@3ryd+1qy9isGs
Thank you for that. There is a linked article about "AI and the Future of Work" linked at the beginning of your source. It is interesting reading:
https://www.imf.org/en/Publications/Staff-Discussion-Notes/Issues/2024/01/14/Gen-AI-Artificial-Intelligence-and-the-Future-of-Work-542379?cid=bl-com-SDNEA2024001
It's 42 pages. On page 15, I found this interesting paragraph:
Older workers may be less adaptable and face additional barriers to mobility, as reflected in their lower likelihood of reemployment after termination. Following job termination, older workers are less likely to secure new employment within a year than young and prime-age workers (Figure 7). Several factors can explain this discrepancy. First, older workers’ skills, though once in high demand, may now be obsolete as a result of rapid technological advances. Moreover, after significant time in a particular location, they may have geographic and emotional ties, such as to a spouse and children, that discourage them from relocation for new job opportunities. Financial obligations accumulated over the years might also make them less likely to accept positions with a pay cut. Last, having invested many years, if not decades, in a particular sector or occupation, there may be a natural reluctance or even a perceptual barrier to a transition to entirely new roles or industries. This may reflect a combination of comfort with familiar settings, concern about the learning curve in a new domain, or perceived age bias. These constraints are likely to be relevant also in the context of AI-induced disruptions.
On page 16, this one:
Historically, older workers have demonstrated less adaptability to technological advances; artificial intelligence may present a similar challenge for this demographic group. After unemployment, older workers previously employed in high-exposure and high-complementarity occupations are less likely to find jobs in the same category of occupation than prime-age workers (Figure 7). This difference in the reemployment dynamics can reflect technological change, changes in workers’ preferences, and age-related biases or stereotypes in the hiring processes in high-complementarity and high-exposure occupations. * Technological change may affect older workers through the need to learn new skills. Firms may not find it beneficial to invest in teaching new skills to workers with a shorter career horizon; older workers may also be less likely to engage in such training, since the perceived benefit may be limited given the limited remaining years of employment. This effect can be magnified by the generosity of pension and unemployment insurance programs. * These channels align with Braxton and Taska (2023), which finds that technology contributes 45 percent of earnings losses following unemployment. This happens primarily because workers lacking new skills
move to jobs where their existing skills are valued but that garner lower wages.
Special note to the part emphasized (emphasis mine).