How much does consumption affect production?
A ewe for a ewe
In a discussion with Benquo over his recent suffering-per-calorie estimates I learned that there have been a few different proponents of incorporating short term elasticities into such estimates. But do empirical short term elasticities really improve our estimates of consumption’s long term effect on production? For example, if I decide to reduce my lifetime consumption of chicken by one, should I expect the long term production of chicken to drop by ~1, ~0, or something in between?
I believe we should have a relatively strong prior that long term production has a roughly 1:1 relationship with consumption, including for small individual decisions. Below are a couple arguments I find compelling, and a major exception that is not a short term elasticity.
Black box economies in general
If I go to a large alien civilization of uncertain economic structure and surprise them by buying(?) one widget, how should I expect that to affect their long term production of widgets? Seems like I should expect it to increase by one, because now they have one less than they used to. If it was originally decided that that widget should be produced; why wouldn’t they decide to replace it when lost?
Neoclassical capitalism in the long term
In a simplified market, I expect there to be a lowest price at which chickens can be reliably produced at scale (“the Cost”). If producers expect the market price to be less than the Cost in the future, they will shut down production to avoid losses. If they expect it to be more than the Cost in the future, they might expand operations to make more profit. In the long term (when we can ignore temporary shocks to the system and producers have time to make adjustments), I expect the equilibrium price of chicken to approach the Cost of chicken (because other prices lead to conditions that push the price back toward the Cost). In other words, my prior is that the “price elasticity of supply” in the arbitrarily long term becomes arbitrarily high (imagine a virtually horizontal supply curve).
How many chickens will be produced at that long term price? However many are worth the Cost to consumers. If 50% of chicken consumers permanently become vegetarians, I expect that eventually the chicken industry will end up producing about 50% fewer chickens at a price similar to today’s.
Similarly if consumption is reduced by just one chicken. My prior is that producers have an unbiased estimate of consumption, and that doesn’t change when I eat one less chicken (so my best guess about their long term estimate of consumption drops by one when I forgo one chicken).
Time breaks the elastic limit
Compare my prior that every chicken forgone causes (in the long term) one less chicken to be produced, to the estimates that it only causes 6% or 76% of a chicken to not be produced (as Peter Hurford points out in the second case, the enormous range in these estimates alone is enough to raise flags).
Those numbers sound plausible in the short term when there’s a backup in the chicken pipeline and a drop in price because producers were caught off guard by the drop in consumption. But if the vegetarians hold their new diets, won’t the producers eventually react to the changed market? When they do I bet the equilibrium price will be somewhere close to the original Cost, and the quantity produced will be about 50% less (not 3% less or even 38% less). I think the thing these elasticity estimates are forgetting is that the producers aren’t satisfied (in the long term) with the lower price that results from a chicken glut caused by vegetarianism. If they were, they’d be producing more chickens now.
Said another way, it all comes down to the difference between producers’ reaction in the short term vs. the long term. In the short term, when someone decides not to eat a chicken, it goes to the next highest bidder (so price drops and production doesn’t change much). But in the long term, producers produce all the chickens that will be demanded at the Cost (they want to produce as many as they can at that price, but if they produce any more, the chickens will be sold at a loss). When one person permanently becomes vegetarian, we should expect that long term size of the industry decreases accordingly.
When the long term Cost changes with industry size
To be clear, if we could actually measure consumption’s effect on long term production in specific cases, it would rarely be exactly 1:1, though my prior is that it will average out to that over time. The exception is if consumption consistently affects the long term price in a particular direction. For example, here are some reasons that I might expect the Cost of chicken to grow or shrink as the size of the chicken industry increases:
Finite inputs such as limited agricultural land (Cost grows with size)
The production process also creates another product like eggs (Cost grows with size if marginal production is used for both)
Gains to scale such as factory farming (Cost shrinks with size)
R&D or innovation (Cost shrinks with size)
Favorable government policies (Cost shrinks with size)
If we have sufficiently certain estimates on any of these effects, we can certainly try to model them, although it would be a very different exercise than using empirical estimates of short-term elasticities. As it is, I have no idea which of the above effects win out (ie, whether the “consumption elasticity of the Cost” is positive or negative in the long term).
I think we would make our estimates more simple and accurate by sticking with the prior that eating one less chicken causes about one less chicken to be produced in the long term.
- Misapplied economics and overwrought estimates by (12 Jan 2015 17:10 UTC; 7 points)
- 's comment on Christmas 2014 Open Thread (Open Thread 7) by (EA Forum; 5 Jan 2015 15:59 UTC; 2 points)
- 's comment on Why Eat Less Meat? by (6 Jan 2015 17:08 UTC; 1 point)
- 's comment on Open thread, Dec. 29, 2014 - Jan 04, 2015 by (5 Jan 2015 15:57 UTC; 1 point)
Basic economics that explains why the cost of chicken will drop. You are ignoring supply curves, and these exist because not all producers are identical. The drive in change of costs is competition among chicken producers.
There is a price for chicken, say 10$ per unit. To make a profit, each producer must produce chicken at less than that price. However, not all producers are making chicken at the same cost. Some are more efficient than others. Some spend 9$ making a unit, some spend 8$. Some could produce chicken for 10$ a unit and don’t.. When demand for chicken drops, the business with 9$ cost lowers production or leaves the industry before the business with 8$ cost. The drop in production is concentrated in the marginal producers. Similarly if the price rose, the potential producer with 10$ costs would start producing.
There is a mirror process among consumers.
This is true in the short term, but in the long term, the dynamic changes for producers:
The producers that know how to make chickens for $8 scale up or their production strategy is replicated by others.
The marginal cost of production (and hence price) keeps falling until all producers are making no profit (relative to opportunity cost of capital)
The industry can scale up/down (in the long term) to meet changing demand, but it can’t drive prices any lower. If prices were any higher the industry would scale up in the short term and keep expanding until the price fell back to the Cost in the long term.
The elasticity of the demand curve changes less than the supply curve in the super long term, but if you agree with me that the supply curve is virtually flat at that point, then the elasticity of the demand curve is negligible (because as the supply curve shifts left and right, the only point on the demand curve that matters is quantity @ price = Cost/Supply price).
No. It’s true long term as well.
What you have listed are forces that drive the cost of production down. However, they cannot flatten all costs. For example, some locations are better for producing chickens than others. Better weather, cheaper labor market, ease of transportation to slaughter, etc. These factors cannot be cloned.
It’s only the marginal producers that have costs at or just below the price.
In the specific example, they could be cloned by expanding in the good locations.
More generally, if you’re claiming that there’s a limited supply of good locations from which to produce chickens, then that reduces to a “finite inputs” argument I discuss in the last section of the OP. (For further discussion see responses to this comment .)
In short, I agree that such effects can create a sloping long term supply curve in some cases, but I also believe that there are other effects that can lead it to slope the opposite direction, and it’s not immediately obvious which wins out. My prior is that the long term supply curve for an arbitrary product is virtually flat.
Said another way, if you’re going to argue that the long term cost-per-widget is higher when producing 2X widgets than X widgets, then you have to argue that the effect of finite inputs outweighs gains to scale and other factors. I haven’t seen such an argument generally or in the case of chickens.
Do you have empirical (as opposed to economics-theoretical) support for this prior?
No, I haven’t looked at the empirical evidence because I didn’t think it would be as convincing as the 2 theoretical arguments I made in the original post; let me know if you are aware of any such analysis.
Would you accept the results we find from an analysis of Big Macs as relevant?
Since Big Macs aren’t generally transported across national boundaries, we can think of the market for Big Macs in each country as largely independent.
While we would both expect various factors such as the price of labor to affect the price of Big Macs differently in each country, you would expect the price of Big Macs to positively correlate with # sold in that country (or possibly # sold per person), right? I would not expect such a correlation. (I think looking across countries is better than in one country across time, since then technology or other time-dependent factors would bias the results.)
If we had time we could control for all the other factors we don’t want to bias the results like price of labor; but maybe even without these we might see some interesting initial patterns.
Heh. It seems we have pronounced… methodological differences :-D
Empirical evidence is nice and often more convincing than theory, but I don’t think it’s necessary for an argument to be convincing (to believe otherwise would be quite… burdensome).
In this case, the original articles I am critiquing used purely theoretical arguments to claim that there will be long term price elasticity of supply, and I think that a theoretical critique is sufficient to show that the strength of their arguments is currently too weak to support the complexity of their theory.
I’m certainly open to any empirical evidence that may exist. Would you find a quick analysis of Big Macs moving (or if not, do you have a suggestion for a different empirical analysis)?
The first question is whether you’re interested in being convincing or in getting an accurate map.
Economics, in particular, is well-known for its fondness for theoretical arguments which tend not to hold up in real life.
You’ll have to specify what you are looking for. In particular, how long is “long term”? What kind of goods or industries you want to include and exclude?
For example, it wouldn’t be hard to find both price and supply (=production) data for major commodities (oil, copper, wheat, etc.). You could plot a scatter graph, attempt to fit a model....
Empirically, some industries are approximately constant-cost, others are increasing- and decreasing-cost. OP mentioned certain factors pushing one way or the other, but ultimately the slope of the long-run supply curve of an industry is determined by which factors predominate, so we’d have to measure it to be sure. What is generally true, however, is that long-run supply is typically highly elastic, so cost doesn’t change much from marginal changes in demand.
What you are effectively claiming is that there are no suboptimal producers of chickens. Unless every producer of chickens is ideally located, ideally managed, ideally staffed, and working with ideal capital there are differences in production costs.
There is a reason, that economics assumes that the amount of a good supplied changes as price changes, and I haven’t seen any argument that exempts the case of chickens.
Also, how does the market create less chickens as demand falls? If there are differences in cost, the highest cost producers leave the market as price falls. Easy to answer with the standard assumptions, but almost impossible with your nonstandard prior.
It’s not that this will ever actually be the case, but the argument is that, in the long term, the market approaches what you would expect with such assumptions (and continues to have short term fluctuations away from that). But yes, even this assumption is clearly not actually true in all cases (as with all assumptions in neoclassical economics); the better question is whether it’s a good simplification (enough to form a reasonable prior) or whether there is a better simplification we can consider (either simpler or more accurate).
The estimates I’m critiquing in the original post assume “short term elasticities are the best prior for long term elasticities” and I am advocating that “a better prior for the long term cumulative elasticity factor is 1″.
The explanation of both of these issues is the short term supply curve (which is not flat). In the short term, if people stop eating chicken, the price drops, and the producers that are (in the short term) able to improve their (expected long term) profits by scaling or shutting down do so.
That is an excellent question, but it requires an additional piece: good for what purpose?
Right. In this case, to answer the question, “If I decide to reduce my lifetime consumption of chicken by one, should I expect the long term production of chicken to drop by ~1, ~0, or something in between?” Which is of demonstrated interest to the authors I am critiquing.
That question seems to have a simple answer: your decision will not affect the long-term production of chicken.
Now I suspect that the real question is “If X million people decide to stop eating chicken, what would happen to the long-term production?” That is a much more complicated question which I don’t think can be answered by moving or bending the supply and demand curves under the ceteris paribus assumption. One reason is that it’s scale-dependent: different magnitude of X gives different answers. If X is small, its effect would be swamped by other factors (e.g. the growing prosperity in the developing world which generally leads to more people eating meat) and at the other end, obviously, if everyone stops eating chicken the production would drop to zero and chicken will become extinct.
Let X and Y have a causal relationship of the following form: if X is set to a given value x, Y takes on a value kx + z, where z is the value of a random variable Z which is independent of X.
What is the expected change in Y caused by a change in X of size dx?
It is k dx.
It does not matter how much the change in X is “swamped” by the variability of Z. It does not matter if it is so large that the change in Y consequent on that change in X cannot possibly be observed. The expected change is still k dx.
Of course, but you are assuming a linear relationship (y =kx + z) and I am unwilling to assume one.
Everything is linear, to first order. We are, after all, considering small changes in X. dx will only have no effect if the gradient of the actual function expressing the causal effect on Y is zero. It still has nothing to do with the magnitude of all the other effects on Y independent of X.
You are talking math and I’m talking empirics. You don’t know the true function (or even whether it exists), all you can do is make approximations and build models (which, to quote George Box, are always wrong but sometimes useful).
A couple of posts up you said ” It does not matter if it is so large that the change in Y consequent on that change in X cannot possibly be observed”—and that’s where we disagree. It doesn’t matter in math, it does matter in reality because you don’t have access to the underlying function.
Those models will say what I said: if delta Y/delta X is measurable for large enough delta X, dY/dX cannot be zero at every point throughout such a change. The average value of dY/dX over that range will be delta Y/delta X. That includes both the model that coincides with reality and all the others.
But no one is talking about every point. If you can only detect delta Y for large changes in X, it does NOT imply that small changes in X will also cause some changes in Y. Step functions are common in reality.
In this context, chicken production utilizes economies of scale. This means that a “unit” of chicken production is quite a large chicken factory. To change the number of chicken factories, you need a sufficiently large change in the relevant factors—if the demand drops by 1 chicken, the number of factories won’t change.
And if you don’t know where the step is, this applies.
I think we’ve come full circle here.
This applies only if you are confident that your action is just one out of a large number of similar actions.
In any case, yes, I think we understand each other’s positions and just disagree.
My “not buying a chicken” seems like it would look very similar to anyone else’s “not buying a chicken”.
OK, so I argue option A, you state option B, and the articles I link argue option C.
I agree it’s a complicated question (in that it requires lots of information to answer precisely and accurately). If you had no empirical data to work with, what would be your best guess/expectation? Also if your answer is proportionally different than in the ‘single chicken’ case, I’d be curious to know why.
If I had no empirical data, I would not be making any guesses in this case.
The “single chicken” case is below the noise floor. Empirically speaking, the consequences are undetectable. And for “many chicken”, how many matters—I don’t think there is a straightforward linear case here.
OK so you have no prior for large cases, you have no prior about the relationship between large cases and small cases, and your guess for small cases is “zero impact”.
My prior for large cases is 1:1 impact, my prior is that the impact in large cases is proportionally similar to the impact in small cases, and therefore my prior for small cases is 1:1 impact.
Let’s be clear about distinguishing between the map and the territory. To what do your priors apply?
Cumulative elasticity = Supply Elasticity/(Supply Elasticity—Demand Elasticity).
A cumulative elasticity factor of one means a demand elasticity of 0.
A completely inelastic demand curve is not to be expected in standard economics, and as such it is an inappropriate prior. Thanks for the math demonstrating my point.
I believe your math skipped a step; it seems like you’re assuming that Supply Elasticity is 1. I actually claim in the original article that “the ‘price elasticity of supply’ in the arbitrarily long term becomes arbitrarily high”. In other words, as “length of ‘term’” goes to infinity, the Supply Elasticity also goes to infinity and the cumulative elasticity factor approaches 1 for any finite Demand Elasticity.
Stepping back, I worry from your sarcastic tone and the reactive nature of your suggestions that you assume that I am trying to ‘beat you’ in a debate, and that by sharing information that helps your argument more than it helps mine, I have made a mistake worthy of mockery.
Instead, I am trying to share an insight that I believe is being overlooked by the ‘conventional wisdom’ of this community and is affecting multiple public recommendations for rational behavior (of cost/benefit magnitude ~2x).
If I am wrong, I would like to be shown to be so, and if you are wrong, I hope you also want to be corrected. If instead you’re just debating for the sake of victory, then I don’t expect you to ever be convinced, and I don’t want to waste my effort.
Oops, I meant to edit that rather than retract. Since I don’t believe there’s a way to un-retract I’ll re-paste it here with my correction (Changing “Supply Elasticity is 1” to “Supply Elasticity is finite”):
I believe your math skipped a step; it seems like you’re assuming that Supply Elasticity is finite. I actually claim in the original article that “the ‘price elasticity of supply’ in the arbitrarily long term becomes arbitrarily high”. In other words, as “length of ‘term’” goes to infinity, the Supply Elasticity also goes to infinity and the cumulative elasticity factor approaches 1 for any finite Demand Elasticity.
Stepping back, I worry from your sarcastic tone and the reactive nature of your suggestions that you assume that I am trying to ‘beat you’ in a debate, and that by sharing information that helps your argument more than it helps mine, I have made a mistake worthy of mockery.
Instead, I am trying to share an insight that I believe is being overlooked by the ‘conventional wisdom’ of this community and is affecting multiple public recommendations for rational behavior (of cost/benefit magnitude ~2x).
If I am wrong, I would like to be shown to be so, and if you are wrong, I hope you also want to be corrected. If instead you’re just debating for the sake of victory, then I don’t expect you to ever be convinced, and I don’t want to waste my effort.
I’m sorry, that is correct. You were describing a supply curve that doesn’t behave normally. So I can’t say anything about demand curves. I apologize for the cheap shot.
In the standard economic models, supply and demand curves have elasticity that is a positive, finite number. Infinitely elastic curves are not possible within the standard models.
The priors I start with, for any market, are that it behaves in a manner consistent with these economic models. The burden of proof is on any claim that some market is behaving in a different manner.
Thanks for acknowledging that.
I think standard economics agrees with your vision of “~always positively-sloping finite supply curves” in the short term, but not necessarily the long term. Here’s a quote from AmosWEB (OK, never heard of them before, but they had the quote I wanted)
Long term supply curves are different than supply curves. They are similarly named, but different concepts.
Supply curves measure supply at a price.
Long term supply curves measure market equilibrium supply as demand changes over time.
The elasticity measurement is the derivative of supply with respect to price. It cannot be applied to long term supply curves.
I agree with your definitions of the two curves, although I don’t know what point you’re making by the distinction.
In either case we can ask, “how much will changes in demand affect equilibrium quantity?” In a constant-cost industry, the answer will be 1:1 in the long-run (as indicated by a flat, or infinitely elastic long-run supply curve), but as you gradually shorten the scope over which you’re looking at the market, making it a shorter- and shorter-run supply curve, it will steepen (elasticity decrease) such that the answer is “less than 1:1″.
First, is that because they are different things it’s not a contradiction to what I said.
The second is that elasticity is not validly applied to long term supply curves, as they are not a function of supply in terms of price.
Buying a widget from this mysterious alien civilization will not cause them to produce one more of it. The fundamental and founding fact of economics is scarcity, so another widget cannot be produced simply because someone would like one. Do you replace everything that is lost to you no matter what?
Never mind the question of just how you are buying this widget....
I don’t really understand the sentence, “In a simplified market, I expect there to be a lowest price at which chickens can be reliably produced at scale (“the Cost”).” It doesn’t sound like something someone trained in economics would say.
In general, as other commenters note, you don’t seem to really take into account marginal factors, the fact that firms are inherently different from one another (even if it’s something like inevitable like location), or how not all industries have the same cost structure.
Markets produce to satisfy demand, and so your conclusion is probably essentially correct, but the reasoning supplied in the post does not really get you there.
Based on the comments to this article I realized a stronger appeal to established economic theory would probably be more convincing. I’ve made that appeal in a separate post: http://lesswrong.com/r/discussion/lw/lj0/misapplied_economics_and_overwrought_estimates/
Neat write-up. I’d say that the scale elasticity of Cost is also irrelevant, since vegetarianism promotion only has a small marginal effect on scale.
Suppose 100 chickens are produced. And, suppose 100% of the population becomes vegetarian. The number of chickens produced will drop to zero.
100 fewer chickens demanded; 100 fewer produced. So, on average, between 1 and 100, the next marginal drop in chicken demand drops production by 1.
Which elicits the question: what is the pattern from 100 down to 0?
Suppose there’s suddenly only one non-vegetarian left. At today’s price, he would demand 1 chicken. Clearly, prices will have to rise if only 1 is produced instead of 100. He might, then, demand only half a chicken at the new, higher price.
That means: an instant drop in demand from 100 to 1 chicken leads to an eventual drop in production of 99.5 chickens. That’s 99.5 fewer produced when 99 fewer are demanded.
Also, an instant drop in demand from 0.5 to 0 leads to a drop in production from 0.5 to zero.
If the function is monotonic, it must be that a drop in demand of X units must lead to an eventual drop in production of X+f(X) units, where f(X) > 0. That’s the only way the math works out.
There is a drop of X chickens produced, to match the drop in quantity demanded at price X. The extra drop of f(X) reflects the fact that even fewer chickens are demanded at the new, higher price that must result.
The relevant question is how does the chicken economy change when one person who used to demand a chicken at the market price switches to demanding no chicken at the market price. In this case, the supply-offered vs price-offered curve is not changed, the demand-offered vs price-offered is shifted down by one chicken on the demand curve at any given price point. If supply and demand curves were real, the new equilibrium would be reduced by LESS than one chicken traded, the price would be down by a tiny bit.
One can be very modern and say that supply and demand curves are not real, but they are. Chickens are produced at a variety of costs by a variety of producers. The low cost producers will change their behavior perhaps not at all with the slight price decrease in the market. But the highest cost producer, the marginal producer, should react if he is rational, and that of course is the assumption.
Consider an easier to understand situation. 1% of the market disappears, suddenly goes vegetarian. In the short run following causality, the chickens they don’t buy that week go on clearance prices, a price set so that as many chickens as got unsold are priced low enough to grab some people who weren’t having chicken that week at market prices. Meanwhile the store lowers its order for chickens next week. Its suppliers fill their needs at a lower price at the chicken auction. chicken producers see the lower auction price and most of them decide to hatch a few less chickens and save on the cost of chicken feed and processing.
You KNOW the market will respond with lower prices if demand is cut 50% There doesn’t seem to be any quantity at which the forces that produce that disappear. Perhaps a reduction of one chicken cannot be measured reliably to have produced a lower production and a lower price, because the systematic noise in these measurements is higher than the change. But that doesn’t mean the effect isn’t there, we measure the effect by turning up the perturbation to a level where the effect is above the noise floor.
If SOME people switch away from chicken towards other food, then every thing we know about markets and production suggests you will see a slightly lower price and a slightly lower equilibrium production level, but the lower price will cause a switch towards chicken consumption among other people who were on the margin of demand. The reduction in net chicken production will be less than one for each demanded chicken removed from the demand curve.
I’m not arguing that supply curves aren’t real; I’m arguing that the super-long term supply curve is virtually flat (the price elasticity of supply is arbitrarily high). I find it compelling to imagine a chicken producer accepting contracts to produce chickens 10 years from now. At what prices and quantities would the producer accept the contracts? I would say it would accept all contracts at or above the Cost, at as high a quantity as possible. With lead time and certainty, the issues that create short term elasticity don’t apply. (And yes, short term market shocks and uncertainties are real, but they’re just built into the profitability model of Cost.)
Under the simplified assumptions we’ve been using, the starting price is the Cost of producing chicken. When the price drops in your scenario, that means chicken producers are operating at a loss. True they might not throw out any chickens if they appropriately adjust price, but they will not be satisfied with the new equilibrium (because the price is too low). Instead they will reduce production, let the price rise back to the starting point, and let the new chicken consumer (who values chicken at slightly less than Cost) drop back out of the market.
If you don’t believe that the producers will keep reducing consumption until the price rises back to the original level and the “new consumers” (who value chicken < Cost) stop buying again, then you have to explain why producers aren’t already producing more chickens than they are (in the absence of any dietary changes). In other words, if “new consumer” demand exists, and producers are still profitable with slightly lower prices, why haven’t they scaled up production to larger than what it actually is today to sell chickens to “new consumers”?
Huh? A flat supply curve means that the producers will produce the same number of chicken regardless of the price at which they can sell them. I don’t see why this should be true in long term.
Not quite. You are implicitly assuming that the Cost is fixed in stone and it isn’t. The chicken producer should accept all 10-year forwards on chicken if and only if he can buy matching forwards on his production inputs, otherwise he is exposed to the risk of, say, the price of feed going up.
I mean “horizontal” rather than “vertical”. In that sense, a flat supply curve means a constant price, not a constant quantity.
I agree, but this is the type of pressure on Cost that I have no expectation of being in any particular direction. As a result, on expectation these perturbations average to zero, and the argument holds. It would be interesting if we had a reason to expect that Cost would go up or down depending on the amount of production. These are the issues my last section in the original article was intended to address.
The received wisdom is that if the demand for chicken feed goes up, the cost and price will go up, if the demand goes down, the cost and price will go down.
If the received wisdom is that a larger chicken industry will increase the price of chicken feed, then my prior is that it’s true in the short term, but not the long term. Chicken feed might be in finite supply, in which case Cost might grow with chicken industry size, but there are other reasons I can imagine Cost might shrink with increasing chicken industry size (listed in original article) and I don’t have enough confidence about any of these factors to break my prior that Cost at industry size 2X is ~Cost at industry size X.
So what you are basically saying is that in the long run the price will be driven close to the lowest average price—right?
Still not quite, as once you recognize that “perturbations” will happen, you need to engage in some risk management (zero mean does not imply zero volatility). In your scenario the chicken producer seems to be fine with the 50% chance of going bankrupt at the delivery time which isn’t a good assumption to make.
Yes; in the long term the producers that have higher-than-average Costs will be driven out of the market.
I’m modeling risk management as part of the typical Cost of doing business, along with things like interest rates, opportunity costs of capital, chicken feed, other inputs, etc. Separating out risk management as a stand-alone variable doesn’t seem to change anything.
Yes you are. And I think this is wrong. And here is why (stated differently from my original reply which also thought it was wrong).
Consider a world in which the entire demand for chickens is one guy who lives on the island of Kauai. In Kauai, chickens run wild through the streets and yards and fields. Since there is this one guy who buys a chicken every week, the shopkeeper scoops up a chicken in his yard on his way to work whenever he knows his chicken customer is coming. Cost of production, close to zero.
Consider an alternative world in which everybody is eating a chicken every day. The chicken producers certainly buy up lots of land in low cost places where it is cheap to build chicken production. This doesn’t fill the need, i.e. price is way above the marginal cost to produce the last chicken. So they build chicken production closer to centers of demand, where real estate and labor are more expensive. This doesn’t meet demand, i.e. price is still higher than the marginal cost to produce the most expensive chicken. Finally, someone builds 10 level chicken coops with HVAC systems, water desalinators to provide drinking water to the chickens, and pays a fortune to process the chicken poop into a benign fertilizer product which they essentially have to give away in order to keep it from stacking up around their chicken coops. Finally demand is met.
The point is, demand is filled by suppliers that pay different costs to create the chickens they sell. The cheapest producer makes the most profit, he is lucky. The most expensive producer makes just enough profit to keep producing, the slightest drop in price will put him out of business.
Even within a given production facility, the marginal cost to produce an extra chicken rises as the “capacity” of the production facility is filled. So for the christmas rush, 10% more chickens are grown, but it raises the costs of the facility 15% because they are paying night-workers instead of day workers, they are having to build dormitories to house their extra workers, they need a higher quality of automation in order to get 10 chickens per cubic foot instead of 8 chickens per cubic foot which their cheaper robots manage, etc.
So in a world where everybody wants chickens a lot, people will spend more to consume chickens because they will spend more to produce chickens, because the cheapest production sites and methods will be saturated before the market is.
I think your scenario is a good illustration of “finite inputs”, which I listed as one of five example ways in which the long term supply curve may not actually be flat (at the end of the original article).
While I think that finite supply is a very real force (that, if strong enough, would create significant long term price elasticity of supply as you claim), the other four examples I mentioned also seem very real to me, and it’s not obvious which ones win out for any particular industry.
If Cost always grew with industry size, products in big industries would always cost more than the same product from equivalent but smaller industries (where both supply and demand is reduced). Intuitively/anecdotally this doesn’t seem to be true; I think the most common reasons it’s not true are “gains to scale”.
Looking at the extremes doesn’t tell you that chicken production is an increasing-cost industry at the margin. Sure input costs are important (the OP agrees—see last section), but there are also economies of scale, R&D investment, and so on pushing the other way, so it’s ultimately an empirical matter whether chicken production is increasing- or decreasing-cost at current levels of production (again I’m just repeating what the OP says).
IMO this issue is actually less relevant than the OP seems to think, because we’re only talking about very small marginal changes to chicken demand, and there’s no way the long-run supply curve is steep enough for that to matter. But one could try to estimate the long-run supply curve at least “locally”, which might settle this issue.
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.