  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.
