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Using Integrated Administrative Data to Identify Youth Who Are at Risk of Poor Outcomes as Adults

2.3 Estimating future outcomes and costs using statistical matching

A statistical record linkage technique was used to help estimate the likely longer-term outcomes of the study populations. This process is discussed in detail in Treasury Analytical Paper 15/01 in the context of earlier analysis of ICD data and is only summarised briefly in this paper.

The approach involved linking data for an older birth cohort (specifically the July 1978 to June 1979 birth cohort) to the data for the 1990/91 birth cohort population to project outcomes for this latter population. Records were linked on the basis of benefit receipt and corrections sentencing rates and patterns when aged 16 to 21 years inclusive as well as on gender and ethnicity. Observed outcomes and costs experienced by the 1978/79 cohort were then used to estimate the outcomes and costs of the 1990/91 cohort up to age 35.

Matching individuals rather than population groups gives us the flexibility to estimate costs for very different subsets of the population. This is particularly important when we are looking to identify specific target populations for investment decisions. The statistical matching method uses real patterns for individuals over time with very similar observed characteristics up to a certain age.

The approach assumes longitudinal patterns of benefit receipt and corrections sentences can be moved around in time from one cohort to another and that, conditional on a set of 'early indicator' matching variables, these patterns remain relevant to later cohorts. The success of this depends on how well we establish good matching criteria and on how relevant these are for forecasting future outcomes. The range of variables used in the matching process also had some significant omissions, such as region and NCEA achievement. As a result, some caution must be taken with analysis based on these characteristics. Differences in groups defined by these characteristics are probably more diluted than the differences in other group comparisons.

We have also not accounted for differences in macro-economic conditions experienced by the 1978/79 cohort and those that may be faced by the 1990/91 cohort in future years. As a result, future outcome estimates will in part reflect the particular patterns of labour demand and unemployment that have occurred over the last 20 years. Ideally, we would like to remove the effects of these macro-economic fluctuations and have a more constant underlying macro-economic picture underpinning the analysis. This remains an issue for further investigation.

Long-run shifts in New Zealand's social assistance policies could also influence the success of the cohort matching if they have affected the outcomes of different birth cohorts very differently. Ideally, we would adjust individuals' outcomes to remove the effects of any secular trends that are external to the individual but affect the outcomes of the cohort as a whole. In practice, however, it may be difficult to do so in an objective way using the data currently available.

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