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1 Introduction

Greater use of population data in the IDI (Integrated Data Infrastructure held at Statistics New Zealand) supports analysis that enables the public sector to identify sub-groups of the population who are at risk of poorer education, welfare, health and corrections related outcomes. Treasury's analytical paper 16/01 describes in detail the creation of the datasets that underpin the work described in this note:

Characteristics of Children at Greater Risk of Poor Outcomes as Adults (http://www.treasury.govt.nz/publications/research-policy/ap/2016/16-01)

In this note we build on that analysis to examine what might be the impact of better social sector performance on long term fiscal trajectories. A number of scenarios are outlined in Burton et al (2016) “The benefits of improved social sector performance”. The analysis in this note includes four sub-populations which are the focus of the scenarios outlined in that report.

The earlier analytical work had three distinct phases:

  1. Individuals in two cohorts (born in 1990/91 or in 1993) were observed in the IDI data from birth through to their early twenties. This enabled us to understand the association between various explanatory characteristics (gender, ethnicity, region, contact with CYF, family welfare history, caregiver corrections contact etc) and education, health, welfare and corrections outcomes as young adults. These relationships are summarised in a set of regression models. We constructed separate models for males and females at each year of age.
  2. The second phase involved estimating longer term outcomes for these people based on statistical techniques that involved matching individuals from younger cohorts to individuals in older cohorts. This enabled us to estimate likely future cost trajectories out to age 34 for each individual.
  3. Finally a “current” (2013) population of children (aged 0 to 14) had their risk of poorer outcomes estimated using the models from the first phase. Future cost trajectories were also estimated for these children using similar statistical matching techniques between cohorts. This analysis gave us a more contemporary picture of risks and future costs.

This note uses the datasets created in this earlier work combined with a strengths-based measure of being “on track at 21”. This measure represents what we hope to see if the cumulative impact of individual, family, community factors and government services mean young people are “on track” for success in adulthood. We define this to be:

  • having attained or enrolled in a course at level four or above (training for skilled employment) or
  • being employed and earning more than two-thirds of median wage for most of their 21st year (approximately the “living wage”) or
  • being self-employed

(Note: we exclude those who served a custodial sentence in their 21st year)

We begin by reviewing the descriptive analysis of the 1993 cohort, showing their interactions with different government agencies up to their early adult years. This is largely drawn from the previous analysis but we have added some hospital event-related data and the “on track at 21” measure. We focus on sub-groups relevant to four scenarios discussed in Burton et al (2016).

We then show how we have constructed risk measures and projections of future fiscal costs for each individual in both the 1993 cohort dataset and the current population dataset.

We then describe how we calculate parameters (cost ratios) which are used in the long-term fiscal modelling of the impacts for each of the four scenarios. These ratios reflect how much we might expect spending on welfare and corrections to reduce under the four scenarios. We do this by modelling changes in the risk distribution and use the observed relationships between risk and future costs in the micro-level population datasets we have created to estimate the possible reductions in future costs.

It should be noted that the results reported in this note have a heavy focus on one cohort (1993). The cohort's interactions with government social agencies reflect the cyclical economic conditions and social policy settings in place during their upbringing, and the quality of the administrative data systems across a twenty year period. Other population cohorts are likely to experience different economic conditions and social policy settings during their lifetimes and some measures of interactions with government agencies will be better recorded in more recent data. For example, more recent cohorts will not necessarily experience the same level of associations between the factors recorded and the labour market or tertiary education participation rates.

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