We use estimates from Frey and Osborne (2017) of how likely automation is to affect occupations. They first identify the tasks of each occupation that may become automated: perception and manipulation tasks, creative intelligence tasks, and social intelligence tasks. Then they use a machine learning algorithm to calculate the probabilities of computerization. We merge their estimates with MSA-level occupational employment and wages from the 2017 Occupational Employment Statistics (OES, from the Bureau of Labor Statistics). The figure shows the probability of automation for each occupation on the horizontal axis, the annual income for each occupation on the vertical axis, and the employment in each occupation by the size of the bubble. Low-paid occupations, such as office and food service jobs, are the most likely to be automated. These occupations also have large employment.
As the table shows, automation increases inequality in every scenario because it tends to displace the lowest-paid workers. For the first (and most extreme) scenario, when all affected employees become unemployed, the Gini coefficient goes from 0.31 to 0.70—it more than doubles. However, this scenario is very extreme; it is used here only as an exercise to understand where automation will most impact the income distribution. The percentile ratios for the case in which workers become unemployed are not provided in the table because the entire bottom half of the distribution would earn zero income, so the 90-50 and 50-10 ratios cannot be calculated. It is more likely that affected employees would earn the minimum wage or take a 20 percent pay cut; but these scenarios are still based on strong assumptions. The Gini coefficient is high in the minimum-wage scenario: 0.43. The 50-10 ratio of 1 for the minimum-wage scenario means that the bottom half of the distribution would earn minimum wage. The scenario that is probably closest to reality, and most useful in this case, is that the affected employees take a 20 percent wage cut. The Gini coefficient increases to 0.35, and the 90-50 ratio increases more than the 50-10 ratio, indicating that income becomes more concentrated at the top of the distribution.
Chosen excerpts by Job Market Monitor. Read the whole story at The Impact of Automation on Inequality | St. Louis Fed
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