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A multiple state model for the working-age disabled population using cross-sectional data

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  • Poontavika Naka
  • María del Carmen Boado-Penas
  • Gauthier Lanot

Abstract

A multiple state model describes the transitions of the disability risk among the states of active, inactive and dead. Ideally, estimations of transition probabilities and transition intensities rely on longitudinal data; however, most of the national surveys of disability are based on cross-sectional data measuring the disabled status of an individual at one point in time. This paper aims to propose a generic method of the estimation of the expected transition probabilities when the model allows recovery from disability using the UK cross-sectional data. The disability prevalence rates are modelled by taking into consideration the effect of age and time. Under some plausible assumptions concerning the death rates among inactive and active people, the estimated prevalence rates of disability are used to decompose survival probabilities in each state.

Suggested Citation

  • Poontavika Naka & María del Carmen Boado-Penas & Gauthier Lanot, 2020. "A multiple state model for the working-age disabled population using cross-sectional data," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2020(8), pages 700-717, September.
    • Handle: RePEc:taf:sactxx:v:2020:y:2020:i:8:p:700-717
    DOI: 10.1080/03461238.2020.1724192
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    Cited by:

    1. Manuel L. Esquível & Nadezhda P. Krasii & Gracinda R. Guerreiro, 2024. "Estimation–Calibration of Continuous-Time Non-Homogeneous Markov Chains with Finite State Space," Mathematics, MDPI, vol. 12(5), pages 1-21, February.

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