1 min and 13 sec to read, 303 words

In this paper, the researchers are looking at differences in job posting from private and public sector in the US.

Talent flows and talent patterns are sometimes undervalued as a metric to understand and track transformation of different sectors, and it is interesting to note that the job postings in the private sector for AI-jobs have gone up significantly, whereas the public sector has remained flat — a sign that the transformation is not yet visible in talent flows.

There is also, of course, the finding that salaries in the private sector are 50% higher on average than in the public sector — but even so, if the demand has not changed for the public sector, it seems as if that matters somewhat less for the talent asymmetries.

It may be an interesting idea to explore setting talent targets for public sector, recruiting targets, in order to ensure that there is transformative capability in the public sector. In order to do this you would also have to have an idea about the talent profile. According to the study, public sector is recruiting more for scientists, with less flexible criteria for experience and education than private sector — and if you are not recruiting developers, your transformative capability will remain low.

All of this is not simple, of course, and you could argue that the transformative capability can be procured, but even public procurement relies on some level of talent to create well-defined tenders and contracts, to define what should be done.

Politicians and decision makers thinking about how to harness AI might want to track talent in a more granular way.

See more here: Makridis, Christos and Alterovitz, Gil, Measuring and Understanding Differences in Private and Public Sector Technology Jobs: Evidence from Artificial Intelligence Job Posting Data (July 10, 2024). Available at SSRN: https://ssrn.com/abstract=4891300 or http://dx.doi.org/10.2139/ssrn.4891300