U.S. flag

An official website of the United States government

Skip Header


Business Dynamics Statistics of Single Unit Firms with Revenue (BDS-SU-REV)

Business Dynamics Statistics of Single Unit Firms with Revenue (BDS-SU-REV)

The Business Dynamics Statistics of Single Unit Firms with Revenue (BDS-SU-REV) is an experimental data product extending the set of statistics published by the Business Dynamics Statistics (BDS) program. The BDS-SU-REV provides year-over-year changes in employment, payroll, and revenue for each quarter of the year for businesses that operate in one location (single-units) and file their taxes under a single Employer Identification Number (EIN). This product relies on IRS annual tax filings to collect revenue information and IRS quarterly tax filings to collect employment and payroll information for the pay periods including March 12th, June 12th, September 12th, and December 12th. Year-over-year employment and payroll changes are calculated between the same point in year t and year t-1 (i.e., June 12th year t-1 to June 12th year t, etc.). Employment and payroll growth is labeled as job and pay creation respectively and employment and payroll decline as job and pay destruction.

A novel innovation in the BDS-SU-REV is the incorporation of annual revenue data for single-establishment firms that consistently maintain positive employment levels each quarter. A positive annual growth in revenue is categorized as revenue creation, while negative growth is categorized as revenue destruction.

Revenue Data Insights

The BDS-SU-REV data reveals that the COVID-19 pandemic had a significant impact on the net real revenue creation rate for single-unit firms. In Figure 1, we observe that in 2019 the average net real revenue creation rate was 8%. However, this rate plummeted to less than 2% in 2020. To construct real revenue measures, we use the Bureau of Economic Analysis (BEA) GDP implicit price deflator, with a base year of 2017.

Figure 1. Annual net real revenue creation rate for all firms active in quarter 4

Source: BDS-SU-REV public tables (2022) from the U.S. Census Bureau Project 7508369: CBDRB-FY24-0446.

Net real revenue creation rate = revenue real creation rate – revenue real destruction rate, where the revenue creation rate is the sum of all gains from revenue-expanding firms from year t-1 to year t including startups. The revenue destruction rate is the sum of all losses from revenue-contracting firms from year t-1 to year t including deaths. Real revenue is adjusted for inflation using the (BEA) GDP implicit price deflator using 2017 as the base year.

The drop in the net revenue creation rate in 2020, shown in Figure 1, was caused by a significant rise in the revenue destruction rate, as shown in Figure 2. However, the revenue destruction rate for firms that exited by the fourth quarter of each year remained relatively constant over time. Most of the real revenue destruction was attributable to firms that remained active, especially in 2020.

Figure 2. Annual real revenue destruction rate for all firms active or exiting in quarter 4

Source: BDS-SU-REV public tables (2022) from the U.S. Census Bureau Project 7508369: CBDRB-FY24-0446.

Figure 3 illustrates the net revenue creation rate by state for 2019 and 2020. In 2019, California, New York, and Texas had net revenue creation rates above 5%, reaching 11%, 17%, and 6%, respectively. However, by 2020, these states experienced negative net revenue creation rates of -3%, -18%, and -1%.

Figure 3. Net real revenue creation rate (%) by State for firms active in quarter 4

Year = 2019
Year = 2020

Source: BDS-SU-REV public tables (2022) from the U.S. Census Bureau Project 7508369: CBDRB-FY24-0446.

In terms of revenue levels for single-unit firms, Figure 4 shows that real revenue has been on a steady rise from around 10 trillion in 2018-Q4 to 11.3 trillion in 2022-Q4, except in the pandemic year 2020, when it decreased from the 2019 level.

Figure 4. Annual real revenue ($ Billion) for all firms active in quarter 4

Source: BDS-SU-REV public tables (2022) from the U.S. Census Bureau Project 7508369: CBDRB-FY24-0446.

Sectors negatively impacted by the pandemic due to reduced revenue include Manufacturing; Arts, Entertainment, and Recreation; Lodging and Food Services; and Other Services as seen in Figure 5. This last category encompasses various businesses, such as auto repair, beauty salons, and personal care services, which tend to rely on in-person interactions.

Figure 5. Net real revenue creation rate (%) by sector for all firms active in quarter 4

Source: BDS-SU-REV public tables (2022) from the U.S. Census Bureau Project 7508369: CBDRB-FY24-0446.

Summary of Published Data

The BDS-SU-REV provides quarterly statistics from 2007 Q1 to 2022 Q4. There are 50 tables, each expressed in nominal and real terms. Tables are stratified by year, quarter, and firm characteristics and contain the following information:

  • Total number of firms
  • Total employment, payroll, and revenue
  • Number of firms opening and closing
  • Jobs,  payroll, and revenue created by continuing firms and new firms
  • Jobs, payroll, and revenue destroyed by continuing firms and closing firms
  • Net job creation,  payroll creation, and revenue creation
  • Payroll per employee for continuing, new, and exiting firms
  • Revenue per employee for continuing, new, and exiting firms

Tables are stratified by the following characteristics or some combination thereof, with 14  one-way tables, 26  two-way tables, and 8 three-way tables. In addition, there is an economy-wide table stratified only by year and quarter:

  • Firm age – number of calendar quarters since the first quarter of positive payroll (six detailed categories and five coarse categories)
  • Firm size – employment count (five detailed categories and three coarse categories)
  • NAICS sector
  • NAICS 3-digit industry
  • NAICS 4-digit industry
  • State
  • Metro/non-metro indicator
  • Rural/urban continuum – based on percentage of the county that is urban (four categories)
  • MSA - Metropolitan Statistical Area (metro areas reported individually, micro areas summed)
  • County
  • SBA Covid Relief for businesses – participation in PPP and other small business assistance programs

Key Innovations

Unlike the main BDS, which measures firm age in years, the BDS-SU-REV measures firm age in quarters. These quarterly single-unit data have numerous advantages. First, because these businesses operate in a single place, the firm, as defined by ownership, is equivalent to the establishment, as defined by location. This simplifies the reporting of business characteristics since age and size are the same for the firm as for the establishment. Second, because the quarterly data allow us to identify the first and last quarters a business had employees, we can date business entry and exit more precisely during the year. Third, the quarterly data also capture large temporary disruptions to the economy that happen in a single year, such as the 2020 COVID-19 pandemic recession or a natural disaster, which would be missed in the traditional BDS annual March-over-March job change estimates. Finally, the quarterly data include payroll, allowing us to calculate payroll creation and destruction in an analogous manner to employment.

While the quarterly employment time series begins in 2007, quarterly payroll data exist back to 1976, allowing us to measure firm age over the same time span as the main BDS. We identify the first quarter a firm had payroll between 1976 Q1 and 2022 Q4 and then measure firm age as the number of calendar quarters between the first and current quarter. In comparison, the main BDS identifies the first year a firm had employment in the pay period that includes March 12th and then calculates firm age as the number of calendar years between the first and current year.  If a firm began in the 2nd, 3rd, or 4th quarters of the year, its birth will not be recognized by the main BDS until the following year in March.  This annual measurement aggregates births over all the quarters of the prior year and reports them at the end of the 1st quarter each year. The quarterly measurement of the BDS-SU-REV disaggregates these births across the four quarters of the year. The same is true for firm exits.

Another innovation of the BDS-SU-REV product is the addition of payroll and revenue measures that mirror the employment measures. These new measures include total payroll, total revenue, payroll creation (year-over-year increase), revenue creation, payroll destruction (year-over-year decrease), revenue destruction, net payroll creation (total creation minus total destruction), net revenue creation, and the portion of payroll and revenue creation attributed to new businesses ('births') and the portion of payroll and revenue destruction attributed to business closures ('deaths').

Note that while the payroll and employment variables are measured at the quarterly level, revenue is only measured annually. However, all revenue measures are presented at the quarterly level, reflecting the annual revenue generated by  businesses active in that quarter. For more information, please refer to the BDS-SU-REV definition.

Finally, we publish the ratio of payroll to employment and revenue to employment for all single-unit firms, and separately for continuing, entering, and exiting firms.  Comparing this ratio for different groups of firms provides information about how average pay per worker and revenue per worker (labor productivity) varies across industries and geography.

NEW: The 2022 BDS-Single Unit Firms covering the years 2007 to 2022 is now available! The 2022 release includes applicable changes and improvements reflected in the 2022 BDS Release.

Download experimental Business Dynamics Statistics of Single Unit Firms with Revenue (BDS-SU-REV) data tables below. 

Data Sources

The BDS-SU-REV is a product of the U.S. Census Bureau and was developed by the Center for Economic Studies (CES). The published statistics were tabulated from the Longitudinal Business Database (LBD), an internal Census data product that tracks firms over time, beginning in 1976. Quarterly employment, payroll and revenue data are sourced from IRS tax records integrated into the Census Bureau’s business data.

The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data used to produce this product (Data Management System (DMS) number: 7508369, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0446).

There are two versions of each BDS-SU-REV table, one with nominal revenue and payroll and one with inflation-adjusted revenue and payroll. For v2022, we use the Bureau of Economic Analysis (BEA) GDP implicit price deflator, with a base year of 2017. Prior versions of this product used a base year of 2012, so note that all measures of real revenue and payroll will automatically increase in v2022 relative to prior versions.

Economy-wide Datasets

Number of Prior Active Quarters Datasets

Industry Datasets

Firm Size Datasets

Firm Age Datasets

Geography Datasets

SBA Covid Relief Datasets

Two-way Datasets with State

Two-way Datasets with Metro/Non-Metro

Two-way Datasets with MSA

Two-way Datasets with Industry

Three-way Datasets with Metro/Non-Metro

Three-way Datasets with State

Two-and Three-way Datasets with Firm Age and Firm Size

Top of Section

Glossary

Business Dynamics Statistics (BDS) – A public dataset on the LBD that describes United States business dynamics across a wide range of measures. Disclosure analysis is performed prior to release to the public to protect the confidentiality of the underlying LBD data.

Business Register (BR) – A comprehensive database of all U.S. business establishments developed and maintained by the U.S. Census Bureau, with data beginning in 1975 and continuing to the present. This is restricted data and is the source data for the Longitudinal Business Database (LBD).

Censoring – A statistical term indicating that a value cannot be known with certainty. Within the BDS, all firms/establishments born prior to 1976 have an unknown birth year and are therefore of an unknown age and are grouped into the age category “Left Censored”.

Data Quality Suppression – Data quality suppressions are made when a cell is determined to be unreliable due to its time series characteristics. Cells suppressed due to data quality concerns will appear as “S”. Disclosure Suppression – Disclosure suppressions are made when a cell has too few firms. Cells suppressed due to containing too few firms will appear as “D”.

Gross Domestic Product (GDP) Implicit Price Deflator – The price index produced by the Bureau of Economic Analysis (BEA), which is used to construct inflation-adjusted (real) payroll measures. The data can be found on FRED using the link below. In using this deflator, we maintain the 2017 base year.

Nominal Variables – Data that are unadjusted for inflation. Nominal data series are in “current” dollar terms.

Real Variables – Data that are adjusted for inflation. Real data series are in “constant” dollar terms.

Structurally Missing Flag – Structurally missing flags are applied to cells that are “structurally zero” or “structurally missing.” These are cells in the firm and establishment datasets where activity is not possible given the nature or structure of the BDS data. These cells appear as “X”. An example of a “structurally zero” cell is the variable ‘estabs_exit’ for firms age ‘0’. These cells will always be ‘0’ given the nature of data for firms age ‘0’. An example of a “structurally missing” cell is any variable for firms age ‘5’ in the years 1978 to 1981. These cells will always be missing for the years 1978 to 1981 because the source data for the BDS – the LBD – begins in 1976.

North American Industrial Classification System (NAICS) – NAICS is the standard used by Federal statistical agencies in classifying business establishments for the purposes of collecting, analyzing, and publishing statistical data released to the U.S. business economy. The system is used by the United States, Canada, and Mexico. Note that the 2022 BDS-SU-REV release is based on the 2017 NAICS vintage.

Top of Section

The BDS-SU-REV product follows the BDS methodology. Payroll creation and destruction are defined analogously to job creation and destruction, namely:

  • Payroll Creation (PayC) – Payroll creation is the sum of all payroll gains from payroll-expanding firms from year t–1, quarter n to year t, quarter n including firm startups.
  • Payroll Destruction (PayD) – Payroll destruction is the sum of all payroll losses from payroll-contracting firm (pleases from year t–1, quarter n to year t, quarter n including firms shutting down.

More precisely, definitions of pay creation, destruction, net change, and net change rate for firms classified in groups are given by:

Pay creation, destruction, net change, and net change rate for firms classified in groups

Total revenue, revenue creation, and revenue destruction are defined the following way:

  • Revenue in quarter n year t (Rev) – Annual Revenue (in thousands of dollars) at single-establishment firms with positive employment in quarter n in year t (active in quarter n in year t). Note that revenue is an annual measure while active status is defined at the quarterly level.
  •  Revenue Creation in quarter n year t (RevC) – Revenue creation is the sum of all revenue gains from revenue-expanding firms from year t–1 to year t including firm startups.
  • Revenue Destruction in quarter n year t (RevD) – Revenue destruction is the sum of all revenue losses from revenue-contracting firms from year t–1 to year t including firm closures.

Time Frame and Single-Unit Firm Universe

The BDS-SU-REV product is available from 2007-2022, which is a substantially shorter time frame than the main BDS. The quarterly IRS employment variables were first collected in 2004 and but the data were not of high enough quality to use for publication until 2005. Our data cleaning algorithm uses year t-2 data to edit year t-1 and year t-1 to edit year t data, resulting in a clean pair of t and t-1 employment and payroll values for each year in the time series.  For this reason, we begin our time series with the 2006-2007 year comparison, using 2005 as the baseline year for editing 2006 data (see Redesigning the Longitudinal Business Database (census.gov), page 45).

For revenue, high quality data to use for publication have only been created for 2017-2022 and hence the revenue columns are only populated beginning with the 2017-2018 comparison. Work is on-going to produce revenue for earlier years and extend the BDS-SU-REV time series backwards in time.

The BDS-SU-REV universe is defined as all the firms that the Census Business Register has determined to be operating in a single physical location during a given year. The main reason to exclude multi-unit firms is that payroll and employment are reported to the IRS at an aggregate level for these types of firms.  The Census Bureau models the allocation of firm payroll and employment to individual establishments within a multi-unit firm for quarter 1 of each year but does not allocate additional quarters. In addition, the scope of the single-unit population is honed to the same industry and geography requirements as the main BDS (see Redesigning the Longitudinal Business Database (census.gov), page 46).

Transitions in Single-unit Status

Each year some firms transition from having multiple establishments to having only one and vice versa. The multi-unit to single-unit transitions (MU-SU) in year t create a set of firms that belong to the single-unit universe, but which don’t have clearly defined employment and payroll flows beyond quarter 1. Establishments that belong to multi-unit firms in year t-1 don’t have quarter 2 – 4 employment and payroll for year t-1 and hence we cannot measure job creation and destruction between year t-1 and t in any quarter except quarter 1. Thus, for these cases, we set all employment flows to zero and simply include the relevant employment in total employment for the year. This assumption treats MU-SU transitions as if they had experienced constant employment across the two years. By definition, they are continuers, having operated in year t-1 and year t, and are not counted as entrants.

If a single-unit firm grows and opens additional establishments, i.e., new locations, it becomes a multi-unit (SU-MU transition). This type of change will remove the firm from the single-unit universe, beginning with the SU-MU transition year, and drop the associated employment from the totals. We do not count any employment flows from this type of transition, nor do we label the firm as an exit.

In the main BDS, the year t establishment count is equal to the year t-1 count plus entrants and minus exits plus the net effect of any scope changes that move establishments in and out of the BDS universe. The same is true to an even larger extent in the BDS-SU-REV tables because of transitions in and out of multi-unit status.  Abstracting away from other scope changes, in the BDS-SU, the year t, quarter n establishment count is equal to the year t-1, quarter n count plus entrants, minus exits, plus MU-SU transitions, and minus SU-MU transitions.  These transitions happen most often in Economic Census years or in the year immediately after an Economic Census. They represent a small percentage of establishments in each year (.1% on average) but sometimes have out-sized effects on more granular cells. In particular, there are occasions where a few large transitions cause relatively large changes in employment without corresponding job creation or destruction. This happens because employment moves in or out of the single-unit universe without being counted as an entrant or exit, as explained above. Research in on-going about how to improve measurement of the timing of these transitions, as well as how to account for the employment flows.

Relationship Between Entrants, Exits, Firm Age, and Number of Prior Active Quarters

Both the main BDS and the BDS single-units rely on year-over-year changes. This approach minimizes the effect of seasonality, or patterns in the data that occur every year due to weather, holiday timing, or other factors that affect the operation of certain types of businesses. By comparing year t data to the same point in year t-1, we measure employment changes due to underlying economic factors instead of changes from one season to the next. However, this method means that a firm entry or exit will generally be counted as such for multiple quarters. For example, if a firm operated continuously until quarter 2 of year t, it will be defined as an exit because it was still in operation in quarter 2 of year t-1.  If it remains closed in quarter 3 of year t, it will again be classified as an exit because it was in operation in quarter 3 of year t-1. The same is true for births. Thus, when reporting all the entrants and exits for a quarter, there is some ambiguity about how many of them, in fact, first happened in that quarter.

For births, this problem is solved by using quarterly firm age.  Age zero entrants in any quarter are “true” births, i.e., opening firms that have never operated before. Entrants between ages one and three in any quarter are firms operating for the first time in that quarter that initially opened in one of the prior three quarters. To mimic this concept for firm exit, we created a count of active quarters in the immediately preceding three quarters. In their first quarter with no payroll or employment, exiting firms will be categorized as having three prior active quarters. In the second quarter after cessation of activity, the exit will have only two prior active quarters, and so on until the fourth quarter when they will have zero prior active quarters. We use the number of prior active quarters as a by-variable in a one-way table to show the precise timing of exits.

Some businesses may close for one or two quarters and then re-open. This may happen for seasonal reasons (winter or summer recreation activities, specialty holiday retail, etc.) but also due to temporary economic shocks. The number of prior active quarters helps to trace out the pattern of exit and reactivation. The number of exits in quarter n with only two prior active quarters (i.e., not a first-time exit) is lower than the number of exits in quarter n-1 with three prior active quarters (i.e., first-time exits) and the difference is due to reactivations, in this case firms that closed for a single quarter and then re-opened. Large jumps relative to the historical time trend of exits with three prior active quarters followed by a return to more normal levels of exits with two prior active quarters in the subsequent quarter can signify a temporary economic disruption followed by a recovery as some businesses re-open.

Payroll and Revenue Deflation

As in any monetary time series, it is important to account for inflation when comparing values over time. The BDS-SU-REV tables include both nominal and real payroll versions. Real payroll is calculated using the GDP Price Deflator from the Bureau of Economic Analysis (BEA). We use the formula: real payroll = current year payroll * (100/GDP deflator) and maintain the Metro/Non-Metro base year of 2017.

Top of Section

Below are links to selected publications related to the BDS-Single Unit Firms.

Beem, Richard, Christopher Goetz, Martha Stinson, Sean Wang, 2022. "Business Dynamics Statistics for Single-Unit Firms," CES Discussion Paper Series, CES-WP-22-57.

Top of Section

Questions? Contact us at ces.bds@census.gov.

Page Last Revised - March 17, 2025
Is this page helpful?
Thumbs Up Image Yes Thumbs Down Image No
NO THANKS
255 characters maximum 255 characters maximum reached
Thank you for your feedback.
Comments or suggestions?

Top

Back to Header