Is your argument about personnel overlap that one could do some sort of mixed effect regression, with location as the primary independent variable and controls for individual productivity? If so I’m so somewhat skeptical about the tractability: the sample size is not that big, the data seems messy, and I’m not sure it would capture necessarily the fundamental thing we care about. I’d be interested in the results if you wanted to give it a go though!
More importantly, I’m not sure this analysis would be that useful. Geography-based-priors only really seem useful for factors we can’t directly observe; for an organization like CHAI our direct observations will almost entirely screen off this prior. The prior is only really important for factors where direct measurement is difficult, and hence we can’t update away from the prior, but for those we can’t do the regression. (Though I guess we could do the regression on known firms/researchers and extrapolate to new unknown orgs/individuals).
The way this plays out here is we’ve already spent the vast majority of the article examining the research productivity of the organizations; geography based priors only matter insomuchas you think they can proxy for something else that is not captured in this.
As befits this being a somewhat secondary factor, it’s worth noting that I think (though I haven’t explicitly checked) in the past I have supported bay area organisations more than non-bay-area ones.
Thanks, that’s very kind of you!