Training Opportunities: Exploring what happens two months later

Region

Learners differed by geographical location in the likelihood of attaining each labour market outcome. An other outcome (i.e. unemployment/out of the labour force) is the most likely outcome in the Northland and Nelson/Marlborough/West Coast regions. A return to Training Opportunities is most likely outcome in the Southern and Canterbury and the odds of further progressive training over other outcomes are highest in the Central and Wellington regions.

Learners in the Bay of Plenty and Eastern Coast regions are most likely to be employed two months after leaving Training Opportunities.

It is not entirely clear whether this variable is showing a geographic isolation effect, an administration effect or a regional labour market effect. Generally learners placed in programmes in areas that are less densely populated have a higher propensity to gain an employment or further training outcome over an other outcome than those placed in metropolitan areas. This points to a partial isolation effect, but one that operates differently for employment than for training outcomes.

It may be that Training Opportunities participants in some more densely populated areas face stiffer competition for jobs than learners in less densely populated areas; an effect that does not seem to apply to learners in the South Island, however.

Regional effects may also reflect the different concentrations of various industries within them, and differing work patterns. For example, some regions have high concentrations of agricultural and horticultural work, and employment in those regions may be more casually and seasonally-based as a consequence, as well as being more susceptible to cycles in world commodity markets.

There is bound to have been some difference in the strength of the labour market between regions across the time period, and this is also likely to have contributed to the variance explained by this variable. It would be worthwhile modelling regional unemployment rates by year as a predictive factor to determine if this is the case.

It may also be that learners in metropolitan areas / certain regions have higher needs and therefore present more complex cases than those in less dense areas, and these needs are not necessarily correlated with other learner-related variables present in the model.

Figure 2 – Odds ratio of  labour market outcome to other outcome category by region

Note: points above 1 indicate more likely to occur than an other outcome, while points below 1 indicate the outcome is less likely to occur than an other outcome.