Last updated June 18, 2018 at 10:29 am
Towns and smaller cities will be most affected by the implementation of automation, according to a new study.
Smaller cities will be most impacted by automation. Credit: TunnelBug/Flickr
Artificial Intelligence and robotics are coming, and they’re going to change the way we live. Understandably, this has caused worry about job losses, the impact on the economy, and how society itself will change.
However, new research has shown that those changes will be felt differently depending on where you live.
According to the findings, towns and smaller cities are more likely to feel the impact of the change, while larger cities are less impacted through increases in numbers of specialised managerial and technical jobs which are not easily automated.
Large cities build their own buffer
Cities are the economic and innovation centre of modern society, and the increased urbanisation of society is due to there being more opportunities for work in larger cities.
In a profile of job types across cities of different sizes, researchers from the Massachusetts Institute of Technology and CSIRO’s Data61 found that large cities tend to have greater levels of innovation, and have more unique occupations and industries – in other words a larger range of industries and jobs.
This in turn creates more specific, specialised jobs such as highly technical positions, filled with employees with more specific and specialised skills. It is predicted that these more specialised employees are less likely to be impacted by implementation of automation.
In these larger cities, the analysis also found greater numbers of managerial positions, which would also be less affected by automation – after all, companies still need management to operate.
This, they suggest, means that in large cities there is a larger number of workers whose skills better prepare them to work with automation technology, while small cities rely more prominently on physical workers, who are more susceptible to automation.
The analysis also highlighted specific occupations, such as mathematician and chemist, as well as specific types of skills, such as computational or analytical skills, that were less likely to be affected by automation. These skills and occupations were also more likely to be found in greater numbers in larger populations, helping explain the increased resilience of large cities.
There is no way of predicting the future job market
The larger cities, with their more abundant industries and opportunities were also more likely to be able to offer retraining and reskilling opportunities to its residents than a small city.
However, it is extremely difficult to predict a future job market. The job market and skills required today are vastly different to those from 20 years ago, and what automation that has been introduced has changed employees’ roles more than replacing them outright.
The roles and jobs may change, but it’s unlikely they will disappear. In fact, many surveys and studies have predicted that the total number of jobs available will increase.
This may mean that employees who are at risk of automation will need retraining into higher-skilled jobs, which will often come with higher pay grades. Additionally, an economy which is entirely automated will fail, as a population in which there is high unemployment means a population unable to pay for goods and services – without customers, companies can’t exist.
There are other economic factors to consider, as well as legal and political factors which may limit or alter the roll out of automation, factors which are difficult to model.
Automation is but the ongoing evolution of work efficiency and technology – a process which has always occurred and always will. However in managing this transition policymakers on a national, and local, level will need to take into account these changes and adjustments and start planning for a future and a job market which will be very different – but we’re not quite sure how just yet.
The research was published in the Journal of the Royal Society Interface
