The report Job Creation and Local Economic Development 2024: The Geography of Generative AI examines the health of regional labour markets and provides new estimates on regional labour shortages. In addition, it provides new findings on the impact of Generative AI on different regions and workers. It examines how AI technologies can be leveraged to address critical labour market challenges and boost productivity growth.
Job Creation and Local Economic Development 2024 - Country Notes: New Zealand
(a) Employment rate for the working age population (15-64 year-olds), 2023
Note: The figure shows the regional values and the national and OECD regional average in the working-age employment rate in 2023. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
In New Zealand the employment rate in 2023 varies across regions, ranging from a low of 71.9% in Northland to 83.3% in Wellington. This represents a difference of 11.4 percentage points, above the average OECD regional dispersion of 10 percentage points. The national employment rate in New Zealand stands at 78.2%, above the OECD benchmark of 69.4%.
(a) Change in the employment rate, 2019 to 2023
Note: The figure shows the difference between 2019 and 2023 for the employment rate for regions in New Zealand, as well as the national and OECD regional average. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
By 2023, over half of (8 out of 12) of New Zealand regions saw their employment recover to at least pre-pandemic levels. In Waikato, Bay of Plenty, Taranaki, and Otago employment did not return to pre-crisis levels. Manawatu-Wanganui experienced the greatest recovery for employment rates, surpassing the pre-pandemic level by 6.4 percentage points. Overall, employment rates are 1.3 percentage points above pre-crisis levels, a weaker recovery than the regional OECD average of 1.5 percentage points.
(a) Change in the gap in the participation rate by age (between 25-64 year-olds and 15-24 year-olds) and gender (between men and women), 2013 to 2023
Note: The figure shows regional values and the national and OECD regional average in the change in the age gap (difference between the prime-age working population (25-64 year-olds) and youth (15-24 year-olds) and the gender gap (difference between men and women) in the participation rate in 2013 and that in 2023. A negative value implies that disparities decreased. The participation rate is defined as the number of employed persons and those looking for work as a share of the population in that subgroup. For gender disparities, it is defined using the working age population of 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Over the past ten years, the gap in participation rates between prime-age and younger workers (age inclusion gap) fell in 10 out of 12 regions in New Zealand, on average by 5.6 percentage points. The age inclusion gap grew by 1.3 percentage points across OECD regions. The biggest decrease in age disparities occurred in Otago at -27.6 percentage points, while the biggest increase was in Bay of Plenty by 9.9 percentage points. Over the same period, the gap in participation rates between male and female workers (gender inclusion gap) fell in 8 out of 12 regions. The gender inclusion gap fell by, on average, 2.3 percentage points. The biggest increase in gender disparities was in Tasman-Nelson-Marlborough by 4 percentage points, while the biggest decrease was in Gisborne at -13.4 percentage points.
(a) Labour productivity in USD 2015 PPP per worker, 2021
Note: The figure shows the regional values and the national and OECD regional average of labour productivity (USD 2015 PPP per worker) in 2021. The parentheses describe the difference in labour productivity relative to the national average. Labour productivity is defined as gross value added, measured in USD 2015 purchasing power parity, per worker.
Source: OECD calculations based on the OECD Regional databases.
(a) Ten-year annual labour productivity growth, 2012 to 2021
Note: The figure shows the regional values and the national and OECD regional average in the ten year annual growth rate of labour productivity (USD 2015 PPP per worker) from 2012 to 2021. Labour productivity is defined as gross value added, measured in USD 2015 purchasing power parity, per worker.
Source: OECD calculations based on the OECD Regional databases.
In 3 out of 12 regions in New Zealand, labour productivity is above the OECD benchmark. Auckland leads labour productivity levels at 22% above the regional average. The lowest labour productivity is observed in Northland at -21% below the national average. Annual labour productivity growth in New Zealand over the past ten years is at 0.5%, below the OECD regional average of 0.9%. The strongest labour productivity growth is observed in Southland at 2% annual growth, and the weakest in Taranaki where labour productivity fell by 2.9% annually.
AI has the potential to transform local labour markets by boosting productivity, creating or destroying jobs, and changing the very nature of some jobs, including job quality. While the full extent of its impact is still uncertain, the effects on jobs or skills will likely be context- and place specific. This report explores both the observed and anticipated impacts of technologies, both AI and non-AI, as they mature and achieve widespread adoption.
Narrow-purposed technologies in local labour markets
Even before the emergence of Generative AI, the impact of automation technologies differed across local labour markets. This measure of risk of automation serves as a useful metric to examine the effects of narrow-purposed technologies, these are, technologies (digital or not) that are intended to help with or take over one or a few specific tasks. The metrics presented below explore the share of jobs at risk of automation given available technologies at the end of 2021.
In New Zealand, on average around 8.2% of workers are considered at high risk of automation, meaning over 25% of its skills and abilities are highly automatable. This is 3.8 percentage points less than the OECD average of 12%. This figure ranges from 5.8% in Wellington to 11.2% in Southland.
(a) Share of employment at high risk of automation in OECD regions (2023)
Note: This figure shows the share of employment at high risk of automation in each region. Risk of automation is defined at the occupation level, where an occupation is considered exposed at high risk of automation if at least 25% of its skills and abilities are automatable. Averages represent the weighted regional average.
Source: OECD calculations based on Lassébie and Quintini, 2022[34], labour force survey and employment by occupation tables.
Regional employment exposed to Generative AI
(a) Share of employment exposed to Generative AI in TL-2 regions, 2023
Note: This figure shows the share of employment exposed to Generative AI in each region. Exposure is defined at the occupation level, where an occupation is considered exposed to Generative AI if at least 20% of its tasks can be done twice as fast with the help of Generative AI. OECD and country averages represent the weighted regional average.
Source: OECD calculations based on Eloundou et al., 2023[49], labour force survey and employment by occupation tables.
In New Zealand, on average around 27.1% of workers are exposed to Generative AI, meaning 20% (or more) of their job tasks could be done in half the time with the help of Generative AI. This is 1.1 percentage points more than the OECD average of 26%. This figure ranges from 17.6% in Southland to 37.8% in Wellington.
(a) Share of employment exposed to Generative AI and at high risk of automation , 2023
Note: This figure shows the share of employment exposed to Generative AI and the share of employment at high risk of automation in each region in each region. Exposure is defined at the occupation level, where an occupation is considered exposed to Generative AI if at least 20% of its tasks can be done twice as fast with the help of Generative AI while an occupation is considered at high risk if at least 25% of its skills and abilities are automatable. Horizontal and vertical lines represent the country average.
Source: OECD calculations based on Eloundou et al., 2023[49], labour force survey and employment by occupation tables.
OECD regions previously only mildly at risk of automation are now significantly exposed to Generative AI and vice versa. There tends to be a negative correlation between the share of exposed workers to Generative AI and a region’s share of workers at high risk of automation.
References
OECD (2024), Job Creation and Local Economic Development 2024: The Geography of Generative AI https://doi.org/10.1787/83325127-en
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