Karolina is co-founder and Director of Research at Charity Entrepreneurship.
She also serves as a Fund Manager at the EA Animal Welfare Fund, and as a board member and consultant for various nonprofits and think tanks.
One point that I feel that we haven’t communicated well enough on is that cost of $27,000 per farm we have in the CEA doesn’t literally mean that we will pay the farm $27,000. As mentioned in the post, “this aims to set a conservative minimal threshold for cost-effectiveness. A high-scale, lower cost strategy (e.g. outreach through farmers associations) could further increase cost-effectiveness.”. We want to test in our CEA the worst possible scenario and it doesn’t mean that this will be the strategy. I will make a note to structure our reports differently in the future to avoid the confusion that what we test under “charity report” is literally also an implementation that the organization is going to go with.
“However, I couldn't see any mention in the report of how the initial work with individual farms could be translated into policy change.”
Sorry if we don’t include the details about the implementation in the charity ideas report. We usually follow up those reports with an “implementation report” to discuss long-term strategy, etc. Those are shared with co-founders who often contribute to them. Still, we prefer not to share them publicly for two reasons i) we don’t want the details of the strategy to potentially negatively affect the campaign ii) the strategy outlines the uncertainties that co-founders have to test at the beginning and how they should adopt the strategy to that, so because the plans are to some extend flexible they could change so we don’t want to create confusion.
More specifically to your point,
“It seems likely that it would increase profits in the Indian egg industry by paying for something (at an estimated cost of $27,000 per farm according to the model) which will likely increase the overall profitability of farms”.
The approach that charity will take is to first try to achieve success for cage-free and feed fort through multiple means that don’t require any support from us and put costs on the producers (e.g., outreach through farmers’ associations and partnership building ). If that would be unsuccessful (e.g., because there is no proof of concept), then try to subsidize an additional cost that farmer would have to take to have a higher level of nutrients in the feed (e.g., if low-nutrient feed would cost 1$, and high-nutrient feed would cost $3, we would subsidize the $2 difference between them). That way, the new situation is that producers’ costs are the same as before the intervention, and hens have a higher nutrient feed at the same time. No change in costs = no change in price = no major change in long-term profitability. If there is still resistance to fortification, only then would we consider a higher level of subsidization to achieve proof of concept and then when more widely adopted. That would be the case only until enough farmers operate like that to push for more systemic change, e.g., new mandatory feed standards in state regulations that are not subsidized at scale. What we model in the CEA is the absolute worst-case scenario, not the scenario that is most likely.
"Although the animal advocate understands that these problems could also be problems for the cage-free campaigns, they think that cage-free is a better ask because it tackles one of the underlying issues of intensive factory farming (confinement), where feed fortification doesn’t."
I agree with this advocate’s opinion that behavioral restrictions (like foraging and movement deprivation) caused by conventional cages and enriched cages are the biggest welfare problem, as you can see on the graph we linked from Cynthia Schuck-Paim and Wladimir Alonso’s forthcoming book, Quantifying Pain in Laying Hens. But keel bone fractures are the second biggest issue in conventional and enriched cages and the biggest in cage-free/aviary systems.
That’s why in places where there is no cage-free production (e.g., in India), we would recommend a focus on cage-free and feed fort, and in places where the shift to cage-free already happened, we want to work on feed fortification to avert keel bone fracture.
When speaking with advocates about it, we only spoke about feed fort in India, instead of cage-free + feed fort in India, so maybe that created confusion.
Hi Gordan! Happy to respond more in-depth but first, I have two clarifying points.
This intervention is for egg-laying hens, not broiler chickens. Egg-laying hens are not used for meat, but I could address your question from the perspective of egg quality. Is that fine?
Also, are you making an argument that feed fort will specifically be more prone to “humane-washing” compared to, e.g. cage-free/broiler campaigns or that all welfare-focused interventions that aim to improve the conditions on the farms are prone to “humane-washing” and therefore may be net-negative in the long term?
On flock size, yes you are understanding this correctly. You make a good point, although the model doesn’t rely much on this factor so I wouldn’t expect it to greatly alter the endline cost-effectiveness. But to double-check, I went back to our model and replaced the current estimate of flock size in 2019 with a range from 25,500 to 59,000 hens (capturing the possibility that flock sizes remained the same as in 2007 and all hens were placed in new farms, as well as the possibility that no new farms were built and existing flock sizes increased). With this adjusted flock size, we get an updated cost-effectiveness of 32 WP/$ and 1.4 chickens helped/$, so the intervention still looks promising. It is likely the endline cost-effectiveness isn’t greatly affected by this change to the flock size number as subsidization costs play a big part in the cost-effectiveness of this intervention. Although a smaller flock size will mean fewer chickens helped per farm, it will also mean less money spent per farm.
On the “humane-washing”, this advocate’s concern applied to all welfare-focused interventions that aim to improve the conditions on the farms e.g. cage-free, broiler campaigns and fish welfare campaigns. As with those, a similar long-term strategy will apply to the feed fortification organization. The end goal is a change of law to establish mandatory regulation on optimal nutrition for hens (similarly to cage-free asks, which aim at a complete ban on cages for hens. We’ve done some research into policy change for feed fortification in India and concluded that because of problems with enforcement and other barriers we would like to provide a proof of concept and transition part of the industry to production with optimal feed first. But in the long term, we would like to see legal requirements that hens are in cage-free systems with optimal feed.
My sense is that the producers are fortifying the feed a little bit, but to maximize production rather than for better welfare. What is optimal for those two goals diverges. A couple of reasons for that:
Feed cost is the largest single item in poultry production and accounts for 60 to 75% of the total production cost. So producers fortify the minimum amount possible that will still make the calculation of cost and benefits positive for them to make a profit, but not necessarily what would make a hen more healthy and happy.
There is also sometimes a lack of knowledge about optimal feeding, because producers only learn about something if a feed company has an incentive to market it. Since there’s no money to be made in e.g. changing calcium timing / particle size, that knowledge doesn’t get passed from the scientific literature to producers.
What is optimal for profit doesn’t equal what is optimal for welfare, and we see a difference in current nutrients and what we found to be optimal. More specifically, we looked at optimal for welfare vs current fortification of calcium, phosphorus, and vitamin D3 standards of the top five egg-producing countries. This table shows the difference between their nutrient standards and the optimum nutrient levels (all sources can be found in Supplement B of our full report, section 2.2.):
Perhaps more important than the mortality is that bone fractures are a leading source of chronic pain for hens, and keel bone fractures aren’t visible to the naked eye. Producers probably fortify their feed enough to keep a sufficient number of the hens alive (again to maximize profit, because some mortality rate is just assumed to always happen and is written into the cost-benefits of production). Mortality may be low enough that the costs of them dying vs the expense of feed isn't worth it.
Thanks for the question! I generally believe that it is hard/impossible to reliably compare CEAs done using different methodologies and approaches. For example, Saulius’ CEA has a different goal than ours and takes into account the overall, average cost-effectiveness of allhistorical work on cage-free campaigns. In contrast, we look at themarginal, future cost-effectiveness of a feed fortification ask. Naturally they will differ a lot. I would expect that marginal cage-free $ would be lower impact than average historical cage-free $.
It’s more informative to compare CEAs within the same methodology, and this intervention is one of the most cost-effective we found. For our future research, we plan to estimate the value of marginal $ spend on an additional cage-free/broiler campaign to have a better sense of the counterfactuals.
That said, to try to answer your questions, I’ll make a back-of-the-envelope calculation (BOTEC):
If I read correctly Saulius estimated that outside of the US 63-210M hens were affected by cage-free pledges for an average of 15 years of impact with a follow-through rate of 76% for an average = 48M-170M hens helped. He estimated that all global cage-free work costs $36-84M. I didn’t find in his estimates how much of that amount was outside the US. Let’s assume that the cost outside the US was half the total cost, so $18M-42M.
Let’s assume that both the impact and the cost are distributed equally among 7 major orgs. That gives us an effect of 6.8M-24M hens helped for $2.6-6.1M per org. That assumes an impact over 15 years. In our CEA we assume 10 years, so let’s adjust the impact for 10 instead of 15 years – that will be 4.5M-16Mhens per org (6.8M-24M*0.66).
That gives us BOTEC CEA of 1-4.5hens per $, compared to our estimate of approx. 1.8 hens per $. It is generally true that the more extensive the CEA, the lower the estimated cost-effectiveness. So given that BOTEC and our prior research I believe that feed fort alone will be comparable with cage-free, and feed fort + cage-free (something we suggest for India) could have ~twice the impact.
Hi Dan! Our CEA is built off the theory of change for this intervention that focuses on the animal welfare effects. We will likely add more cross-cause calculations to our CEA when the results of our work on moral weights by Rethink Priorities come back. Although human welfare doesn’t feature in our CEA, we do consider it in our report more broadly. We believe that this intervention could be a win-win, improving the lives of shrimp and of farmers. For example, an expert informed us that farmers would be keen to work with such an organization, since the intervention could help them improve their resilience to climate change.
On a more object level, based on GW’s research I would expect that there are more cost-effective interventions to increase household income that have less detrimental effects on animal welfare. We recommend that a new organization should avoid an increased stocking density, because we would expect high stocking densities to be overall net-negative from a species-neutral utilitarian framework. However, if it does increase slightly, I’m glad it would have a positive effect on farmers’ income.
A follow-up question: What would this chart look like if all the opportunities you want to fund existed? In other words, to what extent does the breakdown of funding shown here capture Open Phil’s views on cause prioritization vs. reflect limiting factors such as the availability of high-quality funding opportunities, and what would it look like if there were no such limiting factors?
Thanks for adding this, Marcus! Indeed, Vicky - primary author - worked with Daniela Waldhorn from Rethink Priorities while researching this topic. We both cannot wait to read the final report and see your tentative conclusions.
Once your report is published, I will link it in this post to ensure that people can read more from a different angle and see where our research differs.
One thing to note is that CE plans to follow up our shrimp welfare report with an implementation report that looks more at the practicalities, which may lead to some changes in next steps.
Really impressed by your work so far, thanks for sharing this.
Hey Edo, I'm glad to hear that you find our work useful.
I'm curious about how you are using multiple researchers for this. Most steps can be done in parallel, but I wonder- how much do you rely on multiple views on the same analysis, and how do you go about it?
We have one lead researcher for each cause, responsible for conducting comprehensive research in their area; this way, they become experts in their respective fields. But we also want to capitalize on the fact that we are one of only a few organizations conducting research in multiple causes. We’re in a unique spot to learn and cross-apply methodologies and practices from other causes, as Neil Buddy Shah illustrates. Animal advocacy can cross-apply from global health research e.g. a comprehensive system to grade the quality of evidence. In turn, global health can learn from animal advocacy e.g. how to answer questions when there is little information, or when the evidence-base is low. For this reason, after the initial draft of a report is completed, it is peer-reviewed by a researcher from a different cause. On top of that, we have a senior staff member whose work is dedicated to thoroughly reviewing the reports. He looks for contradictory research; challenges crucial assumptions; double-checks key inputs in the CEA; verifies that the strength of evidence has been adequately expressed in the report based on its source; etc. At the end, I analyze the conclusions of the report. So for example, I consider whether any crucial considerations have been missed; if the evidence is strong enough to warrant the conclusion; and if equal rigor has been applied across different charity ideas. We also engage external research reviewers and experts in the field.
I'm always looking to improve our systems, so I'm open to suggestions on how we can do things better.
Also, is there anything that the EA community can do to assist the research process? If so, what could be the most valuable?
Thanks for this question and for facilitating this research group! It seems like a fascinating project, and I cannot wait to see updates from it.
Researching marginal ideas on our priority list would be most valuable (ideally using the same process so it is comparable). Ideas that almost made it to our priority list probably have the highest odds of being better than the idea we recommend, so researching them might change what charities will be started. To get more granular, it would be really helpful to conduct crucial consideration research that may determine whether an intervention merits deeper research. As an example, here are the first ideas that didn’t quite make the list for each cause: 1. Mental health and subjective well-being: Addressing mundane, suboptimal happiness through conditional cash transfers for using gratitude journals
2. Animal welfare: Developing and advocating for pre-hatch sexing to reduce the suffering of male chicks 3. Family planning: Informing parents and girls about future economic opportunities
4. Health and Development Policy: Improving health systems through community monitoring of health problems (e.g. through scorecards, planning meetings, etc.; regional comparison/competition for outcomes-focused government)
You can read about each of these possible interventions in more detail in the linked Idea Prioritization reports.
Out of the areas you mention, I'd be very interested in the following: Animal product alternatives 6/10
Pain relief in developing countries 6/10 Improving science 9/10
Ideas not included on your list: GiveWell recently published its list of areas they are planning to explore. I think some of them might be of interest to donors focused on improving the welfare of the current generation of humans and high-income countries’ problems.
Tobacco, alcohol, and sugar control
Air pollution regulation
Micronutrient fortification and biofortification
Improving government program selection
Improving government implementation
Mosquito gene drives advocacy and research
Mental health (interventions comparison)
Sleep quality improvement
As you know, GW’s research is very diligent. Consequently, it takes a long time to finalize. I would be interested in having preliminary research conducted by other organizations.
Regarding donors focused on animal welfare:
Producers’ outreach, for example,. providing subsidization for farmers interested in higher-welfare farming
CRISPR-based gene drives to address wild animals’ suffering
Insects’ welfare, intervention comparison, for example, reduction of the production of silk, painkillers for insects used in research, etc.
I am currently working on CE’s agenda for the next year in the area of global poverty/health, animal advocacy, and mental health. I will be able to list more areas and research questions worth investigating that CE cannot cover this year at the end of September. I am narrowing down a list of research ideas from 400 ideas (in three cases). Let me know if you are interested in hearing more about it.
Thanks Saulius for pointing this out. We spent a considerable amount of time trying to find a more relevant number, but that was the closes proxy we've found. If you have any other source we could use, that would increase the accuracy of the estimate.
Follow-through rate is lower in the case of companies that affect the highest number of hens.
Financial situation: When companies broke or delayed the commitment, they blamed the recession or claimed that there were insufficient consumer demand and lack of funds for making the necessary changes on farms.
ACE claimed that the success of the campaigns depends on the public perception of targeted issues. I have not fack-check that though.
Characteristics that don't correlate with follow-through:
No correlation between the number of pledges and the % of cage-free eggs in a given country before the campaign. Meaning that campaigns do not seem to be simply riding already existing trends.
I wouldn't be surprised if those factors were somewhat predictive:
If the pledge was made voluntary form the company or was forced upon by strong negative campaign.
If a given company gave itself some wiggle room in the phrasing of their commitments, which they could later be used as a justification for breaking their commitments.
Thanks for the suggestions! As we were discussing above, combining this estimate with a prior estimate using Bayes’ rule might be a solution here. Taking the uncertainty of the model into account, we indeed score this approach quite poorly when it comes to the evidence-base aspect of it. We have a different research template for approaches than the one you linked. I expect to publish the whole report on corporate outreach pretty soon.
When it comes to the step between research questions and the probability distribution, full research, answering each question, can be seen in the full report. In the report, we also address some of the concerns you have with the judgement calls on each of the “qualitative” parameters.
Each update incorporates the weight we put on this factor, the directionality and strength. Those factors, again, rely on other information. With the example, you cited ”what ACE thinks makes an effective campaign” vs “probability that all companies defect in a Prisoner's Dilemma scenario". For example, ACE’s opinion on the importance of public support when launching corporate campaigns is formed based on the intervention report they have researched in November 2014, and as they currently claim “is not up to our current standards.”. The landscape has changed since then. As of recent, we can observe that there is a strong track record of successful corporate campaigns in countries where the society didn’t have sympathetic views toward animals (e.g. Lithuania or Japan). I think we can rely more and more on rigorous and generalizable conclusions from research on real-life examples and on the application of game theory to predict the behaviour of the companies.
I agree I wish we had enough time to flesh out the reasoning for each of the factors. Sadly, due to limited time we are constantly having to make trade-offs about whether we should put time into explaining the reasoning more deeply to the broader community vs discussing with the CE candidates vs researching more to get a deeper internal understanding. We generally plan on going deeply into these factors with the specific entrepreneurs looking to start this project or others, who are going to work/are working in the field in the near term, but not publish much more on the topic publicly after our full report.
Thank you for the suggestion. I agree that we can’t extrapolate the conclusion about predicted follow-through rate based on what percentage of companies have followed through on the commitment so far. I looked at it again, and I think that if analyzed correctly it still provides valuable information, so I will leave it in the model, but I’ll move it to the section on updates based on qualitative data and change the values based on the information below. I think that a good proxy for the information is whether the top 20 biggest companies are making progress and will eventually switch is if they responded to EggTrack. We can break it down to: * Companies that passed their deadline: Whole Foods (2004) - 100% follow-through Costco (2018) - as of July 2017, 78% and 100% converted to liquid *Those that didn’t respond to EggTrack are Walmart, Albertsons, McDonald’s, Target, Sysco, ALDI, Burger King, Tim Hortons, Southeastern, and Wendy's. * Those that did respond and reported progress: Kroger (21% as of 2017), Publix (50%), SUPERVALU (as of 2016, 12%) * Those in 20 that I do not have information on US Foods, IGA, Inc., Associated Grocers of Florida.
I will estimate the value in this cell based on this information unless you have info that could fill in the gaps.
I generally think that with all very uncertain estimates, whatever the result, it should be only treated as a cautious update and be combined with prior estimates of value.
As a meta comment, I think I’m less concerned about an error in one of the parameters than you seem to be because of the different goals of the research. My goal is to reach broadly good conclusions about which intervention should be executed from a given list of options given a limited amount of time, rather than get the right answer to a specific question, even if it takes me an extremely large amount of time. I think that using cluster approach is superior in such cases. If you are using cluster approach, the more perspectives you take into account the lower the odds of your decision being wrong, and so I trade other aspects (eg. number of interventions compared in a given time frame and how accurate a single estimate need to be) differently. One contradicting factor also cannot overpower the whole decision, etc. A completely different method should be used when we are trying to have as accurate beliefs about the world as possible vs getting to a good decision.
When evaluating cost-effectiveness of interventions or charities, GiveWell only looks at how the action affects the most important metric that the charity is trying to adress. For example, when analysing the cost-effectiveness of SMS vaccine reminders, they only take into account the effect on the vaccination rate, but not on breastfeeding rates, which is also promoted by the intervention. We look at the effect on multiple disperse metrics, including health effects, reduced birth rate, woman empowerment, effect on animal welfare and the environment, etc. Additionally, we have not yet determined that condom distribution is going to be the intervention the charity is going to pursue. We are also considering SMS for reproductive health, community reproductive education, advance provision of EC etc.
I agree that there is no obvious way to model it and the method would even depend on the goal of the model, and it might not necessarily cross-apply to seemingly similar cases.
The estimate reflects a probability distribution of the percentage of corporations that have pledged a welfare improvement that will follow through on those pledges. Note here that it doesn’t inform about what percentage of companies in a country that the organization operate will implement the improvement, but rather the percentage of companies out of companies that have already pledged. Here the 39% - 50% is the most plausible outcome, but the model also includes, for example, the small probability of just 5% of companies following-through. We are also trading the accuracy of the result for the value of the information it provides. Of course, I feel fully confident that the true outcome will be somewhere between 0% and 100%, but this result is not that informative when we need to make a call.
I was modelling in mostly having in mind CE’s asks recommendations: food fortification and management of DO levels. That enabled us to narrow it down and make it more generalizable. I agree it won’t be generalizable for other asks, like the one that you used or even for the broiler asks for the same reasons.
Given your aims, you can use my estimates but just give any prior estimate, given that presumably, your priors aren't flat or 1.
An alternative to that method might be estimating number of animals affected rather than percentage of corporations since presumably animals aren't distributed evenly across corporations and so it seems possible that you might hit >x% of animals with x% of corporations. That would require modelling it for a very specific case if you want to get a “usable” result.
Thanks for the suggestions. For our poverty year we mainly focused on GiveWell priority programs although considered some interventions in the hits based area. Next year we plan on writing up some views comparing hits based giving to evidence-based giving and how we think they compare in expected value.
Thank you for the suggestion. I'm always open for ideas on productivity improvements, especially if they directly affect charity entrepreneurs ;)
We generated a list of 100 ideas and prioritized them based on things like expected effect on the general population, on me and Joey, ease of testing, etc. As far as I remember, rotating positions from sitting on an office chair to standing to sit on a ball or laying on a couch are more strongly recommended than any single one of those. I think testing all of the tools you can use to be physically active would be an interesting separate experiment in itself. Have you ever tried a mini-stepper? How did you find the effects of a treadmill compared to a mini-stepper?
Thank you for compiling information on fish oil used in fish feed. As part of the research at Charity Entrepreneurship, I recently published a report exploring fish feed optimization as a potential intervention. We had mostly focused on fishmeal, so you might be interested in complementary research. A lot of crucial considerations that we've explored are also applicable to fish oil. You can find the whole report here.
Water quality including dissolved oxygen is affected by three main categories of causes; one of which is the biological loading and water treatment systems applied by the farmer that includes management of oxygen level. DO level is affected by multiple stable factors (like temperature) but also sporadic factors including overfeeding, swimming activity or CO2 increase, so it is important that the baseline dissolved oxygen level has a safety margin for temporary increases in DO requirements.
Comment by KarolinaSarek on [deleted post]
Thank you for the relevant questions.
Is this only from the animal products the child would have eaten themself? Should the consumption from that child's descendants be included?
Yes, in our preliminary analysis we only include effects in the first generation, adjusted for the possible increase in consumption by other family members due to increased income. We will analyse the impact of prevented consumption in the next generation, but give it smaller weight than direct effect in the first birth averted.
FWIW, TLYCS recommends PSI and DMI, and DMI is one of GiveWell's standout charities, and both do family planning work.
We are aligned more with GiveWell’s methodology and consider their recommendations more representative. Family planning is one of many interventions DMI does with considerably less resources spent on it compare to other interventions.
What is even more important, DMI (and PSI) don’t work with impact on animal welfare in mind. That leads to choice of countries (Burkina Faso, DRC, Mozambique) that are one of the least promising form the perspective of their effect on animals (we have a report on priority countries coming out soon).
Comment by KarolinaSarek on [deleted post]
We are very skeptical about being able to make any progress on far future effects of population given the time cap we put on this report and our general skepticism towards being able to make accurate far future predictions. We use something closest to a "weighted quantitative model" but would only do a more explicit model of this for the top charity ideas we investigate deeper.
Comment by KarolinaSarek on [deleted post]
Broadly we have not considered WAS due to separate reports/views on how to deal with that (coming out soon). In short, epistemically, we tend to take a cluster view, one of which would be a cluster concerned with flow through effects. We think wild animal suffering will often be the most important consideration within flow through effects and we expect flow through effects to carry between 1% and 25% of our endline evaluation of the intervention’s promisingness. Overall, we think the effects other interventions have on wild animal suffering should be considered as a non-trivial factor, but not a dominating one. We will analyze it thoroughly in the next stage of research if this intervention would make to top 3 after shallow research of all asks we consider.
1) As you correctly observed, we didn’t adjust welfare points for population size and odds of feeling pain in this spreadsheet. But we just publish another report summarizing our animal prioritization research where we aggregated information about baseline welfare points, population size, odds of feeling pain, neglectedness, and amount of suffering caused by a smaller number of specific reasons.
Generally, when we are calculating the cost-effectiveness of a given intervention we take into account the number of welfare points “gained” (baseline welfare points changed counterfactually by the intervention) multiplied by odds of feeling pain and number of animals affected.
We also need to adjust for length of life. For example, if the baseline welfare points per year for a cow is -20 and for broiler chicken is -56, but beef cow spends 402 days on a farm, their WP would be multiplied by the percentage of year they spend on the farm, so 402 days / 365 days in a year = 110%, and broiler chicken spend 42 days, then WPs would be multiplied by 12% resulting in: Cow: -22 welfare points per lifetime of an individual Broiler chicken: -6.72 welfare points per lifetime of an individual.
2) The range is the minimum and maximum values of welfare points as rated by our external reviewers. “Total welfare score” (second column) is an average of internal and external reviewer’s ratings.
We pulled the data on odds of feeling pain from Open Phil’s report on consciousness and moral patienthood. The probability of consciousness (as loosely defines by examples in the report) for a given species were estimated based on proxies like last common ancestor with humans, neurobiological features, nociceptive features and other behavioral/cognitive features. In our system, we based weighting of different criteria based on multiple factors including proxying ethical value accuracy (metric and ethical value, encapsulation, directness and gamability) and cross-applicability, including cross-animal applicability. You can read more on that in our previous post.
Some farms (e.g. GAP farms) do have better nutritional practices, although there is not great specific data from them. That being said, there is other evidence that both calcium has an impact and theoretical reasons why a large number of farms would not supplement well. Farms do experiment with food a lot but not generally with welfare in mind. It’s not currently an issue at the front of consumers’ minds.
One of the requirements for Global Animal Partnership (GAP) certified farms is "Hens must be provided with sufficient calcium in their diet to maintain hen's health and eggshell quality." There are approximately 20 chicken farms signed up to this program and they might provide an adequate level of calcium. Welfare of hens is measured individually for every farm, but according to my knowledge, they are not conducting any studies. Fortunately, evidence base for calcium and phosphorus supplementation is pretty strong. For example, according to this analysis for hens at age 462-543 days of life increasing dietary calcium
from 24-25 to 36-40 g/kg decreased mortality by 5.5% (22.8% -> 17.3%) and improved egg production, shell weight (SW) and shell thickness (ST)
from 36-40 g/kg to 49 g/kg by next 5.4% (17.3% - 11.9%) but did not affect egg production but increased SW and/or ST.
Farmers do prepare their own fortified feed premixes, but it is unlikely that they provide an adequate level of nutrients because the currently recommended dosage is not optimal. One study compared turkey's health benefits of currently recommended by National Research Council (NRC) dosage of phosphorus and diets that were 0.06% higher than NRC recommended levels; 0.1% higher than the medium diet, and 0.1% higher than the high diet.In addition to lower body weights, turkeys fed with the NRC recommended diet had higher incidences of bone fractures and reduced the walking ability, indicating that feeding nonphytate phosphorus at levels above NRC recommended levels resulted in improved growth and better skeletal integrity compared to NRC recommended levels. Similarly, the level of calcium can affect skeletal properties and body weight. For example, Tatara et al. (2011) reported improved skeletal properties and increased body weights in turkeys provided with 95% or more of NRC recommended calcium compared to those provided with 85% of NRC recommended calcium. (source, page 281)
Additionally given that economically, phosphorus is the third most expensive component in a non-ruminant diet after energy and protein, it is less likely that chickens in the standard farm have an adequate level of this mineral. One of the biggest feed distributors, DSM, that additionally seems to focus on animal welfare outside of the profitability of having healthy animals, supplement feed with vitamins, but not dietary minerals like calcium.
The evidence is strong enough to research this, ask more deeply, and we are planning to conduct more research to determine the exact level of nutrients in chicken's diet and evaluate the change in welfare points cause by fortification. Interestingly, feed (as well as chickens) is often provided to the farmers by large food companies (e.g. Tyson Foodswho contract out the raising of the birds to the farmers, so we will compare the level of nutrients added by Tyson Food to the optimal dosage to determine if the ask is still more cost-effective than other interventions we are investigating.
Thank you, Joey, for gathering those data. And thank you, Darius, for providing us with the suggestions for reducing this risk. I agree that further research on causes of value drift and how to avoid it is needed. If the phenomenon is explained correctly, that could be a great asset to the EA community building. But regardless of this explanation, your suggestions are valuable.
It seems to be a generally complex problem because retention encapsulates the phenomenon in which a person develops an identity, skill set, and consistent motivation or dedication to significantly change the course of their life. CEA in their recent model of community building framed it as resources, dedication, and realization.
Decreasing retention is also observed in many social movements. Some insights about how it happens can be culled from sociological literature. Although it is still underexplored and the sociological analysis might have mediocre quality, but it might still be useful to have a look at it. For example, this analysis implicate that “movement’s ability to sustain itself is a deeply interactive question predicted by its relationship to its participants: their availability, their relationships to others, and the organization’s capacity to make them feel empowered, obligated, and invested."
The reasons for the value drift from EA seems to be as important in understanding the process, as the value drift that led to EA, e.g. In Joey's post, he gave an illustrative story of Alice. What could explain her value drift was the fact that at people during their first year of college are more prone to social pressure and need for belonging. That could make her become EA and drifted when she left college and her EA peers. So "Surround yourself with value aligned people" for the whole course of your life. That also stresses the importance of untapped potential of local groups outside the main EA hubs. For this reason, it's worth considering even If in case of outreach we shouldn't rush to translate effective altruism
About the data itself. We might be making wrong inferences trying to explain those date. Because it shows only a fraction of the process and maybe if we would observe the curve of engagement it would fluctuate over a longer period of time, eg. 50% in the first 2-5 year, 10% in a 6th year, 1% in for the next 2-3 and then coming back to 10%, 50% etc.? Me might hypothesize that life situation influence the baseline engagement for short period (1 month- 3 years). As analogous for changes in a baseline of happiness and influences of live events explained by hedonic adaptation, maybe we have sth like altruistic adaptation, that changes after a significant live event (changing the city, marriage etc.) and then comes back to baseline.
Additionally, the level of engagement in EA and other significant variables does not correlate perfectly, the data could also be explained by the regression to the mean. If some of the EAs were hardcore at the beginning, they will tend to be closer to the average on a second measurement, so from 50% to 10%, and those from 10% to 1%. Anyhow, the likelihood that the value drift is true is higher than that it's not.
More could be done about the vale drift on the structural level, e.g. it might be also explained by the main bottlenecks in the community itself, like the Mid-Tire Trap (e.g. too good for running local group, but no good enough to be hired by main EA organizations -> multiple unsuccessful job applications -> frustration -> drop out).
Becuase mechanism of the value drift would determine the strategies to minimalize risk or harm of it and because the EA community might not be representative for other social movements, we should systematically and empirically explore those and other factors in order to find the 80/20 of long-lasting commitment.