Sample Allocation to Increase the Expected Number of Publishable Cells in the Survey of Occupational Injuries and Illnesses

• Activity Number: 68
• Type: Topic Contributed
• Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
• Sponsor: Survey Research Methods Section
• Author(s): Diem-Tran Kratzke* and Daniell Toth
• Companies: Bureau of Labor Statistics and Bureau of Labor Statistics
• Keywords: optimum allocation ; constrained optimization ; generalized variance function (GVF) ; establishment survey ; stratified sample ; gradient descent

Abstract

The Survey of Occupational Injuries and Illnesses(SOII) is an establishment survey that provides annual estimates for the incidence count and rate of employer-reported work-related injuries and illnesses. Results of the survey are published by industry for the nation and participating states. Low response rates for some industries within a state result in many of the state industry-level estimates not being published because of quality and/or confidentiality concerns. The SOII sample is stratified by state, ownership, industry, and size. The number of sample units from each sampling stratum is currently determined by the Neyman allocation, which is intended to minimize the expected sample variance of the estimator for total recordable cases given the fixed sample size. Our goal for the study is to develop a new sample allocation to increase the publishability of estimates at the state industry level while constraining the variance for the fixed sample size. In this paper, we explore a method for assigning sample allocation that aims to maximize the number of publishable cells while constraining the variance of the estimator for total recordable cases.