A model-based approach to predict employee compensation components

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Andreea L. Erciulescu, Jean D. Opsomer
{"title":"A model-based approach to predict employee compensation components","authors":"Andreea L. Erciulescu,&nbsp;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.

基于模型的员工薪酬预测方法
对精细官方统计的需求促使研究者探索超越传统的基于调查的估计方法。对于这项工作,挑战源于美国劳工统计局,他们进行了全国薪酬调查,从全国范围内的企业样本中收集薪酬数据。其目的是对由详细的工作特征界定的大量就业领域的工资和非工资部分的薪酬进行预测。调查估计仅适用于这些领域的一小部分。为了实现这一目标,我们开发了一个双变量分层贝叶斯模型,该模型可以共同预测由详细工作特征定义的大量就业领域的工资和非工资补偿成分。我们还讨论了在大规模环境下实施小面积估计方法时遇到的一些实际挑战的解决方案,包括定义预测空间的方法,构建和选择作为模型输入的信息的方法,以及获得稳定的调查方差和协方差估计的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信