The estimates in this release were obtained from the Current Population
Survey (CPS), which provides basic information on the labor force, employment,
and unemployment. The survey is conducted monthly for the Bureau of Labor
Statistics (BLS) by the U.S. Census Bureau using a scientifically selected national
sample of about 60,000 eligible households, with coverage in all 50 states
and the District of Columbia. The earnings data are collected from one-quarter
of the CPS monthly sample and are limited to wage and salary workers (both
incorporated and unincorporated self-employed are excluded). The data, there-
fore, exclude self-employment income.
Information in this release will be made available to sensory impaired
individuals upon request. Voice phone: (202) 691-5200; Federal Relay
Service: (800) 877-8339.
Statistics based on the CPS are subject to both sampling and nonsampling
error. When a sample rather than the entire population is surveyed, there is a
chance that the sample estimates may differ from the "true" population values
they represent. The exact difference, or sampling error, varies depending on
the particular sample selected, and this variability is measured by the stan-
dard error of the estimate. There is about a 90-percent chance, or level of
confidence, that an estimate based on a sample will differ by no more than
1.6 standard errors from the "true" population value because of sampling error.
BLS analyses are generally conducted at the 90-percent level of confidence.
The CPS data also are affected by nonsampling error. Nonsampling errors can
occur for many reasons, including the failure to sample a segment of the popu-
lation, inability to obtain information for all respondents in the sample,
inability or unwillingness of respondents to provide correct information on a
timely basis, mistakes made by respondents, and errors made in the collection
or processing of the data.
A full discussion of the reliability of data from the Current Population
Survey and information on estimating standard errors is available on the BLS
The principal definitions used in connection with the earnings series are
described briefly below.
Usual weekly earnings. Data represent earnings before taxes and other deductions
and include any overtime pay, commissions, or tips usually received (at the main
job in the case of multiple jobholders). Prior to 1994, respondents were asked how
much they usually earned per week. Since January 1994, respondents have been asked
to identify the easiest way for them to report earnings (hourly, weekly, biweekly,
twice monthly, monthly, annually, other) and how much they usually earn in the
reported time period.
Earnings reported on a basis other than weekly are converted to a weekly equi-
valent. The term "usual" is as perceived by the respondent. If the respondent asks
for a definition of "usual", interviewers are instructed to define the term as more
than half the weeks worked during the past 4 or 5 months.
Medians (and other quantiles) of weekly earnings. The median (or upper limit of
the second quartile) is the amount that divides a given earnings distribution into
two equal groups, one having earnings above the median and the other having earnings
below the median. Ten percent of a given distribution have earnings below the upper
limit of the first decile (90 percent have higher earnings); 25 percent have earnings
below the upper limit of the first quartile (75 percent have higher earnings); 75
percent have earnings below the upper limit of the third quartile (25 percent have
higher earnings); and 90 percent have earnings below the upper limit of the ninth
decile (10 percent have higher earnings).
The estimation procedure places each reported or calculated weekly earnings value
into $50-wide intervals that are centered around multiples of $50. The actual value
is estimated through the linear interpolation of the interval in which the quantile
Over-the-year changes in the medians (and other quantile boundaries) for specific
groups may not necessarily be consistent with the movements estimated for the overall
quantile boundary. The most common reasons for this possible anomaly are: (1) There
could be a change in the relative weights of the subgroups. For example, the medians
of both 16- to 24-year-olds and those 25 years and over may rise; but if the lower-
earning 16- to 24-year-olds group accounts for a greatly increased share of the total,
the overall median could actually fall. (2) There could be a large change in the shape
of the distribution of reported earnings, particularly near a quantile boundary. This
could be caused by survey observations that are clustered at rounded values, such as
$250, $300, or $400. An estimate lying in a $50-wide centered interval containing
such a cluster or "spike" tends to change more slowly than one in other intervals.
Wage and salary workers. Workers who receive wages, salaries, commissions, tips,
payment in kind, or piece rates. The group includes employees in both the private
and public sectors but, for the purposes of the earnings series, excludes all self-
employed persons, regardless of whether or not their businesses are incorporated.
Full-time workers. Workers who usually work 35 hours or more per week at their
sole or principal job.
Part-time workers. Workers who usually work fewer than 35 hours per week at their
sole or principal job.
Constant dollars. The Consumer Price Index for All Urban Consumers (CPI-U) is used
to convert current dollars to constant (1982-84) dollars.
Hispanic or Latino ethnicity. Refers to persons who identified themselves in the
enumeration process as being of Hispanic, Latino, or Spanish origin. Persons whose
ethnicity is identified as Hispanic or Latino may be of any race.
Over the course of a year, the size of the nation's labor force and other measures
of labor market activity undergo regularly occurring fluctuations. These recurring
events include seasonal changes in weather, major holidays, and the opening and closing
of schools. The effect of such seasonal variations can be very large.
Because seasonal events follow a more or less regular pattern each year, their
influence on the level of a series can be tempered by adjusting for regular seasonal
variation. These adjustments make nonseasonal developments easier to spot. The season-
ally adjusted figures provide a more useful tool with which to analyze changes in
At the end of each calendar year, the seasonally adjusted data are revised for the
past 5 years when the seasonal adjustment factors are updated. More information on sea-
sonal adjustment is available on the BLS website at www.bls.gov/cps/documentation.htm#sa.