Analysing the course of public trust via hidden Markov models: a focus on the Polish society
Fulvia Pennoni and
Ewa Genge ()
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Ewa Genge: University of Economics
Statistical Methods & Applications, 2020, vol. 29, issue 2, No 10, 399-425
Abstract We investigate public trust among the society by a statistical model suitable for panel data. At this aim, using trust’s levels measured from individual items recorded through a long-term survey we dispose of key variables with appropriate meaning. We account for the repeated and missing item responses by a hidden Markov model using longitudinal sampling weights. Since trust may be conceived as a psychological unobservable process of each person that fluctuates over time we consider observed time-varying and time-fixed individual covariates. We estimate the model parameters by a weighted log-likelihood through the Expectation–Maximization algorithm by using data collected in an East-Central European country like Poland. The latter is a country where the level of support to the national and international institutions is one of the lowest among the European member states. We apply a suitable algorithm based on the posterior probabilities to predict the best allocation to each latent typology. The proposed model is validated by generating out-of-sample responses and we find reasonable predictive values. We disentangle four hidden groups of Poles: discouraged, with no opinion, with selective trust and with fully public trust. We reveal an increasing number of people that are going to trust only some selected institutions over time.
Keywords: Expectation–Maximization algorithm; Missing responses; Panel data; Sampling weights; Trust-building policy discussion (search for similar items in EconPapers)
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