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Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics

Author

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  • Tullio, Federico
  • Bartolucci, Francesco

Abstract

To assess the effectiveness of remittances on the poverty level of recipient households, we propose a causal inference approach that may be applied with longitudinal data and time-varying treatments. The method relies on the integration of a propensity score based technique, the inverse propensity weighting, with a general Latent Markov (LM) framework. It is particularly useful when the interest is in an individual characteristic that is not directly observable and the analysis is focused on: (i) clustering individuals in a finite number of classes according to this latent characteristic and (ii) modelling its evolution across time depending on the received treatment. Parameter estimation is based on a two-step procedure in which individual weights are computed for each time period based on predetermined covariates and a weighted version of the standard LM model likelihood based on such weights is maximised by means of an expectation-maximisation algorithm. Finite-sample properties of the estimator are studied by simulation. The application is focused on the effect of remittances on the poverty status of Ugandan households, based on a longitudinal survey spanning the period 2009-2014 and where response variables are indicators of deprivation.

Suggested Citation

  • Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:91459
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    File URL: https://mpra.ub.uni-muenchen.de/91459/1/MPRA_paper_91459.pdf
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    Cited by:

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    2. F. J. Clouth & S. Pauws & F. Mols & J. K. Vermunt, 2022. "A new three-step method for using inverse propensity weighting with latent class analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 351-371, June.

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    More about this item

    Keywords

    Causal inference; Expectation-maximisation algorithm; Potential outcomes; Weighted Maximum Likelihood;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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