I would expect PhD value to mostly be affected by underlying demographic factors; they’re already structurally on an inflationary trajectory and I expect that to be more important than whether they’re understood to be fake or real. No one thinks Bitcoins contain powerful knowledge but they still have exchange value.
If there’s a demographic model of PhD salary premium with a good track record (not just backtested, has to have been a famous model before the going-forward empirical validation) I might bet strongly against deviation from that. If not, too noisy.
Variance (and thus sigma) for funding could be calculated on basis of historical YOY % variation in funding for all US universities, weighted by either # people enrolled or by aggregate revenue of the institution. Can do something similar for h-index. Obviously many details to operationalize but the level of confusion you’re reporting seems surprising to me. Maybe you can try to tell me how you would operationalize your “dropping pretty sharply” / “drop relatively intensely” claim.
Less than a sigma seems like it can’t really be a clear quantitative signal unless most of the observed variance is very well explained (in which case it should be more than a sigma of remaining variance). Events as big as Stanford moving from top 3 to top 8 have happened multiple times in the last few decades without any major crises of confidence.
I agree the disagreement about academia at large is important enough to focus on, thanks for clarifying that that’s where you see the main disagreement.
I would expect PhD value to mostly be affected by underlying demographic factors; they’re already structurally on an inflationary trajectory and I expect that to be more important than whether they’re understood to be fake or real. No one thinks Bitcoins contain powerful knowledge but they still have exchange value.
If there’s a demographic model of PhD salary premium with a good track record (not just backtested, has to have been a famous model before the going-forward empirical validation) I might bet strongly against deviation from that. If not, too noisy.
Variance (and thus sigma) for funding could be calculated on basis of historical YOY % variation in funding for all US universities, weighted by either # people enrolled or by aggregate revenue of the institution. Can do something similar for h-index. Obviously many details to operationalize but the level of confusion you’re reporting seems surprising to me. Maybe you can try to tell me how you would operationalize your “dropping pretty sharply” / “drop relatively intensely” claim.
Less than a sigma seems like it can’t really be a clear quantitative signal unless most of the observed variance is very well explained (in which case it should be more than a sigma of remaining variance). Events as big as Stanford moving from top 3 to top 8 have happened multiple times in the last few decades without any major crises of confidence.
I agree the disagreement about academia at large is important enough to focus on, thanks for clarifying that that’s where you see the main disagreement.