# Bayesian analysis of Turkish Income and Living Conditions data, using clustered longitudinal ordinal modelling with Bridge distributed random-effects

## TL;DR

This study employs Bayesian clustered longitudinal ordinal modeling with Bridge distributed random-effects to analyze Turkish income and living conditions data, focusing on health outcomes and their associations with socioeconomic factors.

## Contribution

It introduces a novel Bayesian approach using Bridge distributed random-effects for marginal inference in longitudinal ordinal data analysis.

## Key findings

- Differences in health reporting odds across employment status.
- Income level impacts self-reported health.
- Panel year influences health outcome odds.

## Abstract

This paper is motivated by the panel surveys, called Statistics on Income and Living Conditions (SILC), conducted annually on (randomly selected) country-representative households to monitor EU 2020 aims on poverty reduction. We particularly consider the surveys conducted in Turkey, within the scope of integration to the EU, between 2010 and 2013. Our main interests are on health aspects of economic and living conditions. The outcome is {\it self-reported health} that is clustered longitudinal ordinal, since repeated measures of it are nested within individuals and individuals are nested within families. Economic and living conditions were measured through a number of individual- and family-level explanatory variables. The questions of interest are on the marginal relationships between the outcome and covariates that are addressed using a polytomous logistic regression with Bridge distributed random-effects. This choice of distribution allows one to {\it directly} obtain marginal inferences in the presence of random-effects. Widely used Normal distribution is also considered as the random-effects distribution. Samples from the joint posterior density of parameters and random-effects are drawn using Markov Chain Monte Carlo. Interesting findings from public health point of view are that differences were found between sub-groups of employment status, income level and panel year in terms of odds of reporting better health.