Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics
Federico Tullio () and
MPRA Paper from University Library of Munich, Germany
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.
Keywords: Causal inference; Expectation-maximisation algorithm; Potential outcomes; Weighted Maximum Likelihood (search for similar items in EconPapers)
JEL-codes: C33 I32 (search for similar items in EconPapers)
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