The tech sector is often touted as the next big thing for regional economies, representing a huge opportunity to grow our productivity. The ‘weightless’ nature of tech presents a great opportunity to overcome the tyranny of distance, both within New Zealand and for exporting overseas. Unfortunately for economists, it represents something of a slippery fish, with standard industry and occupation classification systems at times struggling to classify and monitor such a dynamic and evolving sector.

Across nearly all our work, we use the ANZSIC industry classification system (dating back to 2006) and ANZSCO occupation classification system (reviewed in 2013). ANZSCO does have some elements of modernity, such as the inclusion of the occupation ‘Chief Information Officer’, however ‘Data Scientist’ are lumped into ‘Information and Organisation Professional Not Elsewhere Counted’ (would Maree Kondo fit in here too?). The 2006-dated industry classification system is more challenging, with an incredibly broad ‘computer system design’ industry capturing virtually all of the ‘cool’ tech sector activities without further breakdown. ‘Computer system design’ includes software as a service (SaaS) firms like Xero and Orion Health, alongside more traditional IT support providers, software development consultancies, website designers, and many other specialties.

Contrast this with the manufacturing industry – under the ANZSIC classification, there are 143 sub-industries within the manufacturing industry. This means we can tell you more about your local potato crisp and corn chip manufacturing industry, than we can about your local Software as a Service (SaaS) industry.

Nearly a decade ago, MBIE developed a tech sector definition based on ANZSIC industries, with the intent of aligning with definitions used by the OECD. This goes beyond ICT, reaching out into high-tech elements of the manufacturing, wholesaling and leasing industries. MBIE’s definition has been fairly widely accepted, and has been adopted by tech industry bodies and regional economic development agencies. It appears that this was designed with ‘tech unicorns’ in mind, attempting to capture high-tech niche firms such as Tait Communications and Rakon. However, just because our ‘tech unicorns’ have used technology to become leaders in their industries, that doesn’t mean that all of the firms in their industries are high-tech. For example, Rocket Lab is absolutely a high-tech manufacturer, but many of its peers in the ‘aircraft manufacturing and repair services’ industry are not high-tech, for example carrying out routine servicing on light aircraft. As a result, the MBIE tech sector definition is incredibly broad, drawing in 116,900 jobs or 4.3% of the total workforce in 2022.

This leads into the challenge of tracking applied-tech firms, such as agri-tech or fin-tech, where technology is being applied to improve non-tech industries. Sharesies as an example of fintech, as they apply technology to offer a new approach to investing in shares. Under the ANZSIC system, Sharesies is classified as a ‘portfolio investment management service’ – lumped in with its traditional, non-tech competitors. An agri-tech firm providing software to help farmers optimise their fertiliser application would likely be grouped into the monolith of ‘computer system design’. A game development studio could be classified in either ‘computer system design’ or in the TV and film industry, depending on the exact nature of their business. This is unfortunate because applied tech is arguably the biggest prize for New Zealand – it would be great to have a few more Xero’s, but applying tech to our large primary sector could make a bigger difference to our productivity, exports and living standards.

KiwiSaaS compiled data from Callaghan Innovation and Stats NZ on 500 businesses whose service is exclusively SaaS. This indicated that the SaaS sector employed 12,500 people in 2020, and had doubled in size over the preceding five years. This provides a useful top-level estimate, but doesn’t give the rich regional or timeseries detail that we’ve become accustomed to with other industries.

Occupations are one way to track the contribution of technology across all industries. For example, a data scientist could be employed by a Kiwifruit packhouse to develop software which helps machines to grade Kiwifruit. In the case of Infometrics, our IT colleagues have created products which enable us to share our economic insights with clients. We can see this by looking at the spread of IT professionals across non-tech industries. For example, there were 66,600 ICT professionals employed across New Zealand in 2021, only 32,000 of which were employed within the tech sector.

Alternatively, we can look at occupations within industries, to focus on higher value parts of the tech sector. SaaS firms may be more likely to hire highly-skilled occupations such as software developers, rather than semi-skilled occupations such as diploma-qualified IT support engineers. Within the 116,900 filled jobs in the tech sector as defined by MBIE, 83,700 of these jobs were in professional or managerial occupations.

The Skills Framework for the Information Age (SFIA) is a technical competency and skills framework. SFIA can be applied to technology professionals to define their specific skills (e.g. data analysis) and corresponding responsibility (follow through to set strategy). SFIA is currently used in New Zealand for IT skill accreditation, but isn’t collected systematically in the same way that industries and occupations are. If collected, SFIA would be a good avenue to understand the breath and complexity of technology used in a range of industries.

Measuring the tech sector is not an impossible task, but it does take a considered approach. Above all else, caution needs to be applied when interpreting statistics relating to the tech sector. In some cases, understanding the overall size of the sector, using the gross measures described above, is still useful. While this approach may overstate the size of the sector, it can still be used for valid comparisons over time (how has your tech sector grown?) and across the country (is your tech sector growing faster than the national average?).

For specific niches, such as gaming or fintech, often primary data collection is the best approach. A carefully designed survey can provide unique insights and be related back to official statistics to understand its representativeness.