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June 2014 Adjusting models of ordered multinomial outcomes for nonignorable nonresponse in the occupational employment statistics survey
Nicholas J. Horton, Daniell Toth, Polly Phipps
Ann. Appl. Stat. 8(2): 956-973 (June 2014). DOI: 10.1214/14-AOAS714

Abstract

An establishment’s average wage, computed from administrative wage data, has been found to be related to occupational wages. These occupational wages are a primary outcome variable for the Bureau of Labor Statistics Occupational Employment Statistics survey. Motivated by the fact that nonresponse in this survey is associated with average wage even after accounting for other establishment characteristics, we propose a method that uses the administrative data for imputing missing occupational wage values due to nonresponse. This imputation is complicated by the structure of the data. Since occupational wage data is collected in the form of counts of employees in predefined wage ranges for each occupation, weighting approaches to deal with nonresponse do not adequately adjust the estimates for certain domains of estimation. To preserve the current data structure, we propose a method to impute each missing establishment’s wage interval count data as an ordered multinomial random variable using a separate survival model for each occupation. Each model incorporates known auxiliary information for each establishment associated with the distribution of the occupational wage data, including geographic and industry characteristics. This flexible model allows the baseline hazard to vary by occupation while allowing predictors to adjust the probabilities of an employee’s salary falling within the specified ranges. An empirical study and simulation results suggest that the method imputes missing OES wages that are associated with the average wage of the establishment in a way that more closely resembles the observed association.

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Nicholas J. Horton. Daniell Toth. Polly Phipps. "Adjusting models of ordered multinomial outcomes for nonignorable nonresponse in the occupational employment statistics survey." Ann. Appl. Stat. 8 (2) 956 - 973, June 2014. https://doi.org/10.1214/14-AOAS714

Information

Published: June 2014
First available in Project Euclid: 1 July 2014

zbMATH: 06333783
MathSciNet: MR3262541
Digital Object Identifier: 10.1214/14-AOAS714

Keywords: Administrative data , auxiliary data , categorical outcome , establishment survey , imputation , missing data , regression trees , Survival analysis

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 2 • June 2014
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