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7 Conclusions

This paper has presented findings from an analysis of Statistics New Zealand's Integrated Data Infrastructure. The analysis looked at the characteristics of young people aged 15 to 24 who were at risk of poor outcomes as adults across welfare, corrections, education and health domains, and attempted to define useful and identifiable target populations at high risk of experiencing these poor outcomes.

The work was undertaken by the Analytics and Insights team at the Treasury in collaboration with other government agencies. It fed into a stream of work being led by the Ministry of Education, resulting in the production of the accompanying A3 document. The analysis presented here represents one of a number of early steps towards using a more data-driven approach to prioritising social assistance initiatives and evaluating the effectiveness of social assistance programmes. The analysis has highlighted a number of characteristics that are predictive of future poor outcomes. Examples include early contact with government agencies such as Child, Youth and Family (CYF), caregiver qualifications and benefit status, geographic location and participation and early outcomes in the education system. These can be used to quantify risk at an individual level and to identify the size and characteristics of at-risk groups of young people at different ages.

The characteristics that are predictive of future outcomes change over time. As young people progress into early adulthood, poor future outcomes become directly evident through contact with the benefit, corrections and health systems. This, combined with the proximity of the outcomes period we are seeking to predict, means that it becomes easier to predict poor outcomes as a young person ages. At the same time, however, these outcomes may become more and more difficult to influence.

It is possible to identify groups of at-risk youth at different ages using a small set of identifying characteristics, such as benefit type and duration, corrections sentencing information and information on a person's early contact with government agencies such as CYF. These predictions are by no means perfect however. Those young people who are identified as being at risk are much more likely to have poor future outcomes than those who aren't, but a large number of people have poor outcomes despite not falling into one of these defined groupings. Approaches to targeting services should be flexible enough to offer services based on particular individual circumstances as well as broad characteristics.

One useful way of targeting services is to focus on specific areas with higher concentrations of at-risk youth. However, there is a tension between targeting those services at areas where a high proportion of youth are at risk (such as Kawerau or Opotiki) and larger centres where large number of at-risk youth live (such as Manukau or Waitakere).

All of the findings in the paper should be treated with some caution given the various caveats associated with the data and methods used as well as the early stage of this type of analysis. There is some scope to improve the results in future, taking advantage of the improvements that are being made to linked administrative data and refining the analytical and estimation methods.

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