Chapter 8 - BLS Handbook of Methods

Chapter 8
National Compensation Measures

T

he Office of Compensation Levels and Trends of the
U.S. Bureau of Labor Statistics (BLS) produces a diverse set of data from the National Compensation Survey (NCS) program and the Work Stoppages program.

IN THIS CHAPTER
NCS Background .......................................................... 1
Description of the NCS ........................................................... 1
NCS Sample Design and Sampling Procedures...................... 2
NCS Data Collection............................................................... 4
NCS Occupational Selection and Classification ..................... 5
Processing the NCS Data: Weighting, Nonresponse
Adjustment, Imputation, and Benchmarking .................... 7
Computations of Compensation Measures and Reliability
of Estimates for All NCS Data Products ............................ 9
Employment Cost Index (ECI) Series................................ 15
Employer Costs for Employee Compensation
(ECEC) Series .................................................................. 15
Occupational Earnings Estimates ..................................... 16
Area-to-Nation and Area-to-Area Pay Comparisons ........ 19
Incidence and Provisions of Benefits ................................ 19
Use and Limitations of NCS Data ........................................ 22
Technical References for NCS, byTopic ............................... 24
Appendix: Major Work Stoppages Program ......................... 26

The links to each of the NCS data products are as follows:
• Occupational earnings by geographic area, http://www.
bls.gov/ncs/ocs/#tables
• Occupational pay comparisons between areas, http://
www.bls.gov/ncs/ocs/payrel.htm
• Employment cost levels and trends, http://www.bls.gov/
ncs/ect/
• Incidence of employer-provided benefits, http://www.
bls.gov/ncs/ebs/
• Benefit plan provisions, http://www.bls.gov/ncs/ebs/
The link to Work Stoppages data products is http://www.
bls.gov/wsp

NCS Background
BLS collection and publication of wage data can be traced
back to the late 19th century, employee benefits data to the
mid-20th century. The NCS, introduced in 1996, collects a
broad range of compensation data that formerly had been
collected by three separate BLS programs. The Occupational
Compensation Survey program published national and local
area wage data for survey years 1991 through 1996. The Employment Cost Index has been published since 1975. The
Employee Benefits Survey program started in 1979 and collected and published data on employee benefits for survey
years 1980 through 1998. The NCS currently collects data
on employee compensation from a large sample of establishments providing data on about 800 detailed occupations in
more than 150 local areas. The number of establishments surveyed for a particular set of estimates is stated in the technical

notes of the original NCS publication on those estimates.

Description of the NCS
The NCS is an establishment-based survey that provides
comprehensive measures of occupational earnings, employer
costs of employee compensation, compensation trends, wages
in one geographic area relative to other geographic areas, the
incidence of employer-provided benefits among workers,
and provisions of employer-provided benefit plans. The Employment Cost Index (ECI)—a Principal Federal Economic
Indicator—is estimated from data collected by the NCS.
The NCS surveys workers in private industry establish-

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ments, and in State and local government, in the 50 States
and the District of Columbia. For the NCS, the term civilian
workers denotes workers in private industry and workers
in State and local government. Establishments with one or
more workers are included in the survey. Major exclusions
from the survey are workers in Federal and quasi-Federal
agencies, military personnel, agricultural workers, workers
in private households, the self-employed, volunteers, unpaid
workers, individuals receiving long-term disability compensation, and individuals working overseas. Currently, the NCS
also excludes individuals who set ther own pay (for example,
proprietors, owners, major stockholders, and partners in unincorporated firms) and family members being paid token
wages; however, these exclusions are being reevaluated.

selves and other areas that are not part of the sample. The
larger the area, the greater is its chance of being selected.
In stage 1, certainty areas are identified, noncertainty areas
are stratified, and the remaining sample areas are selected in
accordance with the NCS Probability-Proportionate-to-Size
(PPS) technique, in which local areas are allocated approximately proportionally to total employment across county
clusters. These clusters consist of Metropolitan Areas, Micropolitan Areas, and Outside Core Based Statistical Areas
(CBSAs), for each of the nine census divisions and for the
United States as a whole. Furthermore, NCS combines most
contiguous Outside CBSA counties within the same Census
(area) division, to create clusters with employment of at least
10,000 and with heterogeneous wage levels. The result is a
list of 436 county clusters formed from an original list of
1,359 Outside CBSA counties. Stage 1 resulted in a sampling of 152 areas, broken out as follows: 57 certainty areas,
60 noncertainty Metropolitan Areas, 22 noncertainty Micropolitan Areas, and 13 noncertainty Outside CBSA county
clusters.

NCS Sample Design and
Sampling Procedures
The NCS sample covers civilian workers, including workers
in private industry establishments and workers in State and
local governments across all 50 States and the District of Columbia. The NCS samples a portion of all occupations in a
portion of all establishments in a portion of all local areas in
the Nation. The statistics compiled from the survey, such as
median weekly earnings, by occupation, in private industry
establishments, are called estimates because they estimate
the actual value for the entire population.
NCS data are collected from probability samples selected
in three stages: (1) a probability sample of geographic areas,
(2) a probability sample of establishments within sampled
areas, and (3) a probability sample of occupations within
sampled establishments. Probability samples are subject to
sampling error and nonsampling error, which are discussed
under the section “Computations of Compensation Measures and Reliability of Estimates for All NCS Data
Products.”

Area definitions
Geographic areas of the United States are defined by the
Office of Management and Budget (OMB) once every 10
years and are based on the results of the most recent decennial census. In December 2003, OMB defined 361 Metropolitan Statistical Areas and 573 Micropolitan Statistical
Areas in the Nation. OMB also defined a list of Combined
Statistical Areas (CSAs) such that adjacent Metropolitan
and Micropolitan Statistical Areas, in various combinations,
form a CSA if specified criteria are met. There are 1,359
counties in the Nation that are not included in either type of
statistical area. Any county not included in a Metropolitan
or Micropolitan Statistical Area is referred to as an Outside
Core Based Statistical Area (CBSA).
To keep NCS products representative of the areas it
surveys, NCS must phase in the new OMB-defined sampled
areas and phase out the old. The previous NCS area sample
was based on the December 1994 OMB definitions. The
1994 area sample consisted of 152 areas; however, because
of a difference between the 1994 and 2003 OMB area definitions, and because of the NCS clustering of Outside CBSAs,
only a portion of the 2003 area sample’s 152 areas include
exactly the same municipal and county areas as those of the
1994 sample.
NCS converted the State and local government sample
of index establishments to the December 2003 OMB area
definitions in December 2007. NCS began the conversion
of the private industry sample of index establishments to
2003 OMB area definitions in December 2008, with the replacement of one-fifth of the private industry sample under
the new area definition each year through 2012. (Definitions
of index and wage-only establishments are described in
detail in the section “NCS Data Collection.”) The transition
has resulted in the collection of private industry data from
227 areas. Thus, NCS publications with reference periods of

Selecting geographic areas (Stage 1)
In stage 1, the NCS selects a sample of geographic areas in
the Nation. The sampling frame is the list of establishments
from which the survey sample is selected. Selection of the
geographic areas has resulted in the methodology currently
used by the NCS. (See the topic “Area Sample Selection”
in the “Technical References for NCS, by Topic” section
at the end of this chapter for references regarding the research and the decisionmaking process by which the current
NCS sample frame was created.) The paragraphs that follow
describe, in general terms, the methods by which the area
sample was selected for the NCS.
For the NCS sample, certainty areas are any individual
areas with employment greater than 80 percent of the sampling interval. (The sampling interval is the total employment
across all areas, divided by the number of areas to be selected.) Certainty areas represent only themselves in NCS
local area estimates; however, smaller areas represent them-

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NCS sample was stratified beginning in August 2007 on
the basis of the 2007 version of NAICS. NCS publications
with collection periods that include August 2007 contain
data from both the 2002 and 2007 NAICS industry codes.
The 2007 NAICS includes revisions across several sectors.
The most significant revisions are in the information sector,
particularly within the telecommunications area; overall, the
change from the 2002 NAICS to the 2007 NAICS had little
effect on the resulting NCS estimates. (For more information about the differences between the 2002 NAICS and the
2007 NAICS, see http://www.census.gov/epcd/naics07/.)

December 2007 through December 2012 may include data
from as many as 227 local areas. (For more information, see
“Phase-in of the Redesigned National Compensation Survey
Area Sample,” on the Internet at https://www.amstat.org/
sections/srms/Proceedings/y2005/Files/JSM2005-000156.
pdf; for a list of current and historical OMB area definitions,
see https://www.census.gov/population/www/metroareas/
metrodef.html.)

Selecting establishments (Stage 2)
In stage 2, the NCS uses the Probability-Proportionateto-Size (PPS) technique to select a sample of private industry and State and local government establishments
within each of the sampled areas. That is, the larger the
establishment, the greater is its chance of being selected.
An establishment is commonly a single economic unit
that engages in one, or predominantly one, type of economic
activity. For private industries in the survey, the establishment usually is at a single physical location, such as a mine,
a factory, an office, or a store, that produces goods or provides services. If a sampled establishment is owned by a
larger entity with many locations, only the employment and
characteristics of the immediate establishment are considered
for the survey. For State and local governments, an establishment can include more than one physical location, such as
a school district, a college, a university, a hospital, a nursing home, an administrative body, a court, a police department, a fire department, a health or social service operation,
a highway maintenance operation, an urban transit operation,
or some other governmental unit within a defined geographic
area or jurisdiction. Each establishment is assigned a sixdigit code from the North American Industry Classification
System (NAICS). When a single physical location encompasses two or more distinct economic activities, it is treated as two or more separate establishments if separate payroll records are available and certain other criteria are met.
The sampling frame, or universe, is the list of establishments from which the survey sample is selected. It is
developed from State unemployment insurance reports.
The most recent month of reference available at the time
the sample is selected is used to develop sampling frames.

Panel structure
The NCS uses a panel structure to rotate establishments in
and out of the survey. A panel is a subset of all establishments sampled for the survey that begin their participation
in the NCS at the same time. Each panel is composed of a
representative cross section of industries and geographic areas throughout the United States that are within the scope of
the survey.
Approximately one-fifth of the private industry sample is
reselected each year. The private industry establishment sample is divided into five panels that enter and exit the survey
on a rotational basis. A panel of establishments is introduced
into the survey once each year. Private industry panels stay in
the survey for 5 years. Establishments that go out of business
or refuse to participate in the survey are not replaced within
the panel; rather, the NCS adjusts for establishments’ and
occupations’ refusals as the panel proceeds. With one panel
of new establishments entering the survey, and one exiting,
each year, the sample is fully replaced over an approximately
5-year period. This practice helps to reduce respondent burden and keep the sample current. When a new replacement
panel is introduced into the survey, field economists conduct
the initial interviews of establishments in the new panel while
updating the establishment records of the other four panels.
Each panel is divided into two parts: index establishments
and wage-only establishments. The definitions and the purpose of this division are described in detail in the section
“NCS Data Collection.”
The State and local government establishment sample includes only one panel, replaced approximately once every
10 years. This arrangement differs from the private industry
5-year rotation because State and local government establishments are, generally, more stable in terms of establishment
births and deaths as well as number employed. NCS replaced
its State and local government index-establishment sample in
its entirety in December 2007, using 2007 NAICS to stratify
the sampling frame for the selection of new establishments.
In areas newly surveyed in 2007, data were collected only
in State and local government establishments. From 2008
through 2012, NCS is using the 2007 NAICS to stratify the
sampling frame in introducing panels of new private industry
establishments from newly selected areas based on the December 2003 OMB area definitions.

Industry classification of establishments
All Federal statistical agencies currently use NAICS for defining industries and classifying survey establishments. The
NCS, which originally used Standard Industrial Classification (SIC) codes to stratify establishments for selection, began a transition from SIC to the 2002 version of NAICS in
2004; the transition was completed in July 2007. NAICS
revises its industry classifications every 5 years to stay current with industrial taxonomy in North America. In selecting new establishment samples, NCS uses the most recent
version of NAICS as one of the stratification variables. The

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omist proceeds to collect wage and benefits data on
all of the workers with the same work attributes in the
matched occupation.

Probability sample of occupations within sampled
establishments (Stage 3)
In stage 3, field economists use a technique to randomly select the jobs to be sampled during the initial contact with the
sampled establishment. (See the section “NCS Occupational
Selection and Classification” for details.)
Occupational classification and the transition to new occupational definitions
Before 2004, the NCS used the Occupational Classification System (OCS), which was based on the 1990 Census of
Population, to classify jobs in its selection and publication
of occupational data. The NCS phased in the use of Standard Occupational Classification (SOC) codes over several
years. The NCS first published Employer Costs for Employee
Compensation (ECEC) estimates using SOC codes in March
2004; the ECI in March 2006; and NCS benefits publications in March of 2007. The NCS first published local area
earnings estimates using the SOC in September 2006 and
followed with the NCS national and Census division publications in September 2007.
Starting in May 2010, the NCS began to use SOC 2010
definitions for initial data collection at an establishment.
Updates (followup data collection at an establishment) will
begin September 2010. The 2010 SOC system contains 840
detailed occupations, aggregated into 461 broad occupations.
(For more information on SOC 2010, see http://www.bls.
gov/SOC/. The first NCS publication tentatively scheduled to
use SOC 2010 is the March 2011 ECI release.)

NCS Data Collection
BLS field economists employ a variety of methods to obtain
data from NCS survey respondents, including personal visits,
mail, telephone, and email. Field economists ask a series of
questions at the initial and subsequent contacts, such as the
following:


What is the primary business activity of the establishment? The field economist determines the correct
industry code for the establishment.



What types of occupations does the establishment
employ? The field economist determines the correct
SOC code and work level for each sampled job.



How many employees are there in each sampled job
that is matched to an occupational description? The
field economist determines how many employees in
the establishment can be defined by the occupational
code for the sampled job.



Do workers in the matched, sampled occupation
work full or part time? Are they union or nonunion
workers? Paid by time or incentive? The field economist determines these three work attributes of the
employee in the matched occupation. The field econ-



What are the employees in the sampled, matched occupation paid? The field economist collects data on
hours and wages from the payroll records covering
the 12th of the month.



What are the duties and responsibilities of the job?
The field economist collects the information and uses
it to determine the number of points for each of the
pay factors of the job. From the sum of the points of
all of the pay factors, the “work level” of each job surveyed is determined. (For more information on pay
factors and work levels, see “National Compensation
Survey: Guide for Evaluating Your Firm’s Jobs and
Pay,” on the Internet at http://www.bls.gov/ncs/ocs/
sp/ncbr0004.pdf.)



How many hours does the employee work? The field
economist collects data on the usual work schedule
of each sampled, matched occupation. This helps to
determine the hourly, weekly, and annual earnings, as
well as the cost of benefits.



What types of benefits do the employees receive? The
field economist gathers data on the availability and
cost to the employer of 18 surveyed types of benefits that are provided to the worker in each sampled,
matched job. The field economist collects summary
plan descriptions (SPDs) of the health and retirement
plans offered by the employer. Data on the availability
of a number of other benefits also are collected. Then
the SPDs are sent to the national office in Washington,
DC, where they are analyzed for data on benefit plan
provisions (the terms of coverage of the plan).

Collection period
A BLS field economist contacts the sampled establishment
for the initial collection of data. Establishments in each
sample panel are divided into two parts: wage-only establishments and index establishments. Wage-only establishments
are contacted once per year during their tenure in the sample,
and only for wage data. Data from wage-only and index establishments are used to produce NCS wage (earnings) publications. Although the ECI and ECEC publications include
estimates of employer costs of total compensation, of which
wages and salaries are a major component, wage data collected from wage-only establishments are not used in the production of ECI and ECEC estimates.
NCS collects local area earnings data from wage-only
establishments over a 14-month period for larger survey
areas and a 4-month period for smaller areas. The data’s reference date is the average payroll reference date over that
14-month or 4-month period. The reference date of NCS data

4

publications is listed in the title of the publication as well as
in every table title. The publication date is included on the
cover page of the publication. (See, for example, the bulletin
New York–Newark–Bridgeport, NY–NJ–CT–PA, National
Compensation Survey, May 2008, U.S. Department of Labor,
Bureau of Labor Statistics, December 2008, on the Internet
at http://www.bls.gov/ncs/ocs/sp/ncbl1197.pdf.) The data
in this publication have a reference date of May 2008 and a
publication date of December 2008.
The reference periods for the national earnings and Census
division earnings estimates are the average of the payroll reference dates of all the local area data collected. For example,
for the 2008 survey data, earnings data were collected from
December 2007 through January 2009 in the 87 larger areas.
For the 140 smaller areas, earnings data were obtained in one
of four 4-month periods over the same timeframe. The average reference date for the 2008 national estimates is July
2008; the 2008 national earnings bulletin has a publication
date of August 2009. (See National Compensation Survey:
Occupational Earnings in the United States, 2008, on the
Internet at http://www.bls.gov/ncs/ncswage2008.htm.) The
reference dates for annual earnings estimates apply to the
NCS annual pay comparisons (also known as pay relatives)
estimates as well. (See http://www.bls.gov/ncs/ocs/payrel.
htm.)
NCS contacts index establishments for data on wages, the
cost of benefits, and the incidence and provisions of benefits.
Data from index establishments are used to produce ECI,
ECEC, and NCS benefits estimates. Although initial data collection occurs at any time of the year, ECI and ECEC updates
are collected over a 6-week period for the pay period that
includes the 12th day of the month for the months of March,
June, September, and December. For example, the news release “Employment Cost Index—June 2009” includes data
collected for the pay period that included June 12, 2009, from
each employer scheduled for an ECI update. The publication
date of the news release was July 31, 2009. (See http://www.
bls.gov/news.release/pdf/eci.pdf for the most recent ECI
news release.) The news release for “Employer Costs for Employee Compensation—June 2009,” includes data collected
for the same pay period—in this example, the pay period that
included June 12, 2009; however, its publication date was
September 10, 2009. (See http://www.bls.gov/news.release/
pdf/ecec.pdf for the most recent ECEC news release.) The
ECEC publication dates are 3 months after the reference
month, while the ECI publication dates are a month after the
reference month.
NCS collects benefits incidence and key provisions data
from index establishments that are being contacted for survey
updates in March of the collection cycle. The March data are
published in the summer or fall of the same year. (See for
example, National Compensation Survey: Employee Benefits
in the United States, March 2009, on the Internet at http://
www.bls.gov/ncs/ebs/benefits/2009/ebbl0044.pdf.)
NCS detailed benefits provisions data are collected from
the private industry index establishments that are in their

initial 14 months in the survey, which, for the NCS, is May
of one year through July of the next year. Annual data are
published in the summer of the next year. (See, for example,
National Compensation Survey: Health Plan Provisions
in Private Industry in the United States, 2008, on the Internet at http://www.bls.gov/ncs/ebs/detailedprovisions/2008/
ebbl0042.pdf, which has a publication date of July 2009.)
Detailed benefits provisions in State and local governments
are collected and published when the government sample is
initiated. The latest NCS detailed benefits provisions publication for State and local government is “National Compensation Survey: Retirement Benefits in State and Local
Governments in the United States, 2007,” U.S. Department
of Labor, Bureau of Labor Statistics, Summary 08-03, published May 2008, on the Internet at http://www.bls.gov/ncs/
ebs/sp/ebsm0008.pdf.

NCS Occupational Selection
and Classification
The NCS collects data on workers who are employed by
the owner of the establishment. Persons working onsite at a
surveyed establishment, but paid by a contracted firm, are
not included in data collection from the establishment. If a
contracted firm is part of the sample, the NCS collects data
on employees of the contracted firm who are working offsite
at other establishments, as well as those working onsite. To
be included in the NCS, employees in sampled occupations
must receive cash payments (cash, check, or direct deposit
payments) from the establishment for services performed,
and the establishment must pay the employer’s portion of
Medicare taxes on those individuals’ wages.
Number of workers in establishment includes workers on
paid vacation or other types of leave; salaried officers, executives, and staff members of incorporated firms; employees
temporarily assigned to other units; and noncontract employees for whom the reporting unit is the permanent duty
station, regardless of whether that unit prepares their paychecks.
In sampling jobs at an establishment, BLS field economists use a method that ensures a random sampling. Field
economists then match employees working in the sampled
jobs with an occupation (as defined in the SOC structure).
Workers are classified into occupations on the basis of the
work performed and the skills required in each occupation,
and not on the basis of their education. For example, an employee trained as an engineer, but working as a drafter, is reported as a drafter. An employee who performs the duties of
two or more occupations is reported in the occupation that requires the highest level of skill or in the occupation in which
the employee spends the most time if there is no measurable
difference in skill requirements. A quote is a sampled job that
has been matched with an SOC occupation; it includes all
workers in the job that have the same occupational attributes:
full-time or part-time status, union or nonunion status, and

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major group Health Care Practitioner and Technical Occupations (code 29-0000). (See the entire list of SOC occupational
categories at http://www.bls.gov/soc/soc_majo.htm.) For
the NCS, occupations can fall into any of 22 major groups;
the NCS excludes major group 23 (SOC code 23-0000), military-specific occupations.

whether they are paid on a time or an incentive basis.
Stages 1 and 2 of the NCS sampling procedures are described in the section titled “NCS Sample Design and Sampling Procedures.” Stage 3, occupational selection and classification, is conducted by field economists during the initial
contact with the sampled establishment. There are four main
steps in this stage:

Determining occupational attributes of the worker. In step
3, for each selected occupation, the field economist records
specific attributes of the worker in the sampled job. Each selected occupation must include only workers with the same
attributes; for example, the occupation cannot include both
full-time and part-time workers. The occupational attributes
of workers, as determined by NCS, are as follows:

1. Selection of establishment jobs by the NCS Probability Selection of Occupations (PSO) technique.
With this technique, the probability of selecting a
given job is proportional to the number of workers
in the job in the establishment
2. Classification of jobs into occupations based on the
SOC system



Full-time/part-time status. The field economist identifies the worker as holding either a full-time job or a
part-time job. For the NCS, full-time and part-time
status is not determined by number of hours worked;
rather, the status is based on the establishment’s definition of those terms.



Time-based/incentive-based pay. The field economist identifies the worker as having time-based or
incentive-based pay, depending on whether any part
of the pay was based directly on the actual production
of the worker, rather than solely on the number of
hours worked. Time workers are those whose wages
are based solely on an hourly rate or salary. Incentive
workers are those whose wages are at least partially
based on productivity payments, such as piece rates,
commissions, and production bonuses.



Union/nonunion workers. The field economist records whether the occupation is filled by union or
nonunion workers. The NCS defines a union worker
as any employee in a union occupation when all of
the following conditions are met: a labor organization
is recognized as the bargaining agent for all workers
in the occupation; wage and salary rates are determined through collective bargaining or negotiations;
and settlement terms, which must include earnings
provisions and may include benefit provisions, are
embodied in a signed, mutually binding collective
bargaining agreement. A nonunion worker is an employee in an occupation not meeting all of the NCSdefined conditions for union coverage.

3. Determining attributes of the worker in the job, such
as full-time or part-time status, union or nonunion
status, and whether the worker is paid on a time or
incentive basis
4. Determining the work level of each job
Selecting occupations. In step 1, the field economist receives
the establishment’s complete list of employees and their job
titles. The field economist then uses the NCS Probability Selection of Occupations (PSO) technique to randomly select
the jobs to be sampled. The number of selected jobs for
which data are collected is based on the establishment’s employment size, according to the following criteria:
Number of employees
Number of jobs selected

1–49
Up to 4

50–249 250 or more
6

8

Exceptions include State and local government units, for
which up to 20 jobs may be selected, and the aircraft-manufacturing industry units—those matching NAICS code 336411—for which up to 32 jobs may be selected.
Classifying jobs. In step 2, the field economist classifies
the sampled jobs into occupational categories based on the
workers’ actual job duties and responsibilities, not on their
job titles.
Today, the Standard Occupational Classification (SOC)
system is used by all Federal statistical agencies to classify
occupations. Under the SOC, a job may fall into any one of
about 800 occupational classifications. Each occupation is
designated by a six-digit code that is part of a hierarchical
structure: detailed occupations are grouped under broad occupations, broad occupations are part of a minor group, and
minor groups are part of a major group. The SOC designates
23 major groups. Major group codes end with 0000, minor
groups codes end with 000, and broad occupation codes end
with 0. For example, the detailed occupation Orthodontists
(code 29-1023) is under the broad occupation Dentists (code
29-1020), which is under the minor group Health Diagnosing
and Treating Practitioners (code 29-1000), which is under

Determining the work level of the job. In step 4, field economists evaluate the job, using a “point-factor” system to determine the work level of a selected occupation. The NCS
system uses four distinct factors:





Knowledge
Job controls and complexity
Contacts
Physical environment

Each factor consists of several degrees, each with an associated description and number of points. Generally, the
6

greater the consequence, complexity, or difficulty of the
factor, the higher is the number of points assigned.
Except for the knowledge factor, the descriptions apply
to all occupational categories. Knowledge has a separate
set of descriptions for each of several broad occupational
categories. For example, the knowledge factor of a professional accounting occupation is based on a different set of
descriptions by which levels and points are assigned than the
knowledge factor of an engineering occupation. The broad
occupational categories for which unique descriptions are
given are as follows:

























may have contributed in different portions to that point total.
Work levels, from lowest to highest, vary by occupation.
Lower levels are found in occupations that require limited
training, such as equipment cleaners, cashiers, and personal
care workers. Higher levels are found in occupations that require extensive knowledge and independent decisionmaking,
such as operations managers, engineers, and lawyers.
During data production, data on work levels are combined
by common traits and estimates are published in four broad
groups, called combined work levels. (See the section “Occupational Earnings Estimates” for details.)

Business Administration
Professional Accounting and Auditing
Information Technology
Professional Mathematics and Statistics
Professional Engineering and Architecture
Engineering and Scientific Technician
Professional Biological and Physical Science
Professional Economics, Sociology, Geography, Psychology, and Similar Jobs
Social, Welfare, and Health Administration
Professional Legal
Administrative Legal
Professional Education
Professional Librarian, Museum Curator, and Archivist
Communications and Arts
Professional Medical
Medical, Hospital, Dental, Public Health, and Veterinary Technician
Protective Service
Investigation, Inspection, and Compliance
Service
Sales
Office and Administrative Support
Miscellaneous Technician
Blue Collar
Pilots and Air Transportation

Determining supervisory responsibilities. According to the
current version of the SOC, “Supervisors of professional and
technical workers usually have a background similar to the
workers they supervise, and are therefore classified with the
workers they supervise. Likewise, team leaders, lead workers
and supervisors of production, sales, and service workers
who spend at least 20 percent of their time performing work
similar to the workers they supervise are classified with the
workers they supervise.”1 Typically, supervisors have the authority to hire, transfer, lay off, promote, reward, and discipline other employees. For the NCS, field economists record
whether the occupation includes supervisory responsibilities
and, if so, the level of responsibility. By NCS definition,
first-line supervisors direct their staff through face-to-face
meetings and are responsible for conducting the employees’
performance appraisals; second-line supervisors typically
direct the actions of their charges through first-line supervisors. NCS also evaluates most supervisory jobs on work
levels based on the four point factors previously described.
A modified approach is used for professional and administrative supervisors when they direct professional workers
and are paid primarily for their supervisory and managerial
skills; the levels of such supervisory jobs are determined on
the basis of the duties and responsibilities of the highest reporting position. (For a complete description of point-factor
leveling and the determination of supervisory levels, refer
to the publication National Compensation Survey: Guide
for Evaluating Your Firm’s Jobs and Pay, on the Internet at
http://www.bls.gov/ncs/ocs/sp/ncbr0004.pdf.)

Processing the NCS Data: Weighting,
Nonresponse Adjustment, Imputation,
and Benchmarking

The job is assigned points for the highest level at which all
requirements are met. This entire process is known as pointfactor leveling. If a specific work level cannot be determined
for a selected occupation, the data for that occupation are recorded as not able to be leveled.
NCS publishes data on 15 work-level categories. The
work levels, which reflect a hierarchy of primary duties
and responsibilities, can be used to compare different occupations that have the same broad occupational knowledge
factors. For example, a level-9 registered nurse would have
a total number of points within the same range as a level-9
pharmacist; however, the factors of knowledge, job controls
and complexity, personal contacts, and physical environment

Participation in the survey is voluntary; therefore, a company
official may refuse to participate in the initial survey or may
be unwilling or unable to update previously collected data
for one or more occupations during a subsequent contact. In
addition, some establishments selected from the sampling
frame may be out of scope or may have gone out of business.
To address the problems of nonresponse and missing data,
the NCS adjusts the weights of the raw data and imputes
1
Standard Occupational Classification User Guide, Classification
Principles, #3, on the Internet at http://www.bls.gov/soc/socguide.htm.

7

missing values, ultimately to ensure that published compensation estimates are representative of compensation in the
civilian, private industry, and State and local government
sectors—for the Nation, broad geographic regions, and local
areas. Beginning in 2006, the NCS implemented a number
of significant changes in the survey, including imputing for
temporary nonresponse situations and benchmarking estimated employment. This section includes a description of the
current imputation and benchmarking methods. (For more
information on recent changes in the NCS methodology
with respect to NCS wage products, see “Change Comes
to the National Compensation Survey Locality Wage Bulletins,” on the Internet at http://www.bls.gov/opub/cwc/
cm20070122ar01p1.htm. For more information on these
changes with respect to the ECI, see http://www.bls.gov/
ncs/ect/ecsm0001.htm, and with respect to the benefits publications, see http://www.bls.gov/ncs/ebs/ebsm0005.htm.)

sponds to the survey but is unable or unwilling to
provide some of the benefits data, occupational classifications, or worker attributes for a given sampled
occupation. Item nonresponse is treated by item imputation in certain situations. In item imputation, missing
values for an item are replaced by values derived from
establishments with similar characteristics that completed the item. For NCS benefits estimates, items can
be imputed for nonresponse at initial and subsequent
data collection. For example, suppose that, during the
initial contact, an establishment reports earnings data
for a sampled occupation but refuses to report whether
those in the occupation receive paid vacation leave;
NCS then imputes the incidence of vacation leave for
the occupation on the basis of the incidence of vacation
leave among similar occupations in similar establishments. For NCS earnings estimates, items are not imputed for item nonresponse during the establishment’s
initial data collection but are so imputed at subsequent
data collections. For example, if a manufacturing establishment gave no information on the earnings per
hour of its full-time, nonunion assembly workers at the
initial collection of data, NCS would not use any of the
data on those workers. However, if the establishment
reported earnings per hour for its full-time, nonunion
assembly workers during the initial collection, but not
in a subsequent collection, NCS would use the most
recent reported hourly earnings of full-time, nonunion
assembly workers in the establishment, adjusted for the
rate of change in hourly earnings of workers in similar
manufacturing establishments, to impute the missing
data.

Weight adjustments and imputation are made in accordance
with the following steps:
1.

2.

An establishment is considered responding if it provided information on at least one usable occupation. An
occupation is classified as usable if the following data
are present: occupational attributes (full-time or parttime schedule; union or nonunion status; and time or
incentive type of pay); work schedule; and wage data.
Wages account for roughly 70 percent of compensation;
therefore, if wage data are not available, other data
from the establishment cannot be used in calculating
estimates. Establishment nonresponse occurs when an
establishment did not provide earnings, occupational
classification, worker attributes, and work schedule
data for any occupation. Establishment nonresponse
during the initial interview is treated with adjustments
that redistribute the weights of nonrespondents to
similar respondents on the basis of characteristics such
as the industry and size class of the establishment. For
example, if the nonresponding establishment was in
the manufacturing industry with an employment of 350
workers, NCS would adjust the weights of responding
manufacturing establishments with 250–499 workers
by a nonresponse factor. This factor is calculated by
dividing the sum of the product of establishment employment and sample weight for responding and nonresponding establishments by the sum of the product of
establishment employment and sample weight for responding establishments. At subsequent interviews of
an establishment, establishment nonresponse is treated
by imputation, in which missing values for an initially
responding establishment are replaced by values from
the original interview, adjusted by the rate of change
among responding establishments. Establishments no
longer in operation or out of the scope of the survey,
and establishments with no workers within the scope
of the survey, are excluded from the survey estimates.
Item nonresponse occurs when an establishment re-

8

3.

A third factor adjusts for any special situations that
may have occurred during data collection. For example, when a sample unit is one of two establishments
owned by a given company and the respondent provides data for both locations combined instead of the
sampled unit, the weight of the sampled unit is adjusted
to reflect the employment data actually collected.

4.

Finally, poststratification, or benchmarking, is the
process of adjusting the weight of each establishment
in the survey to match the most current distribution
of employment by industry. The NCS establishment
sample is drawn from the Quarterly Census of Employment and Wages (QCEW). Because the sample of
establishments used to collect NCS data was chosen
over the past several years, establishment weights
reflect employment when sampled. The benchmark
process updates that weight on the basis of current
employment. Benchmarking ensures that survey estimates reflect the most current industry-ownership
employment counts in proportions consistent with
the private industry, State government, and local government sectors. For example, suppose that 40 private
industry, 10 local government, and 5 State government
units in the service sector were selected from the NCS

sampling frame containing 200,000 private workers,
30,000 local government workers, and 10,000 State
government workers, respectively. Suppose also that,
by the time of survey processing, the private service
sector experienced an employment increase of 10,000
workers, or 5 percent, and no increase in employment
was experienced in local and State government. Then,
if NCS did not benchmark, the sample would underrepresent current private service sector employment.
In this example, the NCS would adjust the sample
weights of the 40 service sector firms to ensure that
the number of private service sector workers in
the sampling frame rises to 210,000. The industryownership employment counts would then reflect
the current proportions of 84 percent for private,
12 percent for local government, and 4 percent for
State government employment in the service sector.

scope of the NCS, by sector and by occupational group, represented by the survey—not the number of workers actually
surveyed. The number of workers represented by the survey
is not intended as an accurate employment count; rather, it
indicates only the relative importance of the occupational
groups studied in the survey.

Computations of Compensation
Measures and Reliability
of Estimates for All NCS Data Products
The NCS sample provides (1) data for the Employment Cost
Index (ECI) series and the Employer Costs for Employee
Compensation (ECEC) series, (2) estimates of occupational
wages by work level, (3) occupational wage comparisons between geographic areas, and (4) data on employer-provided
benefits. This section describes computations and reliability
measures that apply to all NCS data products. At the end of
the section are links to Web pages for each NCS data product;
these pages include a description of the computations and reliability measures specific to the individual product.
NCS estimates are derived from a sample of occupations
selected from the responding establishments. Two types
of errors are possible in an estimate based on a sample
survey: sampling errors and nonsampling errors. Sampling
errors occur because the sample makes up only a part of
the population. The sample used for the survey is one of a
number of possible samples that could have been selected
under the sample design, each producing its own estimate.
A measure of the variation among sample estimates is the
standard error. Nonsampling errors are data errors that stem
from any source other than sampling error, such as data
collection errors and data-processing errors.
Standard errors can be used to measure the precision with
which an estimate from a particular sample approximates
the expected result of all possible samples. The chances are
about 68 out of 100 that an estimate from the survey differs
from a complete population figure by less than the standard
error. The chances are about 90 out of 100 that this difference
would be less than 1.6 times the standard error. Statements
of comparison appearing in NCS publications are significant
at a level of 1.6 standard errors or better. This means that,
for differences cited, the estimated difference is greater than
1.6 times the standard error of the difference. To assist users
in ascertaining the reliability of NCS series, standard errors
for all NCS estimates are available online at http://www.bls.
gov/ncs/.
The ECI, ECEC, and NCS wage and benefits publications all use some variation of balanced repeated replication (BRR) methodology to estimate the standard error.
The procedure for BRR is first to partition the sample into
variance strata composed of single sampling strata or clusters
of sampling strata and then to split the sample units in each
variance stratum evenly into two variance primary sampling
units (PSUs). Next, half-samples are chosen so that each
half-sample contains exactly one variance PSU from each

The benchmark calculation is essentially the same
for all NCS data products; however, the input to the
benchmark calculation differs by data product. The
ECI uses fixed employment weights; the benchmark
adjustment for the ECI is calculated each quarter, currently with the use of 2002 employment counts from
the Occupational Employment Statistics survey. (See
http://www.bls.gov/oes/.) Before December 2006, the
ECEC used only QCEW employment counts for benchmarking; starting with the December 2006 quarter, the
ECEC began using employment data from two BLS
programs—the QCEW and the Current Employment
Statistics (CES) program—for benchmarking. The
CES data are used to update the QCEW data that are
about 6 months old. Combined, data from the two programs provide the appropriate industry coverage and
timeliness needed for the ECEC. Starting with the publication of the March 2007 estimates, the NCS benefits
incidence and key provisions (I&P) series began using
employment data from the two BLS programs as well.
The NCS continues to use employment counts from the
most recent QCEW data to benchmark national, Census
division, and local area wage estimates and for estimates of detailed NCS benefits provisions. (For more
information on ECEC benchmarking, see “Changes
in Calculations for the BLS Employer Costs for Employee Compensation Data, March 2007,” on the Internet at http://www.bls.gov/ncs/ect/sp/ececcalc.pdf.)

NOTE: In NCS benefits and earnings data publications, an
appendix table on survey establishment response in the technical notes shows the number of establishments by sector in
the sampling frame and in the sample; the number responding
to the survey; the number out of scope or out of business;
and the number unable or unwilling to participate in the
survey. A second appendix table shows the estimated number
of workers represented by the survey; this estimate includes
the number of all workers in all establishments within the

9

substantially from expectations in other ways are identified,
the data underlying those estimates are examined in detail to
try to explain the results. When the review staff is convinced
that the data are accurate and that they are based on enough
observations, they designate the data as “fit for use” and the
data are published.
Not all calculated series meet the criteria for publication.
Before any series is published, it is reviewed to make sure
that it meets specified statistical reliability and confidentiality
requirements. The review prevents the publication of a series
that could reveal information about a specific establishment
or have a large sampling error.
The following are links to descriptions of the methods
used to compute estimates for specific NCS products and the
reliability of those estimates:

variance stratum. Choices are not random, but designed to
yield a “balanced” collection of half-samples. For each halfsample, a “replicate” estimate is computed with the same
formula for the regular, or “full-sample,” estimate, except
that the final weights are adjusted. If a unit is in the halfsample, its weight is multiplied by (2 – k); if not, its weight
is multiplied by k. For all NCS publications, k = 0.5, so the
multipliers are 1.5 and 0.5. (Some of the weighting adjustments done as part of the calculation of final weights also are
recalculated for each replicate.)
The BRR estimate of standard error with R half samples is
SE(Yˆ ) 

R
1
(Yˆr – Yˆ ) 2 ,

R(1– k ) 2 r 1

where the summation is over all half-samples r = 1,...,R,
Employment Cost Index (ECI) Series
Yˆr is the rth replicate estimate, and

Employer Costs for Employee Compensation (ECEC)
Series

Yˆ is the full-sample estimate.

Occupational Earnings Estimates
The percent relative standard error data are provided
alongside earnings estimates in NCS earnings and ECEC
publications. ECEC and NCS wage publications display the
standard error as a percentage of the full-sample estimate.

Area-to-Nation and Area-to-Area Pay Comparisons
Incidence and Provisions of Benefits

The percent relative standard error is given by
Employment Cost Index (ECI) Series
The Employment Cost Index (ECI) is a measure of the
change in the cost of labor, free from the influence of employment shifts among occupations and industry categories.
The total compensation series includes changes in wages
and salaries and in employer costs for employee benefits.
The ECI calculates indexes of total compensation, wages and
salaries, and benefits separately for all civilian workers in the
United States (as defined by the NCS), for private industry
workers, and for workers in State and local government, and,
within each of these sectors, by occupational group, worker
attributes, industry group, and establishment characteristic.
Seasonally adjusted series are calculated as well.
Employer costs for employee benefits are collected for
paid leave—vacations, holidays, sick leave, and personal
leave; supplemental pay—premium pay for work in addition
to the regular work schedule (such as overtime, weekends,
and holidays) and for shift differentials, and nonproduction
bonuses (such as yearend, referral, and attendance bonuses);
insurance benefits—life, health, short-term disability, and
long-term disability insurance; retirement and savings benefits—defined benefit and defined contribution plans; and legally required benefits—Social Security, Medicare, Federal
and State unemployment insurance, and workers’ compensation.
The ECI is a Principal Federal Economic Indicator. (Principal Federal Economic Indicators are the major statistical

%RSE  100  SE(Yˆ ) Yˆ .
Data collection and processing errors are mitigated primarily
through quality assurance programs that include the use of
data collection reinterviews, observed interviews, computer
edits of the data (validation), and systematic professional
review of the data. The programs also serve as a training
device to provide feedback to the field economists, or data
collectors, on errors. They provide information as well on the
sources of errors that can be remedied by improved collection
instructions or computer-processing edits. Extensive training
of field economists is conducted to maintain high standards
in data collection.
Once estimates of compensation cost changes, of wage
and compensation cost levels, or of benefit provisions are produced, the estimates are verified, or validated. The focus of
the verification at this stage is a comparison of the estimates
with expectations. Expectations are based on economic conditions, recent trends in similar data, historical relationships
among industries, occupations, union status, region of the
country, types of compensation, and so on. Computer checks
are used to identify anomalies, such as wage changes outside
the historical range. Another set of checks verifies that there
are enough observations supporting each estimate and that
there will be no way in which data from a respondent could
be identified. Once estimates that are anomalies or that differ

10

series that describe the current condition of the economy. For
more detail, see OMB Statistical Policy Directive No. 3, at
http://www.bea.gov/about/pdf/federalregister09251985.
pdf.)

wage level for the cell forward to the current
quarter.
Mt,i

Computation of index series
The ECI is a Laspeyres index. The basic computational
framework for the ECI is the standard formula for an index
number with fixed index weights, modified by the special statistical conditions that apply to the ECI. The text that follows
describes ECI measures of wage changes, but indexes of
changes in benefits and in total compensation (defined by the
NCS as the sum of changes in wages and benefits) are calculated in essentially the same manner.
An index for the ECI is simply a weighted average of the
cumulative average wage changes within each of the ECI
basic cells, with base-period “wage bills” serving as the
fixed weights. A basic cell for the ECI is composed of raw
wage data from a narrowly defined set of workers, sorted by
ownership sector, industry, and occupational groups in which
they work. The ECI cell structure sorts the industry codes
into 1 of 3 ownership sectors: private, State government, or
local government. Workers within private establishments are
sorted into 1 of 58 industry categories that are defined primarily by three-digit NAICS codes. Workers in either State
or local governments are sorted into 13 industry categories;
the government industry categories are as broad as “all
goods-producing industries” and as narrow as “hospitals.”
Each of these private and government industry groups is arrayed across nine aggregate occupational groups, which are
ordered numerically, by their SOC codes. Altogether, there
are 522 private industry occupational cells (58 × 9) and
234 State and local government industry occupational cells
(13 × 9 × 2), for a total of 756 ECI basic cells.
Woo))
For each of these basic cells, a base-period wage bill ((W
is computed, and the wage bill is updated each quarter by
observed rates of change from the ECI survey sample. The
simplified formula for a basic cell is

It  

(Wo ,i M t ,i )

W

can be written as M t ,i  M t – I ,i  Rt ,i ,

where
Rt,i

is the ratio of the current-quarter weighted average wage in the cell to the previous-quarter
weighted average wage in the cell, both calculated in the current quarter from matchedsampled quotes. Using only matched quotes
in the ratio eliminates the inclusion of wage
changes that might be caused by shifting
of workers within establishment jobs. That
is, the ECI sample tracks changes in wages
within establishment jobs and not for individual workers of the establishment. The
weights applied are the “sample weights” of
the survey.

All wage indexes are computed from the following data:

 100,



Average straight-time hourly earnings for six-digit
SOC code occupations, or groups of those occupations, in those sample establishments for which
data are available for both the current and previous
quarters. These earnings are called matched quotes.
In addition to being identified by the six-digit SOC
code, a quote within an establishment is identified
from quarter to quarter by its union status, full-time
or part-time status, method of pay (time or incentive),
and job level.



Employment for each of the basic cells, enumerated
with the use of 2002 employment data from the BLS
OES survey.



Sample weights that reflect both employment in each
establishment or occupation surveyed and the probability of selection of that establishment or occupation.

o ,i

The index computation for a calendar quarter involves five
principal steps:

where
It

is the symbol for the index at period t.

Wo,i

is the estimated base-period wage bill for the
ith cell. The wage bill is the average wage
of workers in the cell at the base period (0),
times the number of workers represented by
the cell. For the ECI, the number of workers
represented by the cell is held fixed.

1. Sampled occupation (quote) weights are applied
to the average occupational hourly wage for every
quote in a sampled establishment that has reported
both current-quarter and previous-quarter wage
data. These data are used to calculate a weighted
average wage for each basic cell (that is, for each
occupational group within each industry) for the
current and previous survey periods.

Mt,i

is the multiplicatively accumulated average
wage change in the ith cell from time 0 (the
base period) to time t (the current quarter). In
essence, Mt,i projects the base-period average

2. The ratio of the current-quarter to the previousquarter weighted average wage is then calculated
for each cell i. This ratio (Rt,i ) is used as an estimate
of the current-quarter (t) wage change for that
11

basic cell and is multiplied by the previous-quarter
(t – 1) cumulative average wage change for the cell
(Mt – 1,i ). The product Mt,i is a measure of the cumulative percentage wage change in the cell since the
base period.

change in the union wages in the cell, and similarly for the
nonunion sector. Therefore, the relative employment of the
union sector in each cell is not held constant over time. Since
the weights of the region, union, and less-incentive-workers
subcells are allowed to vary over time, the indexes for these
series are not strictly comparable to those for the aggregate,
industry, and occupation series.

3. This measure of cumulative percentage wage change
is multiplied by the base-period wage bill (Wo,i ) to
generate an estimate of the current-quarter wage bill
for the cell.

Recent changes to the index computations
For a fixed-weighted index to remain economically relevant
over a span of periods, it is necessary on occasion to make
changes to the computations of the indexes. Beginning with
the release of the March 2006 data, the following major
changes were made in the way the ECI is calculated:

4. Both the current-quarter and previous-quarter wage
bills are then summed over all cells within the scope
of the index. For example, for the manufacturing
wage index, the wage bills would be summed across
all cells in manufacturing.
5. The summed current-quarter wage bill (∑Wo,i M t,i)
is divided by the summed base-period wage bill
(∑Wo,i). The result, when multiplied by 100, is the
current-quarter index (I t). That index is divided
by the previous-quarter index (It – 1) to provide a
measure of quarter-to-quarter change, referred to as
an “index link relative.”
Computations for the occupational and industry group indexes follow the same procedures as those for the overall indexes, except for summation. For example, for an index for a
broad occupational group, the wage bills are summed across
all cells which are a subset of that occupational group, with
indexes for industry groups calculated analogously.
For the private industry nursing care facilities indexes,
however, some caution is warranted. Those indexes are estimated with the use of fixed-employment weights derived
from staffing patterns estimated from the four-digit NAICS
industry group 6231, nursing care facilities, a subindustry
of the larger industry group, nursing and residential care
facilities (NAICS 623). The basic-cell cost weights for the
nursing care facilities were constructed after the basic-cell
fixed weights for 623 were computed and prepared for use
in the index computation system. Consequently, the fixed
weights for the four-digit industry 6231, nursing care facilities, were not directly constructed as linear disaggregates
of NAICS 623. Because the nursing care facilities indexes
are not linearly associated with their higher level aggregates,
they are not strictly comparable to those aggregates.
Computation procedures for measures of change in the
regional, union and nonunion, and excluding-incentiveworkers series differ from those of the national wage indexes
because the sample is not large enough to hold constant the
wage bills at that level of detail. For these subseries, each
quarter the prevailing distribution in the sample (for example, between union and nonunion attributes within each
ownership/industry/occupation cell) is used to apportion the
previous-quarter wage bill in that cell (for example, between
the union and nonunion series). The portion of the wage bill
assigned to the union sector is then moved by the percentage



Indexes were rebased to December 2005 = 100 (from
June 1989 = 100).



New fixed employment weights were introduced,
using 2002 employment counts from the Bureau’s
Occupational Employment Statistics survey;



Industry classification was changed from the Standard
Industrial Classification (SIC) system to the North
American Industry Classification System (NAICS);



Occupational classification was changed from the
Occupational Classification System of the 1990
Census of Population to the Standard Occupational
Classification (SOC); and



Imputation methods were changed.

In 2009, the NCS began publishing continuousseries historical information, to assist data users in
locating ECI data on occupational and industry series
that ran before and after the changes just noted.
(See the subsection “Publication of index series,” to
follow; for more information on all the changes, see
http://www.bls.gov/ncs/ect/sp/ecsm0001.htm.)
Reliability of the index estimates
To assist users in ascertaining the reliability of series,
standard errors for all ECI estimates (excluding seasonally
adjusted series) are available on the BLS Web site at
http://www.bls.gov/ect/ectvar.htm shortly after publication
of the news release.
Publication of index series
The ECI publishes indexes of total compensation, wages and
salaries, and benefits separately for all civilian workers in
the United States (as defined by the NCS), private industry
workers, and State and local government workers, and,
within each of these sectors, by occupational group, worker
attributes, industry group, and establishment characteristic.

12

More than 400 unique index series and their associated quarterly and 12-month changes in employers’ costs are published
quarterly. Seasonally adjusted series are published as well.
In 2008, ECI estimates were published for 14 selected
local areas for the first time; they are now published quarterly. A 15th local area was added in 2009. Local area data
are limited to estimates for total compensation and for wages
and salaries, for 12-month periods beginning with reference
date December 2005 and for subsequent 12-month periods
ending in March, June, September, and December. The data
are available at http://www.bls.gov/ect, as well as in news
releases, for each of the 15 local areas. (For additional information, see “BLS Introduces New Employment Cost Indexes
for 14 Metropolitan Areas,” on the Internet at http://www.
bls.gov/opub/cwc/cm20080922ar01p1.htm. ECI estimates
for the four Census regions and nine Census divisions are
included in tables 6 and 10 of the national “Employment Cost
Index” news release.)
Historical current-dollar ECI series that use industry categories based on the Standard Industrial Classification (SIC)
System and classify jobs into occupational classifications according to the Occupational Classification System (OCS) of
the Census Bureau are available dating from the first publication of each series through December 2005 at http://www.
bls.gov/web/echistry.pdf. ECI current-dollar series based on
the 2002 and 2007 North American Industry Classification
Systems (NAICS) and the 2000 Standard Occupational Classification (SOC) also are available beginning in March 2001,
with December 2005 = 100 as the base period, at http://
www.bls.gov/web/echistrynaics.pdf.
In addition, historical constant-dollar ECI series derived
from the Consumer Price Index for All Urban Consumers
(CPI-U) are available. The constant-dollar series are calculated by converting the CPI-U to the same base as the ECI.
The ECI for each quarter is then divided by the converted
CPI-U for the same reference period. The CPI-U U.S. City
Average All Items is used to compute all series except for
the regional estimates, which use corresponding CPI regional
data. Historical constant-dollar ECI series that use industry
categories based on the SIC and that classify occupations according to the OCS are available, dating from the first publication of each series through December 2005, except for seasonally adjusted series, at http://www.bls.gov/web/ecconst.
pdf. ECI constant-dollar series based on the 2002 and 2007
North American Industry Classification Systems (NAICS)
and the 2000 Standard Occupational Classification (SOC)
also are available beginning in March 2001, with December
2005 = 100 as the base period, at http://www.bls.gov/web/
ecconst-naics.pdf.
Historical continuous-series listings are available for all ECI
occupational and industry series that existed before the March
2006 revisions and continued afterwards. This historical listing,
which uses December 2005 = 100 as the base period, is available
online at http://www.bls.gov/web/eci/ecicois.pdf.
NOTE: Official ECI series—those designated for use by agencies
of the Federal Government—are based on the SIC and OCS through
December 2005 and on NAICS and SOC from March 2006 forward.

Seasonal adjustment
Over the course of a year, rates of change in the cost of
wages and benefits, as measured in the Employment Cost
Index (ECI), reflect events that follow a more or less regular
pattern. These events include expansions and contractions
of economic activity that occur in specific periods of the
year, such as increased work in the construction industry
during warm weather. For another example, ECI 3-month
rates of change for wage-and-benefit costs in State and local
governments, which include State and local education as a
substantial part, show larger rates of increase in September,
reflecting new contracts associated with the beginning of new
school sessions. Such regular patterns in an economic time
series are typically referred to as seasonal effects. The process
of estimating and removing these effects from an economic
series is called seasonal adjustment. Seasonal adjustment
makes it easier for analysts to observe long-run and other
movements in an economic time series, exclusive of seasonal
effects. Economists and other researchers are particularly
interested in observing cyclical and long-run movements of
economic series to better understand the economic behavior
of various sectors of the economy.
In evaluating changes in a seasonally adjusted series, it
is important to note that seasonal adjustment is an approximation based on past experience. Seasonally adjusted estimates have a broader margin of error than the original data
on which they are based, because they are subject to errors
associated with seasonal factor estimation in addition to sampling and nonsampling errors.
Seasonal adjustment is performed with the X-12 ARIMA
program developed by the time-series staff in the Statistical
Research Division of the Census Bureau, U.S. Department
of Commerce. The X-12 ARIMA program includes enhancements to the X-11 variant of the Census Method II seasonal
adjustment program, as well as the X-11 ARIMA program
developed by Statistics Canada.
At the beginning of each calendar year, seasonal adjustment factors are calculated for use during the coming
year. The seasonal factors for the coming year are published
on the BLS Web site. Revisions of seasonally adjusted indexes and 3-month percent changes for the most recent 5
years also are published on that Web site.
ECI series are seasonally adjusted by either direct or indirect methods. In the direct method, an original or unadjusted
index is divided by its seasonal factor. In the indirect method
(also called composite seasonal adjustment), the seasonally
adjusted index is calculated as a weighted sum of seasonally
adjusted index components. (For more information about seasonal adjustment issues, see “Transitional Employment Cost
Indexes for seasonal adjustment,” on the Internet at http://
www.bls.gov/opub/mlr/2008/04/art3full.pdf. For more information on the ECI conversion to new industry and occupational classification systems, see “Seasonal adjustment in the
ECI and the conversion to NAICS and SOC,” on the Internet
at http://www.bls.gov/opub/mlr/2006/04/art3full.pdf.)
The following Monthly Labor Review articles also are
informative: “Introducing 2002 weights for the Employment

13

Cost Index,” April 2006, pp. 28–32, on the Internet at http://
www.bls.gov/opub/mlr/2006/04/art5full.pdf; and “Accounting
for missing data in the Employment Cost Index,” Monthly Labor
Review, April 2006, pp. 22–27, on the Internet at http://www.bls.
gov/opub/mlr/2006/04/art4full.pdf.

effect for Fiscal Years 2001–06.

ECI data uses and limitations
The ECI has been designated a Principal Federal Economic Indicator by the Office of Management and Budget. It is the only
measure of labor costs that treats wages and salaries and total
compensation consistently and that provides consistent subseries
by occupation and industry. The ECI is used by the Federal Reserve Board to monitor the effects of fiscal and monetary policies
and to formulate those policies. It enables analysts and policymakers to assess the effects of labor cost changes on the economy,
both in the aggregate and by sectors. The ECI is particularly important in studies of the relationships among prices, productivity,
labor costs, and employment. The ECI also is used to determine
increases in Medicare payments to hospitals and doctors and as a
labor cost escalator in long-term contracts.
In determining data to be used in contract negotiations, it is
important to note that differences by bargaining status may be
due to factors other than union status, such as occupational and
industry mix. An important consideration in choosing a series
for escalation is the sampling error. (For more information, see
http://www.bls.gov/ect/escalator.htm.)
To update wage data from any source to the most recent
quarter, see “Aging Wage Data Using the Employment Cost
Index,” on the Internet at http://www.bls.gov/opub/cwc/
cm20080122ar01p1.htm.



US economic policy decisions. The Federal Reserve
uses the ECI as a major economic indicator for
monetary policy decisionmaking.



Escalator clauses in collective bargaining contracts.
Wage escalator clauses can allow for a pay increase
that is dependent upon the ECI.



Escalator clauses in U.S. government contracts.
Various ECI series are used as labor cost escalators
in U.S. government contracts. For example, the
production and logistics division of the Department
of Defense uses both the wages and salaries cost
series and the benefits costs series as escalators in
numerous defense contracts, including contracts
for computer research. The Contracts Division of
the Environmental Protection Agency uses the total
compensation private industry/white-collar series as
the designated cost escalator in at least 10 contracts
for systems design services.



Adjustments to Medicare reimbursements for hospital,
physician, and related services. The U.S. Department
of Health and Human Services, Centers for Medicare
and Medicaid Services, uses data from the ECI to
determine allowable increases in reimbursements
to hospitals, skilled-nursing facilities, home health
care organizations, and physicians under Medicare’s
Prospective Payment Systems (PPS). The PPS
designates the level of reimbursement for Medicarecovered services, in accordance with the diagnosis and
geographic location of care. PPS reimbursements are
adjusted annually on the basis of a number of factors,
including changes in compensation for medical and
related personnel. (For more information, see “The
Employment Cost Index and the Impact on Medicare
Reimbursements,” on the Internet at http://www.bls.
gov/ncs/ect/medicare2008_impact.pdf.)



School district property taxes. The Pennsylvania Department of Education uses the ECI as a measure of
inflation to determine the maximum increase allowed
for school district property taxes. Pennsylvania began
using the index in 2005 after the General Assembly
passed, and the Governor signed into law, Act 72,
titled “The Homeowner Tax Relief Act.” This tax
reform initiative was designed to reduce the property
tax burden on Pennsylvania residents.



Economic price adjustments in long-term purchase
contracts. Long-term purchase contracts may specify
that the ECI is to be used to adjust the labor cost
portion of contracts.



Ecalator clauses in foreign government contracts.

Examples of ECI data uses
 Federal pay adjustments. The ECI is used to
determine Federal white-collar pay adjustments
under the Federal Employees Pay Comparability Act.


Active-duty military pay adjustments. In November
2003, Congress passed a permanent law requiring that
annual basic pay increases for active-duty military
personnel be indexed to the annual increase in the
ECI for Fiscal Year 2007 and beyond. (Section 602 of
the Fiscal Year 2004 National Defense Authorization
Act; and P.L. 108-136, November 24, 2003; 117 Stat.
1498, amending 37 USC 1009.) However, each year
since Fiscal Year 2004, Congress has enacted a special
“National Defense Authorization Act” that sets the
annual military increase at a level superseding that
of 37 USC 1009. Prior to this legislation, the wages
of active-duty military personnel had been linked to
the annual percent increase in the General Schedule
(GS) Federal civil service pay scale under the Federal
Employees Pay Comparability Act of 1990. In 1999,
with a widening pay gap between military and private
industry pay, Congress enacted legislation that tied
annual military pay increases to the annual increase
in the ECI plus 0.5 percent. The legislation was in

14

published by the CES program is typically used.
For private and government establishments, the employment data were apportioned on the basis of the sampling weights assigned to the Employment Cost Index (ECI)
sample. The ECI, which measures the change in employer
costs for employee compensation, is calculated with fixed
2002 employment counts in order to prevent employment
shifts among occupations and industries from influencing
the measurement. Therefore, changes over time in the ECEC
series will differ from those in the ECI.
Historical ECEC data appear in three listings, all available
at http://www.bls.gov/ect/#tables. The first historical listing
covers data for the March reference periods from 1986 to
2001. These data use the Standard Industrial Classification
(SIC) and Occupational Classification System Manual
(OCSM) classification systems. The second listing contains
data for the March, June, September, and December reference
periods from March 2002 to December 2003. These data also
are based on the SIC and OCSM. The final listing includes
data for March 2004 to the current reference period. These
data are based on the NAICS and the SOC classification
system. Beginning with the March 2004 quarter, historical
data based on the NAICS and the 2000 SOC system became
available. The new historical tables are available on the Internet site http://www.bls.gov/ncs/ect/home.htm or upon
request. Information on how costs are calculated appears in
“Measuring Trends in the Structure and Levels of Employer
Costs for Employee Compensation,” on the Internet at http://
www.bls.gov/opub/cwc/archive/summer1997art1.pdf.
ECEC estimates are shown as costs per hour worked for
total compensation (wages and benefits), expressed both as
dollar amounts and as percentages of compensation. ECEC
estimates are computed for various costs c, including wages,
individual benefits, combinations of benefits, total benefits,
and total compensation (total wages plus total benefits).
The ECEC estimates of percent of total compensation
are calculated from cost aggregates and then rounded to
the published level of precision. This method provides the
most precise estimates of the percent of total compensation;
however, estimates calculated from the published cost estimates may differ slightly from those calculated from the unpublished cost aggregates.
The formula for the mean hourly cost c for domain D is

Foreign governments sometimes use ECI series in
contracts with U.S. firms. For example, the Swiss
government uses the series on durable-goods manufacturing as a wage escalator in a contract with a U.S.
firm that makes computers.


Economic consulting and forecasting. Various ECI
series are used in models for economic forecasting,
including forecasting ECI values for clients’ use in
budgeting and other activities. ECI series also are
used in developing inflation indexes of personnel
costs and other costs for elementary and secondary
schools and for colleges.

Employer Costs for Employee Compensation
(ECEC) Series
The Employer Costs for Employee Compensation (ECEC)
series measures the average cost to employers for wages
and salaries, and for benefits, per employee hour worked.
The ECEC series provides quarterly data on employer costs
per hour worked for total compensation, wages and salaries,
total benefits, and the following types of benefits: paid
leave—vacations, holidays, sick leave, and personal leave;
supplemental pay—premium pay for work in addition to
the regular work schedule (such as overtime, weekend, and
holiday work) and for shift differentials, and nonproduction
bonuses (such as yearend, referral, and attendance bonuses);
insurance benefits—life, health, short-term disability, and
long-term disability insurance; retirement and savings benefits—defined benefit and defined contribution plans; and legally required benefits—Social Security, Medicare, Federal
and State unemployment insurance, and workers’ compensation. Cost data are presented both in dollar amounts and as
percentages of total compensation. The ECEC uses current
weights to reflect the composition of today’s labor force.
Beginning with the December 2006 data, the NCS
implemented new benchmarking procedures for ECEC
estimates. Current employment weights are used to calculate
cost levels. The weights are derived from two BLS programs:
the Quarterly Census of Employment and Wages (QCEW) and
the Current Employment Statistics (CES) survey. Combined,
these programs provide the appropriate industry coverage and
currency of data needed to match the ECEC. All other NCS
data products are benchmarked with QCEW data only. (For
more information, see “Changes in Calculations for the BLS
Employer Costs for Employee Compensation Data, March
2007,” at http://www.bls.gov/ncs/ect/sp/ececcalc.pdf.)
In most instances, private industry employment weights
used in the ECEC are total employment estimates for twodigit industry groups, such as utilities (NAICS 22) or wholesale trade (NAICS 42). In a few cases, more detailed private
industry employment weights are used. These cases include
four-digit educational establishments—elementary and secondary schools (6111), junior colleges (6112), and colleges
and universities (6113)—as well as the six-digit aircraftmanufacturing industry (336411). For State and local governments, a more aggregated level reflecting the level of detail

YˆcD

W Y

W
qD

qD

'
q cq
'
q

,

where
D
Wq'

15

is the domain of interest,
is the final quote weight for quote q, calculated
as in the description of the final quote weight
in the section on the calculation of wage
levels, with one additional factor included to

account for changes in the employment distribution, and
Yˆcq

When respondents do not provide all the data needed,
a procedure for assigning missing values is used. This imputation procedure is comparable to that used for the Employment Cost Index (ECI). (For a description, see “Accounting for missing data in the Employment Cost Index,”
in the April 2006 issue of the Monthly Labor Review, on the
Internet at http://www.bls.gov/opub/mlr/2006/04/art4abs.
htm).

is the mean hourly cost c for quote q.

In addition, the formula for the mean hourly cost c as a percentage of total compensation is
PcD 

YˆcD
 100,
Yˆ

An example of ECEC data use
 Costs associated with employee compensation. The
International Union of United Automobile, Aerospace
and Agricultural Implement Workers of America
(UAW) has posted tables using ECEC data on its Web
site. The data are given in “The Union Advantage at a
Glance,” which has appeared every quarter since December 2006. The tables highlight wage and benefit
data from the ECEC for union and nonunion workers
in private industry and for goods-producing and
service-providing industries. (See http://www.uaw.
org/facts/09/unionadvantage1208.pdf. NOTE: Links
to non-BLS Internet sites are provided for your convenience and do not constitute an endorsement.)

TD

where
YˆcD

is the mean hourly cost c for domain D, as
before, and

YˆTD

is the mean hourly cost for total compensation
for domain D.

ECEC data use and limitations
Differences between the State and local government and
private industry sectors stem from factors such as variation
in work activities and in occupational structures. Manufacturing and sales, for example, make up a large part of
private-industry work activities but are rare in State and local
government. Professional and administrative support occupations (including teachers) account for two-thirds of the State
and local government workforce, compared with less than
one-half of private industry. A detailed examination of differences in compensation levels and trends between private
industry and State and local government is found in “Cost
of Employee Compensation in Public and Private Sectors,”
Monthly Labor Review, May 1993, on the Internet at http://
www.bls.gov/opub/mlr/1993/05/contents.htm, and in
“Compensation Cost Trends in Private Industry and State
and Local Governments,” Compensation and Working Conditions, fall 1999, at http://www.bls.gov/opub/cwc/archive/
fall1999art2.pdf.
For more information on the calculation procedure, see
“Changes in Variance Estimation Calculations for the BLS
Employer Costs for Employee Compensation Data, March
2007,” at http://www.bls.gov/ncs/ect/sp/ececvmet.pdf.
The relative standard errors (RSEs) for all estimates are
available at http://www.bls.gov/ncs/ect/#tables shortly after
the quarterly news release is issued.
For a detailed explanation of how to use standard error
data to analyze differences in changes over time, see “Analyzing Year-to-Year Changes in Employer Costs for Employee Compensation,” Compensation and Working Conditions, spring 1998, at http://www.bls.gov/opub/cwc/
archive/spring1998art3.pdf. This article supplements an
article titled “Explaining the Differential Growth Rates of the
ECI and ECEC” from the summer 1997 issue of Compensation and Working Conditions. The latter article, available at
http://www.bls.gov/opub/cwc/archive/summer1997art2.
pdf, examines how differences in the construction of these
measures contribute to differing trends.

Occupational Earnings Estimates
Data on earnings of civilian workers and for workers in
private industry and State and local government, the two
components of the civilian sector as defined by the NCS,
are published for the Nation, Census divisions, and selected
Metropolitan Statistical Areas (MSAs). Earnings data are
presented as mean and median hourly, weekly, and annual
earnings (along with hours worked weekly and annually);
as percentiles; by selected worker attributes (such as full
time and part time, and union and nonunion); and by establishment characteristics (such as number of employees and
geographic area).
To calculate earnings for various periods (hourly, weekly,
and annual), the NCS collects data on work schedules. For
hourly workers, scheduled hours worked per day and per
week, exclusive of overtime, are recorded. The number of
weeks worked annually is determined as well. For salaried
workers, field economists record the typical number of
hours actually worked; salaried workers who are exempt
from overtime provisions often work beyond the assigned
work schedule. Estimates for hours worked are given in the
NCS earnings publications. Earnings estimates for aircraft
pilots, flight engineers, and flight attendants include flight
pay and flight hours only; these estimates may not reflect
the total earnings and hours worked. (For more information
on work schedules, see http://www.bls.gov/opub/cwc/
cm20080722ar01p1.htm.)
The NCS publishes earnings estimates for occupational
groups and detailed occupations, and earnings estimates also
are presented by work level. Work levels represent a ranking
of the duties and responsibilities within an occupation, and
the latter permit comparisons of wages across occupations.
The NCS also provides earnings estimates by combined work
level.
16

The broad groups and the combined work levels are as
follows:
Group I

Group II

Group III

Work
Levels 1–4

Work
Levels 5–8

Work
Work
Levels 9–12 Levels 13–15

(2) Mean weekly earnings

 (Y X W H A )
 ( X W H A )
ql

qD l

Group IV

qD l

ql

ql

q

q

q

q

q

q

3) Mean hourly earnings
Earnings formulas
The following is a list of earnings estimates provided by the
NCS and the formulas that describe them:

 (Y X W A )
 ( X W A )

qD l

(1) Mean annual earnings

qD l

 (Y X W )
 ( X W )

qD l

qD l

ql

ql

ql

ql

ql

ql

q

q

q

q

(4) Total employment

q

 ( X

q

qD l

Wq )

ql

In the preceding formulas,

Exhibit 1. Concepts and Definitions of Wages
and Salaries
Wages and salaries, or earnings, are defined as regular
payments from the employer to the employee as compensation for straight-time hourly work or for any salaried work performed. The following components are
included as part of earnings:
Incentive pay, including commissions, production
bonuses, and piece rates
Cost-of-living allowances
Hazard pay
Payments of income deferred because of participation in a salary reduction plan
Deadhead pay, defined as pay given to transportation workers returning in a vehicle without
freight or passengers
The following items are not considered part of
straight-time earnings, and no data on such items are
collected by the NCS:
Uniform and tool allowances
Free or subsidized room and board
Payments made by third parties (for example, tips)
On-call pay
The following forms of payments are considered
benefits and not part of straight-time earnings:
Payments for shift differentials, defined as extra
payment for working a schedule that varies from
the norm, such as night or weekend work
Premium pay for overtime, holidays, and weekends
Bonuses not directly tied to production (such
as Christmas and profit-sharing bonuses)

D

is the domain of interest (for example, occupation ×
level or occupational group × level),

q

is the quote,

l

the wage record,

Yql

is the annual wage rate in formula (1), the weekly
wage rate in formula (2), and the hourly wage rate
in formula (3) of a particular worker or group of
workers in a particular quote,

Xql

is the number of workers who receive a particular
earnings rate,

Hq

is the number of weekly hours paid to a particular
worker and is assumed to be the same for each
worker in a quote (NOTE: Weekly hours paid are
used only in computing average hourly earnings),

Aq

is the number of annual weeks worked by a
particular worker, which is assumed to be the same
for each worker in a quote, and

Wq

is the individual weight.

The individual weight is calculated by dividing the final
quote weight by the number of employees in the quote.
The final quote weight for local area occupational earnings
estimates is the product of (1) the reciprocal of the

17

probability of selecting the establishment, given the set of
sample areas; (2) a correction factor to adjust for cases in
which data are collected for a different number of employees
than data should be collected for; (3) the establishment
nonresponse adjustment factor; (4) the occupational
nonresponse adjustment factor; and (5) the probability of
selection of an occupation interval—that is, the number of
eligible employees divided by the number of occupational
selections. For national and Census division estimates, the
final quote weights are a product of the same factors and one
additional factor: the reciprocal of the probability of selecting
the sample area in which the establishment is located. The
benchmark factors are aggregated for geographical areas,
Census divisions, and national earnings computations. The
individual weight contains an additional benchmark factor to
account for changes in the employment distribution.

Relative standard errors are provided for each of the
earnings estimates. (For information on relative standard
errors and standard errors calculated by the NCS, see the
section “Computations of Compensation Measures and
Reliability of Estimates for All NCS Data Products.”)
Examples of NCS earnings data use
 Negotiating wage contracts. Mean and median
wages for occupations and for occupational groups
in an area can be used as a point of departure for
wage negotiations. If certain occupations are not
published, data on “benchmark occupations”—those
occupations which may be common in a number
of establishments—may be used to compare an
employer’s pay with pay in the area.

(5) Hourly earnings percentiles
Hourly earnings percentiles in NCS wage publications are
computed from earnings reported for individual workers
in sampled establishment jobs and their scheduled hours
of work. Establishments in the survey are asked to report
earnings of individual workers for each sampled job. For
the calculation of percentile estimates, the individual-worker
hourly earnings are appropriately weighted and then arrayed
from lowest to highest within the establishment. If the
establishment reports only the average earnings of all of the
workers in a sampled job, rather than the earnings of each
worker in the sampled job, the job’s average hourly earnings
are appropriately weighted for the number of workers in the
sampled job. Then the resulting average is arrayed along
with all other individual-worker earnings and occupational
average earnings, from lowest to highest, within the
establishment. The published 10th, 25th, 50th, 75th, and 90th
percentiles designate position in the earnings distribution
within each published occupation. At the 50th percentile—
the median—half of the hours are paid the same as or more
than the rate shown and half are paid the same as or less
than the rate shown. At the 25th percentile, one-fourth of the
hours are paid the same as or less than the rate shown. At the
75th percentile, one-fourth of the hours are paid the same as
or more than the rate shown. The 10th and 90th percentiles
follow the same logic.
Earnings publications
The NCS annually publishes national, Census division, and
local area occupational earnings estimates on mean hourly
earnings, mean and median weekly and annual earnings,
and weekly and annual hours, for civilian workers (as
defined by the NCS), private industry workers, and State
and local government workers. Occupational earnings data
are published for some major and minor industry groups, by
worker attributes (such as collective bargaining status) and by
establishment characteristics (such as number of workers in
the establishment). Percentile earnings by worker attributes
and establishment characteristics also are published.

18



Determining compensation rates. Private companies,
labor organizations, and government agencies use the
NCS to help determine compensation rates for pay
ranges or merit increases.



Determining prevailing wage rates. Legislation such
as the Service Contract Act and the Davis-Bacon
Act require employers to pay the “prevailing wage
rate” of the area for certain types of work. In such
cases, a Federal Government agency, such as the
Employment Standards Administration (ESA), may
use BLS survey data as a tool in determining the
prevailing rate. The survey results, however, are not
automatically “the prevailing rate.” BLS neither sets
nor enforces prevailing wage rates.



Setting compensation rates for workers with different
duties and responsibilities. The NCS frequently
is used to help evaluate wage rates for different
job levels of an occupation. Job levels represent
the different duties and responsibilities within the
occupation. Levels are derived from generic standards
used for all occupations or for certain occupational
groups, so occupational pay can be compared at each
job level. A common point-factor analysis is applied
to each occupation to measure the requirements
of the position and derive the job levels. Each job
selected can be slotted into a work level based on nine
factors: knowledge, supervision received, guidelines,
complexity, scope and effect, personal contacts,
purpose of contacts, physical demands, and work
environment.



Comparing geographical area wages. Frequently,
private companies use wage data to identify areas for
expansion or relocation. Individuals find wage data
useful for choosing an area to seek employment.



Evaluating wage distributions. Wages are presented in
many formats, including percentiles (10th, 25th, 50th,

in pay levels. To perform this assessment, a test of statistical
significance is conducted.
The test constructs a 90-percent confidence interval which
assumes that the given area’s true pay level is equal to the
Nation’s or to another area’s. The confidence interval is constructed so that there is a 90-percent probability that the pay
relative calculated from any one sample is contained within
the confidence interval. If, from a single sample, a calculated
pay relative falls within the confidence interval, then the pay
relative is not statistically significant and the hypothesis that
the area’s true pay level is equal to the Nation’s or to another
area’s is accepted. However, if the pay relative falls outside
of the constructed confidence interval, then the pay relative
is statistically significant at the 10-percent level. In that case,
the hypothesis that the given area’s pay level is equal to the
pay level for the Nation or some other comparison area is rejected and one can conclude with reasonable confidence that
the true pay level is different from that in the base area.
Regression models, such as the ones used in the pay relative methodology, are subject to specification error. The significance test does not measure specification error. However,
care was taken to minimize this form of error by an extensive
search across specifications for the model that performs
best in terms of predictive accuracy. (For more details, see
Maury B. Gittleman, “Pay relatives for metropolitan areas in
the NCS,” Monthly Labor Review, March 2005, pp. 46–53,
on the Internet at http://www.bls.gov/opub/mlr/2005/03/
art4full.pdf; and “Pay Relatives for U.S. Census Regions
and Divisions, 2006,” on the Internet at http://www.bls.gov/
opub/cwc/cm20080826ar01p1.htm.)

75th, and 90th). Percentiles describe the distribution
of an occupation’s employment by the average wage
rates for its jobs.
 Paying market wage rates. Many users want to pay
at or above the mean wage rate to attract top-quality
professionals into a job that is hard to fill. Users can
access data on percentiles to target a specific value at
which to compete.


Federal pay adjustments. Under the Federal Employees Pay Comparability Act, wage data are used
to determine pay adjustments by locality for Federal
white-collar workers.

Area-to-Nation and Area-to-Area Pay Comparisons
The NCS publishes area-to-Nation and area-to-area pay
comparisons, also known as pay relatives, for Metropolitan
Statistical Areas. The pay relatives are calculated for civilian
workers in each of nine major occupational groups. For the
NCS, civilian workers are composed of workers in private industry and State and local government. The estimates are expressed as the ratio of workers’ earnings in one area relative
to the earnings of workers in a base area, which can be the
Nation or another local area. Multivariate regression modeling controls for many interarea differences in employment
composition. NCS annually publishes area-to-Nation pay
relatives and area-to-area pay relatives. Area estimates that
differ significantly from the national average are noted as
such.
Multivariate regression analysis controls for the following
10 factors:


Occupational type



Industry type



Work level



Full-time/part-time status



Time/incentive status



Union/nonunion status



Ownership type



Profit/nonprofit status



Establishment employment



Payroll reference date

Incidence and Provisions of Benefits
The NCS collects and annually publishes data on the incidence of employer-provided benefits and on the provisions
(terms) of employee benefit plans, for civilian workers (as
defined by NCS), workers in private industry, and State and
local government workers. The employer-provided benefits
data include the following:

Changes in pay relatives from year to year do not necessarily
imply changes in underlying economic conditions. Because
the NCS is a sample survey, pay relatives are subject to
sampling error, which means that they may differ from the
true pay relatives one would derive from sampling the entire
population. It is important to assess whether area-to-area or
area-to-Nation differences are likely to be the result of sampling error or whether they are attributable to true differences

19



Health care (medical, dental, vision, and prescription
drug plan coverage, and employee and employer
premiums for single coverage and family coverage)
and the percentage of establishments offering health
benefits;



Retirement plan coverage (defined benefit and defined
contribution) and the percent of establishments
offering retirement benefits;



Life, short-term disability, and long-term disability
insurance coverage;



Paid leave (sick, vacation, jury, personal, and family),
paid holidays, unpaid family leave, and nonproduction
bonuses and stock options;



Health promotion benefits;



Pretax benefits; and



“Quality of life” benefits, such as long-term care
insurance, a flexible-workplace option, and subsidized
commuting.

number of workers with access to the plan, times
100 and rounded to the nearest 1 percent. Because
the computation of takeup rates is based on the
number of workers collected, rather than the rounded
percentage estimates, the takeup rates in published
tables may not equal the ratio of participation to
access.

In addition, NCS provides more extensive, detailed data on
provisions for two major benefit areas: health insurance and
retirement plans.



Medical premiums. Estimates of employer and employee
medical premiums include participants in all medical plans,
with calculations for both single and family coverage. The
calculations are based, not on actual decisions regarding
medical coverage made by employees within the occupations, but rather on the assumption that all employees in the
occupation have identical coverage.

Establishment offering a benefit. The concept of
benefits “offered” currently is used in terms of the
percent of all establishments that make a benefit
available for use.

For a list of benefits terms used by the NCS and their definitions, see National Compensation Survey: Glossary of Employee Benefits Terms (published July 2010), available on the
Internet at http://www.bls.gov/ncs/ebs/glossary20092010.
htm and http://www.bls.gov/ncs/ebs/glossary20092010.pdf.

Leave benefits for teachers. Primary, secondary, and special
education teachers typically have a work schedule of 37 or
38 weeks per year. Because of this work schedule, they generally are not offered vacation or holidays. In many cases, the
time off during winter and spring breaks during the school
year is not considered as vacation days for the purposes of
this survey.

Formulas used to calculate NCS estimates of benefits
Access. The formula for the percentage of employees with
access to a benefit area such as life insurance, for domain
D, is

The NCS measures of employer-provided benefits are as
follows:

AD
where



Incidence of benefits. The percent of all workers
that are provided a particular benefit plan. Incidence
can refer to either rates of access to, or rates of
participation in, a benefit plan.



Provisions of benefits. The terms of a benefit plan.
For example, a medical plan might charge a $20
copayment for a doctor’s office visit.



Access to a benefit. Employees are considered to
have access to a benefit if the benefit is available for
their use. Access is expressed as a percentage of all
workers with access.





W X

W
qD

qD

'
q

q

'
q

 100,

D

is the domain of interest,

Wq'

is the final quote weight for q, calculated as
described in the previous section on the calculation
of ECEC estimates, and

Xq

is 1 if the quote has access to the benefit being
estimated and 0 otherwise.

Participation. The formula for the percentage of employees
participating in a benefit area such as medical care, for
domain D, is

Participation in a benefit plan. Employees in
contributory plans are considered as participating
in an insurance or retirement plan if they have paid
required contributions and fulfilled any applicable
service requirements. Employees in noncontributory
plans are counted as participating regardless of
whether they have fulfilled the service requirements.

ID

W P

W
qD jq

qD

'
q qj

'
q

 100,

where

Takeup rates. Takeup rates are the percentages of
workers with access to a plan who participate in
the plan. Takeup rates are computed as the number
of workers participating in a plan, divided by the

20

D

is the domain of interest,

Wq'

is the final quote weight for quote q,

calculated as described in the previous section on
the calculation of ECEC estimates, and

Pqj

is the percentage of workers in quote q who are
participating in plan j.

Other estimates of incidence, such as the percentage of participants in a benefit area or subset of a benefit area, can be
computed in a similar manner, in which the base includes only
those workers who participate in the benefit. For example,
to calculate the percentage of medical insurance participants
in domain D participating in fee-for-service plans, a ratio
is calculated such that the denominator is the same as the
numerator of the previous formula and the numerator is of the
same form as well, except that the summation is restricted to
those participants in fee-for-service plans.

g

= occupation within establishment i,

j

= plan in occupation g in establishment i,

WPEigj

= weighted plan employment of record igj,

OccFWig

= final benchmarked quote weight for
occupation g in establishment i,

Xig

= 1 if quote ig meets the condition set in the
quote (row) condition
= 0 otherwise,

Yigj

= 1 if plan igj meets the condition set in the
base (denominator) plan condition
= 0 otherwise,

Average (Means). The formula for the average flat monthly
employee contribution for medical insurance, for domain D,
is

W Y P
Yˆ 
W P
qD jq

= 1 if plan igj meets the condition set in the
additional (numerator) plan condition

Zigj

'
q qj qj

D

qD jq

'
q qj

= 0 otherwise, and

,
Pigj

= percent of workers in occupation g and
establishment i who are participating in
plan j.

where
D

Calculation of percentiles
Percentiles of benefit provisions are calculated with data only
from those workers with plans that include the provision. The
following percentiles p are calculated: 10, 25, 50 (median),
75, and 90.
The pth percentile is the value Qigj , where plan value of
a quantity for a specific benefit or a subset of a benefit area,
such that

is the domain of interest,
is the final quote weight for quote q, calculated as
described in the previous section on the calculation
of ECEC estimates,

Wq'
Yqj

is the average monthly employee contribution for
plan j in quote q, and

Pqj

is the percentage of workers in quote q who are
participating in plan j.

Other means, such as the average annual deductible for
medical insurance, can be calculated by a similar formula.
In all cases, the averages include only those workers with the
provision.
The weighted plan employment of a record is calculated
by multiplying the final benchmarked quote weight by the
participation rate for only those plans in the quote that meet
the specific conditions defined by the quote condition and the
plan conditions. The formula is

the weighted plan employment (WPEigj) across
plans with a value less than Qigj is less than p
percent of the total weighted plan employment
and



the weighted plan employment (WPEigj) across
plans with a value more than Qigj is less than
(100 – p) percent of the total weighted plan
employment.

It is possible that there are no specific plan records igj for
which both of these properties hold. This occurs when there
exists a plan for which the WPEigj of records whose value is
less than Qigj equals p percent of the total weighted plan employment. In that situation, the pth percentile is the average
of Qigj and the value on the record with the next-lowest value.
The Qigj values must be sorted in ascending order.

WPEigj = (OccFWig ) × Xig × Yigj × Zigj × Pigj ,
where
i



= establishment,

21

A distinction between percentiles in wage and benefits
publications
For NCS earnings publications, hourly earnings percentiles
are computed from earnings reported for individual workers
in sampled establishment jobs and their scheduled hours
of work. Establishments in the survey are asked to report
individual workers’ earnings for each sampled job within the
establishment. If the establishment reports only the average
earnings of all of the workers in a sampled job, rather than
the earnings of each worker in the sampled job, the job’s
average hourly earnings are appropriately weighted for the
number of workers in the sampled job. Then the resulting
average is arrayed, along with all other individual-worker
earnings and occupational average earnings of sampled
occupations within the establishments, from lowest to
highest. Wage percentiles as a worker characteristic, which
are published in NCS benefits publications, are based on an
array of individual-worker hourly earnings. Estimates of
benefits for a worker group’s average hourly pay within six
national earnings intervals are calculated from data published
in the most recent issue of National Compensation Survey:
Occupational Earnings in the United States. An example of
such an estimate is “Twenty-six percent of private industry
workers who receive hourly wages below the 10th percentile
of the wage distribution had access to medical care benefits
through their employer.”
Use and limitations of NCS benefits data
Standard errors are available for incidence estimates from
2008 onward. In 2009, NCS published its first benefits
estimates that include imputed data. (See http://www.
bls.gov/ebs/ for the latest benefits news release; for more
information on NCS imputation for benefits data, see
“BLS Resumes Estimation of Sample Errors for Benefits
Measures,” Compensation and Working Conditions Online,
May 22, 2008, on the Internet at http://www.bls.gov/opub/
cwc/cm20080520ar01p1.htm.)
Examples of Use of NCS Benefits Data
 Planning and improving company benefits. NCS
data commonly are used as a guide when companies
choose the provisions for their benefit plans. In addition, companies may improve benefit packages to
remain competitive in the labor market. For example,
a computer company may have a difficult time finding
qualified computer engineers, or a car dealership may
not be able to attract the best salesperson. Instead of
simply raising the wage, many companies will enhance or add benefits.




Aiding collective bargaining negotiations. Collective
bargaining units go through renegotiation of their
contracts at various times. The bargaining unit
may want to add a new benefit, such as subsidized
commuting, to an agreement. The bargaining unit and
the employer can use NCS benefits data to assist them
in making decisions.



Understanding health benefits data. Health benefits
data are broken out into average contributions for
medical coverage and average plan limits. A new
company can reference these averages when it selects
group health plan coverage, comparing the averages
with proposals that health plan companies have given
the new company. An established company can
compare its current premiums paid for health benefits
with nationwide averages. This comparison helps
the established company assess its health benefits or
negotiate contracts with health benefit companies.



Assessing and formulating public policy. NCS
benefits data were used to design defined benefit and
savings and thrift plans for Federal employees. In the
debate over a universal health care system, benefits
data on employee premium sharing was considered
in formulating proposals. Data on the amount of
retirement income from employer plans have helped
to frame the debate over Social Security reform.
Policymakers used NCS benefits data in drafting the
Family and Medical Leave Act of 1993.



Researching current benefit issues. Students, consultants, and researchers use benefits data frequently.
Students may be writing a thesis or trying to identify
a noteworthy item on which to focus an assignment.
Consultants may be trying to recommend benefitrelated actions to a company or provide supporting
data to clients. Researchers sometimes want to
investigate a particular issue pertaining to benefits or
may focus on a few years of previous data to develop
research on trends or other benefit issues.

Use and limitations of NCS Data
NCS data have a variety of uses. For example, NCS data are
used in economic analysis. Knowledge of levels, structures,
and trends of pay rates and benefit practices is required in
the analysis of current economic developments and in studies
relating to wage dispersion and differentials. The NCS
provides unique measurement of the labor market, in that it
collects data on earnings, employer costs of compensation,
and benefits under the same survey methodology and
definitions, allowing employer costs to be linked to specific
benefit practices.
Also, Federal, State, and local government agencies use
NCS estimates in administering compensation and in the formulation of public policy on compensation. The data are of
value to Federal and State mediation and conciliation services

Lowering turnover rates. To attract and retain workers,
employers may provide additional benefits. These
prospective benefits may be traditional or emerging
benefits. Employers can search NCS benefits data to
evaluate benefits that employees are currently being
offered nationwide.

22

provides not only consistent series for compensation and for
the two components of compensation—wages and benefits—but
also consistent subseries by occupation and industry. The ECI is
used by the Federal Reserve Board to monitor the effects of fiscal
and monetary policies and to formulate those policies. It enables
analysts and policymakers to assess the effects of labor cost
changes on the economy, both in the aggregate and by sectors.
The ECI is particularly important in studies of the relationships
among prices, productivity, labor costs, and employment.
For additional information, see the subsection
“Examples of Data Use” for each data product in the
section “Computations of Compensation Measures and
Reliability of Estimates for All NCS Data Products.”
For an overview, see “Earnings and Other Compensation
Data at BLS: What Users Seek and What We Offer,”
Compensation and Working Conditions Online, February
26, 2003, on the Internet at http://www.bls.gov/opub/cwc/
cm20030224ar01p1.htm.
Although NCS compensation measures have many uses,
their limitations must be kept in mind. The data are subject
to sampling error, which may cause deviations from the
results that would be obtained if the actual records of all
establishments could be used. Nonsampling error is present
in surveys as well. (See the section “Computations of
Compensation Measures and Reliability of Estimates for
All NCS Data Products” for more information.)

and to State employment compensation agencies in judging
the suitability of job offers. In addition, NCS data are used by
government agencies to


Evaluate benefits packages,



Analyze contract settlements,



Aid in collective bargaining negotiations, and



Index Medicare payments.

NCS data are used in private industry to


Adjust wages in long-term contracts,



Evaluate benefit packages,



Analyze contract settlements,



Aid in collective bargaining negotiations,



Guide decisions in locating businesses or plants, and



Assist in administering wages and salaries.

In determining data to be used in contract negotiations,
it is important to note that differences in bargaining status
estimates (union versus nonunion estimates) may be due
to factors other than union status, such as occupational and
industry mix. An important consideration in choosing a series
for escalation is the sampling error. (For more information,
see http://www.bls.gov/ect/escalator.htm.)
The Employment Cost Index, a Principal Federal
Economic Indicator, is the only measure of labor costs that

Standard errors are available for estimates of the incidence
and provisions of benefit plan coverage from 2008 onward.
(For more information, see “BLS Resumes Estimation of
Sample Errors for Benefits Measures,” Compensation and
Working Conditions Online, May 22, 2008, on the Internet at
http://www.bls.gov/opub/cwc/cm20080520ar01p1.htm.)

23

Technical References for NCS, by Topic

Area sample selection
The criteria for defining Metropolitan, Micropolitan, and
Combined Statistical Areas are published in the Federal
Register (65 FR 82228–82238, December 27, 2000), on
the Internet at http://www.whitehouse.gov/omb/fedreg/
metroareas122700.pdf.
The criteria for defining Core Based Statistical Areas are
published in the Federal Register (65 FR 82228–82238,
December 27, 2000), on the Internet at http://www.
whitehouse.gov/omb/fedreg/metroareas122700.pdf.

Seasonal adjustment
Branch, Raphael E., James A. Buszuwski, Albert E. Schwenk,
and Mark Gough, "Transitional Employment Cost Indexes
for seasonal adjustment," Monthly Labor Review, April
2008, pp. 25–39, on the Internet at http://www.bls.gov/
opub/mlr/2008/04/art3full.pdf.
Branch, Raphael E., and Lowell Mason, "Seasonal
adjustment in the ECI and the conversion to NAICS and
SOC," Monthly Labor Review, April 2006, pp. 12–21, on
the Internet at http://www.bls.gov/opub/mlr/2006/04/
art3full.pdf.

Izsak, Yoel, Lawrence R. Ernst, Erin McNulty, Steven
P. Paben, Chester H. Ponikowski, Glenn Springer,
and Jason Tehonica, "Update on the Redesign of the
National Compensation Survey," 2005 Proceedings of
the American Statistical Association, Section on Survey
Research Methods, Alexandria, VA: American Statistical
Association, 2005, pp. 3150–3158, on the Internet at
http://www.amstat.org/sections/srms/Proceedings/
y2005/Files/JSM2005-000156.pdf.

Survey concepts
Braden, Bradley R., and Stephanie L. Hyland, "Cost of
Employee Compensation in Public and Private Sectors,"
Monthly Labor Review, May 1993, pp. 14–21, on the
Internet
at
http://www.bls.gov/opub/mlr/1993/05/
art2full.pdf.
Buckley, John E., “Fifty years of BLS surveys on Federal
employees’ pay,” Monthly Labor Review, September,
2009, pp. 36-46, on the Internet at http://www.bls.gov/
opub/mlr/2009/09/art3full.pdf.

Izsak, Yoel, Lawrence R. Ernst, Steven P. Paben, Chester
H. Ponikowski, and Jason Tehonica, "Redesign of the
National Compensation Survey," 2003 Proceedings of
the American Statistical Association, Section on Survey
Research Methods, Alexandria, VA: American Statistical
Association, 2003, pp. 1978–1985, on the Internet at
https://www.amstat.org/sections/srms/Proceedings/.

Buckley, John E., "Pay Relatives for U.S. Census Regions and
Divisions, 2006," Compensation and Working Conditions
Online, August 26, 2008, on the Internet at http://www.
bls.gov/opub/cwc/cm20080826ar01p1.htm.

Tehonica, Jason, "New Area Sample Selected for the National
Compensation Survey," Compensation and Working
Conditions Online, April 25, 2005, on the Internet at
http://www.bls.gov/opub/cwc/cm20050318ar01p1.htm.

Ford, Jason L., “The New Health Participation and
Access Data from the National Compensation Survey,”
Compensation and Working Conditions Online, October
26, 2009, on the Internet at http://www.bls.gov/opub/
cwc/cm20091022ar01p1.htm.

Tehonica, Jason, Lawrence R. Ernst, and Chester H.
Ponikowski, "Phase-in of the Redesigned National
Compensation Survey Area Sample," 2005 Proceedings
of the American Statistical Association, Section on Survey
Research Methods, Alexandria, VA: American Statistical
Association, 2005, pp. 2993–2997, on the Internet at
https://www.amstat.org/sections/srms/Proceedings/
y2005/Files/JSM2005-000156.pdf.

Gittleman, Maury B., "Pay Relatives for Metropolitan Areas
in the NCS," Monthly Labor Review, March 2005, pp.
46–53, on the Internet at http://www.bls.gov/opub/
mlr/2005/03/art4full.pdf.
Kramer, Natalie, and Alan Zilberman, “New Definitions of
Employee Access to Paid Sick Leave and Retirement
Benefits in the National Compensation Survey,”
Compensation and Working Conditions Online, December
23, 2008, on the Internet at http://www.bls.gov/opub/
cwc/cm20081219ar01p1.htm.

Benchmarking
"Changes in Calculations for the BLS Employer Costs for
Employee Compensation Data, March 2007," on the
Internet at http://www.bls.gov/ncs/ect/sp/ececcalc.pdf.

24

Lettau, Michael K., Jonathan Lisic, Jesus Ranon, Bradley
D. Rhein, Thuy T. Shipp, and Sarah J. Stafira, “Local
Area Employee Benefits Estimates for 15 Metropolitan
Areas,” Compensation and Working Conditions Online,
September 28, 2009, on the Internet at http://www.bls.
gov/opub/cwc/cm20090924ar01p1.htm.

2007," on the Internet at http://www.bls.gov/ncs/ect/sp/
ececvmet.pdf.
Lettau, Michael K., Mark A. Loewenstein, and Aaron T.
Cushner, "Explaining the Differential Growth Rates of the
ECI and ECEC," Compensation and Working Conditions,
summer 1997, pp. 15–23, on the Internet at http://www.
bls.gov/opub/cwc/archive/summer1997art2.pdf.

Moehrle, Thomas G., "The Cost and Incidence of Referral,
Hiring, and Retention Bonuses," Compensation and
Working Conditions, winter 2000, pp. 37–42, on the
Internet
at
http://www.bls.gov/opub/cwc/archive/
winter2000art4.pdf.

Ojo, Omolola E., and Jonathan J. Lisic, "BLS Resumes
Estimation of Sample Errors for Benefits Measures,"
Compensation and Working Conditions Online, May 22,
2008, on the Internet at http://www.bls.gov/opub/cwc/
cm20080520ar01p1.htm.

Morton, John, "Variable pay," Compensation and Working
Conditions Online, January 21, 2003, on the Internet at
http://www.bls.gov/opub/cwc/cm20030121yb02p1.
htm.

Schwenk, Albert E., "Measuring Trends in the Structure and
Levels of Employer Costs for Employee Compensation,"
Compensation and Working Conditions, summer 1997,
pp. 3–14, on the Internet at http://www.bls.gov/opub/
cwc/archive/summer1997art1.pdf.

National Compensation Survey: Guide for Evaluating Your
Firm's Jobs and Pay, October 2003, on the Internet at
http://www.bls.gov/ncs/ocs/sp/ncbr0004.pdf.
Schumann, Richard, "Work schedules," Compensation and
Working Conditions Online, July 22, 2008, on the Internet
at http://www.bls.gov/opub/cwc/cm20080722ar01p1.
htm.

Walker, Martha A.C., and Bruce Bergman, "Analyzing
Year-to-Year Changes in Employer Costs for Employee
Compensation," Compensation and Working Conditions,
spring 1998, pp. 17–27, on the Internet at http://www.bls.
gov/opub/cwc/archive/spring1998art3.pdf.

Schwenk, Albert E., "Compensation Cost Trends in Private
Industry and State and local Governments," Compensation
and Working Conditions, fall 1999, pp. 13–18, on the
Internet
at
http://www.bls.gov/opub/cwc/archive/
fall1999art2.pdf.

Use of fixed weights and index estimation
Costo, Stephanie L., "Introducing 2002 weights for the
Employment Cost Index," Monthly Labor Review, April
2006, pp. 28–32, on the Internet at http://www.bls.gov/
opub/mlr/2006/04/art5full.pdf.

Weinstein, Harriet, and Elizabeth Dietz, "Towards a Working
Definition of Compensation," Compensation and Working
Conditions, summer 1996, pp. 3–9, on the Internet at http://
www.bls.gov/opub/cwc/archive/summer1996art1.pdf.

Ruser, John W., "The Employment Cost Index: What is it?"
Monthly Labor Review, September 2001, on the Internet at
http://www.bls.gov/opub/mlr/2001/09/art1full.pdf.
Schwenk, Albert E., "Measuring Trends in the Structure and
Levels of Employer Costs for Employee Compensation,"
Compensation and Working Conditions, summer 1997,
pp. 3–14, on the Internet at http://www.bls.gov/opub/
cwc/archive/summer1997art1.pdf.

Imputation methods
Yi, Song, "Accounting for missing data in the Employment
Cost Index," Monthly Labor Review, April 2006, pp.
22–27, on the Internet at http://www.bls.gov/opub/
mlr/2006/04/art4full.pdf.

Schwenk, Albert E., "BLS Introduces New Employment
Cost Indexes for 14 Metropolitan Areas, Compensation
and Working Conditions Online, September 24, 2008,
on the Internet at http://www.bls.gov/opub/cwc/
cm20080922ar01p1.htm.

Stafira, Sarah, “Recent Modification of Imputation Methods
for National Compensation Survey Benefits Data,”
Compensation and Working Conditions Online, August
28, 2009, on the Internet at http://www.bls.gov/opub/
cwc/cm20090825ar01p1.htm.

NCS conversion to new industry and occupational
classification systems
Branch, Raphael E., and Lowell Mason, "Seasonal
adjustment in the ECI and the conversion to NAICS and
SOC," Monthly Labor Review, April 2006, pp. 12–21, on

Variance measurement
"Changes in Variance Estimation Calculations for the BLS
Employer Costs for Employee Compensation Data, March

25

the Internet at http://www.bls.gov/opub/mlr/2006/04/
art3full.pdf.

2009, on the Internet at http://www.bls.gov/opub/cwc/
cm20090518ar01p1.htm.

Smith, James E., and Robert W. Van Giezen, "Change
Comes to the National Compensation Survey Locality
Wage Bulletins," Compensation and Working Conditions
Online, January 22, 2007, on the Internet at http://www.
bls.gov/opub/cwc/cm20070122ar01p1.htm.

Kramer, Natalie, "Earnings and Other Compensation
Data at BLS: What Users Seek and What We Offer,"
Compensation and Working Conditions Online, February
26, 2003, on the Internet at http://www.bls.gov/opub/
cwc/cm20030224ar01p1.htm.

Weinstein, Harriet G., and Mark A. Loewenstein, "Comparing
Current and Former Industry and Occupation ECEC
Series," Compensation and Working Conditions Online,
August 25, 2004, on the Internet at http://www.bls.gov/
opub/cwc/cm20040823ar01p1.htm.

Schwenk, Albert E., “BLS Introduces New Employer Costs
for Employee Compensation Data for Private Industry
Workers in 15 Metropolitan Areas,” Compensation
and Working Conditions Online, September 28, 2009,
on the Internet at http://www.bls.gov/opub/cwc/
cm20090921ar01p1.htm.

Use of NCS data products
Buckley, John E., “Beyond Basic Benefits: Employee
Access to Other Types of Benefits, 1979–2008,”
Compensation and Working Conditions Online, May 29,
2009, on the Internet at http://www.bls.gov/opub/cwc/
cm20090527ar01p1.htm.

Shelly, Wayne M., “Aging Wage Survey Data Using the
Employment Cost Index,” Compensation and Working
Conditions Online, January 29, 2008, on the Internet at
http://www.bls.gov/opub/cwc/cm20080122ar01p1.htm.
Wiatrowski, William J., "BLS at 125: Using historic
principles to track the 21st-century economy," Monthly
Labor Review, June 2009, pp. 3–25, on the Internet at
http://www.bls.gov/opub/mlr/2009/06/art1full.pdf.

Buckley, John E., “Recent Modifications of Employee
Benefits Data in the National Compensation Survey,”
Compensation and Working Conditions Online, May 29,

26

Appendix: Major Work Stoppages Program

T

he Bureau compiles data on work stoppages—strikes
or lockouts—involving 1,000 or more workers for at
least a full day or shift. Such data have been collected
since 1947, with detailed information available since 1993.
Detailed work stoppages data include monthly and annual
listings of companies and governments involved in a work
stoppage, along with the name of the union involved in the
dispute, the location of the stoppage, the North American
Industry Classification System (NAICS) code, the beginning
and ending dates of the dispute, the number of workers idled
by the stoppage, the number of days of idleness during the
reference month, and the cumulative number of days of
idleness from the beginning of the work stoppage.

Sources of information
Information on work stoppages is obtained from reports
from the Federal Mediation and Conciliation Service, State
labor market information offices, BLS Strike Reports from
the Office of Employment and Unemployment Statistics,
and media sources. One or both parties involved in the
work stoppage (employer, union, or other organization) is
contacted to verify the duration and number of workers idled
by the stoppage.

Definitions and methods
A work stoppage is a strike or lockout. Because of the
complexity of most labor-management disputes, BLS makes
no attempt to distinguish between strikes and lockouts in its
statistics. A strike is a temporary stoppage of work by a group
of employees to express a grievance, enforce a demand, or
protest the terms, conditions, or provisions of a contract. A
lockout is a temporary withholding or denial of employment
by management, typically during a labor dispute. The group
of employees involved in a strike or lockout may or may not
be members of a union.
The number of workers involved includes all workers made
idle for one shift or longer in establishments directly involved
in a stoppage. Workers involved include those who initiate
the strike, as well as others in the establishment who honor
picket lines or are idled because the facility is closed down.
This number does not account for secondary idleness—
that is, the effects of a stoppage on other establishments or
industries whose employees may be made idle as a result of
shortages of material or services.
The number of days of idleness is computed by multiplying
the number of workers idled during the period by the number
of workdays lost, based on a 5-day workweek (Monday
through Friday), excluding Federal holidays. The cumulative

Availability of data
Data for the major work stoppages series are uninterrupted
and date back to 1947. For statistics on monthly and annual
work stoppages and detailed monthly data since 1993, see
http://www.bls.gov/wsp/. Monthly detailed data include
both monthly and cumulative totals for each work stoppage,
as well as the number of stoppages beginning and in effect
during the month. Annual data present the cumulative totals
for the calendar year. You may obtain a searchable worksheet
containing data on major work stoppages from 1993 to the
present by sending an e-mail request to WorkStoppagesInfo@
bls.gov.
From 1947 to 2007, the Bureau of Labor Statistics had
acted under the mandate of the Taft-Hartley Act to solicit
collective bargaining agreements and make them available in
a publicly accessible file. In September 2007, responsibility
for the maintenance of collective bargaining agreements
and for the continued collection of these agreements was
officially moved to the U.S. Department of Labor, Office
of Labor-Management Standards. (For more information,
see “Collective Bargaining Agreements File Moves to New
Home,” Compensation and Working Conditions Online,
November 30, 2007, on the Internet at http://www.bls.gov/
opub/cwc/cb20071128ar01p1.htm.)

number of days of idleness also is computed for each work
stoppage beyond the beginning reference month.

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