Without looking at data, my prior on meat production being an increasing cost industry is extremely high.
Meat production is a commodity product. Commodity products compete mainly on price, and so we should expect the industry to be fairly normal (i.e. the sort of industry discussed in econ 101 courses) in terms of competition, costs, etc.
Meat has a number of costly inputs, including feed, water, land and labor. If you have several commodity inputs, you can expect that at least one of them would be a potential bottleneck to increasing the scale of the industry at constant cost. For example, we know that water usage is definitely increasing cost, because the next efficient option after depleting our reservoirs is desalination which is stupid expensive on agriculture scales (and this flows into feed prices).
Meat production is an extremely large and mature industry. We should expect that large and mature industries have scaled up to the point where marginal costs are increasing, because they have had the time and intelligence invested in them to pick the low-hanging and high-hanging productivity fruit.
People who eat meat would like to eat more of it, if only it were cheaper. For example, if MacDonalds introduced a third-pounder for the same price as a quarter pounder, most people would buy it. Since this situation exists, it is probably difficult to provide meat at cheaper prices holding other relevant factors stable. One can imagine another industry where people have no particular desire to purchase more of the given product if the price were lower, such as child car seats and so the industry is limited in size before it achieves minimum cost scale.
Of course, in some sense this dodges another question, which is what is your prior probability prior to. If you had never heard of economics, and were just talking about abstract categories which industries either were a member of or were not a member of, then perhaps you could presume a 50% meta-prior. If someone were to take all the industries in the world at a very fine-grained level, and they presented it to you, perhaps then a 50% probability would be warranted as well. To be honest, I'm not sure what the probability would be in that situation, if you were looking at, like beekeeping and video game production and taxi driving as example categories. But from that point to the point of meat production specifically we have many pieces of knowledge that become our prior before we actually get to the collecting real data. My prior of an industry being increasing marginal cost, weighted by the number of times it gets discussed in newspaper articles, or weighted based on the amount of revenue it generates per year, is much higher than 50%.
Edit: I see that this is somewhat repetitive of what you wrote in your other comment. Oh well, it's already written.
Great! This is the only 'complete' argument I've seen that our prior for animal products industries should be that they are increasing-cost rather than constant-cost. I'm not as confident as you seem to be, but that's more of a quibble at this point, and I'm glad we agree on the meta-prior!
The challenge then is to convince Norwood and Lusk that we want to know the long-run impact of consumer choices on animal production, not the short-run! They're clearly estimating short-run elasticities since (a) their supply curves are way too steep, even for an increas...
I believe that a small piece of rationalist community doctrine is incorrect, and I'd like your help correcting it (or me). Arguing the point by intuition has largely failed, so here I make the case by leaning heavily on the authority of conventional economic wisdom.
The question:
How does an industry's total output respond to decreases in a consumer's purchases; does it shrink by a similar amount, a lesser amount, or not at all?
(Short-run) Answers from the rationalist community:
The consensus answer in the few cases I've seen cited in the broader LW community appears to be that production is reduced by an amount that's smaller than the original decrease in consumption.
Animal Charity Evaluators (ACE):
Peter Hurford:
Compassion, by the Pound:
These answers are all correct in the short-run (ie, when the “supply curve” doesn’t have time to shift). If there is less demand for a product, the price will fall, and some other consumers will consume more because of the better deal. One intuitive justification for this is that when producers don’t have time to fully react to a change in demand, the total amount of production and consumption is somewhat ‘anchored’ to prior expectations of demand, so any change in demand will have less than a 1:1 effect on production.
For example, a chicken producer who begins to have negative profits due to the drop in price isn't going to immediately yank their chickens from the shelves; they will sell what they've already produced, and maybe even finish raising the chickens they've already invested in (if the remaining marginal cost is less than the expected sale price), even if they plan to shut down soon.
(Long-run) Answers from neoclassical economics:
In the long-run, however, the chicken producer has time to shrink or shut down the money-losing operation, which reduces the number of chickens on the market (shifts the "supply curve" to the left). The price rises again and the consumers that were only eating chicken because of the sale prices return to other food sources.
As a couple of online economics resources put it:
Policonomics:
AmosWEB*:
[I left out the similar explanations of the increasing- and decreasing-cost cases from the quote above.]
In other words, while certain market characteristics (increasing-cost industries) would lead us to expect that production will fall by less than consumption in the long-run, it could also fall by an equal amount, or even more.
Short-run versus long-run
Economists define the long-run as a scope of time in which producers and consumers have time to react to market dynamics. As such, a change in the market (e.g. reduction in demand) can have one effect in the short-run (reduced price), and a different effect in the long-run (reduced, constant, or increased price). In the real world, there will be many changes to the market in the short-run before the long-run has a chance to react to to any one of them; but we should still expect it to react to the net effect of all of them eventually.
Why do economists even bother measuring short-run dynamics (such as short-run elasticity estimates) on industries if they know that a longer view will render them obsolete? Probably because the demand for such research comes from producers who have to react to the short-run. Producers can't just wait for the long-run to come true; they actively realize it by reacting to short-run changes (otherwise the market would be 'stuck' in the short-run equilibrium).
So if we care about long-run effects, but we don't have any data to know whether the industries and increasing-cost, constant-cost, or decreasing-cost, what prior should we use for our estimates? Basic intuition suggests we should assume an industry is constant-cost in the absence of industry-specific evidence. The rationalist-cited pieces I quoted above are welcome to make an argument that animal industries in particular are increasing-cost, but they haven't done that yet, or even acknowledged that the opposite is also possible.
Are there broader lessons to learn?
Have we really been messing up our cost-effectiveness estimates simply by confusing the short-run and long-run in economics data? If so, why haven't we noticed it before?
I'm not sure. But I wouldn't be surprised if one issue is, in the process of trying to create precise cost-effectiveness-style estimates it's tempting to use data simply because it's there.
How can we identify and prevent this bias in other estimates? Perhaps we should treat quantitative estimates as chains that are no stronger than their weakest link. If you're tempted to build a chain with a particularly weak link, consider if there's a way to build a similar chain without it (possibly gaining robustness at the cost of artificial precision or completeness) or whether chain-logic is even appropriate for the purpose.
For example, perhaps it should have raised flags that ACE's estimates for the above effect on broiler chicken production (which they call "cumulative elasticity factor" or CEF) ranged by more than a factor of 10x, adding almost as much uncertainty to the final calculation for broiler chickens as the 5 other factors combined. (To be fair, the CEF estimates of the other animal products were not as lopsided.)