COVID-19 is likely to speed up the adoption of automation, as firms seek to increase their productivity and lower physical interaction within workplaces. An OECD survey found that over 26% of Australian firms reported an increase in the take-up of new technology or automation because of COVID-19, with firms in New South Wales (27.0%), Victoria (35.1%), and Australian Capital Territory (35.7%) reporting take-up rates higher than the Australian average. Historically, automation has gained traction in the wake of economic shocks, when human labour become relatively more expensive as firms’ revenues decline. During these periods, employers are more likely to seek labour productivity increases by shedding lower-skilled workers and replacing them with technology and higher-skilled workers. The adoption of automation technologies in Australia’s industries has been going on for some time, with Australia being a world leader in mining equipment automation. The need to reduce the frequency and duration of human-to-human contact could further speed up the adoption of automation, as experts warn of the increasing threat of future pandemics.
Australia faces a lower risk of losing jobs to automation than on average across the OECD. Recent OECD estimates show that about 36% of jobs are at high or significant risk of being automated in Australia, compared to the OECD average of about 46%. Food Preparation Assistants, Handicraft and Printing Workers, and Stationary Plant and Machine Operators are the three occupations facing the highest average risk of automation in Australia. Given Australia’s occupational structure, personal service workers represent the largest number of at-risk workers, with 314 000 workers who might lose their job or experience significant change due to automation. Men tend to face a higher risk than women, since men are over-represented in occupations related to trades, manufacturing, and mining. Youth and Indigenous workers face a particularly high risk, given that they tend to be employed in occupations entailing more routine and repetitive tasks than the rest of the population.
In Australia, the risk of automation varies at the local level, with some regions more at risk than others. Automatable tasks are more prevalent in certain occupations and sectors, and neither occupations nor sectors are evenly distributed within national borders. Therefore, regions with a higher proportion of jobs relying on routine tasks are likely to experience more disruption, whereas places where more jobs require tacit skills will face lower levels of risk. Tacit skills are based on experience and intuition instead of formal rules, and are therefore more difficult to replicate through mechanical processes or standard algorithms. Moreover, the net-zero carbon transition could lead to job losses in sectors such as mining among others, which likely will not have a significant impact at the national level, but may lead to regional labour market disparities.
The Australian Capital Territory faces the lowest risk of automation across states and territories in Australia. About 29.3% of jobs are at risk of becoming automated in the Australian Capital Territory – 21.3% facing high risk and 8.0% vulnerable to significant change. New South Wales (33.9%), Victoria (34.0%) and Northern Territory (34.5%) also face a lower risk of automatable jobs than the Australia average. However, regional differences in the risk of automation are pronounced within all Australian states and territories. For example, regional variation in the share of jobs at risk of automation amounts to more than 15% in New South Wales, ranging from 40.2% in Hunter Valley exc. Newcastle to 24.6% in Sydney-North Sydney and Hornsby. Looking across all regions, Mackay-Isaac-Whitsunday is the region in Australia facing the highest risk of automation, with about 41.2% of jobs (or 32 000 jobs) at risk. On the other hand, Sydney-North Sydney and Hornsby is the region facing the lowest risk (24.6% of jobs, or 50 000 jobs).
The drivers of job loss due to automation might differ across regions, even when they face similar risks of automation. For example, in rural and remote regions, where agriculture is a prevalent source of income, the risk of losing jobs to automation might be mainly related to the prevalence of low-skilled occupations in agriculture, which could be potentially replaced by machines. On the other hand, the concentration of administrative jobs in regions where a large number of companies are located, might cause the risk of automation in these regions to be related mainly to those occupations.