Also, “GDP (as it’s actually calculated) measures production growth in the least-revolutionized goods” still seems like basically the right intuitive model...
I don’t think this is quite right, but I think digging a little deeper here can be informative. In your apples and brass example, there was no technological progress in producing apples, but we still measured real GDP growth of 1.36 using year 1 prices. So real GDP growth doesn’t just measure what’s happening in the least-revolutionized goods, but it certainly does get dragged down by stagnation in one sector.
As an interesting contrast, consider what would happen if producing apples and brass both became 2x more productive in year 1, causing the price of both goods to fall to $0.50. If we still have $4.50 nominal spending in year 1 (and apples and brass have similar income elasticities), we’ll spend $2.25 to buy 4.5 apples and $2.25 to buy 4.5 units of brass. Now real GDP growth calculated with year 0 prices = $9/$6 = 1.5, and real GDP growth calculated with year 1 prices = $4.5/$3 = 1.5.
This contrast sheds light on a couple points: First, the disconnect between measuring GDP growth using year 1 prices vs. year 0 prices is driven by the large change in the relative amount of each good consumed. So if the growth in our consumption of houses (and everything else) since 1960 had matched the growth in our consumption of transistors, measuring GDP growth using today’s prices or 1960s prices wouldn’t matter as much.
Second, and I think more interestingly, it seems that measured GDP growth can be higher when we have relatively slow growth in a lot of industries than when we have extremely rapid growth in just a few industries, even if those rapidly going industries are a significant portion of the economy. In your example, brass started off as 50% of the economy and had 10x productivity grow, and we still only measured real GDP growth of 1.36. In my example, both industries saw relatively slow productivity growth of 2x, but we measured a higher real GDP growth rate of 1.5.
This implies that to the extent that GDP growth today is (a) mostly driven by technological progress in information technology, and (b) at least in the same ballpark as historical GDP growth, it must be the case that information technology is progressing much faster than any particular industry’s technology ever did during the times of stronger growth in the 19th and 20th centuries. Put another way, using this mental model as a guide, the high GDP growth rates of the 19th and 20th centuries seem to be much due much more to the broadness of technological progress at that time than to the actual rate of growth in any of the industries. This is all right in line with your point about stagnation and Jason Crawford’s post, but for me it really helps to clarify how important the broadness of growth is for measured GDP growth.
This was a very informative read. One thing I’ve been wondering about a lot lately is to what extent innovation relies on exploiting unpriced externalities, and I think this piece provides a good example.
As you note, the key issue is that safety in a factory involves externalities, and the workman’s comp laws effectively internalized the cost of those externalities to the firms. If the workman’s comp laws had been in place from the beginning (or if the externalities had otherwise been internalized from the beginning), how would that have changed the course of innovation and technological progress? Would innovation have slowed down, or would we have missed out on some innovations altogether, because it would have been too costly for firms to operate when they were fully internalizing all their external costs from the beginning?
I’ve mostly been wondering this in the context of pollution, but I think it applies to worker safety too after reading this.