Latent variable models for multivariate dyadic data with zero inflation: Analysis of intergenerational exchanges of family support

TL;DR

This paper introduces advanced latent variable models incorporating zero inflation and binary indicators to analyze intergenerational family support exchanges, providing a nuanced understanding of help dynamics.

Contribution

The paper develops novel multivariate zero-inflated latent variable models with MCMC estimation for analyzing complex dyadic support data.

Findings

Identified significant predictors of help exchange levels.

Revealed patterns of reciprocity in intergenerational support.

Demonstrated model effectiveness on UK survey data.

Abstract

Understanding the help and support that is exchanged between family members of different generations is of increasing importance, with research questions in sociology and social policy focusing on both predictors of the levels of help given and received, and on reciprocity between them. We propose general latent variable models for analysing such data, when helping tendencies in each direction are measured by multiple binary indicators of specific types of help. The model combines two continuous latent variables, which represent the helping tendencies, with two binary latent class variables which allow for high proportions of responses where no help of any kind is given or received. This defines a multivariate version of a zero inflation model. The main part of the models is estimated using MCMC methods, with a bespoke data augmentation algorithm. We apply the models to analyse exchanges of help between adult individuals and their non-coresident parents, using survey data from the UK Household Longitudinal Study.