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 and 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: Australia
(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 Australia the employment rate in 2023 varies across regions, ranging from a low of 76.4% in Tasmania to 82.2 % in Canberra region (ACT). This represents a difference of 5.8 percentage points, below the average OECD regional dispersion of 10 percentage points. The national employment rate in Australia stands at 78.1%, 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 Australia, 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, all of (8 out of 8) of Australian regions saw their employment recover to at least pre-pandemic levels. Tasmania experienced the greatest recovery for employment rates, surpassing the pre-pandemic levels by 5.1 percentage points. Overall, employment rates are 3.7 percentage points above pre-crisis levels, a stronger recovery than the OECD average of 1.5 percentage points for employment rates.
(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 7 out of 8 regions in Australia, on average by 1.5 percentage points. The age inclusion gap grew by 1.3 percentage points across OECD regions. The biggest decrease in age disparities occurred in South Australia at -6.1 percentage points, while the biggest increase was in Western Australia by 0.3 percentage points. Over the same period, the gap in participation rates between male and female workers (gender inclusion gap) fell in 8 out of 8 regions. The gender inclusion gap fell by, on average, 3.6 percentage points. The smallest decrease in gender disparities was in Canberra region (ACT) by -0.6 percentage points, while the biggest decrease was in Western Australia at -5.4 percentage points.
(a) Share of youth not in employment, education or training among the youth working-age population, 2022
Note: The figure shows the regional values and the national and OECD regional average in the NEET rate (not in employment, education or training) for the youth working-age population (15-24 year-olds) in 2022.
Source: OECD calculations based on the OECD Regional databases.
In Australia, all (8 out of 8 regions with available data) have youth not in employment, education, or training (NEET) rates below the OECD benchmark of 16.8%, while the national mean stands at 11%. The highest rate of youth exclusion is observed in Queensland at 14.5%, while the lowest rate is in Canberra region (ACT) at 5.3%. This underscores the uneven opportunities for youth across the country. In Australia, all (8 out of 8 regions with available data) have youth not in employment, education, or training (NEET) rates below the OECD benchmark of 16.8%, while the national mean stands at 11%. The highest rate of youth exclusion is observed in Northern Territory at 14.5%, while the lowest rate is in Canberra region (ACT) at 5.3%. This underscores the uneven opportunities for youth across the country.
(a) Labour productivity in USD 2015 PPP per worker, 2020
Note: The figure shows the regional values and the national and OECD regional average of labour productivity (USD 2015 PPP per worker) in 2020. 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 2020
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 2020. 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 8 out of 8 regions in Australia, labour productivity is above the OECD benchmark. Western Australia leads labour productivity levels at 25% above the national average. The lowest labour productivity is observed in Tasmania at -19% below the national average. Annual labour productivity growth over the past ten years is at 0.9%, close to the the OECD average of 0.9%. The strongest labour productivity growth is observed in Western Australia at 1.5% annual growth, and the weakest in Victoria where labour productivity increased by 0.4% annually.
(a) Differences of regional labour market tightness (vacancies over unemployment) relative to the national average, 2022.
Note: This figure shows the difference in labour market tightness between regions and the national level (in %) on the horizontal axis. Labour market tightness is defined as the number of vacancies over unemployment.
Source: Own elaboration based on Lightcast, EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.
In Australia, the extent of labour shortages varies by region. Taking labour market tightness (i.e. vacancies divided by unemployment), as a proxy, Northern Territory is the region that faces the most severe labour shortages with 46% more vacancies per unemployed person than Australia as a whole. In contrast, South Australia is the region that experiences the least severe labour shortages, as it has 42% fewer vacancies per unemployed person than Australia on average.
The following tightness estimates for green and ICT jobs come with a small change in the methodology. Rather than dividing vacancies by employment—as done for the aggregate tightness estimates—tightness for green and ICT jobs is estimated as the ratio of vacancies to employment in each occupational group, as information on an unemployed person’s last job is not available in most countries.
(a) Difference between the labour market tightness of green jobs and the average job in the labour market by region (in %), 2022.
Note: This figure shows the difference between the labour market tightness of green jobs and the average job in the respective labour market (in %) on the horizontal axis. Labour market tightness is defined as the number of vacancies over employment. Occupations that have at least 10% of their tasks classified as green are defined as green jobs, following “Job Creation and Local Economic Development 2023: Bridging the Great Green Divide” (OECD 2023). The country average refers to the employment-weighted average of regions and the OECD average referes to the unweighted average of countries.
Source: Own elaboration based on Lightcast, EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), Australian Bureau of Statistics, and OECD (2023).
Australia experiences higher shortages for green jobs than for the average job. Specifically, there are on average 6% more vacancies per employed person in green jobs than for the average job in Australia compared to 29% in the OECD. Tightness among green jobs is highest in Western Australia, where green jobs show 27% more vacancies per employed person, and lowest in Victoria, where green jobs are -2% tighter than the average job.
(a) Difference between the labour market tightness of green jobs and the average job in the labour market by region (in %), 2022.
Note: This figure shows the difference between the labour market tightness of ICT jobs and the average job in the respective labour market (in %) on the horizontal axis. Labour market tightness is defined as the number of vacancies over employment. ICT specialists are defined as “workers who have the ability to develop, operate and maintain ICT systems, and for whom ICT constitute the main part of their job”, following Eurostat (Eurostat 2024). The country average refers to the employment-weighted average of regions and the OECD average referes to the unweighted average of countries.
Source: Own elaboration based on Lightcast, EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), Australian Bureau of Statistics, and Eurostat (2024).
Australia experiences higher shortages among ICT jobs than for the average job, as there are on average 81% more vacancies per employed person in ICT jobs than in the average job in Australia. This compares to 117% higher ICT tightness in the OECD. Tightness among ICT jobs is highest in South Australia, where ICT jobs are 102% tighter than the average job, and lowest in Northern Territory, where ICT jobs have 23% more vacancies per unemployed person.
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 Australia, on average around 8.9% of workers are considered at high risk of automation, meaning over 25% of its skills and abilities are highly automatable. This is 3.1 percentage points less than the OECD average of 12%. This figure ranges from 6.1% in Northern Territory to 10.4% in Western Australia.
(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 considereded exposed at high risk of automation if at least 25% of its skills and abilites automatable
Source: OECD calculations based on (Lassébie and Quintini, 2022[34]), labour force survey and employment by occupations tables.
Regional employment exposed to Generative AI
(a) Share of employment exposed to Generative AI - 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 considereded exposed to Generative AI if at least 20% of its tasks can be done double as fast with the help of Generative AI. OECD and conuntry averages represents the weighted regional average.
Source: OECD calculations based on (Eloundou et al., 2023[49]) , labour force survey and employment by occupations tables.
In Australia, on average around 28% 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 2 percentage points more than the OECD average of 26%. This figure ranges from 24% in Tasmania to 30.3% in New South Wales.
(a) Share of employment exposed to Generative AI and at high risk of automation - (2023)
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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