A model‐based approach to predict employee compensation components

Andreea L. Erciulescu and Jean D. Opsomer

Journal of the Royal Statistical Society Series C, 2022, vol. 71, issue 5, 1503-1520

Abstract: The demand for official statistics at fine levels is motivating researchers to explore estimation methods that extend beyond the traditional survey‐based estimation. For this work, the challenge originated with the US Bureau of Labor Statistics, who conducts the National Compensation Survey to collect compensation data from a nationwide sample of establishments. The objective is to obtain predictions of the wage and non‐wage components of compensation for a large number of employment domains defined by detailed job characteristics. Survey estimates are only available for a small subset of these domains. To address the objective, we developed a bivariate hierarchical Bayes model that jointly predicts the wage and non‐wage compensation components for a large number of employment domains defined by detailed job characteristics. We also discuss solutions to some practical challenges encountered in implementing small area estimation methods in large‐scale settings, including methods for defining the prediction space, for constructing and selecting the information that serves as model input, and for obtaining stable survey variance and covariance estimates.

Date: 2022
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