Calibrating the Empirical Bayes to Decision-Based Estimates in the Annual Survey of Public Employment and Payroll

• Activity Number: 207
• Type: Invited
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Survey Research Methods Section
• Author(s): Justin Nguyen*+ and Joseph Barth
• Companies: U.S. Census Bureau and U.S. Census Bureau
• Keywords: Government Units ; Small Area Estimation ; Empirical Bayes ; Decision-based ; Benchmarking

Abstract

The Governments Division of the U.S. Census Bureau uses small area estimation techniques for several of its surveys. The Annual Survey of Public Employment and Payroll (ASPEP) yields estimates of the number of federal, state, and local government civilian employees and their gross payrolls. The ASPEP sample design is based on state and type of government as strata from which a proportional-to-size sampling design is applied. Estimation of government totals at the state and functional level, e.g., air transportation, public welfare, hospitals, etc. are produced. This estimation motivates the small area estimation methodology that enables the production of reliable estimates in the level of aggregation small cells where direct estimators show some limitations. We used Empirical Bayes (EB) models to estimate the totals for the cells. At the state and national level aggregates,the totals obtained from the direct estimates are reliable due to big data. Furthermore, we obtain other reliable totals, Decision-based estimates, from which we benchmark on. In this paper, we show how to use the EB estimation, and then benchmark the estimates to the direct estimates and Decision-based totals.