Prioritising animal welfare over global health and development?

Summary

  • Corporate campaigns for chicken welfare increase wellbeing way more cost-effectively than the best global health and development (GHD) interventions.

  • In addition, the effects on farmed animals of such interventions can at least influence prioritisation within GHD, and those on wild animals might determine whether they are beneficial or harmful.

  • I encourage Charity Entrepreneurship (CE), Founders Pledge (FP), GiveWell (GW), Open Philanthropy (OP) and Rethink Priorities (RP) to:

Corporate campaigns for chicken welfare increase nearterm wellbeing way more cost-effectively than GiveWell’s top charities

Corporate campaigns for chicken welfare are considered one of the most effective animal welfare interventions. A key supporter of these is The Humane League (THL), which is one of the 3 top charities of Animal Charity Evaluators.

I calculated the cost-effectiveness of corporate campaigns for broiler welfare in human-years per dollar from the product between:

  • Chicken-years affected per dollar, which I set to 15 as estimated here by Saulius Simcikas. Note Saulius estimates broiler and cage-free campaigns affect 41 chicken-years per dollar, 2.73 (= 4115) times as much as the broiler campaigns on which I am relying.

  • Improvement in welfare as a fraction of the median welfare range when broilers go from a conventional to a reformed scenario[1], assuming:

    • The time broilers experience each level of pain defined here (search for “definitions”) in a conventional and reformed scenario is given by these data (search for “pain-tracks”) from the Welfare Footprint Project (WFP).

    • The welfare range is symmetric around the neutral point[2], and excruciating pain corresponds to the worst possible experience.

    • Excruciating pain is 1 k times as bad as disabling pain[3].

    • Disabling pain is 100 times as bad as hurtful pain.

    • Hurtful pain is 10 times as bad as annoying pain.

    • The lifespan of broilers is 42 days, in agreement with section “Conventional and Reformed Scenarios” of Chapter 1 of Quantifying pain in broiler chickens by Cynthia Schuck-Paim and Wladimir Alonso.

    • Broilers sleep 8 h each day, and have a neutral experience during that time.

    • Broilers being awake is as good as hurtful pain is bad. This means being awake with hurtful pain is neutral, thus accounting for positive experiences[4].

    • Median welfare range of chickens, which I set to RP’s median estimate of 0.332.

  • Reciprocal of the intensity of the mean human experience, which I obtained supposing humans:

    • Sleep 8 h each day, and have a neutral experience during that time.

    • Being awake is as good as hurtful pain is bad. This means being awake with hurtful pain is neutral, thus accounting for positive experiences.

I computed the cost-effectiveness in the same metric for the lowest cost to save a life among GW’s top charities from the ratio between:

  • Life expectancy at birth in Africa in 2021, which was 61.7 years according to these data from OWID.

  • Lowest cost to save a life of 3.5 k$ (from Helen Keller International), as stated by GW here.

The results are in the tables below. The data and calculations are here (see tab “Cost-effectiveness”).

Intensity of the mean experience as a fraction of the median welfare range

Broiler in a conventional scenario

Broiler in a reformed scenario

Human

-2.59*10^-5

-5.77*10^-6

3.33*10^-6

Broiler in a conventional scenario relative to a human

Broiler in a reformed scenario relative to a human

Broiler in a conventional scenario relative to a reformed scenario

-7.77

-1.73

4.49

Intensity of the mean experience as a fraction of that of the mean human experience

Broiler in a conventional scenario

Broiler in a reformed scenario

-2.58

-0.574

Improvement in chicken welfare when broilers go from a conventional to a reformed scenario as a fraction of...

The median welfare range of chickens

The intensity of the mean human experience

2.01*10^-5

2.01

Cost-effectiveness (human-years per dollar)

Corporate campaigns for broiler welfare

30.1

Lowest cost to save a life among GW’s top charities

0.0176

Corporate campaigns for broiler welfare relative to lowest cost to save a life among GW’s top charities

1.71 k

According to my results, corporate campaigns for broiler welfare are 1.71 k times as effective as the lowest cost to save a life among GW’s top charities. I am not surprised. Here I got a ratio 6.78 (= 11.6/​1.71) times as high, essentially because I used a moral weight 7.26 (= 2.41/​0.332) times as high as RP’s median welfare range (which I used above). This was not available at the time, but I trust it much more than my previous estimate, so I think the lower ratio of 1.71 k is more accurate.

To get a ratio of 1:

  • Everything else equal, the median welfare range of chickens (relative to humans) would have to be 1.94*10^-4 (= 0.332/​(1.71 k)), which is 97.2 % (= 1.94/​2.00) the one I guessed here for nematodes. I do not see this being possible.

  • Assuming broiler welfare is worth zero outside hedonism, this would have to be given a weight of 0.0586 % (= 1/​(1.71 k)). This is very much against what Bob Fischer says here. “Even if hedonic goods and bads (i.e., pleasures and pains) aren’t all of welfare, they’re a lot of it. So, probably, the choice of a theory of welfare will only have a modest (less than 10x [i.e. at least 10 % weight for hedonism]) impact on the differences we estimate between humans’ and nonhumans’ welfare ranges”.

So the takeaway to me is that corporate campaigns for chicken welfare increase nearterm wellbeing robustly more cost-effectively than GW’s top charities, which are plausibly among the best GHD interventions.

Effects of global health and development interventions on animals are neglected and unclear

GHD interventions decrease mortality or increase economic growth. These tend to increase the consumption of farmed animals (see meat-eater problem), or impact net forest area, thus changing the number of animals. To illustrate, I show in the next sections the effects on animals of GW’s top charities may at least influence which countries they should target, or even determine whether they are beneficial or harmful. Nonetheless, these considerations have not been researched by GW.

Farmed animals

The table below contains the relative reduction in the cost-effectiveness of saving lives due to increased consumption of poultry caused by saving lives in each of the countries targeted by GW’s top charities analysed here. I have focussed on poultry because I think there is especially good data from WFP on the conditions of chickens. I got the estimates from the product between:

  • Absolute value of the intensity of the mean experience of broilers in a reformed scenario as a fraction of the median welfare range of chickens relative to the intensity of the mean human experience, which I estimated to be −1.73 in my early cost-effectiveness analysis.

  • Median welfare range of chickens, which I set to RP’s median estimate of 0.332.

  • Production of poultry per capita in 2019 in each country as a fraction of the global one to the power of 1.5.

    • I computed the fraction from these and these data from Our World in Data (OWID).

    • 1.5 instead of 1 such that each doubling of poultry consumption per capita makes the conditions of farmed chickens 1.41 (= 2^0.5) times as bad. This is a very rough approximation, as I expect the lives of farmed chickens to be positive for low poultry consumption per capita, and eventually become negative as it increases, which will arguably happen. From these data from OWID, the population of chickens in Africa increased 2.98 % (= (1.81/​1.20)^(1/​(2014 − 2000)) − 1) per year between 2000 and 2014.

The data and calculations are here (see tab “Poultry”).

Country

Consumption of poultry per capita in 2020 as a fraction of the global one (%)

Relative reduction in the cost-effectiveness of saving lives due to poultry (%)

Mean of the countries below

13.1

3.24

Burkina Faso

12.4

2.50

Cameroon

18.9

4.73

Chad

2.34

0.205

Cote d’Ivoire

16.0

3.67

Democratic Republic of Congo

0.659

0.0307

Guinea

5.51

0.744

Kenya

7.83

1.26

Mali

16.0

3.68

Mozambique

22.4

6.07

Niger

4.86

0.615

Nigeria

6.72

1.00

South Sudan

30.6

9.71

Togo

30.3

9.56

Uganda

9.27

1.62

World

100

57.4

These results suggest accounting for poultry does not matter much for GHD interventions. Among the countries targeted by GW’s top charities, the relative reduction in the cost-effectiveness of saving lives ranges from 0.0307 % for the Democratic Republic of Congo to 9.71 % for South Sudan.

Nevertheless, I believe the results above underestimate the reduction in cost-effectiveness, because:

  • I have not accounted for other farmed animals. From my estimates here, the negative utility of farmed chickens is only 14.5 % (= 1.74/​12.0) of that of all farmed animals globally. This suggests accounting for all farmed animals would lead to a reduction in cost-effectiveness for the mean country of 22.4 % (= 3.24/​14.5), which is not negligible. So accounting for the effects of GHD interventions on farmed animals may lead to targeting different countries.

  • I have used the current consumption of poultry per capita, but this, as well as that of other farmed animals, will tend to increase with economic growth. I estimated the badness of the experiences of all farmed animals alive is 12.1 times the goodness of the experiences of all humans alive, which suggests saving a random human life results in a nearterm increase in suffering.

On the other hand, greater economic growth may be associated with moral circle expansion, and lead to technological innovations that can increase the welfare of farmed animals, or make alternatives more convenient, cheaper and tastier[5]. An additional major uncertainty is the welfare range of chickens. I have used RP’s median estimate, but the 5th and 95th percentile are 0.602 % (= 0.002/​0.332) and 2.61 (= 0.869/​0.332) times as large. Furthermore, as Julian Jamison noted, assuming disabling pain is 10 (instead of 100) times as bad as hurtful pain leads to broilers in a conventional scenario having positive lives[6].

Overall, I am quite uncertain about the magnitude of the effect on farmed animals, but think it may well lead to at least different prioritisation within GHD interventions. So I believe it should be integrated in cost-effectiveness analyses of GHD interventions. This will involve further research, for instance, on forecasting how prevalent will factory-farming become in low-income countries.

Wild animals

The table below contains the absolute value of the relative variation in the cost-effectiveness of saving lives due to changes in the population of wild terrestrial arthropods caused by increased deforestation. I do not know whether the variation corresponds to an increase or decrease, as I am quite uncertain about whether wild arthropods have good or bad lives (see this preprint from Heather Browning and Walter Weit). I got the estimates from the product between:

  • Decrease in forest area per capita in 2015, which I computed from these and these data from OWID. As a 1st approximation, I assume net change is forest area is directly proportional to population.

  • Decrease in density of terrestrial arthropods due to deforestation, which I estimated to be 280 M/​ha following this.

  • Intensity of the mean experience of wild terrestrial arthropods as a fraction of that of humans, which I estimated to be 0.200 % here (see 4th column of table).

The data and calculations are here (see tab “Wild terrestrial arthropods”).

Country

Decrease in forest area per capita in 2015 (m^2)

Decrease in the number of wild terrestrial arthropods per capita in 2015

Absolute value of the relative variation in the cost-effectiveness of saving lives due to wild terrestrial arthropods

Mean of the countries below

20.5

574 k

1.15 k

Cameroon

24.3

681 k

1.36 k

Mali

0

0

0

Mozambique

89.1

2.50 M

4.99 k

Niger

6.17

173 k

346

Nigeria

8.88

249 k

497

Togo

3.96

111 k

222

Uganda

11.0

308 k

616

World

6.93

194 k

388

The results suggest the increase in human welfare from GW’s top charities saving lives is much smaller than the increase/​decrease in that of wild terrestrial arthropods, since the absolute values of the relative variation in cost-effectiveness are much higher than 1. Nonetheless, these are quite uncertain because they are (in my model) directly proportional to the welfare range of silkworms. I have used RP’s median estimate, but the 5th and 95th percentile are 0 (= 00.002) and 36.5 (= 0.073/​0.002) times as large.

All in all, I can see the impact on wild animals being anything from negligible to all that matters in the nearterm. So, as for farmed animals, I think more research is needed. For example, on forecasting net change in forest area in low-income countries.

Note the impact on wild animals may also be the major driver of the overall nearterm effect of interventions which aim to improve the welfare of farmed animals[7]. For example, corporate campaigns for chicken welfare will tend to make chicken and eggs more expensive, which can lead to an increase in the consumption of beef, and therefore more deforestation, thus decreasing the population of wild terrestrial arthropods. Nevertheless, I think the positive/​negative impact on wild animals is much larger for interventions which focus on reducing the consumption of farmed animals (like ones around abolitionism), instead of improving their living conditions.

Regarding the impact of human diet on animal welfare (of both farmed and wild animals), Michael St. Jules suggested Matheny 2005, this and these posts from Brian Tomasik, this post from Carl Shulman, and Fischer 2018.

Miscellaneous thoughts on organisations aligned with effective altruism

As far as I can tell, organisations aligned with effective altruism do not consider the effects of GHD interventions on animals. Below is some brief additional discussion, by alphabetical order of organisation.

Charity Entrepreneurship

CE seemingly has strong reasons to account for effects on animals. According to CE’s weighted animal welfare index, the “total welfare score (with evidence)” of:

  • “FF [factory-farmed] broiler chicken” is −1.75 (= −56/​32) times that of a “human in a low middle-income country”, which is 1.01 times the value of −1.73 I got for broilers in a reformed scenario in my early cost-effectiveness analysis (see 1st table).

  • “Wild bug[s]” is −1.31 (= −42/​32) times that of a “human in a low middle-income country”, which is 656 times the value of 0.200 % I used in my early estimation of the effects on animals.

These suggest the impacts of GHD interventions will be similar to what I estimated for farmed animals, and 3 orders of magnitude as large for wild animals.

Founders Pledge

As part of FP’s prioritisation, Stephen Clare and Aidan Goth published 3 years ago this analysis[8] comparing the cost-effectiveness of THL and Against Malaria Foundation (AMF), which is one of GW’s top charities. According to its Guesstimate model, the cost-effectiveness of THL is 852 (= 230.027) times that of AMF, which (considering the uncertainty involved) is pretty close to the ratio of 1.71 k I got in my early cost-effectiveness analysis.

Stephen and Aidan highlighted the moral weight of chickens relative to humans as a major uncertainty. However, this has meanwhile been narrowed down thanks to RP’s (great!) moral weight project. Maybe FP has not focussed much on animal welfare[9] due to other considerations, such as not having a fund for it (see FP’s funds).

GiveWell

GW determines the value of consumption and saving lives as a function of age based on surveys of its team, donors and beneficiaries (see here). I think it would make some sense to include questions about the importance of animals in such surveys. Nonetheless, I think it would be much better to combine RP’s median welfare ranges with empirical evidence about how further away from the neutral point (as a fraction of the median range) is the mean experience of animals. Something like what I did, but way more in-depth!

I believe it would be hard for people to come up with good estimates describing the importance of animals in surveys. As Bob Fischer commented here:

The upshot of Jason’s post on what’s wrong with the “holistic” approach to moral weight assignments, my post about theories of welfare, and my post about the appropriate response to animal-friendly results is something like this: you should basically ignore your priors re: animals’ welfare ranges as they’re probably (a) not really about welfare ranges, (b) uncalibrated, and (c) objectionably biased.

Welfare ranges are not the sole determinant of the importance of animals, but they are a key input. So trusting our priors regarding them will imply coming up with an inaccurate assessment of how much consideration we should give to animals. Moreover, I suppose GW’s team, donors and beneficiaries would not naturally be open to the possibility of defining moral weights as a function of the country, but that arguably makes sense given consumption of animals and deforestation vary across countries (and so do effects on animals). Alas, the moral weight of saving a life can even be negative under some circumstances (although killing people is still bad!).

Additionally, for the sake of transparency, it would be good if GW described in their website how they think about effects on animals. 8 months ago, I asked GW for feedback on this post related to the meat-eater problem. I was told my message was passed to the research team, but I have not heard back.

Open Philanthropy

From OP’s global health and wellbeing cause prioritisation framework:

When it comes to other outcomes like farm animal welfare or the far future [not so far if you think existential risk in the next 100 years is around 16], we practice worldview diversification instead of trying to have a single unified framework for cost-effectiveness analysis.

I think diversification makes sense in general, but the details matter. There is a (somewhat remote) sense in which a fossil fuel company is practising worldview diversification if it is decreasing its own emissions while increasing extraction of fossil fuels such that it overall contributes to global warming. However, if the goal really is mitigating global warming, it makes sense to focus on the overall contribution of the company to it.

Saving lives increases the nearterm welfare of humans, but it decreases that of farmed animals, and has unclear effects on wild animals. I think effects on farmed animals are sufficiently clear to be integrated into cost-effectiveness analyses, and that we should invest more resources into understanding those on wild animals (relative to global health and wellbeing interventions, at the margin).

Related to learning more, I am glad OP has supported RP’s moral weight project. At the same time, I wonder whether it should have happened before it directed hundreds of millions of dollars towards GHD interventions. Not only because of their effects on animals, but owing to animal welfare interventions increasing wellbeing way more cost-effectively, as I showed in my early cost-effectiveness analysis. This is in agreement with OP’s post on worldview diversification[10]:

  • If you value chicken life-years equally to human life-years, this implies that corporate campaigns do about 10,000x as much good per dollar as top charities. If you believe that chickens do not suffer in a morally relevant way, this implies that corporate campaigns do no good.[3]

  • One could, of course, value chickens while valuing humans more. If one values humans 10-100x as much, this still implies that corporate campaigns are a far better use of funds (100-1,000x). If one values humans astronomically more, this still implies that top charities are a far better use of funds. It seems unlikely that the ratio would be in the precise, narrow range needed for these two uses of funds to have similar cost-effectiveness.

The value of chickens depends on how much weight one gives to hedonism, about which Alexander Berger (OP’s co-CEO) writes[11]:

We think that most plausible arguments for hedonism end up being arguments for the dominance of farm animal welfare. We seem to put a lot of weight on those arguments relative to you, and farm animal welfare is OP GHW’s biggest area of giving after GiveWell recommendations. If we updated toward more weight on hedonism, we think the correct implication would be even more work on FAW, rather than work on human mental health.

In the same comment, Alexander mentions:

We [OP] think it is a mistake to collapse worldviews in the sense that we use them to popular debates in philosophy, and we definitely don’t aim to be exhaustive across worldviews that have many philosophical adherents. We see proliferation of worldviews as costly for the standard intellectual reason that they inhibit optimization, as well as carrying substantial practical costs, so we think the bar for putting money behind an additional worldview is significantly higher than you seem to think. But we haven’t done a good job articulating and exploring what we do mean and how that interacts with the case for worldview diversification (which itself remains undertheorized). We appreciate the push on this and are planning to do more thinking and writing on it in the future.

If OP’s worldviews are not supposed to correspond to popular debates in philosophy, and having more is costly, should the ones of nearterm animal and human welfare be unified? I agree worldview diversification “remains undertheorized”.

I asked Alexander and Lewis Bollard at the end of January whether they thought this analysis about the effects of terrestrial arthropods on the cost-effectiveness of GiveWell’s top charities was any relevant, but I have not heard back.

Rethink Priorities

RP’s Worldview Investigations Team seems perfectly positioned to study how to account for the effects on animals of GHD interventions, and figure out what the greater cost-effectiveness of corporate campaigns to increase wellbeing implies.

I asked here whether RP’s GHD team was considering addressing effects on animals in their work, but I have not heard back (and was downvoted). I had also contacted RP about the post on terrestrial arthropods at the end of January, and was told my message was forwarded to the GHD team, but I have not heard back either.

Complex cluelessness should not be ignored

I do not think it is fair to ignore the effects on animals because they look like a crucial consideration. We are in a case of complex cluelessness, not one of simple cluelessness where very uncertain effects can be ignored based on evidential symmetry. Me looking now to the right might ultimately create a storm somewhere, but just as well prevent it, so we can ignore these considerations. In contrast, increasing population size will robustly lead to greater consumption of food, which has certain impacts on farmed and wild animals.

I agree that, mathematically, E(“overall effect”) > 0 if:

  • “Overall effect” = “nearterm effect on humans” + “nearterm effect on animals” + “longterm effect”.

  • E(“nearterm effect on humans”) > 0.

  • E(“nearterm effect on animals”) = k_1 E(“nearterm effect on humans”).

  • E(“longterm effect”) = k_2 E(“nearterm effect on humans”).

  • k_1 + k_2 = 0.

That being said, setting k_1 + k_2 to 0 seems unfair under complex cluelessness. One could just as well say k_1 + k_2 = −1, in which case E(“overall effect”) = 0. Since I am not confident |k_1 + k_2| << 1, I am not confident either about the sign of E(“overall effect”), nor about whether GW’s top charities are beneficial or harmful.

Let me try to illustrate how I think about this with an example (originally commented here). Imagine the following:

  • Nearterm effects on humans are equal to 1 in expectation.

    • This estimate is very resilient, i.e. it will not change much in response to new evidence.

  • Other effects (on animals and in the longterm) are −1 k with 50 % likelihood, and 1 k with 50 % likelihood, so they are equal to 0 in expectation.

    • These estimates are not resilient, and, in response to new evidence, there is a 50 % chance the other effects will be negative in expectation, and 50 % chance they will be positive in expectation.

    • However, it is very unlikely that the other effects will in expectation be between −1 and 1, i.e. they will most likely dominate the expected nearterm effects.

What do you think is a better description of our situation?

  • The expected overall effect is 1 (= 1 + 0) in expectation. This is positive, so the intervention is robustly good.

  • The overall effect is −999 (= 1 − 1 k) with 50 % likelihood, and 1,001 (= 1 + 1 k) with 50 % likelihood. This means the expected value is positive. However, given the lack of resilience of the other effects, we have little idea whether it will continue to be positive, or turn out negative in response to new evidence. So we should not act as if the intervention is robustly good. Instead, it would be good to investigate the other effects further, especially because we have not even tried any hard to do that in the past.

Am I uncertain about the value of killing people too?

No, killing people is bad! Not saving lives has drastically different consequences from killing people, which is much more anti-cooperative. For what it is worth, I think I am much more against killing than the median citizen. For example, I suspect most people would be in favour of militarily supporting Ukraine even if it was known that it increased the number of people killed in the Russo-Ukrainian War, whereas I would tend to prefer whatever prevented the most war deaths.

However, for the same reasons I am not confident about whether saving lives is good or bad, I do not know whether a random person dying (without being killed) is beneficial or harmful.

I do not know whether saving lives is good longterm

One can argue saving lifes is robustly good longterm (k_2 >> k_1) based on the capability approach to human welfare, despite nearterm effects on humans plus animals being unclear. I am sympathetic to this argument, but think it is too general. There are obvious benefits of being able to live a long and healthy life, but I also worry about humans having the capability of factory-farming animals whose lives are pretty bad. Note the title of the post is “the capability approach to human welfare” (emphasis mine). Interestingly, I have recently listened to Martha Nussbaum on the Clearer Thinking podcast, and it looks like her book Justice for Animals: Our Collective Responsibility attempts to extend the capability approach to non-human animals.

In the same way it is better to focus on differential progress over economic growth, I would rather increase good capabilities over all capabilities, and it is unclear to me what is the net effect of increasing population at the margin. There are many indirect longterm effects. The answer may vary too, depending on factors like year, country and age.

I believe saving lives would more easily be good if there were much fewer humans, because in that case it would decrease the risk from extinction, which is good given my presumption that the expected value of the future is positive. I am open to the possibility that saving lives is a good proxy for longterm value for the current population too, but this is not obvious to me. I think it warrants empirical investigation, for example, into impacts on democracy levels. This in particular seems to be a neglected topic. From Kono 2009 (emphasis mine):

Although many people have argued that foreign aid props up dictators [and so might GHD interventions?], few have claimed that it props up democrats, and no one has systematically examined whether either assertion is empirically true. We argue, and find, that aid has both effects. Over the long run [what matters most?], sustained aid flows promote autocratic survival because autocrats can stockpile this aid for use in times of crisis. Each disbursement of aid, however, has a larger impact on democratic survival because democrats have fewer alternative resources to fall back on.

In addition, I tend to think it would be a surprising and suspicious convergence if saving lives as cost-effectively as possible was the best way to improve the longterm future. I would expect metrics more closely related to existential risk to be better. For example:

Additionally, it is worth keeping in mind longtermist interventions can save lives quite cost-effectively too. For example:

  • The cost-effectiveness of 3.95 bp/​G$ I estimated here for longtermism and catastrophic risk prevention (for method 3 with truncation) naively corresponds to saving a life for 316 $ (= 1/​(3.95*10^-4*8)), which is 11.1 (= 3500316) times as cost-effective as the lowest cost among GW’s top charities (from Helen Keller International).

  • Joel Tan estimated lobbying for arsenal limitation is 5 k times as cost-effective as GW’s top charities. “The headline cost-effectiveness will almost certainly fall if this cause area is subjected to deeper research”. “That said, results are robust, insofar as the low-confidence tractability estimates can drop by three whole magnitudes and still leave the intervention to be comfortably more cost-effective than GiveWell[’s top charities]”.

Note these interventions would look even more cost-effective after accounting for their effect on the far future.

What would I like to see?

Thinking at the margin, I would say scope-sensitive ethics imply prioritising animal welfare over global health and development. I think the scale of the welfare of farmed animals and wild terrestrial arthropods is 12.0 and 253 k times as large as that of humans, so accounting for them seems crucial a priori.

So I encourage organisations, especially the ones I discussed above aligned with effective altruism, to:

Acknowledgements

Thanks to Jeff Kaufman, Michael St. Jules, and Sanjay Joshi for feedback on the draft.

  1. ^

    From this page of WFP, broilers in reformed scenarios have an average daily gain of 45 to 46 g/​d, whereas ones in conventional scenarios have 60 and 62 g/​d.

  2. ^

    This assumption influences the improvement in welfare as a fraction of the median welfare range, but not the cost-effectiveness of corporate campaigns for broiler welfare in human-years per dollar. For example, if welfare could range from something as good as disabling pain is bad to excrutiating pain, the welfare range would become 50.05 % (= (1 + 1 k)/​(2 k)) as large. Consequently, the improvement in welfare as a fraction of the median welfare range would become 1.998 (= 10.5005) times as large, but so would the intensity of the mean human experience. As a result, the cost-effectiveness in human-years per dollar would remain the same, since it is directly proportional to the improvement in welfare as a fraction of the median welfare range, and to the reciprocal of the intensity of the mean human experience.

  3. ^

    I encourage you to check this post from algekalipso, and this from Ren Springlea to get a sense of why I think the intensity can vary so much.

  4. ^

    This assumption affects the (signed) intensity of the mean experience of broilers, but not the improvement in their welfare when they go from a conventional to a reformed scenario, because the lifespan of broilers and value of them being alive is the same in both scenarios. As a consequence, the assumption does not impact the cost-effectiveness of corporate campaigns for broiler welfare.

  5. ^

    Thanks to Sanjay Joshi for noting this point.

  6. ^

    The intensity of the mean experience as a fraction of the median welfare range would be 8.24 %, instead of −777 %.

  7. ^

    Thanks to Michael St. Jules for noting this point. I had thought about it, but had not written it down, possibly due to motivated reasoning.

  8. ^

    If I recall correctly, the one which got me thinking about comparisons between animal welfare and GHD interventions!

  9. ^

    Their only report on animal welfare was published in November 2020.

  10. ^

    Thanks to Michael for noting these points.

  11. ^

    Thanks to Michael for letting me know about Alexander’s comment.

  12. ^

    See section “Climate damage is increasing non-linearly” in this report from FP.

  13. ^