Data Mining Methods for Describing Federal Government Career Trajectories and Predicting Employee Separation

Kimberly Healy, Dan Lucas () and Cherilyn Miller
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Kimberly Healy: Pennsylvania State University
Dan Lucas: Pennsylvania State University
Cherilyn Miller: Pennsylvania State University

A chapter in Advances in Service Science, 2019, pp 83-94 from Springer

Abstract: Abstract Data mining methods can be applied to human resources datasets to discover insights into how employees manage their careers. We examine two elements of career trajectories in federal government HR data. First, we apply association rule mining and sequential pattern mining to understand the prevalence and direction of interdepartmental transfers. Then we apply logistic regression and decision tree induction to understand and predict employee separation. In this specific application, we find that interdepartmental transfers are uncommon, except between branches of the armed services and out of these branches to the Department of Defence. We also find that demographics, compensation, and political transitions are significant factors for retention, but they account for only a small portion of the probability of a federal employee leaving service. We expect these methods would perform better in industry with a small amount of additional data gathered upon hiring and exit interviews.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-04726-9_9

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DOI: 10.1007/978-3-030-04726-9_9

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