CEF is 76 % for chicken meat according to Figure 8.2 of Norwood and Lusk (2011). I do not know the time horizon to which this refers to, and would not be surprised if it generalised badly to stopping farms in Poland. However, if it did, shifting the supply curve of chicken leftwards by 1 kg would decrease its demand/​supply by 0.24 kg (= 1*(1 − 0.76)). Did you account for this when coming up with your guesses for the years of impact?
To clarify my question above, are these estimates supposed to account for adjustments broiler producers can make to expand output along the existing supply curve (with the same farms and technology):
Higher stocking density – place more chicks per m² of barn space (within legal or contractual limits).
Increase batch frequency – shorten downtime between flocks (reduce cleaning/​resting days).
Longer grow-out period – delay slaughter so birds reach heavier weights.
Optimized feed formulation – shift to higher-energy or higher-protein rations if the higher output value outweighs feed costs.
Reduced mortality through management tweaks – e.g. stricter biosecurity, better litter quality, ventilation adjustments.
Use of feed additives or growth promoters (where legal) – probiotics, enzymes, coccidiostats to improve feed conversion.
Labor allocation – more intensive monitoring of flocks to reduce disease losses and increase uniformity.
Energy use adjustments – e.g. heating/​cooling more aggressively to maintain optimal bird growth conditions.
Extending usable facilities – keep older barns in service longer or run temporary housing (e.g. tents, converted sheds) to squeeze a bit more capacity.
Tightening contract terms with integrators – e.g. accepting higher chick placements per house if integrators provide them.
My understanding is that your estimates are just supposed to account for the decrease in the number of broiler farms, not the above. If so, I think the impact of Stop the Farms is 24 % (= 1 − 0.76) as large as you estimated based on the cumulative elasticity factor for chicken meat of 76 % presented in Figure 8.2 of Norwood and Lusk (2011).
No, I did not think about the effects you listed when choosing these numbers, at least not explicitly. I don’t remember what exactly went through my head when I imputed these numbers. I think I was just trying to imagine what a chicken production graph would look like with or without campaign. Naively thinking, blocking a farm would postpone the production by at least 1-2 years, because that’s how long it probably takes to get planning permits and build a farm. But 1-2 years felt like too optimistic though, so I was conservative, but probably not conservative enough.
Either way, those figures of years of impact are guesses in the spirit of If It’s Worth Doing, It’s Worth Doing With Made-Up Statistics, not estimates. I was supposed to finish the project and had no idea how to estimate these things, so I entered somewhat random numbers. Please don’t take them seriously. I think you would be much better off ignoring them, and coming up with a new estimate from scratch. Clearly you are thinking about this much more deeply than I was.
Btw, another effect to consider is anticipation. Investors in Poland already know that new farms face a high risk of being blocked or delayed by protests. Given this, they may (a) decide not to build farms at all (but someone else might build them instead), (b) shift their plans to other countries where protests are less likely, or (c) submit more applications than they really need, expecting some to be blocked. Since the campaign has been active for years, it’s possible the market has already adapted to the reality that building new farms in Poland is unusually difficult, and has found alternative ways to meet the demand.
To clarify my question above, are these estimates supposed to account for adjustments broiler producers can make to expand output along the existing supply curve (with the same farms and technology):
Higher stocking density – place more chicks per m² of barn space (within legal or contractual limits).
Increase batch frequency – shorten downtime between flocks (reduce cleaning/​resting days).
Longer grow-out period – delay slaughter so birds reach heavier weights.
Optimized feed formulation – shift to higher-energy or higher-protein rations if the higher output value outweighs feed costs.
Reduced mortality through management tweaks – e.g. stricter biosecurity, better litter quality, ventilation adjustments.
Use of feed additives or growth promoters (where legal) – probiotics, enzymes, coccidiostats to improve feed conversion.
Labor allocation – more intensive monitoring of flocks to reduce disease losses and increase uniformity.
Energy use adjustments – e.g. heating/​cooling more aggressively to maintain optimal bird growth conditions.
Extending usable facilities – keep older barns in service longer or run temporary housing (e.g. tents, converted sheds) to squeeze a bit more capacity.
Tightening contract terms with integrators – e.g. accepting higher chick placements per house if integrators provide them.
My understanding is that your estimates are just supposed to account for the decrease in the number of broiler farms, not the above. If so, I think the impact of Stop the Farms is 24 % (= 1 − 0.76) as large as you estimated based on the cumulative elasticity factor for chicken meat of 76 % presented in Figure 8.2 of Norwood and Lusk (2011).
No, I did not think about the effects you listed when choosing these numbers, at least not explicitly. I don’t remember what exactly went through my head when I imputed these numbers. I think I was just trying to imagine what a chicken production graph would look like with or without campaign. Naively thinking, blocking a farm would postpone the production by at least 1-2 years, because that’s how long it probably takes to get planning permits and build a farm. But 1-2 years felt like too optimistic though, so I was conservative, but probably not conservative enough.
Either way, those figures of years of impact are guesses in the spirit of If It’s Worth Doing, It’s Worth Doing With Made-Up Statistics, not estimates. I was supposed to finish the project and had no idea how to estimate these things, so I entered somewhat random numbers. Please don’t take them seriously. I think you would be much better off ignoring them, and coming up with a new estimate from scratch. Clearly you are thinking about this much more deeply than I was.
Thanks for the context, Saulius!
Btw, another effect to consider is anticipation. Investors in Poland already know that new farms face a high risk of being blocked or delayed by protests. Given this, they may (a) decide not to build farms at all (but someone else might build them instead), (b) shift their plans to other countries where protests are less likely, or (c) submit more applications than they really need, expecting some to be blocked. Since the campaign has been active for years, it’s possible the market has already adapted to the reality that building new farms in Poland is unusually difficult, and has found alternative ways to meet the demand.
Thanks, Saulius! That makes sense.