Abstract
Janice Lent, Stephen Miller, and P. Cantwell (1994)
"Composite Weights for the Current Population Survey," Proceedings
of the Section on Survey Research Methods, American Statistical
Association, 420-424.
Each month, the Bureau of Labor Statistics (BLS) publishes
labor force estimates for the U.S. resident population and a
variety of its demographic subgroups, e.g., teenagers,
Hispanics. Published figures include estimated numbers of
persons employed, unemployed, and not in the labor force, as
well as relevant rates such as unemployment rates. These
statistics are computed using data from the Current
Population Survey (CPS), a monthly household survey the
Census Bureau conducts for the BLS.
The CPS sample is a two-stage probability sample of housing
units, covering the entire U.S. Each new sample unit remains
in the sample for four months, leaves the sample for eight
months, and then re-enters for another four months. One
quarter of the sample is new (or re-entering) each month,
while half of each month's sample comes from the sample for
the same calendar month one year earlier. This
"four-eight-four" sample rotation scheme results in
positive correlation between CPS estimates from different
months, improving measures of change over time. The positive
correlation is further increased by composite estimation.
Composite estimation is the last in a series of estimation
steps performed on CPS data, prior to seasonal adjustment.
Unlike weighting techniques, composite estimation does not
affect CPS micro data; composite estimates are computed using
estimated totals from the various rotation groups—groups of
respondents who enter the sample together. Since the
composite estimates incorporate information from several
months' data, users cannot compute composite estimates from
only one month's micro data.
In this paper, we present a method of computing composite
weights for the CPS micro data weights that incorporate the
effect of composite estimation. Data users would compute
composite estimates by simply adding these weights, using
only one month's CPS data. This method, suggested by Fuller
(1990), also allows us to tailor the composite estimator by
varying coefficients to the correlation structures of major
labor force categories, thus improving reliability. Section 2
provides a brief overview of current CPS estimation
procedures, including composite estimation. In Section 3, we
describe the process of selecting compositing coefficients
for different labor force categories. Section 4 contains
results of an empirical study of two variants of Fuller's
composite weighting method, as applied to CPS data.
Last Modified Date: July 19, 2008
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