Affiliations: [a] Economic Research Service, US Department of
Agriculture, Room S-2059, 1800 M Street NW, Washington, DC 20036-5831, USA.
E-mail: [email protected]
| [b] Georgetown Public Policy Institute, 3600 N Street NW,
Washington, DC 20007, USA | [c] William Davidson Institute, University of Michigan,
Ann Arbor, MI 48109-1234, USA
Abstract: Answering essentially any question with sample data requires variance estimates
and these estimates depend critically on the sample design. The design
information necessary to estimate variances for sample statistics from the US
Current Population Survey (CPS) is not publicly released in order to protect
respondent confidentiality. To circumvent this problem, the US Census Bureau
provides a variance estimation methodology but it is only valid for a few
specific point estimates. This paper discusses shortcomings of the Census Bureau
methodology and proposes an alternative, general approximation methodology that
produces variance estimates for a significantly wider class of statistics,
including regression analysis. The proposed approach is based on resorting the
data and assigning subsequent observations to synthetic clusters in a manner
that creates similarities with the actual CPS sample. The synthetic design
approach successfully approximates a baseline for comparison in 34 of the 37
sample estimates considered.
