An extended Markov-switching model approach to latent heterogeneity in departmentalized manpower systems

Everestus O. Ossai, Uchenna C. Nduka, Mbanefo S. Madukaife, Akaninyene U. Udom and Samson O. Ugwu

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 19, 6957-6976

Abstract: In recent works in manpower planning interest has been awakened in modeling manpower systems in departmentalized framework. This, as a form of disaggregation, may solve the problem of observable heterogeneity but not latent heterogeneity; it rather opens up other aspects of latent heterogeneity hitherto unaccounted for in classical (non departmentalized) manpower models. In this article, a multinomial Markov-switching model is formulated for investigating latent heterogeneity in intra-departmental and interdepartmental transitions in departmentalized manpower systems. The formulation incorporates extensions of the mover-stayer principle resulting in several competing models. The best manpower model is chosen based on the optimum number of hidden states established by the use of Expectation-Maximization iterative algorithm for estimation of the model parameters and a search procedure for assessing model performance against one another. The illustration establishes the usefulness of the model formulation in highlighting hidden disparities in personnel transitions in a departmentalized manpower system and in avoiding wrong model specification.

Date: 2024
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DOI: 10.1080/03610926.2023.2255322

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