Andrew Yang on economics: what the evidence says · JRE #1245
“so the uh so bain says you're looking at uh between 20 and 30 percent of jobs subject to automation by 2030 which is pretty soon it's like 11 years from now mckinsey says about 25 percent uh the White House, literally their last day in office, they issued a report saying, hey, guys, we're going to automate away all the jobs and then turn the lights off. They said 83% of jobs that make less than $20 an hour will be subject to automation by 2030.”
What the evidence says
The 83% figure Yang attributes to the Obama White House is real but is characterized loosely. It comes from a December 2016 Council of Economic Advisers report, "Artificial Intelligence, Automation, and the Economy," which states that jobs paying under $20/hour "would come under pressure from automation" at a rate of 83%, versus 31% for jobs paying $20-40/hour and 4% for jobs above $40/hour. That figure is a probability-of-automation-risk estimate derived from the 2013 Frey and Osborne methodology, applied by wage tier -- it is not a prediction that 83% of those jobs will actually be eliminated or automated by any specific date, and the report itself does not attach a 2030 deadline to that statistic (Yang's framing that the White House said they'd 'automate away all the jobs and then turn the lights off' is rhetorical exaggeration not present in the source). The Bain and McKinsey figures Yang cites (20-30% and ~25%, respectively) are consistent with the general range of automation-exposure estimates circulating from those firms in this period, but could not be independently re-verified against a live, fetchable McKinsey source at time of review, so this fact-check relies on the one document that was directly confirmed: the White House report. Collapsing distinct methodologies (task-level automation potential vs. wage-tier risk-of-pressure probabilities vs. workforce transition estimates) into a single comparable 'percent of jobs automated by 2030' figure overstates the certainty and consensus behind the numbers, particularly the 83% figure, which describes exposure/risk rather than a realized or forecasted outcome.