This news release presents statistics from two major surveys, the Current
Population Survey (CPS; household survey) and the Current Employment Statistics
survey (CES; establishment survey). The household survey provides information
on the labor force, employment, and unemployment that appears in the "A" tables,
marked HOUSEHOLD DATA. It is a sample survey of about 60,000 eligible households
conducted by the U.S. Census Bureau for the U.S. Bureau of Labor Statistics (BLS).
The establishment survey provides information on employment, hours, and
earnings of employees on nonfarm payrolls; the data appear in the "B" tables,
marked ESTABLISHMENT DATA. BLS collects these data each month from the payroll
records of a sample of nonagricultural business establishments. Each month
the CES program surveys about 142,000 businesses and government agencies,
representing approximately 689,000 individual worksites, in order to provide
detailed industry data on employment, hours, and earnings of workers on nonfarm
payrolls. The active sample includes approximately one-third of all nonfarm
For both surveys, the data for a given month relate to a particular week or
pay period. In the household survey, the reference period is generally the
calendar week that contains the 12th day of the month. In the establishment
survey, the reference period is the pay period including the 12th, which may or
may not correspond directly to the calendar week.
Coverage, definitions, and differences between surveys
Household survey. The sample is selected to reflect the entire civilian
noninstitutional population. Based on responses to a series of questions on
work and job search activities, each person 16 years and over in a sample
household is classified as employed, unemployed, or not in the labor force.
People are classified as employed if they did any work at all as paid employees
during the reference week; worked in their own business, profession, or on their
own farm; or worked without pay at least 15 hours in a family business or farm.
People are also counted as employed if they were temporarily absent from their jobs
because of illness, bad weather, vacation, labor-management disputes, or personal
People are classified as unemployed if they meet all of the following criteria:
they had no employment during the reference week; they were available for work at
that time; and they made specific efforts to find employment sometime during the
4-week period ending with the reference week. Persons laid off from a job and
expecting recall need not be looking for work to be counted as unemployed. The
unemployment data derived from the household survey in no way depend upon the
eligibility for or receipt of unemployment insurance benefits.
The civilian labor force is the sum of employed and unemployed persons.
Those persons not classified as employed or unemployed are not in the labor
force. The unemployment rate is the number unemployed as a percent of the
labor force. The labor force participation rate is the labor force as a
percent of the population, and the employment-population ratio is the
employed as a percent of the population. Additional information about the
household survey can be found at https://www.bls.gov/cps/documentation.htm.
Establishment survey. The sample establishments are drawn from private
nonfarm businesses such as factories, offices, and stores, as well as
from federal, state, and local government entities. Employees on nonfarm
payrolls are those who worked or received pay for any part of the reference pay
period, including persons on paid leave. Persons are counted in each job
they hold. Hours and earnings data are produced for the private sector for
all employees and for production and nonsupervisory employees. Production
and nonsupervisory employees are defined as production and related employees
in manufacturing and mining and logging, construction workers in construction,
and nonsupervisory employees in private service-providing industries.
Industries are classified on the basis of an establishment's principal
activity in accordance with the 2017 version of the North American Industry
Classification System. Additional information about the establishment survey
can be found at https://www.bls.gov/ces/.
Differences in employment estimates. The numerous conceptual and methodological
differences between the household and establishment surveys result in important
distinctions in the employment estimates derived from the surveys. Among these are:
--The household survey includes agricultural workers, self-employed workers
whose businesses are unincorporated, unpaid family workers, and private
household workers among the employed. These groups are excluded from the
--The household survey includes people on unpaid leave among the employed.
The establishment survey does not.
--The household survey is limited to workers 16 years of age and older.
The establishment survey is not limited by age.
--The household survey has no duplication of individuals, because
individuals are counted only once, even if they hold more than one
job. In the establishment survey, employees working at more than one
job and thus appearing on more than one payroll are counted separately
for each appearance.
Over the course of a year, the size of the nation's labor force and the levels
of employment and unemployment undergo regularly occurring fluctuations. These
events may result from seasonal changes in weather, major holidays, and the opening
and closing of schools. The effect of such seasonal variation can be very large.
Because these 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, such as
declines in employment or increases in the participation of women in the labor
force, easier to spot. For example, in the household survey, the large number of
youth entering the labor force each June is likely to obscure any other changes
that have taken place relative to May, making it difficult to determine if the
level of economic activity has risen or declined. Similarly, in the establishment
survey, payroll employment in education declines by about 20 percent at the end
of the spring term and later rises with the start of the fall term, obscuring the
underlying employment trends in the industry. Because seasonal employment changes
at the end and beginning of the school year can be estimated, the statistics can be
adjusted to make underlying employment patterns more discernable. The seasonally
adjusted figures provide a more useful tool with which to analyze changes in
month-to-month economic activity.
Many seasonally adjusted series are independently adjusted in both the household
and establishment surveys. However, the adjusted series for many major estimates,
such as total payroll employment, employment in most major sectors, total employment,
and unemployment are computed by aggregating independently adjusted component series.
For example, total unemployment is derived by summing the adjusted series for four
major age-sex components; this differs from the unemployment estimate that would be
obtained by directly adjusting the total or by combining
the duration, reasons, or more detailed age categories.
For both the household and establishment surveys, a concurrent seasonal adjustment
methodology is used in which new seasonal factors are calculated each month using all
relevant data, up to and including the data for the current month. In the household
survey, new seasonal factors are used to adjust only the current month's data. In the
establishment survey, however, new seasonal factors are used each month to adjust the
three most recent monthly estimates. The prior 2 months are routinely revised to
incorporate additional sample reports and recalculated seasonal adjustment factors.
In both surveys, 5-year revisions to historical data are made once a year.
Reliability of the estimates
Statistics based on the household and establishment surveys 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 component of this difference that occurs
because samples differ by chance is known as sampling error, and its variability
is measured by the standard 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.
For example, the confidence interval for the monthly change in total nonfarm
employment from the establishment survey is on the order of plus or minus 110,000.
Suppose the estimate of nonfarm employment increases by 50,000 from one month to
the next. The 90-percent confidence interval on the monthly change would range from
-60,000 to +160,000 (50,000 +/- 110,000). These figures do not mean that the sample
results are off by these magnitudes, but rather that there is about a 90-percent
chance that the true over-the-month change lies within this interval. Since this
range includes values of less than zero, we could not say with confidence that
nonfarm employment had, in fact, increased that month. If, however, the reported
nonfarm employment rise was 250,000, then all of the values within the 90- percent
confidence interval would be greater than zero. In this case, it is likely (at
least a 90-percent chance) that nonfarm employment had, in fact, risen that month.
At an unemployment rate of around 6.0 percent, the 90-percent confidence interval
for the monthly change in unemployment as measured by the household survey is
about +/- 300,000, and for the monthly change in the unemployment rate it is about
+/- 0.2 percentage point.
In general, estimates involving many individuals or establishments have lower
standard errors (relative to the size of the estimate) than estimates which are based
on a small number of observations. The precision of estimates also is improved when
the data are cumulated over time, such as for quarterly and annual averages.
The household and establishment surveys are also affected by nonsampling error,
which can occur for many reasons, including the failure to sample a segment of the
population, 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.
For example, in the establishment survey, estimates for the most recent 2 months
are based on incomplete returns; for this reason, these estimates are labeled
preliminary in the tables. It is only after two successive revisions to a monthly
estimate, when nearly all sample reports have been received, that the estimate is
Another major source of nonsampling error in the establishment survey is the
inability to capture, on a timely basis, employment generated by new firms. To
correct for this systematic underestimation of employment growth, an estimation
procedure with two components is used to account for business births. The first
component excludes employment losses from business deaths from sample-based
estimation in order to offset the missing employment gains from business births.
This is incorporated into the sample-based estimation procedure by simply not
reflecting sample units going out of business, but imputing to them the same
employment trend as the other firms in the sample. This procedure accounts for
most of the net birth/death employment.
The second component is an ARIMA time series model designed to estimate the
residual net birth/death employment not accounted for by the imputation. The
historical time series used to create and test the ARIMA model was derived from
the unemployment insurance universe micro- level database, and reflects the actual
residual net of births and deaths over the past 5 years.
The sample-based estimates from the establishment survey are adjusted once a
year (on a lagged basis) to universe counts of payroll employment obtained from
administrative records of the unemployment insurance program. The difference
between the March sample-based employment estimates and the March universe counts
is known as a benchmark revision, and serves as a rough proxy for total survey
error. The new benchmarks also incorporate changes in the classification of
industries. Over the past decade, absolute benchmark revisions for total nonfarm
employment have averaged 0.3 percent, with a range from -0.7 percent to 0.6 percent.
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.