Appendix A: Translating MPI workforce forecasts to learner enrolment numbers

The aim of this analysis was to use the existing workforce and skill forecasts completed by MPI and NZIER, to estimate what learner numbers might be required in 2025 for different levels of qualification.

This was a first run through of the model and was completed with a tight time-frame and limited resources. While there are improvements that can be made, this forms the foundation of a methodology to translate workforce estimates into learner enrolment estimates.

Summarising the workforce forecasts

The workforce forecasts developed by MPI and NZIER apply three scenarios to predict the workforce of different food and fibre industries in 2032 – 1)Business as usual, 2) Increased use of technology, and 3)transformed sector. They present workforce forecasts for the industry as a whole, and also by MPI designation ‘core production’, ‘core processing/manufacturing’, ‘strongly connected’ and ‘relevant. ’ In most instances for the industries covered by MPI we are specifically interested in the production workforce, with seafood processing being an exception.

These forecasts further provide information on the extent of change expected in the number of people within each industry covered by MPI (which is a subset of the 14 Muka Tangata industry groups) by ‘skill mix’ which was classified into three categories – 1) Managed – people who are entry level/semi-skilled and are supervised daily, 2) semi-autonomous – people who can work independently, typically not actively managed, and 3) managers.

In MPI’s report of the food and fibre workforce they do extensive mapping of these ‘skill’ titles to roles/occupations in specific industries – which was based on interviews with industry representatives. In this workforce report industries are split in a finer detailed way than in the forecast report – e.g. instead of ‘horticulture’ which is used for the forecasting – skill mix to role/occupation is provided for specific horticulture industries like ‘Apples & Pears’

Methodology for mapping occupation to qualification (level)

We collated information from the ‘employment pathway’ of every single qualification under Muka Tangata’s remit (over 100 qualifications) to provide a list of qualifications and associated pathway occupations. We then attempted to match this to the roles/occupations provided by MPI for each category of skill (managed, semi-autonomous, manager). This was straightforward for some occupations and very hard for others, as detail on occupations varied.

We worked with the qualifications team to map all qualifications (and complex apprenticeships) to our 14 industry groups (which at the time of analysis was still a work in progress). Some qualifications sat across multiple industries and some were industry specific. Given our inability to see what specific strands provider-based learners are undertaking, with differing impact per qualification, for this first run we counted all those enrolled in a qualification.

Given time constraints, for this first run we used this qualification to occupation mapping as a way to gauge the relationship between the NZQF level of a qualification and associated skill category (managed, semi-autonomous, manager) for each industry. Once we incorporated enrolment counts for each qualification, this provided us with a count of enrolments by industry group, at specific NZQF levels, with skill mix titles.

Methodology for calculating required number of learners

Now that we had a table that provided estimates of the number of learners enrolled at different skill mix levels for each industry group, we could apply the percentage change expected in that workforce by 2032 to the learner count to estimate the change in learners required if the training rate were to continue at status quo (which is not ideal as it is below desired rates). Then we could estimate the annual change required to reach that target by 2032 – which could be used to inform estimates required by 2025.

To provide the functionality to enable us to do more detailed analysis once we had finalised information on the extent to which we wish to upskill different parts of the current workforce, we also set up a model that estimated change in learner numbers required if the industry was 10% more skilled overall. Our engagement information currently suggests this would differ by industry and by level of qualification – and in most instances the stated desire would equate to a higher number than this.

Opportunities for improving and refining this approach going forward

Time and resource constraints mean this analysis represents a minimum viable product with room for improvement.

The mapping of qualifications (and complex apprenticeships and micro credentials) to industry group has now been finalised after thorough review and there have been some changes in assignment. Future iterations of this model should use the most up to date mapping.

Currently the workforce forecasts use the category horticulture.

The qualifications taken as a whole also better match the broader title of horticulture and are copied across all horticulture relates industry groups. Therefore, the information for vegetables, fruit and nursery, turf and gardening is near identical (with the exception of enrolment changes based on complex apprenticeship mappings). Grapes and wine also has very similar information but cellar operations qualifications are also included in this grouping (though enrolment numbers are not large). The recommendation here is to just refer to horticulture as a whole and to assign learners based on proportion of the workforce.
This could be improved by using strand information where available (ITR learners), although this requires us to do a strand to industry group mapping which is a work in progress and still leaves some issues. The absolute best-case scenario would be to source course information for students which we do not have available to us currently.

Until strand or industry can be sourced for all learners, thought needs to go into how to account for learners in multiple industry qualifications. The model perhaps could be improved by weighting these counts based on workforce and skill category size – i.e if there are lots of ‘managed’ staff in one industry then we might assume qualification 2218 would have more learners from that industry.

The model would be improved through industry consultation to ensure the mapping of NZQF level of qualification to skill category was sensible.

The model could be improved by working with industry and using our evidence base to identify the % change in upskilling of our current workforce required.

Differential completion rates and retention rates could be incorporated into the model.