Alwyn Poole has been working through a major data project on the state of education in New Zealand and says a lot of information is emerging.

The Ministry of Education in NZ has as their motto that “we shape an education system that delivers equitable and excellent outcomes”.

The data tells a very different story. Our system is stratified in terms of socio-economics (decile), ethnicity, gender, schools types (faith based vs value-free).

Even these comments on the most recent PISA results say we differ from the equity in other nations.

“The average difference between advantaged and disadvantaged students in reading is 96 points, compared to an average of 89 in OECD countries.”

“The score difference in science between the 10% of students with the highest scores and the 10% of students with the lowest scores is one of the largest among PISA-participating countries and economies.”

“In New Zealand, 15-year-old students have a strong fear of failure.”

“Unfortunately, the PISA study highlights a persistent challenge for New Zealand, which is our high rates of bullying. Fifteen percent of 15-year-olds report being frequently bullied – double the OECD average.”

As a point of political interest, here is the political oversight of the work electorates for ambition in our country based on the University Entrance achievements.

Electorate Member of Parliament 2020 UE % for School Leavers
Taranaki-King Country Barbara Kuriger (Nat) 19
Manurewa Arena Williams (Lab) 20
Christchurch East Poto Williams (Lab) 24
Mangere Hon Apito Willian Sio (Lab) 24
Northland Willow Jean-Prime (Lab) 25
East Coast Kiri Allen (Lab) 28
West Coast Tasman Damien O’Connor (Lab) 28
Hamilton West Dr Gaurav Sharma (Lab) 29
Papakura Hon Judith Collins (Nat) 29
Contrast: No.1 Epsom David Seymour (Act) 84


The data set is in spreadsheet form and includes sheets on

– high-school size – largest to smallest

– number of 2020 leavers – largest to smallest.

– Level 3 NCEA and above percentage for school leavers for each school 2018-20 (ranked highest to lowest for 2020).

– UE percentage for school leavers for each school 2018-20 (ranked highest to lowest for 2020).

– Each school’s UE percentage for leavers for 2020 compared to decile mean and ranked highest to lowest across all schools.

– Level 3 NCEA and above gap to UE for leavers 2018-20 and ranked smallest to largest for 2020.

– Retention percentage until 17 years of age for each school and ranked highest to lowest.

– Percentage for each school of their leavers going onto L7+ Degree study – ranked highest to lowest.

– The anonymised data mean for full attendance across deciles 2018-20.

– UE percentage by school governance and school type.

It should be of interest to every school, politician, parent and anyone interested in improving NZ’s education system and future society.

Inquiries for the data set to


  1. An interesting article Alwyn. And, yes, you can often pull a lot of stuff out of large/medium datasets. Of course, data should never be interpreted in isolation. 2020 was something of an usual year so I’d be concerned about making any comparisons with previous years data!! A lot of it is obviously Quant data, and at quite a high level – so not broken down enough to do too much with it. Do you have any Qual data too, that could be a useful addition? Does the data include info around access to devices, internet, how many or average devices in the household etc etc (as per the lockdown period in 2020). StatsNZ could have some data that you could modify or use to do some imputation. Have you run any regressions over it? Have you extracted or built any factors, functions, or segments (through PCA, FA, or Discriminant or Clustering algorithms) – I know that is a bit hard with high level datasets but it can be done? As a former statistician, I’d be interested in what you extract from it.


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