Employment and Labor Income Data

OVERVIEW

Labor Income represents the total value of all forms of employment income paid throughout a defined economy during a specified period of time. Labor Income is the sum of Employee Compensation and Proprietor Income. Employee Compensation (EC) is the total remuneration of employees (e.g. wages and salaries, benefits, payroll taxes) in return for their work on domestic production and reflects the total payroll cost to employers. Proprietor Income (PI) consists of payments received by self-employed individuals and unincorporated business owners and reflects the current-production income of sole proprietorships, partnerships, and tax-exempt cooperatives.

Employment in IMPLAN is an industry-specific mix of full-time, part-time, and seasonal employment. It includes both Wage and Salary Employment and Proprietor Employment. It reflects an annual average that accounts for seasonality and follows the same definition used by the Bureau of Labor Statistics (BLS) and Bureau of Economic Analysis (BEA). 

Source data on income and employment are inherently incomplete. Due to data disclosures and undercoverage, no one dataset provides enough information to create a complete IMPLAN database. The most important three datasets IMPLAN relies on for our Employment and Labor Income estimates are:

• Bureau of Labor Statistics (BLS) Census of Employment and Wages (CEW)
• Census Bureau County Business Patterns (CBP)
• Bureau of Economic Analysis (BEA) Regional Economic Accounts (REA) data

In general, CEW data provide the county level Industry structure for IMPLAN, while CBP data are used to make non-disclosure adjustments to CEW data. REA data are used as controls for data not covered by CEW and proprietors. For some Special Industries (farm, construction, and government), additional data sources are used that provide either more current data or more geographic or sectoral specificity.

More information about the estimation procedures can be found in Accounting for Non-Disclosures, Exclusions, and Undercoverage in Employment Data.

DATA SOURCES

BLS CENSUS OF EMPLOYMENT AND WAGES (CEW)

The CEW dataset is one of the most important datasets used in IMPLAN database development. These data provide the industry structure for the states and counties and the "ground truth" for IMPLAN data. There can be many differences between CBP and CEW, but in the end, if wage and salary employment doesn't exist in CEW, it won't exist in IMPLAN data sets. The data are provided by the U.S. Department of Labor as part of the Unemployment Insurance (UI) Program.

The CEW dataset provides annual average wage and salary establishment counts, employment counts, and payrolls by county at the 6-digit NAICS code level. The primary source of this data is from state unemployment insurance (UI) programs. The data is then supplemented by the BLS using their Annual Refiling Survey (ARS) and the Multiple Worksite Report (MWR) data. This captures more than 95% of employment. Since these data only capture employees covered by the UI program, the data set cannot capture self-employed persons, railway employment, certain religious organizations, military, elected officials, or any other establishments that have their own social insurance program and/or do not pay into the UI program. Since most farm employment is self employment, CEW data misses much of the farm data. Read Accounting for Non-Disclosures, Exclusions, and Undercoverage in Employment Data for more information about how IMPLAN accounts for this.

CENSUS COUNTY BUSINESS PATTERNS (CBP)

County Business Patterns (CBP) is a program run by the U.S. Department of Census. It reports employment as a count of employees by industry (at the 6-digit NAICS level) during the week of March 12. This is a point-in-time estimate and not an annual average. The CBP employment data excludes most government employees and farm sectors.

Data at the 6-digit NAICS level of detail include: total number of establishments, total first quarter employment, first quarter current year and total annual payroll, and a breakdown of the number of firms for 12 different employment size classes. As might be expected with 6-digit level specification, there are significant disclosure problems. Prior to 2019, even when the Industry data was non-disclosed, CBP provided the number of firms by employee size class. This allowed IMPLAN to develop estimates for non-disclosed income and employment data. Since 2019, however, the CBP has adopted a policy under which they no longer provide establishment counts in cases in which the establishment count is less than three. See Accounting for Non-Disclosures, Exclusions, and Undercoverage in Employment Data for more information about how this policy change has affected IMPLAN’s processes. The CBP data give a picture of the industrial structure of a region and are used to adjust the CEW data for non-disclosure. There is a time lag, generally one year, between the current year and the most recent CBP data, but an industrial structure generally changes slowly over time. There are virtually no disclosure problems with the national-level CBP data.

BEA REGIONAL ECONOMIC ACCOUNTS (REA)

The final set of employment and income information is the Bureau of Economic Analysis's (BEA) Regional Economic Accounts (REA) data. This dataset is the most inclusive available and provides information on sectors such as agriculture, construction, and railroads not directly available through CBP or CEW. It also provides information on self-employment and proprietor income. 

The major drawback to these data is that they are only available at the 3-digit NAICS level for state and county income and at the 3-digit and 2-digit level for state and county employment, in addition to the data being lagged a year. IMPLAN therefore uses Bureau of Labor Statistics (BLS) Census of Employment and Wages (CEW) data to project REA data to the current data year and disaggregate to a more detailed Industry Scheme.

The BEA REA data provide a means to estimate proprietor employment and income, allowing for completion of the IMPLAN labor income data. The information used in developing IMPLAN data in this section is the following:

• 3-digit State level wage and salary income - SA7 tables
• 3-digit State level wage and salary employment - SA27 tables
• 3-digit State level employee compensation (wage and salary plus other labor income and benefits) - SA06 tables
• 3-digit State level total income (wage and salary and self-employment) - SA05 tables
• 3-digit State level total employment (wage and salary and self-employment) - SA25 tables
• 3-digit County level total income (wage and salary and self-employment) - CA05 tables
• 3-digit County level employee compensation (wage and salary plus other labor income and benefits) - CA06 tables
• 2-digit County level total employment (wage and salary and self-employment) - CA25 tables
• 6-digit disclosed CEW state and county employment and income data aggregated to the 3-digit NAICS, BEA sectoring scheme (used to project the REA data to the current data year).

BEA employment and income data are also subject to non-disclosure rules; therefore, estimates are made for non-disclosed values.

Additionally, unlike the CEW and CBP data, which give information on all counties and independent cities in the U.S., the BEA has combined independent cities with their neighboring counties in their REA data series. In Virginia, there are currently 24 such combinations. In 1994 and earlier datasets, WI also had one of these combined regions. These regions require special processes to split into separate Federal Information Processing Standards (FIPS) counties. Here is a list of the REA Counties and their Combined Cities and City ID:

VIRGINIA

ALBEMARLE; CHARLOTTESVILLE 901
ALLEGHANY;CLIFTON FORGE; COVINGTON 903
AUGUSTA; STAUNTON; WAYNESBORO 907
CAMPBELL; LYNCHBURG 911
CARROLL; GALAX 913
DINWIDDIE; COLONIAL HEIGHTS; PETERSBURG 918
FAIRFAX; FAIRFAX CITY; FALLS CHURCH 919
FREDERICK; WINCHESTER 921
GREENSVILLE; EMPORIA 923
HENRY; MARTINSVILLE 929
JAMES CITY; WILLIAMSBURG 931
MONTGOMERY; RADFORD 933
PITTSYLVANIA; DANVILLE 939
PRINCE GEORGE; HOPEWELL 941
PRINCE WILLIAM; MANASSAS; MANASSAS PARK 942
ROANOKE; SALEM 944
ROCKBRIDGE; BUENA VISTA; LEXINGTON 945
ROCKINGHAM; HARRISONBURG 947
SOUTHAMPTON; FRANKLIN 949
SPOTSYLVANIA; FREDERICKSBURG 951
WASHINGTON; BRISTOL 953
WISE; NORTON 955
YORK; POQUOSON 958

The 3-digit CEW employment and income data are used to proportion the REA data into its component counties with alternative proxies being used where CEW data are unavailable or incomplete.

COMPARISON OF DATA SOURCES

We often receive questions about our source data for Employment and Labor Income. These questions typically arise when comparisons are made between IMPLAN Employment estimates and public employment data provided by government agencies. This section describes important differences among the datasets and provides some illustrative comparisons. Read Accounting for Non-Disclosures, Exclusions, and Undercoverage in Employment Data for more detailed information about how IMPLAN Employment estimates are generated.

Differences across the datasets are attributable to several factors:

• Different coverage of Industries – e.g., one may cover farms and another may not.
• Different levels of detail reported – e.g., 3-digit versus 6-digit NAICS.
• Different types of Employment included – e.g., Wage and Salary Employment only, versus Wage and Salary Employment plus Proprietor Employment.
• Differences in dataset release timing – e.g., the lag between dataset reference years and publication years may differ.
• Different data collection methods – e.g., using IRS data to identify businesses vs. participation in unemployment insurance (UI) programs, and surveying businesses once per year versus multiple times per year.
• Different classification of the same business establishment – e.g., one dataset might consider a business establishment as a producer of boats (NAICS 336612, IMPLAN sector 364) and another might consider that same business establishment as a producer of ships (NAICS 336611, IMPLAN sector 363).

The different agencies that produce these datasets provide their own descriptions of coverage and methods, including comparisons to other datasets. Readers of this article who are interested in more-detailed information should consult these pages:

• CBP: https://www.census.gov/programs-surveys/cbp/about.html
• CEW: http://www.bls.gov/cew/cewover.htm and http://www.bls.gov/news.release/cewqtr.tn.htm
• BEA REA: http://www.bea.gov/regional/methods.cfm

COMPARISON TABLE

[1]Noise infusion is a method of disclosure avoidance in which values for each establishment are perturbed prior to table creation by applying a random noise multiplier to the magnitude data (i.e., characteristics such as first-quarter payroll, annual payroll, and number of employees) for each company. Disclosure protection is accomplished in a manner that results in a relatively small change in the vast majority of cell values. Each published cell value has an associated noise flag, indicating the relative amount of distortion in the cell value resulting from the perturbation of the data for the contributors to the cell. The flag for ‘low noise’ (G) indicates the cell value was changed by less than 2 percent with the application of noise, and the flag for ‘moderate noise’ (H) indicates the value was changed by 2 percent or more but less than 5 percent. Cells that have been changed by 5 percent or more are suppressed from the published tables. Additionally, other cells in the table may be suppressed for additional protection from disclosure or because the quality of the data does not meet publication standards. Though some of these suppressed cells may be derived by subtraction, the results are not official and may differ substantially from the true estimate. See https://www.census.gov/programs-surveys/cbp/technical-documentation/methodology.html for more information.

Written April 18, 2024

Updated July 2, 2024