Article by: Asst.Prof. Suwan Juntiwasarakij, Ph.D., MEGA Tech Senior Editor
Novelty and technology always spark public concerns within society in a great length. Often, the public are not only able to fully comprehend the consequences, but also failed to rationalize emotional response to such. For decades, countless studies have arrived at potential job losses across industries resulting from intelligence machines replacing human workforce. Moreover, such studies issue a warning that digitization will fundamentally transform the nature of work.
According to PwC, the estimated proportion of existing job at high risk of automaton by the early 2030s varies significantly by county. These estimates range from only around 20-25% in some East Asian and Nordic economies with relatively high average education levels, to over 40% in Eastern European economies where industrial production, which tends to be easier to automate, still accounts for a relatively high share of total employment. Countries like UK and the US, with services-dominated economies but also relatively long ‘tails” of lower skilled workers, could see intermediate levels of automation in the long run.
PwC has analyzed a dataset compiled by the OECD that looks in detail at tasks involved in the jobs of over 200,000 workers across 20 countries including a group of academic researchers. Therefore, the estimation of the proportion of existing jobs that might be of high risk of automation by the 2030s for each of the countries studied, different industry sector, occupations within industries, and workers of different genders, ages, and education levels. Therefore, PwC has identified how this process might unfold over the period to the 2030s in three overlapping waves.
Algorithm waves focused on automation of simple computational tasks and analysis of structured data in areas like finance, information, and communication. This has already been underway. Augmentation wave focused on automation of repeatable tasks such as filling in forms, communicating, and exchanging information through dynamic technological support, and statistical analysis of unstructured data in semi-controlled environments such as aerial drones and robots in warehouses. This has also been underway and is likely to reach maturity in the late 2020s. Autonomy wave focused on automation of physical labor and manual dexterity, and problem solving in dynamic real-world situations that require responsive actions, such as in manufacturing and transport (e.g. driverless vehicles). These technologies are under development already, but may only come to full maturity on an economy-wide scale in the 2030s.
Potential job automation rate by country across waves
Potential job at high risk of automation
Potential impacts by type of worker
Potential job at high risk of automation
As a result of differences in labor market structures, education and skill levels, and government polices across the counties, the relative impacts of these two components varies between countries, which gives rise to differences in estimated automation levels we it can be distinguished into four groups.
Industrial economies, for example, Germany, Slovakia, and Italy, could see relatively higher automation rates in the long run. These countries are typically characterized by jobs that are relatively automatable and (relatively to the OECD average) more concentrated in industry sectors with higher potential automation rate.
Service-dominated economies, for example, the US, UK, France, and the Netherlands, have jobs that are on average relatively more automat able based on their characteristics, but also a greater concentration on services sectors that tend to be less automatable on average than industrial sector.
Asian countries, for example, Japan, South Korean, Singapore, and Russia, have jobs that are relatively less automatable overall but with relatively high concentrations of employment in industrial sectors with relatively high potential automation rates.
Nordic countries, for example, Finland, Sweden, and Norway (in additional to New Zealand and Greece outside this region) have jobs that are on average relatively less automatable and in industry sectors with relatively lower potential automation rates.
Potential impact across countries by employment shares and automatability of jobs
Relative impact from employment shares across industry e.g. manufacturing
Relative impact from the automatability of jobs e.g. educational job requirements
Potential impact of job automation over-time across the four country groups
Task automation across the three waves
The rank order of potential impact over-time across the four country groups
Task composition for manufacturing, financial and insurance, and education sectors
Potential impact of job automation over-time across workers by age group
