The rise of automation and artificial intelligence (AI) is changing the nature of work across industries and occupations.
🚀 With the rise of ChatGPT, many of us fear for our jobs. Can we get more insights on which types of jobs are more susceptible to disruption? This article attempts to answer this question based on seminal and far-seeing work from Oxford researchers that is still relevant 10 years later. Let’s get started! 👇
A comprehensive study by researchers estimated the probability of computerization for 702 detailed occupations and found that around 47% of US employment is in the high-risk category. These jobs could be automated relatively soon, perhaps over the next decade or two.
The study predicts that most workers in transportation and logistics occupations and the bulk of office and administrative support workers and labor in production occupations are at risk. Surprisingly, a substantial share of employment in service occupations, where most US job growth has occurred over the past decades, is highly susceptible to computerization.
The following measuring framework helps us understand some computerization dimensions of traditional jobs:
The study provides evidence that wages and educational attainment exhibit a strong negative relationship with the probability of computerization.
This finding implies a discontinuity between the nineteenth, twentieth, and twenty-first centuries in the impact of capital deepening on the relative demand for skilled labor.
While nineteenth-century manufacturing technologies largely substituted for skilled labor by simplifying tasks, the Computer Revolution of the twentieth century caused a hollowing-out of middle-income jobs:
🧑💻 "The expansion in high-skill employment can be explained by the falling price of carrying out routine tasks by means of computers, which complements more abstract and creative services. Seen from a production function perspective, an outward shift in the supply of routine informational inputs increases the marginal productivity of workers they are demanded by. For example, text and data mining has improved the quality of legal research as constant access to market information has improved the efficiency of managerial decision-making – i.e. tasks performed by skilled workers at the higher end of the income distribution. The result has been an increasingly polarised labour market, with growing employment in high-income cognitive jobs and low-income manual occupations, accompanied by a hollowing-out of middle-income routine jobs. This is a pattern that is not unique to the US and equally applies to a number of developed economies (Goos, et al., 2009)"
The study predicts a truncation in the current labor market polarization trend, with computerization primarily confined to low-skill and low-wage occupations.
The study’s findings imply that as technology progresses, low-skill workers will need to reallocate to tasks that are non-susceptible to computerization, such as those requiring creative and social intelligence.
👉 Key Insight: Low-skill workers will have to acquire creative and social skills to win the race.
As algorithms for big data are now rapidly entering domains reliant upon pattern recognition and can readily substitute for labor in a wide range of non-routine cognitive tasks, and advanced robots are gaining enhanced senses and dexterity, allowing them to perform a broader scope of manual tasks, it is important to prepare the workforce for the future of work.
Action steps:
👉 Are you in one of those areas that have a high likelihood of disruption, such as office work, administrative, support, service, or sales?
👉 What can you do to move towards job that have a lower probability of computerization such as management, tech, computer, engineering, science, education, legal, or community service?
You may also want to check out our learning material on technological disruptions and how to prepare for them in a rapidly changing world:
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