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Research Using Administrative Data to Support the Work of the Expert Panel on Modernising Child, Youth and Family

4 Projecting fiscal costs at a micro-data level

The scenarios we examined using the IDI dataset for the LTFM analysis each identified a sub-population that would be the main focus for improved social services delivery:

The individual-level datasets (both the 1993 cohort and the current dataset of children) enabled us to identify these populations directly, and estimate their risk profiles and their projected fiscal costs.

4.1  Calculating projected fiscal costs at a micro-data level

In Treasury's earlier analytical work statistical record linkage was used to help estimate the likely longer-term outcomes of the study population. The approach involved linking data for an older birth cohort (specifically the July 1978 to June 1979 birth cohort) to the data for the 1993 birth cohort, to simulate the likely 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 the basis of gender and ethnicity. Observed outcomes and costs experienced by the 1978/79 cohort were then used to estimate the outcomes and costs of the 1993 cohort up to age 35.

Using a similar matching technique the outcomes of the current population of children aged 0 to 14 years are estimated by linking each of them to an individual from the 1993 cohort. Records are linked on the basis of the child's contact with child and protection services, caregivers' benefit receipt, caregivers' corrections sentencing history, and some early secondary school enrolment data (for the 13 and 14 year olds) as well as gender and ethnicity. The link through to the 1978/79 birth cohort provides outcome and cost projections to age 35 for all children aged 0 to 14 years.

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.

Figure 7: Projected fiscal cost trajectories by NCEA attainment (1993 cohort)
Figure 7: Projected fiscal cost trajectories by NCEA attainment (1993 cohort).

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 approach depends on how well we establish good matching criteria and on how relevant these are for forecasting future outcomes. We have also not accounted for differences in macro-economic conditions experienced by the two cohorts. 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 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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