I made a stupid simple model where hiring in all age brackets rose slowly until 2021 and then fell slowly. That produces very similar looking graphs, because the many engineers that were hired at the peak move up the demographic curve over time. Normalizing the graph to 2022 levels, as the paper seems to do, hides the fact that the actual hiring ratios didn't change at all.

https://docs.google.com/spreadsheets/d/1z0l0rNebCTVWLk77_7HA...

▲

juxtaposicion 5 days ago | parent | next [-]

I'm not sure I understand. Your model shows that different group buckets (eg 20-24yo vs 25-29yo) peak at different years (in your figure, 2022 vs 2024) despite being driven by the same dynamics. Is that expected? I (naively?) expected the same groups to rise, fall and have peaks at the same times.

▲

jamii 5 days ago | parent [-]

One of the dynamics is that people get older so they move into different buckets.

We can make the model way simpler to make it clearer. Say in 2020 we hired 1000 20-24yo, 1000 25-29yo etc and then we didn't hire anyone since then. That was five years ago, so now we have 0 20-24yo, 1000 25-29yo, 1000 30-34yo etc and 1000 retirees who don't show up in the graph.

Each individual year we hired the exact same number of people in each age bracket, and yet we still end up with fewer young people total whenever hiring goes down, because all the people that got hired during the big hiring spike are now older.

	▲	juxtaposicion 5 days ago \| parent [-]
		Got it, thanks! Yeah, so it makes sense that any age-bucketing like this would have a similar effect

▲

tangotaylor 5 days ago | parent | prev [-]

Wow, that's hilarious. So essentially hiring could be identical across all age groups, but due to a glitch in the analysis (young people don't stay young, who knew?), it appears that younger people are losing jobs more than the rest.