A Comparison of Selected Official Wage Measures

January 11, 2023 R47381

A Comparison of Selected Official Wage
January 11, 2023
Measures
Lida R. Weinstock
The U.S government produces several different wage and wage-related measures, each with its
Analyst Macroeconomic
own source data, methodology, and uses. Wages can be a catchall term for several different
Policy
concepts, including actual wages, earnings, and compensation. Colloquially, people often use

these terms interchangeably to refer to the money people receive from their jobs. However,
statistical agencies such as the Bureau of Labor Statistics (BLS), Census Bureau, and Bureau of

Economic Analysis (BEA) make definitional distinctions among such terms.
Much of the source data for wage or similar measures comes from surveys, each of which has its own definitions and
delineations of data. Three such surveys are the Current Population Survey (CPS), the Current Employment Statistics (CES)
Survey, and the Quarterly Census of Employment and Wages (QCEW). These surveys are used to construct data series that
all measure wages from the perspective of workers and are all able to be used in time series analysis. However, they have a
number of methodological differences including, but not limited to, periodicity, workers covered, and aggregation method.
These differences are illustrative of the range of methodologies available when the government creates wage measures and of
the differences analysts and policymakers must consider when examining wage data and statistics.
Each source and series of wage data has advantages and disadvantages, and none is inherently superior to any other. Rather,
the purpose and issue of interest to the person seeking to examine wages generally determines which data are most helpful
and suited to a particular analysis. For example, the types of compensation included, perspective of the data (employer or
worker), and available disaggregations are all considerations that may call for using one series over another.

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A Comparison of Selected Official Wage Measures

Introduction
To say that wages are important may be an obvious statement from the perspective of the
individual. After all, wages tend to be a major source of income for individuals of working age.
Wages allow individuals to purchase the things they need to live, including housing, food, and
utilities, to name a few. What may be less obvious is why wages, and specifically if and how they
grow, are important to the overall functioning of the economy. Wage growth is often used as a
proxy for economic conditions and standard of living. If, over time, wages rise (in inflation-
adjusted terms), then workers can afford more or better goods and services, thereby increasing
their standard of living. In addition, if wages are growing, aggregate demand (total spending)
would generally be strong and economic conditions therefore robust. As a result, if and by how
much wages grow can have important policy implications for a wide array of issues.1
How to actually define and measure wages is complicated. Colloquially, people often use terms
such as wages, earnings, compensation, pay, or income to mean the money they receive from
their jobs. However, statistical agencies such as the Bureau of Labor Statistics (BLS), Census
Bureau, and Bureau of Economic Analysis (BEA) make distinctions among such terms. For
simplification, this report generally uses the term wages to refer to money received from working
a job, though terms specific to particular agencies or surveys will be used when series definitions
and data are described.
In part because of this definitional issue, there are numerous government measurements of wages.
This stands in contrast to many other economic concepts, for which there is only one main source
of data. For example, while the federal government produces one statistic for gross domestic
product, it produces numerous different measures of wages, compensation, and earnings at the
aggregate level. Individual measures of wages discussed in this report include different categories
of compensation, such as benefits or bonuses, and cover different groups of workers. Therefore,
this report specifically defines each measure and its methodology. Further, there is not one
specific measure preferred over others by all policymakers, economists, academics, or journalists.
Each measure is different from the next and may cover different groups of wage earners. To this
end, which measure of wages a policymaker chooses to consider can have major implications for
policy.
Wages are not the same thing as income. In general economic theory, personal income is equal to
the maximum amount an individual can consume without affecting his or her wealth. Wages,
therefore, are a part of income but do not necessarily constitute all of it. Income may additionally
include transfer payments from the government (e.g., Social Security and unemployment
insurance payments) and capital income from investments (e.g., dividends, capital gains).2
Another way to measure income is to distinguish its source as coming from one of the two main
factors of production: labor and capital. Labor income is the amount of gross domestic product
that is paid out in the form of wages, salaries, and benefits.3

1 For more information about official labor force statistics, see CRS Report R47241, Workforce and Labor Policy:
Resources for Congressional Staff, coordinated by Abigail R. Overbay.
2 John Ruser, Adrienne Pilot, and Charles Nelson, Alternative Measures of Household Income: BEA Personal Income,
CPS Money Income, and Beyond, BEA, November 2004, https://www.bea.gov/system/files/papers/
AlternativemeasuresHHincomeFESAC121404.pdf. For information on personal income, see CRS In Focus IF10501,
Introduction to U.S. Economy: Personal Income, by Lida R. Weinstock. For a discussion of income trends and the
economy, see CRS Report R44705, The U.S. Income Distribution: Trends and Issues, by Sarah A. Donovan et al.
3 Different agencies have differing exact definitions of this concept, which will be addressed in a later textbox. For a
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A Comparison of Selected Official Wage Measures

This report covers selected official measures that measure wages from the perspective of workers
and illustrate a range of methodologies.4 The following sections discuss these measures, their uses
and methodologies, and considerations for when to use different types of measures. A companion
report, CRS Report R47380, Average Wage Growth and Related Economic Trends in 2022, by
Lida R. Weinstock, discusses recent trends in wages and their interactions with the broader
economy.
Measurement Concepts
Wage data may be aggregated and presented in various ways. The fol owing ways of presenting data hold
important implications for how that data should be interpreted.
Nominal data refers to data that have not been adjusted for inflation. For example, the dol ar amount on a
paycheck is a nominal amount.
Real data refers to data that have been adjusted for inflation. Real data provide a sense of how the purchasing
power of money has changed over time. For example, if an individual makes $50,000 in nominal terms in a year, in
real terms that amount is actually lower because price level increases mean that $50,000 can buy less at the end of
the year than it could in the beginning. Real data are often referred to as nominal data minus inflation. In reality,
nominal data are converted to real data using price indices calculated by several agencies.
Average data refers to data points representative of a usual value within a distribution. Mathematically, averages
are determined by adding together each data point in a distribution and then dividing by the number of data
points.
Median data refers to data points representative of the middle of a distribution. Median data tend to be
influenced less by outliers than average data are, but depending on the distribution of data, they may not be very
representative of a typical data point.
Seasonal adjustment refers to the process by which data are adjusted for seasonal factors. Seasonally adjusting
data smooths out fluctuations that are due to seasonal and calendar influences. For example, predictable seasonal
patterns that could affect labor force data include school schedules (such as graduations) and major holidays.5
Selected Wage Series
There are many different sources of data and methods for aggregation and calculation of wages
and wage growth in the United States. Each source and methodology produces a unique measure
that is not necessarily comparable to any other measure. Importantly, many measures cover
different groups of individuals, which may be important in policy decisions that target specific
groups of individuals, or otherwise benchmark against a specific wage measure. These
differences among measures can result in data that show different levels and trends. To parse any
differences in trends, it can be helpful to understand the differences in the measures. This section
first provides an overview of a selection of common official wage measures that provide
information on compensation from the perspective of the worker at the individual and aggregate
level. A detailed methodology discussion follows the overview.

general overview, see Michael D. Giandrea and Shawn Sprague, “Estimating the U.S. Labor Share,” BLS, February
2017, https://www.bls.gov/opub/mlr/2017/article/estimating-the-us-labor-share.htm.
4 Public data are not inherently better or more widely used than are private data. One of the most widely used sources
of data in the United States is private. Automatic Data Processing (ADP) is a private company that provides human
resources management software and other services, including payroll services, for many U.S. companies. ADP
provides monthly data on labor force trends, including annual pay. For more information, see ADP, “ADP Pay
Insights,” https://payinsights.adp.com/.
5 BLS, “What Is Seasonal Adjustment?,” https://www.bls.gov/cps/seasfaq.htm.
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A Comparison of Selected Official Wage Measures

Overview of Selected Series
For ease of comparison and methodology discussion, the series in this section are organized by
the surveys their data are sourced from. The series come from three different surveys: the Current
Population Survey (CPS), the Current Employment Statistics Survey (CES), and the Quarterly
Census of Employment and Wages (QCEW). The CPS is a household survey, while the CES and
QCEW are establishment surveys. These surveys are discussed in more detail in the following
methodology section. Basic information on each survey, its periodicity, and available data
breakdowns is provided here.
Series from the Current Population Survey (CPS)
 BLS Usual Weekly Earnings: This series is a quarterly measure of wages based
on what individuals estimate they “usually” earn in a week. Data are provided by
quartile (buckets that divide the distribution into four parts), with the second
quartile representing the median wage. This series can be broken down by sex,
race, ethnicity, and age. This series may be less accurate than other sources as it
is based on individuals’ estimates. However, this series provides more
information on the overall distribution of wages than other series and may
therefore be useful to compare the wages of lower and higher wage workers.
 BLS Median Hourly Earnings: This series is an annual measure of median wages
for workers paid hourly rates. This series can be broken down by sex, race,
ethnicity, and age. Median hourly earnings is a specific measure that is not
representative of the total labor force and is not provided frequently. Nonetheless,
this series may be useful in the context of tracking and understanding the wages
and characteristics of hourly workers.
 Federal Reserve Bank of Atlanta Wage Tracker: This tracker measures annual
median hourly wage growth and provides data on a monthly basis. Data can be
broken down by several demographic and job characteristics such as educational
attainment, gender, and job stayers versus switchers. Unlike other series, the
tracker only provides nominal (i.e., not inflation-adjusted) data, which is less
useful in tracking purchasing power over time. The tracker does calculate median
wage growth by tracking wage growth for individuals and then selecting the
median growth rate. The growth in other series would be a change over time in
the average or median wage at one point in time compared to another. For this
reason, this series may provide more information on how individuals’ wages are
changing over time.
Series from the Current Employment Statistics Survey (CES)
 BLS Average Hourly and Weekly Earnings: BLS provides these series on average
wages in dollar terms at both the hourly and weekly level. These series are
provided on a monthly basis and can be broken down by industry. Average hourly
earnings is one of the most commonly cited measures of wages and is part of the
monthly BLS Employment Situation data release. These series can be a useful
source of high frequency data.
Series from the Quarterly Census of Employment and Wages (QCEW)
 BLS Average Weekly Wage: This quarterly series provides average weekly wage
data by locality, industry, ownership (private or government), and establishment
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A Comparison of Selected Official Wage Measures

size. This series is published with a lag and, therefore, is often less current than
other quarterly or monthly series. The detailed locality breakdowns make this
series useful in studying wage trends down to the county level and comparing
wages and wage trends across localities at a level not always possible with other
series.
 BLS Total Wages: This series is similar to the average weekly wage series except
that wages are provided as a summed total instead of an average. BLS
additionally provides annual aggregates for this series. Measures of total as
opposed to individual wages can be useful in analyzing the health of the
economy overall, as opposed to just workers.
 BEA Wages and Salaries by Industry: This series provides wage data at the
monthly, quarterly, and annual levels for total wages with breakdowns by
industry. The advantage of using this series over the total wages BLS series is
that it is higher frequency and BEA includes estimates of total military wages.
Military personnel are typically not included in wage data, making this series one
of the most inclusive sources from the perspective of which workers are covered.
The monthly series is imputed from quarterly data and, therefore, may not be as
accurate as other monthly wage data.
Detailed Methodology of Selected Series
As with the above overview, this section is organized by data source and it includes basic
information on each survey with relevant definitions. Tables for each survey are provided and
include details on the methodology, advantages, and limitations of each series using data from a
particular survey.
CPS
The CPS is administered by the Census Bureau and collects labor force and demographic
information on a monthly basis from surveys of a nationally representative sample of
households.6 Results are used to compile data published by BLS—a unit within the Labor
Department—and the Federal Reserve Bank of Atlanta. For series published by BLS, data are
characterized as earnings and specifically measure wage and salary earnings before taxes and
other deductions from the main job of an individual. They do not include any benefits such as
employer-provided health insurance. These earnings data are only for wage and salary workers,
which does not include any kind of self-employed worker. In this case, wage and salary workers
includes workers ages 16 and over who receive wages, salaries, commissions, tips, payments in
kind, or piece rates.7 Of the series below, the two BLS series track median earnings, while the
Federal Reserve Bank of Atlanta series tracks median earnings growth.

6 Despite the CPS being a monthly survey, BLS collects earnings data from only a quarter of the sample each month
and therefore publishes earnings data only as quarterly or annual averages. See Census Bureau, Design and
Methodology: Current Population Survey—America’s Source for Labor Force Data, October 2019, p. 79,
https://www2.census.gov/programs-surveys/cps/methodology/CPS-Tech-Paper-77.pdf.
7 BLS, “Handbook of Methods: Current Population Surveys,” https://www.bls.gov/opub/hom/cps/pdf/cps.pdf; and
Census Bureau, Design and Methodology: Current Population Survey.
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Table 1. Series Based on Current Population Survey Data
Groups
Series
Methodology
Covered
Periodicity
Advantages
Limitations
BLS Usual
Respondents who are
Wage and salary Quarterly
Broad measure
Responses
Weekly
employed are asked to
workers
of many types of about usual
Earnings
report usual earnings
workers.
earnings are
before taxes, including
Provides wage
likely to be
overtime, commissions,
breakdowns by
estimated and
or tips usually received.
demographic
not exact.
Respondents are allowed
characteristics.
Household
to interpret usual, but if
survey has a
asked, interviewers
smaller sample
provide the fol owing
size than
definition: “more than half
establishment
the weeks worked during
surveys and is
the past four or five
subject to
months.” Data available
greater sampling
by percentile or median.
error.
BLS
Employed respondents
Hourly wage
Annual
Provides
Represents only
Median
who report usual earnings workers
estimates of the
a subsection of
Hourly
hourly are assumed to be
number of
workers and,
Earnings
paid hourly. Employed
hourly paid
therefore, may
respondents who do not
workers at and
not be
report usual earnings in
near the federal
representative
hourly increments are
minimum wage.
of the total
asked if they are paid
labor force.
hourly. The hourly rates
Published less
of both groups of
frequently than
respondents are included
other sources.
in this series. Median data
are published.
Federal
Using CPS hourly earnings Wage and salary Monthly
Provides a
Provides only
Reserve
microdata, the Atlanta
workers. This
measure that
growth percent
Bank of
Fed calculates the wage
tracker
tracks the same
change statistics
Atlanta
growth of specific
additionally
individual over
and not dol ar
Wage
individuals in the current
excludes
time. (Other
amounts. Owing
Tracker
month from the same
individuals with
median wage
to methodology
month in the previous
top-coded
growth
requirement for
year. Using the median of
earnings,a
measures track
individuals to
these individual wage
imputed
the median
have earnings in
growth data, median wage earnings, pay
wage in one
current and
growth points are then
below the
month
prior year,
smoothed by calculating a
federal
compared to
dataset has a
three-month moving
minimum wage
the median
higher share of
average. Some
for tip-based
wage in another
older, more
breakdowns are also
workers, and
month.)
educated
available as a 12-month
agricultural jobs.
Relatively high
workers than
moving average.
frequency data.
do other
measures using
CPS data.
Sources: BLS, “Handbook of Methods: Current Population Survey,” https://www.bls.gov/opub/hom/cps/; Census
Bureau, “Design and Methodology: Current Population Survey—America’s Source for Labor Force Data,”,
https://www2.census.gov/programs-surveys/cps/methodology/CPS-Tech-Paper-77.pdf; and Federal Reserve Bank
of Atlanta, “Wage Growth Tracker,” Methodology, https://www.atlantafed.org/chcs/wage-growth-tracker?panel=
2.
Notes:
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a. The Atlanta Fed defines top-coded earnings as those in which the product of usual hours and usual hourly
wages exceeds an annualized wage of $100,000 before 2003 and $150,000 after 2003. BLS computes top-
codes such that the product of usual hours times usual hourly wage does not exceed an annualized wage of
$150,000. Generally, top-coded data refer to data points above an upper bound ($150,000 in this case) that
are censored to ensure the confidentiality of the data. For more information on CPS usual hourly earnings
top-coding, see for example, https://www.census.gov/programs-surveys/cps/technical-documentation/
methodology/topcoding-of-usual-hourly-earnings.html.
CES
The CES is a monthly survey of businesses and government agencies that gathers establishment
employment information, including wages. It is administered and used to compile published data
by BLS. CES data are characterized as earnings or payrolls. In this context, earnings is defined as
the gross actual returns to employees excluding benefits, irregular bonuses, retroactive items, and
payroll taxes. CES is an establishment survey, meaning that workers who do not work for
establishments are not included.8 Excluded workers include proprietors, the unincorporated self-
employed, unpaid volunteer or family employees, domestic employees, government employees,
and military personnel.9
Table 2. Series Based on Current Employment Statistics Survey Data
Groups
Series
Methodology
Covered
Periodicity
Advantages
Limitations
BLS Average
Average hourly
Nonfarm wage
Monthly
Widely used
More sensitive
Earnings
earnings are
and salary
economic
to outliers and
calculated by
workers,
indicator;
composition of
dividing
excluding
provides input
employment
aggregate
government
into other
than are other
payrol sa by all
workers.
major economic measures.
employee
indicators;
hours. Average
provides
weekly earnings
breakdowns by
are calculated
industry.
by multiplying
Relatively high
average weekly
frequency data.
hours and
Uses actual
average hourly
payrol data, not
earnings.
respondent
estimates.
Source: BLS, “Handbook of Methods: Current Employment Statistics,”, https://www.bls.gov/opub/hom/ces/.
Notes:
a. BLS defines aggregate payrolls as total regular pay earned by employees before deductions of any kind.
Bonuses, retroactive pay, in-kind payments, and employer benefits are excluded.

8 Establishment is defined as “economic units that produce goods or services, usually at a single physical location, and
are engaged in one or predominantly one type of economic activity.” Colloquially, establishments may be referred to as
businesses, but BLS makes definitional distinctions between the two terms. See BLS, “Glossary,” https://www.bls.gov/
bls/glossary.htm.
9 BLS, “Technical Notes for the Current Employment Statistics Survey,” https://www.bls.gov/web/empsit/cestn.htm;
BLS, “Handbook of Methods: Current Employment Statistics,” https://www.bls.gov/opub/hom/ces/home.htm; and
BLS, “CES Frequently Asked Questions,” https://www.bls.gov/web/empsit/cesfaq.htm.
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In addition to the nominal wage data, BLS also provides a dedicated news release for real wage
data based on CES.10
QCEW
The QCEW is a quarterly aggregation of employment information reported by employers.
Information is collected by BLS and used to compile data released by BLS and BEA. The QCEW
uses state unemployment insurance administrative data, Annual Refiling Survey11 data, and
Multiple Worksite Report12 data. QCEW data are characterized as pay, defined as “the total
compensation paid, including bonuses, stock options, severance pay, profit distributions, the cash
value of meals and lodging, tips and other gratuities, and, in some states, employer contributions
to certain deferred compensation plans (such as 401(k) plans), during the calendar year,
regardless of when the services were performed.”13
Table 3. Series Based on Quarterly Census of Employment and Wages Data
Groups
Series
Methodology
Covered
Periodicity
Advantages
Limitations
BLS Average Establishment wage
All employees
Quarterly
Provides
Publication
Weekly Pay
data are summed for
of employers
detailed locality
quarterly with
specific industry,
subject to state
data. Uses
up to a five-
geography, and size
and federal
information filed month lag.
subdomains. Data are
unemployment
by
imputed for
insurance laws.
establishments,
establishments that fail
not survey
to respond in a timely
respondent
manner. Averages are
estimates.
then calculated from
the subdomain totals.
BLS Total
Establishment wage
All employees
Quarterly,
Provides
Publication
Wages
data are summed for
of employers
Annual
detailed locality
quarterly with
specific industry,
subject to state
data. One of the up to a five-
geography, and size
and federal
few sources of
month lag.
subdomains. Data are
unemployment
total wage data.
imputed for
insurance laws.
establishments that fail
to respond in a timely
manner.

10 For the latest release, see https://www.bls.gov/news.release/realer.nr0.htm.
11 The Annual Refiling Survey is a questionnaire that asks businesses to verify general information including industry
classification. See BLS, “Annual Refiling Survey (ARS) Respondents,” https://www.bls.gov/respondents/ars/
home.htm.
12 The Multiple Worksite Report is a form that asks employers with multiple locations for employment and wage data
for all locations covered under a single unemployment insurance account in a given state. See BLS, “Multiple Worksite
(MWR) Respondents,” https://www.bls.gov/respondents/mwr/home.htm.
13 BLS, “Handbook of Methods: Quarterly Census of Employment and Wages,” https://www.bls.gov/opub/hom/cew/
home.htm.
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Groups
Series
Methodology
Covered
Periodicity
Advantages
Limitations
BEA Wages
BEA adjusts QCEW
All workers
Monthly,
Provides
Current
and Salaries
data by estimating
including
Quarterly,
detailed
monthly and
by Industrya
wages and salaries of
nonfarm, farm,
Annual
industry data.
quarterly
military personnel,b
and military.
Includes military estimates are
adjusting estimates for
Wages and
personnel. Uses
not published by
employees that are not
salaries excludes
information filed detailed
ful y captured (such as
proprietors, but
by
industry.
agricultural workers),
proprietors’
establishments,
Because of lags
and adjusting for
income is
not survey
in QCEW data,
definitional differences
included as a
respondent
current monthly
in the QCEW and
different line
estimates.
and quarterly
national income and
item in the BEA
estimates are
product accounts. Total Personal
extrapolated
wages by legal form and Income tables.
based on
sector are estimated
historical data.
using additional
industry payrol data
from the Economic
Census. QCEW
quarterly estimates are
usually available five
months after the end of
a quarter, so BEA
estimates current
quarterly wages and
salaries by
extrapolating monthly
estimates from
historical personal
income data (which are
based on most recent
QCEW data).
Sources: BLS, “Handbook of Methods: Quarterly Census of Employment and Wages,” https://www.bls.gov/
opub/hom/cew/; and BEA, NIPA, Handbook, Chapter 10: Compensation of Employees, https://www.bea.gov/
resources/methodologies/nipa-handbook/pdf/chapter-10.pdf.
Notes:
a. BEA additionally produces a series on total compensation of workers, which includes supplements to wages
and salaries—employer payments that are made on behalf of employees, such as employer contributions to
pension funds. These sorts of employer payments are not within the scope of this report.
b. Wages and salaries of military personnel are estimated using military budget data and includes cash wages
and in kind compensation.
Related Series
The below list of data series is not an all-inclusive list. Rather, this list includes a selection of series published by
the U.S. government that measure concepts related to wages, earnings, and compensation and, therefore, may be
useful to economists and policymakers. These series either provide wage data from a perspective other than
workers, are not useful in analyzing trends, or are concepts related to wages but more expansive than just the
money received from a job.
BEA Personal Income: “the income that U.S. residents get from paychecks, employer-provided supplements
such as insurance, business ownership, rental property, Social Security and other government benefits, interest,
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and dividends.”14 Personal income data are aggregate and available at the monthly level. Data can be found at
https://www.bea.gov/data/income-saving/personal-income.
Census American Community Survey (ACS) Household and Family Income: “the sum of the amounts
reported separately for wage or salary income; net self-employment income; interest, dividends, or net rental or
royalty income or income from estates and trusts; Social Security or Railroad Retirement income; Supplemental
Security Income (SSI); public assistance or welfare payments; retirement, survivor, or disability pensions; and all
other income.”15 ACS household and family income data are available on an annual basis and include median and
mean values. Data are available at https://www.census.gov/programs-surveys/acs/data.html.
BLS National Compensation Survey (NCS) Employment Cost Index (ECI): “a measure of the change in
the employer costs of labor, independent of employment shifts among occupations and industry categories.”
Indexes are available for total compensation, wages and salaries, and benefits for all civilian workers, private sector
workers, and state and local government workers.16 Data are available at the quarterly level. Data can be found at
https://www.bls.gov/ncs/.
BLS NCS Employer Costs for Employee Compensation (ECEC): this series “measures the average costs
to employers for wages and salaries, and benefits, per employee hour worked.” Data are available at the quarterly
level for total compensation, wages and salaries, total benefits, and selected benefits.17 Data can be found at
https://www.bls.gov/ncs/.
BLS Occupational Employment and Wage Statistics (OEWS) Wages: this survey provides average,
median, and several percentiles of wages. The survey covers “wage and salary workers in nonfarm establishments
and does not include the self-employed, owners and partners in unincorporated firms, household workers, or
unpaid family workers.”18 The estimates are constructed from samples of businesses that report to state
unemployment insurance programs. OEWS data are useful in providing locality and industry breakdowns but are
less useful for comparing two or more points in time.19 Data are available annually with a May reference date by
occupation, locality, and industry at https://www.bls.gov/oes/tables.htm.
BLS & BEA Labor Share: Labor share is generally defined as the portion of output that accrues to workers. BLS
calculates labor share by sector as the ratio of labor compensation in a given sector to current dol ar output.20
Data are available at the quarterly level at https://www.bls.gov/productivity/data.htm. BEA additionally provides the
portion of gross domestic income that is paid to labor in the form of compensation of employees. BEA data are
also available quarterly in the National Income and Product Accounts, Table 1.11, at https://apps.bea.gov/iTable/
iTable.cfm?reqid=19&step=2#reqid=19&step=2&isuri=1&1921=survey.
Comparing Wage Data
With so many series to choose from, how can it be determined which is the best wage measure to
consider? The answer may sometimes be obvious depending on the use of the data. For example,
one might choose CES average wage data or Atlanta Fed wage growth data because they are
available monthly. Median data might be preferred to average data or vice versa depending on
whether there is concern about outliers in the data.21 Certain sources offer useful disaggregations
by individual characteristic, locality, or industry. Different measures cover different groups of

14 BEA, “Income and Saving,” https://www.bea.gov/resources/learning-center/what-to-know-income-saving.
15 Census Bureau, American Community Survey and Puerto Rico Community Survey 2020 Subject Definitions, 2020,
https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2020_ACSSubjectDefinitions.pdf.
16 BLS, “Handbook of Methods: National Compensation Measures,” https://www.bls.gov/opub/hom/ncs/pdf/ncs.pdf.
17 BLS, “Handbook of Methods: National Compensation Measures.”
18 BLS, “Occupational Employment and Wage Statistics Overview,” June 22, 2022, https://www.bls.gov/oes/
oes_emp.htm.
19 BLS, “Occupational Employment and Wage Statistics Frequently Asked Questions,” https://www.bls.gov/oes/
oes_ques.htm.
20 BLS, “Handbook of Methods: Productivity Measures: Business Sector and Major Subsectors: Calculation,”
https://www.bls.gov/opub/hom/msp/calculation.htm.
21 Median data tend to be more representative of a “typical” value because their value is not affected by the value of
outliers. Averages, however, may be a more inclusive measure because all values are included in the calculation.
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A Comparison of Selected Official Wage Measures

workers, from narrow to broad. Additionally, the definitional differences in wages, earnings, and
compensation might make one series more attractive than another. For example, a compensation
measure such as the ECI may provide a more accurate snapshot of the cost of employing workers
to companies than an average earnings measure of pay received by employees (such as from the
CES). In short, which data may be most useful depends on context, and there is no “best”
measure for all situations. This final section discusses a few general things to consider when
deciding on a wage measure to use in any given situation.
How Are the Data Measured?
One of the main differences in the series discussed in this report is whether data are provided as
averages or medians. Average data are more vulnerable to changes in the composition of the
workforce than are median or total data. For example, average data may increase more
significantly than median data if low-wage workers drop out of the labor force. In this case the
data may not provide a full picture of workers’ well-being. Median data represent the middle or
typical value of a distribution and, therefore, may be more useful than average data in analyzing
point estimates or short-term trends. Average and median data are usually both reasonable choices
for longer-term trend analysis, and the decision between the two is more about preference in this
situation.
Another key difference in how data are measured and presented is whether the data represent total
wages or individual wages. Individual wages give a view of what a median or average worker
makes. This is a generally intuitive measure because it is easily comparable to a minimum wage
or the personal wage of the interested party. However, total wages can be useful in analyzing
aggregate trends and can give a better look at concepts like the current size and state of the labor
market.
Which Workers Are Included?
The types of workers included in the wage series discussed in this report range from narrow to
broad. More narrow measures, such as wages for hourly workers, may be useful in analyzing
questions about such a group, while broader measures may be more useful in providing a
snapshot of the labor force as a whole. While the broadest measures of wages may seem the most
useful or comprehensive at first glance, there are reasons why economists or policymakers may
want to use a less inclusive measure. For example, in the context of unemployment insurance
policy, it may be more useful to look at wages that include only workers covered by
unemployment insurance. It can also simplify analysis to use a measure that does not include
agricultural workers owing to the seasonality of this work, the complications of self-employment,
unpaid family employment, hobby farmers, and the unknown number of undocumented workers
in the agriculture industry.22

22 Federal Reserve Bank of St. Louis, “Nonfarm Payrolls: Why Farmers Aren’t Included in Jobs Data,” July 3, 2019,
https://www.stlouisfed.org/open-vault/2019/july/nonfarm-payrolls-why-farmers-not-included.
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A Comparison of Selected Official Wage Measures

Which Types of Compensation Are Included?
Different measures include different types of compensation given that definitions of wages
change from survey to survey. For example, compensation that is not part of base salaries is not
always included in measures. Benefits, taxes, and bonuses are not included in all measures. Series
based on QCEW data include total compensation, whereas series based on CPS and CES data do
not include benefits or irregular compensation. A total compensation measure is useful for
understanding all forms of payment workers receive, whereas some of the more narrowly defined
measures may give a better understanding of the typical amount of money a worker takes home in
a specified period and budgets for living expenses.
What Breakdowns Are Available?
One of the most obvious ways to decide on a wage series is by how that series can be broken
down to measure certain subgroups. Industry, locality, and demographic breakdowns may all be
useful under certain circumstances. It can be particularly useful to choose series in this way when
comparisons are being made between some total and a subcategory. Where possible, using the
same type of series within one analysis is advantageous for isolating differences solely based on
factors such as race, age, or state, for example.

Author Information

Lida R. Weinstock

Analyst Macroeconomic Policy

Disclaimer
This document was prepared by the Congressional Research Service (CRS). CRS serves as nonpartisan
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under the direction of Congress. Information in a CRS Report should not be relied upon for purposes other
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