I saw cumulative elasticity factor being used for impact estimations of veganism. I get it: if some people stop buying chicken, and now only 900 are sold at $1, the price might drop to $0.9. Then the question of how much production contracts is where elasticities come in. But with production shifts it feels different, messier. If we assumed that vegans would start eating chicken again once the price falls, then even the veganism case would be just as confusing.
Thanks, @saulius. I am tagging you because I updated the comment.
Below is a sketch of how I am imagining the change in demand/supply would evolve over time. It would initially be equal to the shift in supply Delta_Q_S (negative of the annual production of the affected farm), then increase to Delta_Q at equilibrium after Delta_t_eq (where Delta_Q = (1 - CEF)*Delta_Q_S), then remain at Delta_Q until Delta_t_S after the initial shift (where Delta_t_S is the duration of the shift in the supply curve), then increase by -Delta_Q_S to Delta_Q—Delta_Q_S (as a result of a new farm starting operations), and then decrease to 0 over Delta_t_eq. The total change in demand/supply is the integral of the line below over time. The shaded areas are equal, so the total change in demand/supply is Delta_Q*Delta_t_S = (1 - CEF)*Delta_Q_S*Delta_t_S. This directly depends on CEF = E_S/(E_S—E_D), so I would say the elasticities very much matter.
I would estimate Delta_t_S from “probability of i) farm being built in the original place”*”delay of the start of the farm’s operations given i)” + “probability of ii) farm being built elsewhere”*”delay of the start of the farm’s operations given given ii)” + “probability of iii) farm not being built”*”lifetime of the farm”. It looks like your estimates for the years of impact refer to Delta_t_S. If so, for CEF equal to 76 % (as in Figure 8.2 of Norwood and Lusk (2011)), I believe the impact is 24 % (= 1 - CEF) as large as you estimated.
For CEF equal to 76 %, and Delta_t_S equal to 0.695 years (= (20.9*1.0 + 67.5*0.6)/(20.9 + 67.5)), which is your estimate for the years of impact weighted by the number of broilers affected, the reduction in the demand/supply of chicken meat is 16.7 % (= (1 − 0.76)*0.695) of the annual production of the targeted farms.
I am taking a break from research and I won’t try to understand what you wrote here because it’s currently over my head and that’s not the type of thing I want to focus on right now in life. You can talk with Jakub Stencel if you are interested on improving my estimates, he would tell you whether it’s worth it (he is on the forum you can just tag him). But you might also need to talk to someone who say works on the stop the farms campaign for some context. They say that there will be protests against new farms no matter where they are built within Poland because of the network Anima created, but in most other countries it’s not happening. To me the bigger question is if the campaign is even net-positive, because it might just shift production to countries where it’s more difficult to improve conditions for farmed animals.
Some random thoughts about your message: * I did look into elasticities during my project and other elasticities of chicken can be found in this very old ACE spreadsheet and in this paper which analyzes elasticity some years ago in Turkey and says “According to the supply and demand functions for chicken meat, supply elasticity is 0.377 and demand elasticity is 0.030”. I remember comparing the two and getting wildly different numbers. * I will say that tI still don’t understand what you mean that “the reduction in the demand/supply of chicken meat is 16.7 % (= (1 − 0.76)*0.695) of the annual production of the targeted farms.” I mean, if we say closed a farm of a million broilers, in the very short term at least, surely the reduction of the number of broilers farmed in the world is one million. It’s not like those other chickens instantly appear somewhere else. So to me the question is still how quickly market goes back to equilibrium. Your variable Delta_t_S and my guess of 0.695 years of impact seem like two different things by the way. Maybe the reduction in production for a closed farm is 16.7% × “the lifetime of a farm”? Plus a bit more because it would take the market some time to adjust?
Thanks, Saulius. I was looking into this because I started working with Animal International 2 weeks ago, but I like public conversations (as long as confidential information is not shared).
this paper which analyzes elasticity some years ago in Turkey and says “According to the supply and demand functions for chicken meat, supply elasticity is 0.377 and demand elasticity is 0.030”. I remember comparing the two and getting wildly different numbers.
These elasticities imply a cumulative elasticity factor of CEF = 92.6 % (= 0.377/(0.377 + 0.030)). In this case, 1 - CEF = 7.4 %, which is 30.8 % (= 0.074/0.24) of the value implied by Figure 8.2 of Norwood and Lusk (2011).
I will say that tI still don’t understand what you mean that “the reduction in the demand/supply of chicken meat is 16.7 % (= (1 − 0.76)*0.695) of the annual production of the targeted farms.”
If the operations of a farm are delayed by Delta_t_S years, I would estimate the integral of the reduction in demand/supply over all time to be Delta_Q*Delta_t_S = (1 - CEF)*Delta_Q_S*Delta_t_S = (1 − 0.76)*”annual production of the delayed farm”*Delta_t_S.
I mean, if we say closed a farm of a million broilers, in the very short term at least, surely the reduction of the number of broilers farmed in the world is one million. It’s not like those other chickens instantly appear somewhere else.
I agree. This is represented in my drawing by the change in demand/supply starting at −1.
So to me the question is still how quickly market goes back to equilibrium. Your variable Delta_t_S and my guess of 0.695 years of impact seem like two different things by the way. Maybe the reduction in production for a closed farm is 16.7% × “the lifetime of a farm”? Plus a bit more because it would take the market some time to adjust?
Here is a description of my graph which may help:
At t = 0, there is a leftwards shift in the supply curve (less supply for the same price) because the operations of a new farm were supposed to start then, but they are delayed due to Stop the Farms.
From t = 0 to t = Delta_t_eq, the market adjusts, with other farms (not necessarily in Poland) producing more, and stocks (not necessarily in Poland) being spent to offset the reduced supply.
At t = Delta_t_eq, the market reaches the new equilibrium.
From t = Delta_t_eq to t = Delta_t_S, the market stays at the new equilibrium.
At t = Delta_t_S, there is a rightwards shift in the supply curve (greater supply for the same price) because the operations of a new farm start then (not necessarily in Poland).
From t = Delta_t_S to t = Delta_t_S + Delta_t_eq, the market adjusts, with other farms (not necessarily in Poland) producing less, and stocks (not necessarily in Poland) being saved to offset the increased supply.
My understanding isthat your estimates for the years of impact refer to Delta_t_S, which is the expected duration of the delay of the start of the operations of a new farm. So it seems to me that the impact should be 24 % (= 1 - CEF) of what you estimated for CEF = 76 %.
I saw cumulative elasticity factor being used for impact estimations of veganism. I get it: if some people stop buying chicken, and now only 900 are sold at $1, the price might drop to $0.9. Then the question of how much production contracts is where elasticities come in. But with production shifts it feels different, messier. If we assumed that vegans would start eating chicken again once the price falls, then even the veganism case would be just as confusing.
Thanks, @saulius. I am tagging you because I updated the comment.
Below is a sketch of how I am imagining the change in demand/supply would evolve over time. It would initially be equal to the shift in supply Delta_Q_S (negative of the annual production of the affected farm), then increase to Delta_Q at equilibrium after Delta_t_eq (where Delta_Q = (1 - CEF)*Delta_Q_S), then remain at Delta_Q until Delta_t_S after the initial shift (where Delta_t_S is the duration of the shift in the supply curve), then increase by -Delta_Q_S to Delta_Q—Delta_Q_S (as a result of a new farm starting operations), and then decrease to 0 over Delta_t_eq. The total change in demand/supply is the integral of the line below over time. The shaded areas are equal, so the total change in demand/supply is Delta_Q*Delta_t_S = (1 - CEF)*Delta_Q_S*Delta_t_S. This directly depends on CEF = E_S/(E_S—E_D), so I would say the elasticities very much matter.
I would estimate Delta_t_S from “probability of i) farm being built in the original place”*”delay of the start of the farm’s operations given i)” + “probability of ii) farm being built elsewhere”*”delay of the start of the farm’s operations given given ii)” + “probability of iii) farm not being built”*”lifetime of the farm”. It looks like your estimates for the years of impact refer to Delta_t_S. If so, for CEF equal to 76 % (as in Figure 8.2 of Norwood and Lusk (2011)), I believe the impact is 24 % (= 1 - CEF) as large as you estimated.
For CEF equal to 76 %, and Delta_t_S equal to 0.695 years (= (20.9*1.0 + 67.5*0.6)/(20.9 + 67.5)), which is your estimate for the years of impact weighted by the number of broilers affected, the reduction in the demand/supply of chicken meat is 16.7 % (= (1 − 0.76)*0.695) of the annual production of the targeted farms.
I am taking a break from research and I won’t try to understand what you wrote here because it’s currently over my head and that’s not the type of thing I want to focus on right now in life. You can talk with Jakub Stencel if you are interested on improving my estimates, he would tell you whether it’s worth it (he is on the forum you can just tag him). But you might also need to talk to someone who say works on the stop the farms campaign for some context. They say that there will be protests against new farms no matter where they are built within Poland because of the network Anima created, but in most other countries it’s not happening. To me the bigger question is if the campaign is even net-positive, because it might just shift production to countries where it’s more difficult to improve conditions for farmed animals.
Some random thoughts about your message:
* I did look into elasticities during my project and other elasticities of chicken can be found in this very old ACE spreadsheet and in this paper which analyzes elasticity some years ago in Turkey and says “According to the supply and demand functions for chicken meat, supply elasticity is 0.377 and demand elasticity is 0.030”. I remember comparing the two and getting wildly different numbers.
* I will say that tI still don’t understand what you mean that “the reduction in the demand/supply of chicken meat is 16.7 % (= (1 − 0.76)*0.695) of the annual production of the targeted farms.” I mean, if we say closed a farm of a million broilers, in the very short term at least, surely the reduction of the number of broilers farmed in the world is one million. It’s not like those other chickens instantly appear somewhere else. So to me the question is still how quickly market goes back to equilibrium. Your variable Delta_t_S and my guess of 0.695 years of impact seem like two different things by the way. Maybe the reduction in production for a closed farm is 16.7% × “the lifetime of a farm”? Plus a bit more because it would take the market some time to adjust?
Thanks, Saulius. I was looking into this because I started working with Animal International 2 weeks ago, but I like public conversations (as long as confidential information is not shared).
These elasticities imply a cumulative elasticity factor of CEF = 92.6 % (= 0.377/(0.377 + 0.030)). In this case, 1 - CEF = 7.4 %, which is 30.8 % (= 0.074/0.24) of the value implied by Figure 8.2 of Norwood and Lusk (2011).
If the operations of a farm are delayed by Delta_t_S years, I would estimate the integral of the reduction in demand/supply over all time to be Delta_Q*Delta_t_S = (1 - CEF)*Delta_Q_S*Delta_t_S = (1 − 0.76)*”annual production of the delayed farm”*Delta_t_S.
I agree. This is represented in my drawing by the change in demand/supply starting at −1.
Here is a description of my graph which may help:
At t = 0, there is a leftwards shift in the supply curve (less supply for the same price) because the operations of a new farm were supposed to start then, but they are delayed due to Stop the Farms.
From t = 0 to t = Delta_t_eq, the market adjusts, with other farms (not necessarily in Poland) producing more, and stocks (not necessarily in Poland) being spent to offset the reduced supply.
At t = Delta_t_eq, the market reaches the new equilibrium.
From t = Delta_t_eq to t = Delta_t_S, the market stays at the new equilibrium.
At t = Delta_t_S, there is a rightwards shift in the supply curve (greater supply for the same price) because the operations of a new farm start then (not necessarily in Poland).
From t = Delta_t_S to t = Delta_t_S + Delta_t_eq, the market adjusts, with other farms (not necessarily in Poland) producing less, and stocks (not necessarily in Poland) being saved to offset the increased supply.
My understanding is that your estimates for the years of impact refer to Delta_t_S, which is the expected duration of the delay of the start of the operations of a new farm. So it seems to me that the impact should be 24 % (= 1 - CEF) of what you estimated for CEF = 76 %.
Best wishes for your new projects!