The practical part of me says that one single chicken will be absorbed into fluctuations in breeding, premature chicken deaths on the farm, and a supermarket’s expected excess chicken that gets donated or thrown into the garbage. Dealing with live animals and perishable products brings uncertainty and inefficiency.
Americans eat ~8 billion chickens a year. The uncertainty and inefficiency present in the market may well prevent the loss of a single chicken from ever being noticed. Those elasticity estimates are made by working with some fraction of total consumption and then extrapolating the results to individual chickens. The change you get from having 1% of the population turn vegetarian is different from having 50% go vegetarian. Go high enough and production starts getting cut faster than the fall in demand. Restrict supply, market chicken as a luxury item, and gouge the hell out of the price.
The conclusion is that an individual’s change in consumption will have practically zero impact, that the change of a nontrivial minority will run into elasticity, that change of large swaths of the population will reach the 1:1 ratio, and that change in a large majority may well go past the 1:1 ratio.
I think that “practically zero” means “practically zero as a fraction of the whole”, which is true but not directly relevant. (In the same way, donating to a charity that feeds starving people has “practically zero” effect on the problem of starvation, curing someone of cancer has “practically zero” effect on the problem of cancer, etc.)
It’s true that measurement is noisy. It’s true that this means that a one-chicken change may go completely unnoticed. But for the same reason a one-chicken change may (with lower probability) lead to a substantial overcorrection. On average I would expect that if my chicken consumption goes down by 1/year, the production of chickens for eating will go down by about 1/year, for the sorts of reasons that erratim gives.
[EDITED to fix a trivial typo (missing close-quote).]
On average I would expect that if my chicken consumption goes down by 1/year, the production of chickens for eating will go down by about 1/year, for the sorts of reasons that erratim gives.
My issue is that there’s a fair amount of waste built in. The chicken you don’t buy is probably just going straight to the rubbish heap. A large supermarket is already throwing away hundreds of pounds of meat each year. For example, British chain Tesco said that in the first six months of 2012, some 28,500 metric tons of their food was wasted. With just under 6,800 stores, that’s over 8 metric tons per store, per year.
To get the retailer to buy less chicken, you’d have to cut consumption enough to exceed their threshold for allowable waste.
I think that “practically zero” means “practically zero as a fraction of the whole, which is true but not directly relevant. (In the same way, donating to a charity that feeds starving people has “practically zero” effect on the problem of starvation, curing someone of cancer has “practically zero” effect on the problem of cancer, etc.)
I meant in absolute terms. If you donate to a charity, that money’s going to help someone. Curing someone of cancer drops the cancer population by one. With chickens, there’s the aforementioned waste problem where you may have to meet certain thresholds before you see any change.
To get the retailer to buy less chicken, you’d have to cut consumption enough to exceed their threshold for allowable waste.
This strikes me as compatible with what gjm said in the sentence before the one you quoted. Some chicken-buying decisions will make no difference, and others are going to have a disproportionate effect by hitting some threshold. In aggregate, chicken purchases by a supermarket have to equal their chicken sales (plus inventory breakage), so a pretty good guess for the expected impact of buying one less chicken is that one less chicken is going to be produced. Richard Chappell discusses a very simple model here. I haven’t seen believable models where in the long run there is substantial deviation from one-for-one.
Yes; another way to think of this is, “How do you model waste?”
If you think waste is best modeled by a fixed percentage of all production, then our best guess about the waste is that it changes proportionally with consumption. We don’t get to magically assign our consumption to the ‘waste’ category without highly specific information (such as, “I found it in a dumpster”).
If you expect the percentage of waste to grow/shrink with industry size, that could be an argument for slightly less/more than 1:1 effect (I’d put it in the “Gains to scale” category, even if it were negative). But I’ve never seen someone make that argument or attempt to model it.
Richard Chappell discusses a very simple model here.
Thanks for sending this; the ‘chunky fallacy’ comes up frequently when discussing this issue. Unfortunately, he explicitly endorses using short term elasticities at the end of his article.
There is undoubtedly some slop built in to the system, both to cover ordinary fluctuations in demand (which is, after all, stochastic), and because inventory control is itself expensive and difficult and only worth doing up to a certain level of precision.
That said, there’s a fallacy here, the same one as in this recent post (addressed here, e.g.). In brief, what matters is not whether you cause stores to waste measurably less food with certainly, but the expected amount of change in food waste due to your actions, especially over the long term.
In your model, how can you tell how close a market is to reaching the thresholds you suggest? In other words, if we are somewhere in the middle of converting a “large swath” of the population to vegetarianism, how can we tell if we’re at a point of 1 effect?
My guess is that we can’t distinguish between those cases, in which case the best we can do is to average out over all long periods of time/market states and estimate that our long term effect is 1:1 (even though, in every case, it probably isn’t exactly that).
I mostly brought that up as something to keep in the back of your mind when working with a simplified linear model. You could see bifurcations and nonlinear behavior in real life.
The practical part of me says that one single chicken will be absorbed into fluctuations in breeding, premature chicken deaths on the farm, and a supermarket’s expected excess chicken that gets donated or thrown into the garbage. Dealing with live animals and perishable products brings uncertainty and inefficiency.
Americans eat ~8 billion chickens a year. The uncertainty and inefficiency present in the market may well prevent the loss of a single chicken from ever being noticed. Those elasticity estimates are made by working with some fraction of total consumption and then extrapolating the results to individual chickens. The change you get from having 1% of the population turn vegetarian is different from having 50% go vegetarian. Go high enough and production starts getting cut faster than the fall in demand. Restrict supply, market chicken as a luxury item, and gouge the hell out of the price.
The conclusion is that an individual’s change in consumption will have practically zero impact, that the change of a nontrivial minority will run into elasticity, that change of large swaths of the population will reach the 1:1 ratio, and that change in a large majority may well go past the 1:1 ratio.
I think that “practically zero” means “practically zero as a fraction of the whole”, which is true but not directly relevant. (In the same way, donating to a charity that feeds starving people has “practically zero” effect on the problem of starvation, curing someone of cancer has “practically zero” effect on the problem of cancer, etc.)
It’s true that measurement is noisy. It’s true that this means that a one-chicken change may go completely unnoticed. But for the same reason a one-chicken change may (with lower probability) lead to a substantial overcorrection. On average I would expect that if my chicken consumption goes down by 1/year, the production of chickens for eating will go down by about 1/year, for the sorts of reasons that erratim gives.
[EDITED to fix a trivial typo (missing close-quote).]
My issue is that there’s a fair amount of waste built in. The chicken you don’t buy is probably just going straight to the rubbish heap. A large supermarket is already throwing away hundreds of pounds of meat each year. For example, British chain Tesco said that in the first six months of 2012, some 28,500 metric tons of their food was wasted. With just under 6,800 stores, that’s over 8 metric tons per store, per year.
To get the retailer to buy less chicken, you’d have to cut consumption enough to exceed their threshold for allowable waste.
I meant in absolute terms. If you donate to a charity, that money’s going to help someone. Curing someone of cancer drops the cancer population by one. With chickens, there’s the aforementioned waste problem where you may have to meet certain thresholds before you see any change.
This strikes me as compatible with what gjm said in the sentence before the one you quoted. Some chicken-buying decisions will make no difference, and others are going to have a disproportionate effect by hitting some threshold. In aggregate, chicken purchases by a supermarket have to equal their chicken sales (plus inventory breakage), so a pretty good guess for the expected impact of buying one less chicken is that one less chicken is going to be produced. Richard Chappell discusses a very simple model here. I haven’t seen believable models where in the long run there is substantial deviation from one-for-one.
Yes; another way to think of this is, “How do you model waste?”
If you think waste is best modeled by a fixed percentage of all production, then our best guess about the waste is that it changes proportionally with consumption. We don’t get to magically assign our consumption to the ‘waste’ category without highly specific information (such as, “I found it in a dumpster”).
If you expect the percentage of waste to grow/shrink with industry size, that could be an argument for slightly less/more than 1:1 effect (I’d put it in the “Gains to scale” category, even if it were negative). But I’ve never seen someone make that argument or attempt to model it.
Thanks for sending this; the ‘chunky fallacy’ comes up frequently when discussing this issue. Unfortunately, he explicitly endorses using short term elasticities at the end of his article.
Exactly.
There is undoubtedly some slop built in to the system, both to cover ordinary fluctuations in demand (which is, after all, stochastic), and because inventory control is itself expensive and difficult and only worth doing up to a certain level of precision.
That said, there’s a fallacy here, the same one as in this recent post (addressed here, e.g.). In brief, what matters is not whether you cause stores to waste measurably less food with certainly, but the expected amount of change in food waste due to your actions, especially over the long term.
In your model, how can you tell how close a market is to reaching the thresholds you suggest? In other words, if we are somewhere in the middle of converting a “large swath” of the population to vegetarianism, how can we tell if we’re at a point of 1 effect?
My guess is that we can’t distinguish between those cases, in which case the best we can do is to average out over all long periods of time/market states and estimate that our long term effect is 1:1 (even though, in every case, it probably isn’t exactly that).
I mostly brought that up as something to keep in the back of your mind when working with a simplified linear model. You could see bifurcations and nonlinear behavior in real life.