A Bayesian Approach to Predicting Disengaged Youth

TL;DR

This paper introduces a Bayesian method for predicting NEET status among youth, emphasizing the importance of covariate partitioning to identify effective intervention factors, with implications for policy and clinical decision-making.

Contribution

It develops a novel Bayesian approach that partitions covariates into modifiable and control groups, improving the identification of intervention-relevant factors.

Findings

Importance of covariate partitioning varies with control variables

Different models yield different key predictive factors

Implications for targeted intervention strategies

Abstract

This article presents a Bayesian approach for predicting and identifying the factors which most influence an individual's propensity to fall into the category of Not in Employment Education or Training (NEET). The approach partitions the covariates into two groups: those which have the potential to be changed as a result of an intervention strategy and those which must be controlled for. This partition allows us to develop models and identify important factors conditional on the control covariates, which is useful for clinicians and policy makers who wish to identify potential intervention strategies. Using the data obtained by O'Dea (2014) we compare the results from this approach with the results from O'Dea (2014) and with the results obtained using the Bayesian variable selection procedure of Lamnisos (2009) when the covariates are not partitioned. We find that the relative importance of predictive factors varies greatly depending upon the control covariates. This has enormous implications when deciding on what interventions are most useful to prevent young people from being NEET.