Bunch of thoughts on Baumol effect & Jevons paradox
Key points. The Baumol effect & Jevons paradox are 2 claims regarding the effect of increasing efficiency of a good or sector. Although not incompatible, they are at odds; one suggesting relative decline, the other suggesting absolute growth. I examine these & find that they are often defined & discussed in a confused way. I suggest that thinking in terms of sector specific ‘Kondratiev waves’ may be more helpful. This suggests a boom for AI but a bust for non AI software with the former overwhelming the latter as far as effect on the overall economy.
Baumol effect vs Jevons paradox -- a trillion $ show down?
Prima facie the Baumol effect is bearish for sectors with larger gains in efficiency whereas Jevons paradox is bullish [N-1]. So unsurprising Satya Nadella wants us to know about Jevons paradox. Jevons paradox any way seems to be much better known probably b/c of its connection to climate change concerns.
Some misc thoughts on this topic…
Also starring Kondratiev waves. Cameo appearance -- ‘Keynesian utopia’ [N0].
N-1. Ironically Jevons was the pessimist & predicted a return to Malthusian equilibrium once the UK's coal reserves were mostly depleted; roughly analogous to more recent especially pessimistic takes on peak oil. Baumol was just trying to understand wage growth in jobs w/o productivity growth.
N0. By which I mean “For three hours a day [work shift] is quite enough to satisfy the old Adam in most of us!”.
§ 1. My definitions.
Let's say x alone (not y) becomes more efficient meaning… Either increased productivity ie more output from x. Or reduced cost from x's marginal cost curve falling.
(Generalized) Baumol effect -- x becomes more efficient -> y becomes more expensive
This is ubiquitous. Remember than only relative cost really matters. So if x becomes less expensive, the only way to avoid this is if y ceases to be produced or if it gets pushed far enough down its marginal cost curve. And if x becomes more productive & this causes the output of x to become less expensive, the same reasoning applies. Since the generalized Baumol effect can manifest in counter intuitive ways, it's still a useful thing to know. Most often the Baumol effect is used to explain why wages rise in sectors w/o much productivity growth.
‘Strong’ Baumol effect [N1] -- if a sector becomes more efficient, it shrinks relative to the economy
Unfortunately most sources (including Wikipedia?) do not clearly distinguish between the 2 Baumol effects
Jevons paradox also has 2 different forms, but these are quite similar to each other. The designations 1 & 2 are arbitrary.
Jevons paradox 1 -- x becomes more productive -> demand for x increases
Stated abstractly this is not surprising, but again it can manifest in counter intuitive ways. Eg supposedly more efficient use of a natural resource often leads to greater exploitation of that resource.
Jevons paradox 2 -- x becomes less costly -> revenue from x increases
A problem with Jevons paradox is distinguishing a ‘true’ Jevons paradox from a ‘pseudo’ Jevons paradox. An apparent Jevons paradox need not reflect efficiency gains specific to that good; other economic growth may be a confounding factor. The “Let's say x alone (not y) becomes more efficient” bit at the start of this section excludes the latter possibility, but this important distinction seems to often be elided. Eg increased demand for coal in the 1800s UK likely reflects improvements in many processes not simply energy efficiency of coal [N1.5]
N1. Not actually stronger in the mathematical sense, but in practice it likely is.
N1.5. In principle these possibilities could be distinguished by counter factual modeling of a scenario in which coal magically becomes cheaper or more efficient but marginal cost curves (not marginal cost) are otherwise unchanged.
§ 2. Discussion.
Whereas the generalized Baumol effect is ubiquitous, Jevons paradox & the strong Baumol effect are more uncertain. In the case of US manufacturing, the strong Baumol effect only shows up in the early 20th century. Until then that sector was growing relative to the rest of the economy in terms of employment, productivity, revenue & value added. So it seems the strong Baumol effect is some thing that shows up late so AI is probably safe for the time being.
But what about non AI software? Humans did not evolve to do computer programming -- a human doing computer programming is like GPT4 playing chess. AI will likely drastically reduce the cost of making non AI software. This is a mature industry w/o the explosive growth potential of AI so the strong Baumol effect is looking probable to me.
What does this mean for a company like Microsoft? Microsoft's deep pockets have only given it a slim edge relative to Anthropic & Deepseek. Non AI software is likely to go into relative decline in the near future. On the other hand Microsoft has so many people hooked on Azure, Outlook etc it will likely continue to make fat profits for a while. What does all this mean for the market value of Microsoft & other software companies? I remain neutral on that question.
The emergence of the strong Baumol effect provides some (additional) mechanistic basis for sector specific Kondratiev waves [N2]. These could produce economy wide Kondratiev waves depending on how they line up.
Arguably both the Baumol effect & Jevons paradox are a bit over played. The relative decline of the US manufacturing sector to some extent reflects government actions that channel more & more $ into dysfunctional sectors [N3] & raise the cost of living via mandates & restrictions. In a Keynesian utopia w/o such things people could easily live off the manufacturing bonanza (w/o doing much work) so possibly the economy would be smaller but the relative size of the manufacturing sector is likely larger meaning a more modest Baumol effect. The problem with Jevons paradox in this regard is discussed at the end of § 1.
N2. I see no reason why Kondratiev waves should be very close to 53 years long. But if we take Microsoft's IPO in 1986 as the beginning of a Kondratiev wave in non AI software, + 53 years gives 2039 as the end which seems plausible. The AI boom should more than offset this.
N3. In particular education & health care.
Hzn