{"title":"A model-based approach to predict employee compensation components","authors":"Andreea L. Erciulescu, Jean D. Opsomer","doi":"10.1111/rssc.12587","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"100","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/rssc.12587","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 4
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.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.