In other words: "Simple language is more impactful."

Hey Stijn, a few critical points on this.

I'm worried about claiming any specific petitions are "easily thousands of times more effective than most other petitions", for reasons similar to this post.

I'm unsure how you're judging 'tractability' here, but I'm doubtful about the tractable routes to change for some of these. For example, the shrimp change.org petition made for a class project with ~400 signatures. Even if this petition got 10x or 100x the signatures, I don't understand the Theory of Change that results in any person/group/organisation making meaningful change. (For some petitions, like UK Parliament ones, there is a clearer route to impact, but it still requires lots more work and luck for the debate to become actual change).

Even if some petitions are super impactful compared to others, petitions might not be impactful compared to other interventions. This is somewhat offset by petitions being really 'cheap' (low time, non-fungible time, low/no funding costs). However, if you're recommending people sign petitions they don't know much about, they might reasonably want to spent time researching the issues, which increases time from ~10-15 sec to ~10-15 mins, which is a non-trivial amount of time.

Regardless, I made this really simple website to visualise your 10 recommended petitions more clearly (like 2-30 mins with ChatGPT). Would be open to working on this more, depending on yours and other's thoughts/responses to the concerns above!

Hi Brad, really good post, appreciate it! I've got one positive, one question, and one challenge.

Positive: The analysis of which industries are more amenable to Profit for Good seems interesting. It would be great to see more about which are industries are likely best/worst, and especially why (which you have partly done here).

Question: Does this model apply to publicly held companies, or could it be adapted for them? I imagine a large portion of the $100Tn you mentioned is from publicly traded companies. I also assume there's a competitive advantage to public ownership, (but I only think this because lots of the largest companies seem to do it). However, the model you propose seems to require private/foundation ownership.

Challenge: Even if Profit for Good is advantageous in general, it doesn’t mean that the most impactful Profit for Good is advantageous. For example, the most successful companies might focus on causes the public already cares about, like cancer research (which is probably less impactful than, say, GiveWell). This is especially relevant for causes like ending factory farming. Many interventions raise meat prices (intentionally or unintentionally), which might deter customers, and in the worst case could result in a comparative disadvantage.

Would like to hear your thoughts or pushbacks

This is interesting. What generally happens when you point out the ~inconsistency? Do people tend to reject Speciesism, reject anti-humanism, or accept/defend maintaining both? (Or something else!)

I think this idea and article are great. This (decision-relevant/skill-building work as a social group) seems like exactly like what EA Groups should be doing. The article is well-written, clear and potentially important.

I don't have enough knowledge to respond to your questions, but here's some thoughts:

• Digging wells in Niger seems to be cost-effective, however I wouldn't necessarily generalise that digging wells is cost-effective. (You don't do this, just pointing out for others.)
  • As you say, a lot of the country is on a large aquifer. This might make this intervention very good in Niger, but not scalable to other places.
  • Similarly, you've taken maximum values for rate reductions due to Niger having a larger burden. This wouldn't translate to other places.
• There's no data here about the overhead of Wells4Wellness (for example salary costs). This could change calculations.
• With regards to your Question 4: What do you expect the 'major quality of life improvement' to look like?
  • (I ask this both genuinely, my knowledge of this area is poor, and as a 'coaching-style' question to answer your question).

Having said that, do you know if W4W is likely to have room for significantly more funding? It seems like a good organisation to support!

This is a great resource, very detailed and something I think all group organisers should be aware of! Great work on it.

Two questions:

1. How likely do you think it is that this is a complete list? (Or, how likely do you think it is that a relevant organisation isn’t on this list?)
2. Do you have plans to maintain this list as orgs open/close/change, and if so roughly how often?

Hey! Firstly - massive kudos for this post and your marketing efforts. That's a LOT of work done in total. A couple of thoughts:

1. Do you know what the breakdown of attendees by outreach method was? The amount of stuff done might make this an unusually useful sample of what works.
2. Your in-lecture pitches might actually have decreased the number of attendees to a first meeting (in a good way)!
   1. I can imagine last year, several people came to the first meeting thinking "EA sounds potentially interesting, but I don't have enough info to know if I'll like it. Let me go to their first meet and find out."
   2. I can imagine this year, several people heard the pitch, and made the judgement that EA wasn't for them, so they didn't turn up to the first meeting.
3. I think the number attendees at the first meeting is largely unimportant/Goodharting compared to the number of attendees at the (say) fifth meeting.
   1. EA is quite high-commitment as societies go ("Hey, you should come and change your whole life plan to help others"). Heavy-tailed impact and such.
   2. I think the more interesting question is whether increased marketing resulted in a higher quality/fit (ie. likely to stay around and take points really seriously) of attendee, than pure number. The fifth meeting attendance might be a partial datapoint for this.

Here are some less important/certain factors that I think you could also take into account with your model:

• This intervention can't prevent first incidents, which might make it much less effective.
  • Intuitively, I agree the harm from the first incident is likely larger than subsequent incidents. At a complete guess, I'd say the first incident is maybe 20-25% of total harm.
  • This intervention by nature cannot prevent first incidents (reporting requires an incident to take place).
  • The linear model therefore (perhaps significantly) overestimates the benefits of this intervention.
• The bar for 'interacting with' 30 children might be high.
  • A teacher sees a child regularly over a long period of time. They therefore build a rapport that could lead to disclosures.
  • Doctors or police (mostly) see children relatively few times over a short period. It seems less likely they would be disclosed to because of the weaker rapport.
  • However, this might be outweighed by these professions being more likely to discover CSA (eg. noticing signs of CSA during a medical checkup; investigating other crimes which correlate with CSA offences).
• Not all disclosures result in stoppages (sadly).
  • More importantly, the important factor is not whether a disclosure causes a stoppage, but how much quicker a stoppage occurs after disclosure, compared to no disclosure.
  • Depending on the length and complexity of the investigative process, this might not prevent much harm (although I hope I'm wrong).
• It might be better to say an average of 1.5 years extra without disclosure.
  • This is half the time of the average CSA 'cycle', and assumes that each disclosure happens at a 'random' point.
  • The 1 year is also sensible, because I assume the chance of disclosing is proportional to the length of abuse taking place.
  • However, maybe the opposite is true. After a few incidents disclosure is likely, but after several incidents it becomes 'normalised' in some way, and the chance of disclosing drops dramatically.
  • This could make the intervention more or less cost effective, depending on how disclosure rates correlate with length of CSA.

Thanks for writing this Siobhan, and sorry this comment is very late. I currently see a few key issues (this comment), and a couple of broader concerns (future comment).

1. 1 in 20 children will experience CSA (at some time). This does not mean 1 in 20 children are experiencing CSA (at the current time).
   1. On average, a child experiencing CSA experiences it for 3 years, at a random point between age 3 and 18 (15 years).
      1. (I'm ignoring children under 3, since it is unlikely they can report, so this intervention probably doesn't help them much.)
   2. For a given child, there is a 1 in 20 chance they experience CSA over 15 years, and the average duration is 3 years (1/5th of the 15 years), so there is a (1 in 20 * 1/5th = ) 1 in 100 chance that a given child is experiencing CSA in a given year.
   3. So the correct value for Assumption 2 is that 1 in 100 children are (over the 12 months) experiencing CSA. This makes the intervention less cost effective.
2. I think the £29.7K/year figure is wrong, and £<19.8K/year a better figure.
   1. Using the stats from your cited report:
   2. On your assumptions, this intervention causes 1 less year of CSA per disclosure, and the benefit per year is 1/3 of the harm to the victim ('cost as a consequence' in the link).
   3. Therefore, the intervention saves (1/3 x £59,300 = ) £19,800 per disclosure.
   4. On your assumptions, this intervention doubles the number of disclosures (from 10% chance to 20% chance).
   5. This does not double the 'cost in response' costs, because these are 'top down' costs. (See Section 2.5 of the report). However, doubling the number of reports probably would require an increase in 'costs in response'.
   6. I don't know what a sensible increase would be, but it would require more spending, and thus reduce the cost saved below the £19,800 above. This makes the intervention less cost effective.
3. I think the 1 in 20 figure might be wrong, and 1 in 10 a better figure.
   1. The 1 in 20 figure (4.8%) comes from asking a group of 11-17-year-olds.
   2. Asking 18-24-year-olds instead gave a figure of 1 in 10 (11.3%).
   3. The first number will be an underestimate (An 11-year-old might be asked, and truthfully say no, but then experience CSA at 14 years old. This method of asking skews the percentages downwards because the group asked are only partway through the 'relevant period' of under-18). This makes your intervention more cost-effective.

Combining these, the new cost effectiveness is (1/5 * (19.8/29.7) * 2 * £4450 = ) £1190 averted per professional per year, which is £1680-£2520 per DALY.

I think it's possible that I've misunderstood some/all of these, so would appreciate sanity checks from others.