I think you can ask the question without any specific measure of “research output”.
How do you speed up research on cancer? Train more researchers, hoping they will generate research positions, or create more research positions, thinking that it will entice people to train for these positions?
One approach pulls another pushes. One generates demand, the other generates supply.
Take this question, apply whatever metric you like and let us see what the correct answer is!
Very interesting research, thanks for the pointer.
I disagree with your conclusion, though:
“Training more Ph.D.s in some targeted areas might fail to improve research output in these areas. In this instance, supply-side economics fails.”
The Soviet emigres published and were cited more than there peers pre-collapse, as economics would predict: Eliminating positions in the USSR pushed out marginal producers, leaving a more talented cohort.
Where we diverge from classical economic thinking is that Americans in “Soviet” fields declined. However, the paper suggests older, tenured professors were less affected: it was the young mathematicians that got edged out. Instead of competing on any metric of quality, Americans competed on age, and this seemed to produce a lower quality cohort.
Thus I don’t think you can conclude that overproducing young mathematicians will lower the output quality: My read of the paper is that it’s specifically an influx of older, well-established applicants edging out the young that causes the problem.
It should not be surprising that economics fails to predict outcome in this type of market, because it is very far from a free market. This means that it can be very hard (at least to me) to make sense of it.
I’m not going to argue specific points here, but I’ll just say that there is not a lot of difference between adding already trained foreign Ph.D.s or training new Ph.D.s.
Agreed on the trickiness of predicting non-free markets. And the USSR situation is doubly tricky, because it was a discontinuity: a one-time sudden large influx of talent.
Your last point is actually what I find most interesting about this topic. What is the difference between training a new Ph.D. and hiring an older, established researcher? My own impression of the world is that very different dynamics can exist when you pull in all senior, established people, vs. a start-up of exclusively young, vs. a mixed group, especially in a very long term endeavor like science.
Very often, government programs have a limited duration. For example, the program can decide to sustain research on computer security or AI for the next 5 years, and then it stops. So there are many discontinuities.
Academic departments tend to have a fixed number of positions allocated by the University. Sometimes they can hire at any rank. However, they are usually encouraged (and often required) to hire younger professors, mostly because older professors are more expensive. That is, there is open age discrimination in academia against older people. As far as I know, there is no planning to ensure that you have a mix of young and older professors except maybe that if several professors are due to retire, the department may get to open a few positions earlier to avoid a big and sudden turnover. Of course, government research laboratories have more flexibility when they hire. They also don’t have tenure so it is possible, in theory, to let go older and less productive researchers. I would think that many research laboratories would try harder than academic departments to strike a balance between older and younger researchers. Note also that professors are typically expected to do research more or less independently. In research laboratories, co-workers are more likely to be collaborators.
Itmansays:
They also don’t have tenure so it is possible, in theory, to let go older and less productive researchers.
In regard to training more PhDs: one should consider openings in industry (and industry labs) as well. Is this number growing or shrinking (I am not sure)?
I’d say it’s time to rate “proliferation of different ideas” more highly than “research output”!
@Jeremy
I think you can ask the question without any specific measure of “research output”.
How do you speed up research on cancer? Train more researchers, hoping they will generate research positions, or create more research positions, thinking that it will entice people to train for these positions?
One approach pulls another pushes. One generates demand, the other generates supply.
Take this question, apply whatever metric you like and let us see what the correct answer is!
Very interesting research, thanks for the pointer.
I disagree with your conclusion, though:
“Training more Ph.D.s in some targeted areas might fail to improve research output in these areas. In this instance, supply-side economics fails.”
The Soviet emigres published and were cited more than there peers pre-collapse, as economics would predict: Eliminating positions in the USSR pushed out marginal producers, leaving a more talented cohort.
Where we diverge from classical economic thinking is that Americans in “Soviet” fields declined. However, the paper suggests older, tenured professors were less affected: it was the young mathematicians that got edged out. Instead of competing on any metric of quality, Americans competed on age, and this seemed to produce a lower quality cohort.
Thus I don’t think you can conclude that overproducing young mathematicians will lower the output quality: My read of the paper is that it’s specifically an influx of older, well-established applicants edging out the young that causes the problem.
@Paul
It should not be surprising that economics fails to predict outcome in this type of market, because it is very far from a free market. This means that it can be very hard (at least to me) to make sense of it.
I’m not going to argue specific points here, but I’ll just say that there is not a lot of difference between adding already trained foreign Ph.D.s or training new Ph.D.s.
@Daniel
Agreed on the trickiness of predicting non-free markets. And the USSR situation is doubly tricky, because it was a discontinuity: a one-time sudden large influx of talent.
Your last point is actually what I find most interesting about this topic. What is the difference between training a new Ph.D. and hiring an older, established researcher? My own impression of the world is that very different dynamics can exist when you pull in all senior, established people, vs. a start-up of exclusively young, vs. a mixed group, especially in a very long term endeavor like science.
@Paul
They also don’t have tenure so it is possible, in theory, to let go older and less productive researchers.
This is only theory: http://www.youtube.com/watch?v=rL_J-Yl55K0
In regard to training more PhDs: one should consider openings in industry (and industry labs) as well. Is this number growing or shrinking (I am not sure)?