Technology innovation is the key driver of the economy in the long run. Therefore, we assume that technology automatically finds a way into the economy, driving growth and development. However, MIT economist Daron Acemoglu and Economics Professor at Boston University Pascual Restrepo co-authored a study that reveals otherwise. The study suggests that robotics technology is more likely to be adopted by countries with a relatively older population.
According to the paper ‘Demographics and Automation’, demographic changes such as ageing are important factors that lead to the adoption of robotics. The paper argues that ageing leads to greater industrial automation. This is because a shortage of middle-aged workers specialising in manual production tasks leads to the greater adoption of robots to automate those production tasks.
The study is a part of a series of papers by Acemoglu and Restrepo on automation, robots and the workforce. The duo has studied the multiple layers of demographic, technological and industry-level data collected from the early 1990s until the mid-2010s. It is supported by Google, Microsoft, the National Science Foundation, the Sloan Foundation, the Toulouse Network on Information Technology, and the Smith Richardson Foundation.
Findings of the study
The study suggests that as far as robots’ adoption is concerned, ageing alone accounts for 35 per cent, and another 20 per cent of the variation in robots’ imports among 60 countries. The ageing population is defined by the ratio of workers of age 56 years and above to workers between ages 21 and 55.
For instance, South Korea, which accounts for the most rapidly ageing population, implements robotics most intensively. The country houses leading large enterprises — Hyundai, LG, Samsung, KT and Hanwha — which largely focus on the robotics business. As of 2019, for every 10,000 workers, South Korea installed 855 units of industrial robots.
Competing in the robot implementation numbers, Germany and Japan indulge in 350 robots for every 10,000 workers. On the contrary, the US only deploys 228 robots per 10,000 human workers.
Source: Statista
Acemoglu said that the paper’s findings suggest that the top countries deploying robots do not do so for it is the next ‘amazing frontier’ but because these countries have a shortage of middle-aged blue-collar labour.
Collating industry-level data from across 129 countries, the authors concluded that the theory of robotics adaptation being directly linked to the age of a country’s population holds for non-robotic automation technologies as well. These essentially include numerically controlled machinery and automation machine tools. However, they did not find this theory holding true for non-automated machinery such as non-automated machine tools and computers.
US-focused trends
After globally examining the trends of robot adaptation, Acemoglu and Restrepo studied if the same theory would apply in the US’ metros. The findings of studying 700 cities revealed that in the US, the investment in robots has been much faster and intensive in the metropolitan areas between 1990 and 2015, where the population is getting older at a much faster rate.
The study further revealed that a 10-percentage point increase in local population ageing led to a 6.45 per cent increase with robot integrators (firms specialising in the installation and maintenance of industrial robots) in those areas.
Having said that, Acemoglu and Restrepo dismiss speculations related to robots replacing industry workers. Instead, the duo suggests that there is a difference between adopting robotics due to labour shortage and adopting automation for cost-cutting and labour replacement. They validate this with examples of Germany’s adaptation of robotics as opposed to the US. In Germany, robots have entered industries to make up for the shortage of middle-aged workers. The US, on the contrary, has deployed robots to replace the younger workforce.
Acemoglu and Restrepo plan to continue their study to study and analyse the effects of AI on the workforce and understand the relationship between economic inequality and workplace automation.
