he doesn't compare against the exponential fit: the exponent is obvious, has a reasonable empirical justification.

The theoretical justifications are entirely different, though. It seems reasonable to me to suppose there's some minimal intelligence to be wealth-producing in an industrial society, and the smart fraction estimates that well and it predicts gdp well. But, it also seems reasonable to treat log(gdp) as a more meaningful object than gdp.

It's also bothersome that the primary empirical prediction of the smart fraction model (that there is some stable gdp level that you hit when everyone is higher than the smart fraction) is entirely from the extrapolated part of the dataset, and this doesn't seem noticeably better than the exponential model, whose extrapolations are radically different.

Granting that Dickerson published in 2006 and he wrote the smart fraction essay in 2002 he could at least have updated.

Yeah; I'm curious what they'd have to say about the relative merits of the two models. I'll see if I can get this question to them.

You need to delete any trailing whitespace in your indented R terminal output.

Fixed, thanks!

but what is this b here and why is it being added?

It's an offset, so that it's an affine fit rather than a linear fit: the gdp level for a population with no people above 108 IQ doesn't have to be 0. Turns out, it's not significantly different from zero, but I'd rather discover that than enforce it (and enforcing it can degrade the value for m).