Variance Estimation for Decision-Based Estimators with Application to the Annual Survey of Public Employment and Payroll

• Activity Number: 243
• Type: Contributed
• Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Government Statistics
• Author(s): Yang Cheng*+ and Slud Eric and Carma Hogue
• Companies: U.S. Census Bureau and University of Maryland and U.S. Census Bureau
• Keywords: survey design ; decision-based estimation ; re-sampling method ; bootstrap ; mean squared error

Abstract

In 2009, two major surveys in the Governments Division of the Census Bureau were redesigned to reduce sample size, save resources, and improve the precision of the estimates. We developed a new decision-based estimation method in which the collapsing of strata either by state and government type or by small and large size was determined by a series of hypothesis tests of the equality of fitted coefficients in linear relationships between attributes and their values in the previous census year. In this research, we study design-based variance estimation by a bootstrap method, for the new decision-based stratified regression estimates applied to the Annual Survey of Public Employment and Payroll. The bootstrap method, which goes beyond available theory, is validated through a small simulation study. We use the data from the 2007 Census of Governments Employment to illustrate our methods.