater than the corresponding certainty cutoff, that firm is selected into the SAS sample with certainty.

Next, all firms not selected with certainty are subjected to sampling. To be eligible for the initial sampling, a firm has to have nonzero payroll in 2014. The firms are stratified according to their major industry and their estimated revenue (on a 2012 basis). Within each noncertainty stratum, a simple random sample of firms is selected without replacement.

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For kind-of-business classifications where there are substantial numbers of taxable and tax-exempt establishments, establishments are classified based on the Federal income tax filing requirement for the establishment or organization. This classification is based primarily on the response to an inquiry on the 2012 Economic Census questionnaire. Establishments that indicated that all or part of their income is exempt from Federal income tax under provisions of section 501 of the IRS code are classified as tax-exempt; establishments indicating no such exemption are classified as taxable. All government-operated hospitals are classified as tax-exempt. For establishments without a report form, the tax status classification is based upon administrative data from other Federal agencies. For selected kind-of-business classifications that are comprised primarily of tax-exempt establishments, all establishments in those classifications are defined as tax-exempt. All establishments in the remaining kind-of-business classifications (comprised primarily of taxable establishments) are defined as taxable.

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New establishments acquired by an alpha/firm in sample, even if under new or different EINs, may be added to the sample as part of the initial sampling unit’s representation, i.e., with the same initial sampling weight. For noncertainty firms, additional evaluation may be done in some instances to determine the feasibility of adding the new establishments by evaluating the effect of the new establishments on the industry estimates.

EINs with an active payroll filing requirement on the IRS Business Master File (BMF) are said by the Bureau to be "BMF active" and EINs with an inactive payroll filing requirement are said to be "BMF inactive."

Each quarter, we check against the current Business Register to determine if any EINs on SAS have become BMF inactive. Typically, we do not canvass BMF inactive EINs or inactive firms during the reference year. Likewise, if any EIN in SAS that was BMF inactive in a previous reference year, or is part of an inactive sampling unit on SAS, is now BMF active on the current Business Register, we again include these EINs, or sampling units, in the canvass. In both cases, we only tabulate data for that portion of the reference year that these EINs reported payroll to the IRS.

Additionally, each quarter, we update the sample to represent EINs issued since the initial sample selection. These new EINs, called births, are EINs that have an active payroll filing requirement on the IRS BMF. An active payroll filing requirement indicates that the EIN is required to file payroll for the next quarterly period. The Social Security Administration attempts to assign industry classification to each new EIN.

We sample EIN births on a quarterly basis using a two-phase selection procedure. To be eligible for selection, a birth must either have no industry classification or be classified in an industry within the scope of the Service Annual Survey, the Annual Wholesale Trade Survey (AWTS), or the Annual Retail Trade Survey (ARTS), and it must meet certain criteria regarding its quarterly payroll. In the first phase, we stratify births by broad industry groups and a measure of size based on quarterly payroll. A relatively large sample is drawn and canvassed to obtain a more reliable measure of size, consisting of revenue in two recent months and a new or more detailed industry classification code. We contact births by telephone if they have not returned their questionnaire within 30 days.

Using this more reliable information, in the second phase we subject the selected births from the first phase to probability proportional-to-size sampling with overall probabilities equivalent to those used in drawing the initial SAS sample from the December 2015 Business Register. Because of the time it takes for a new employer firm to acquire an EIN from the IRS and because of the time needed to accomplish the two-phase birth-selection procedure, we add births to the sample approximately nine months after they begin operation.

We include births that are selected in the quarterly birth-selection procedure in August and November of the calendar year in the initial mailing of the SAS letters in the following calendar year. To better represent all EIN births in the reference year, and specifically to account for the time it takes to identify and select new EINs, we traditionally add births to the SAS sample in the same calendar year if they are selected in February or May of that calendar year. We will mail a letter to these births as a supplement to the initial survey mailing for that calendar year.

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Total estimates are computed using the Horvitz-Thompson estimator, i.e. the sum of weighted reported or imputed data, for all selected sampling units that meet the sample canvass and tabulation criteria.  The weight for a given sampling unit is the reciprocal of its probability of selection into the SAS sample. These estimates are input to a benchmarking procedure. Variances are estimated using the method of random groups and are used to determine if measured changes are statistically significant.

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The current sample was introduced with the 2016 Service Annual Survey. This sample is designed to produce estimates based on the 2012 North American Industry Classification System (NAICS). In order to maintain the time series for each industry, an operation was performed to link estimates from the prior and new samples. For the linking operation to occur, for the 2016 Service Annual Survey, two years of data were collected (2015 and 2016) from units in the new sample. The linking is done so that the new sample estimates are implicitly benchmarked using results of the 2012 Economic Census because they are linked to previously benchmarked estimates from the prior sample.

Beginning survey year 2019, estimates have been benchmarked using results of the 2017 Economic Census. For more information on benchmarking to the 2017 Economic Census, please visit https://www.census.gov/programs-surveys/sas/technical-documentation/methodology/benchmarkprocess.html.

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Because the SAS sample is selected from a sampling frame of firms with paid employees, the following methodology, which is used to link the samples, is applied to employer-only estimates. Note, the following methodology applies to the majority of NAICS covered in SAS. Some NAICS follow unique linking methods which are detailed at the end of this section.

Total Revenue

Total Revenue estimates from the new sample for reference year 2015 and subsequent years are linked to the prior sample estimates by multiplying the Horvitz-Thompson estimates from the new sample by an adjustment ratio. The resulting total revenue estimates are referred to as "modified" revenue estimates. The years prior to 2015 remain unchanged and are referred to as “fixed” revenue estimates. The revenue adjustment ratio is calculated as follows:

• The numerator is the 2015 total revenue estimate from the prior sample. This estimate is census-adjusted based on the 2012 Economic Census for employer firms. This has been modified from a 2007 NAICS basis to a 2012 NAICS basis.
• The denominator is the 2015 Horvitz-Thompson total revenue estimate from the new sample. This is for employer firms and already on a 2012 basis.

Expenses

The following method is used to produce "modified" estimates for total expenses. First, the revenue ratio described above is multiplied by the Horvitz-Thompson estimate for total expenses for 2015 and subsequent years. Then, the estimates for 2012 through 2015 from the prior sample are input into a benchmarking program. Using this program, the expense estimates for 2013 and 2014 are revised in a manner that:

• Uses the census-benchmarked estimate for 2012 from the prior sample and the “modified” estimate for 2015 from the new sample as constraints, resulting in no revision to the 2012 census-benchmarked or 2015 “modified” estimate.
• Minimizes the sum of squared differences between the year-to-year changes of the input expense estimates and revised expense estimates for 2012 through 2015.

Revenue from Electronic Sources

Revenue from Electronic Sources is a new item beginning in survey year 2017 (see here). Estimates for Revenue from Electronic Sources are modified using the following procedure. The Horvitz-Thompson estimate is multiplied by the revenue ratio for 2017 and subsequent years.

Detail Revenue Items

Estimates for data items that sum to total revenue (i.e., detail revenue items) are linked using the following procedure. The Horvitz-Thompson estimate for the given data item is multiplied by the revenue ratio for 2015 and subsequent years. Years prior to 2015 are “fixed” detail revenue estimates.

Detail Expense Items

Estimates for data items that sum to total expenses (i.e., detail expense items) are linked using the following procedure. The Horvitz-Thompson estimate for the given data item is multiplied by the  revenue ratio for 2020 and subsequent years.

Other Items

The same method used for detail revenue items is used for any other data item that does not sum to total revenue or total expenses.

“Modified” estimates for any sums of data items are obtained by adding the “modified” estimates of the data items that comprise the sum. Also, “modified” estimates at aggregate industry levels are computed by summing the “modified” estimates for the appropriate detailed industries comprising the aggregates.

Unique Linking Methods

As a result of the 2012 NAICS Revision, NAICS 221119 was split across NAICS 221114, 221115, 221116, 221117, and 221118. For this reason, the new industries were linked at an aggregated level using the general linking methods described above. After “modified” estimates at the aggregate level were calculated, these estimates were raked to the detailed NAICS levels.

A similar method was used for NAICS 488991 and 488999. These NAICS were benchmarked to the 2012 Economic Census at the five-digit aggregate level. After the “modified” estimates were calculated at the aggregate level, the estimates were raked to the six-digit levels.

The following methods produce “modified” estimates for NAICS 521.

• Total revenue “modified” estimates for 2015 and subsequent years are set equal to the current sample “unmodified” estimates. Years 2012 through 2015 are linked using the benchmarking program; that is, 2012 and “modified” 2015 estimates serve as constraints, while 2013 and 2014 will become “modified”.
• Total expense “modified” estimates for 2015 and subsequent years are set equal to the current sample “unmodified” estimates. Total expense estimates for years prior to 2015 will not be published.
  
• Detail expense “modified” estimates for 2020 and subsequent years are set equal to the current sample “unmodified” estimates. Detail expense estimates for years prior to 2020 will not be published.
• There are no detail revenue items collected for NAICS 521.

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The published estimates may differ from the actual, but unknown, population values. For a particular estimate, statisticians define this difference as the total error of the estimate. When describing the accuracy of survey results, it is convenient to discuss total error as the sum of sampling error and nonsampling error. Sampling error is the error arising from the use of a sample, rather than a census, to estimate population values. Nonsampling error encompasses all other factors that contribute to the total error of a sample survey estimate. See the following sections below for further descriptions of sampling error and nonsampling error. Data users should take into account the estimates of sampling error and the potential effects of nonsampling error when using the published estimates.

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The sampling error of an estimate based on a sample survey is the difference between the estimate and the result that would be obtained from a complete census conducted under the same survey conditions. This error occurs because characteristics differ among sampling units in the population and only a subset of the population is measured in a sample survey. The particular sample used in this survey is one of a large number of samples of the same size that could have been selected using the same sample design. Because each unit in the sampling frame had a known probability of being selected into the sample, it was possible to estimate the sampling variability of the survey estimates. 

Common measures of the variability among these estimates are the sampling variance, the standard error, and the coefficient of variation (CV), which is also referred to as the relative standard error (RSE). The sampling variance is defined as the squared difference, averaged over all possible samples of the same size and design, between the estimator and its average value. The standard error is the square root of the sampling variance. The CV expresses the standard error as a percentage of the estimate to which it refers. For example, an estimate of 200 units that has an estimated standard error of 10 units has an estimated CV of 5 percent. The sampling variance, standard error, and CV of an estimate can be estimated from the selected sample because the sample was selected using probability sampling. Note that measures of sampling variability, such as the standard error and CV, are estimated from the sample and are also subject to sampling variability.  They do not measure any systematic biases in the estimates.  

We estimate variances for published statistics (totals, ratios, and percent changes) using the method of random groups. To implement the random group method of variance estimation, we assign a random group number to each sampling unit at the time of sample selection. Then, for each tabulation level at which estimates are produced, we compute variance estimates using the assigned random group numbers. We use 16 random groups (G=16) to estimate variances for the Service Annual Survey.

The Census Bureau recommends that individuals using published estimates incorporate this information into their analyses, as sampling error could affect the conclusions drawn from these estimates.

The estimate from a particular sample and its associated standard error can be used to construct a confidence interval. A confidence interval is a range about a given estimator that has a specified probability of containing the average of the estimates for the parameter derived from all possible samples of the same size and design. Associated with each interval is a percentage of confidence, which is interpreted as follows. If, for each possible sample, an estimate of a population parameter and its approximate standard error were obtained and using a t-statistic with 15 (=G-1) degrees of freedom, then:

Approximately 90 percent of the possible samples, the interval from 1.753 standard errors below to 1.753 standard errors above the estimate would include the average of the estimates derived from all possible samples.

In the example above, the margin of error (MOE) associated with the 90 percent confidence interval is the product of  1.753 and the estimated standard error. 

To illustrate the computation of a confidence interval for an estimate of total revenue, assume that an estimate of total revenue is $10,750 million and the CV for this estimate is 1.8 percent, or 0.018. First obtain the standard error of the estimate by multiplying the total revenue estimate by its CV. For this example, multiply $10,750 million by 0.018. This yields a standard error of $193.5 million. The upper and lower bounds of the 90-percent confidence interval are computed as $10,750 million plus or minus 1.753 times $193.5 million. Consequently, the 90-percent confidence interval is $10,411 million to $11,089 million. If corresponding confidence intervals were constructed for all possible samples of the same size and design, approximately 9 out of 10 (90 percent) of these intervals would contain the average of the estimates derived from all possible samples.

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Nonsampling error encompasses all factors other than sampling error that contribute to the total error associated with an estimate. This error may also be present in censuses and other nonsurvey programs.  Nonsampling error arises from many sources: inability to obtain information on all units in the sample; response errors; differences in the interpretation of the questions; mismatches between sampling units and reporting units, requested data and data available or accessible in respondents' records, or with regard to reference periods; mistakes in coding or keying the data obtained; and other errors of collection, response, coverage, and processing.  Although no direct measurement of nonsampling error was obtained, precautionary steps were taken in all phases of the collection, processing, and tabulation of the data in an effort to minimize its influence.  Precise estimation of the magnitude of nonsampling errors would require special experiments or access to independent data and, consequently, the magnitudes are often unavailable.

A potential source of bias in the estimates is nonresponse. Nonresponse is defined as the inability to obtain all the intended measurements or responses about all selected units. Two types of nonresponse are often distinguished. Unit nonresponse is used to describe the inability to obtain any of the substantive measurements about a sampled unit. In most cases of unit nonresponse, the questionnaire was never submitted to the Census Bureau after several attempts to elicit a response. Item nonresponse occurs either when a question is unanswered or the response to the question fails computer or analyst edits.

For both unit and item nonresponse, a missing value is replaced by a predicted value obtained from an appropriate model for nonresponse. This procedure is called imputation and uses survey data and administrative data as input.

The Census Bureau recommends that individuals using these estimates factor in this information when assessing their analyses of these data, as nonsampling error could affect the conclusions drawn from the estimates.

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Economic surveys at the Census Bureau are required to compute two different types of response rates: a unit response rate and a weighted item response rate.

The next few paragraphs provide details about the types and status of units used to collect and tabulate data. Though important, they are not essential to understanding the response rate measures and readers may continue to the description of the two types of response rates.

A survey unit is an entity selected from the underlying statistical population of similarly-constructed units. Examples of survey units for different economic programs include establishments, Employer Identification Numbers (EIN), firms, state and local government entities, and building permit-issuing offices. For the Service Annual Survey, the survey unit is either an EIN/establishment or firm/alpha/company. EIN survey units consist of only one establishment while company survey units are comprised of two or more establishments owned or controlled by the same firm.

A reporting unit is an entity about which data are collected. Reporting units are the vehicle for obtaining data and may or may not correspond to a survey unit for several reasons. First, the composition of the originally-sampled entity can change over the sample’s life cycle, as noted above. Second, for some surveys, an entity may request (or the Census Bureau may ask the entity) to report data in several separate pieces corresponding to different parts of the business or other entity type. For example, a large, diverse company in a company-based collection may request a separate form for each region or kind of business in which it operates or may ask to report separately for each of its establishments to align with their record keeping practices. For SAS, reporting units are usually created to facilitate the collection and tabulation of data by industry.

A tabulation unit houses the data used for estimation (or tabulation, in the case of a census). As with reporting units, the tabulation units may not correspond to a survey unit. Some programs consolidate establishment- level data to a company level to create tabulation units, so that the tabulation unit is often equivalent to the survey unit. Other programs create artificial units that split a reporting unit’s data among the different industries in which the reporting unit operates. In this case, the tabulation unit represents a portion of a survey unit. For SAS, the tabulation unit is the reporting unit.

For each survey, the statistical period describes the reference period for the data collection. For example, an annual program might collect data on the prior year’s business activity; the statistical period refers to the prior year, but the data are collected in the current calendar year.

During a given statistical period, all three types of units can be active, inactive, or ineligible. An active unit is in business and is in-scope for the program during the statistical period. An inactive unit is not operating or is not in-scope during the statistical period but is believed to have been active in the past and can potentially become active and in-scope in the future. Finally, a survey unit may become ineligible and excluded from subsequent computations due to a change in industry classification or ceasing to conduct business operations. All units are considered active until verified evidence otherwise is provided.

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The Unit Response Rate (URR) is defined as the percentage of reporting units in the statistical period, based on unweighted counts, that were eligible for data collection or of unknown eligibility that responded to the survey. URRs are indicators of the performance of data collection for obtaining usable responses. For a reporting unit to be classified as a response, a respondent must provide either total revenue or total expenses. Responses may be obtained by mail, telephone, or internet.

The Total Quantity Response Rate (TQRR) is defined as the percentage of the estimated (weighted) total of a given data item reported by the active tabulation units in the statistical period or from sources determined to be equivalent-quality-to-reported data. The TQRR is an item-level indicator of the “quality” of each estimate. In contrast to the URR, these weighted response rates are computed for individual data items, so that a survey may produce several TQRRs per statistical period and release. The TQRR is a weighted measure that takes the size of the tabulation unit into account as well as the associated sampling parameters. To compute the TQRR for a particular estimate, it is necessary to determine the source of the final tabulated value of the associated data item for each tabulation unit. This value could be directly obtained from respondent data, indirectly obtained from other equivalent quality data sources, or imputed.

The URRs and TQRRs for 2022 total revenue and total expenses for employer firms at the published NAICS sector levels are as follows:

For additional information about response rates, see the Census Bureau’s Statistical Quality Standard D.3., <https://www.census.gov/about/policies/quality/standards/appendixd3b.html>.

Estimates with a coefficient of variation greater than 40 percent or with a total quantity response rate less than 60 percent have been suppressed from publication. These estimates have been replaced with an "S" in the published tables. Estimates with a coefficient of variation between 30 percent and 40 percent have been provided and denoted with “(s)”. For more information, see the Census Bureau's Standards for Releasing Information Products,  <https://www.census.gov/about/policies/quality/standards/standardf1.html>.

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