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).
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.
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.
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)?
Empirical evidence is nice and often more convincing than theory, but I don’t think it’s necessary for an argument to be convincing
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.
empirical evidence
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.
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.
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″.
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.
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.
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
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.
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).
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.
cannot be zero at every point throughout such a change
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.
That question seems to have a simple answer: your decision will not affect the long-term production of chicken.
OK, so I argue option A, you state option B, and the articles I link argue option C.
That is a much more complicated question
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 you had no empirical data to work with, what would be your best guess/expectation?
If I had no empirical data, I would not be making any guesses in this case.
Also if your answer is proportionally different than in the ‘single chicken’ case, I’d be curious to know why.
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.
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.
Cumulative elasticity = Supply Elasticity/(Supply Elasticity—Demand Elasticity).
A cumulative elasticity factor of one means a demand elasticity of 0.
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.
Thanks for the math demonstrating my point.
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”):
Cumulative elasticity = Supply Elasticity/(Supply Elasticity—Demand Elasticity). A cumulative elasticity factor of one means a demand elasticity of 0.
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.
Thanks for the math demonstrating my point.
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.
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)
As a perfectly competitive industry reacts to changes in demand, it traces out positive, negative, or horizontal long-run supply curve due to increasing, decreasing, or constant cost.
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″.
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.