I’m a strategist at Affinity Impact, the impact initiative of a Taiwanese family.
Have you compared your analysis to this previous EA Forum post? Are there different takeaways? Have you done anything differently and if so, why?
Here’s the math on moral/financial fungibility:...You’re probably better off eating cow beef and donating the $6.03/kg to the Good Food Institute
Here’s the math on moral/financial fungibility:
You’re probably better off eating cow beef and donating the $6.03/kg to the Good Food Institute
Is refraining from killing really morally fungible to killing + offsetting? Would it be morally permissible for someone to engage in murder if they agreed to offset that life by donating $5,000 to Malaria Consortium? I don’t mean to be offensive with this analogy, but if we are to take seriously the pain/suffering that factory farming inflicts on animals, we should morally regard it in a similar lens to inflicting pain/suffering on humans.
So, no, moral acts are not necessarily fungible. It is better to not eat meat in the first place than to eat meat and donate the savings to farm animal charities (even if you could save more animals). This is obvious from a rights moral framework but even consequentialists would consider financial offsetting dangerous and unpalatable. The consequences of allowing people to engage in immoral acts + offsetting would be a treacherous and ultimately inferior world.
So your calculations are not the cost of eating meat but rather, the cost of saving animals. You have not estimated the cost of chicken/cow suffering (which would require estimating utility functions and animal preferences), but rather, the cost of alleviating suffering. Your low-cost numbers don’t imply that eating meat is inconsequential, but rather, that it’s very cost-effective to help chickens and cows. GiveWell’s $5,000 per human life doesn’t make human life cheap, it means we have an extraordinary opportunity to help others at a very low cost to ourselves.
Thanks, Sanjay, I’m sharing a basic model I’ve written that highlights the trade-off for impact investments that seek both social impact and financial returns. This isn’t specifically about ESG but the key ideas still apply. The upshot: the investment must produce annually one percent of a same-sized grant’s social benefit for every one percent concession on its financial return. I construct impact investing’s version of the Security Market Line and quantitatively define what ‘impact alpha’ means.
This model was written a couple of years ago but since then, I actually haven’t applied it much. That’s because it’s hard to quantify impact, which is a key input that the model requires (and an input that any model will obviously require). There’s no established and easy way to monetize impact, especially given impact’s tremendous heterogeneity. Comparing the value of a year’s education versus a year’s health is hard enough. What about quantifying the counterfactual impact that a business has? Or that of the investor investing into the business? So modeling is helpful but at this stage, I think data is what we actually need most.
I agree with Michael that a 70% allocation to US stocks is way too high. US stocks’ outperformance against international developed stocks can almost entirely be explained by the increase in the US market’s valuation (which shouldn’t be assumed to continue and indeed, is more likely to reverse). See AQR’s analysis on pg 6 here. Also, what about Emerging Market stocks? This should certainly get some allocation as well, especially if you’re focused on the next 100 years. China and India will increasingly be key economic players and have capital markets that will outgrow the US in importance. In fact, 6 of the 7 largest economies in the world in 2050 are likely to be emerging economies. When it comes to investing, beware of simply extrapolating the past into the future! The US markets have done well because the US has been the dominant country in the 20th century. This is unlikely to continue during this century.
A 10% global bonds/90% global stocks portfolio is likely to be more robust and not suffer from a USD/US historical bias. Keep it simple and avoid picking bond/stock market winners.
This paper is relevant to your question.
Abstract: This article asks how sustainable investing (SI) contributes to societal goals, conducting a literature review on investor impact—that is, the change investors trigger in companies’ environmental and social impact. We distinguish three impact mechanisms: shareholder engagement, capital allocation, and indirect impacts, concluding that the impact of shareholder engagement is well supported in the literature, the impact of capital allocation only partially, and indirect impacts lack empirical support. Our results suggest that investors who seek impact should pursue shareholder engagement throughout their portfolio, allocate capital to sustainable companies whose growth is limited by external financing conditions, and screen out companies based on the absence of specific ESG practices that can be adopted at reasonable costs. For rating agencies, we outline steps to develop investor impact metrics. For policymakers, we highlight that SI helps to diffuse good business practices, but is unlikely to drive a deeper transformation without additional policy measures.
I don’t think it makes sense to compound the model distributions (e.g. from 1 year to 10 years). Doing so leads to non-intuitive results that are difficult to justify.
1) Compounded model results (e.g. 10x impact in 10 years) are highly sensitive to the arbitrarily assumed shape, range, and skewness parameters of the variable distributions. Also, these results will vary wildly from simulation to simulation depending on the sequence of random draws. This points to the model’s fragility and leads to unnecessary confusion.
2) The parameter estimates may use annualized growth rates, but they need not correspond to an annual time frame. Indeed, it is more realistic to make estimates for longer horizons because short-term noise averages out (i.e. Law of Large Numbers). In other words, it is far easier to estimate a variable’s expected mean than its underlying distribution. Estimates for the expected mean will already be highly uncertain. I don’t think it’s possible to reasonably defend distribution assumptions of the variables themselves.
The exercise is to compare giving-today vs. investing-to-give-later. The post usefully identifies key variables in this consideration. I think the most it can do is propose useful estimates of these variables’ expectations over the long run (i.e. their averages over time) and their key uncertainties (i.e. Knighting uncertainty and not quantifiable distribution parameters). If the expectations’ net sum is above 1, it makes sense to give later. If it falls below 1, it makes sense to give now. Reasonable areas of uncertainty can be further discussed and debated. Already, there will be much irreconcilable (rational) disagreement. Compounding returns using arbitrary distribution parameters won’t (and shouldn’t) reconcile any differences and likely confuses the matter.
A 7% real investment return over the long-term is in my opinion, highly aggressive. World real GDP growth from 1960 through 2019 is 3.5%. Since the proposed fund expects to invest over “centuries or millennia,” any growth rate faster than GDP eventually takes over the world. Piketty’s r > g can’t work if wealth remains concentrated in a fund with no regular distributions.
Even in the shorter run, it’s unrealistic to expect the fund to implement a leveraged equity-only strategy (or analogous VC strategy):
1) A leveraged approach may not survive (e.g. will experience −100% returns). Even if the chance is small over a given year, this will be increasingly likely over a longer horizon. Dynamic leverage strategies can be implemented to reduce this risk but this likely reduce returns too.
2) A high-risk strategy will result in extremely painful drawdowns. In bad times, any fiduciary running the fund will face enormous pressure to shift to a more conservative strategy. During the Great Depression, US equities declined by nearly 90% during the course of just 3 years, even without leverage. Sticking to the same approach in the face of a potentially worse decline is nearly unimaginable.
3) A consistently leveraged portfolio approach has never been done before over long investment periods. Foundation/university endowments are probably in the most analogous position and few apply leverage. Harvard tried a modest 5% leverage during the 2000’s, and it blew up during the Financial Crisis.
4) Any successful strategy will be mimicked and thus face increasing competition and declining returns. If the fund grows to any significant size, it will start facing competition from itself. For example, Yale’s legendary endowment has seen declining returns from a ~9.5% real rate over the past 20 years to a ~5.5% one over the past decade. Similarly, given Berkshire Hathaway’s large size, it’s now increasingly difficult for Warren Buffet to beat the stock market.
Indeed, the proposed fund may actually have to be quite conservative for it to survive over time (through broad diversification even into low-return assets) and be accepted by the world (to avoid scrutiny or excess taxation). In my opinion, when investing over centuries with an unprecedented strategy, I would characterize a 2-4% real return (broad asset class diversification that keeps up with world GDP) as reasonable, and a 5%+ real return (all equity with or without leverage) as aggressive.
Hi Carl, thanks for your response and for posting the links. I have now retracted my initial strong downvote of your comment.
I understand and am sympathetic of the view that altruists investing to donate should be a lot more risk-seeking than when investing to fund their own future consumption. My concern was entirely based on your recommendation to invest long term in leveraged ETF’s. I did not think this is a good idea because leveraged ETF’s can have realized returns that deviate substantially from its underlying index in a bad and unexpected way. Given current market conditions of elevated volatility, they are especially dangerous and more likely to have poor performance. The original EA post was about taxes and likely from someone with limited investment experience. I thought your advice could actually be harmful and lead to distressing investment results.
From your links, I saw that Brian Tomasik conducted simulations of leveraged ETF’s and concluded that altruists should consider them as an effective way to apply leverage. I did not review his work in detail but it does alleviate my concern of holding leveraged ETF’s over long periods. Still, as discussed in the links you shared, this should be done with caution and with awareness of the complicating role that other factors play (e.g. fees, choice of portfolio to lever, market conditions). If investors are unaware of these risks and complexities, there could be a backlash.
Since your comment now contains a cautionary disclaimer and the various links that clearly indicate the challenges involved with leverage, I think it’s unlikely to be misinterpreted anymore. Thank you for your response!
You should NOT be holding leveraged ETF’s for long periods of time (i.e no more than a day or two). When held for a year, a 3x leveraged ETF will not deliver 3x the returns of the underlying index. In fact, it is quite possible given high current volatility, that the ETF delivers negative returns even when the underlying index is positive. For more info, see ‘Why Leveraged ETFs Are Not a Long Term Bet.’
Hauke’s calculation simply determines a standard Benefit/Cost ratio. If it costs $10 to avert a tonne of CO2 that provides benefits of $417 (in damages averted), this Benefit/Cost ratio equals 41.7. This ratio should be directly comparable to Copenhagen Consensus ‘Social, economic, and environmental benefit per $1 spent.’ For the Post-2015 Consensus, ‘Climate Change Adaption’ is listed as providing a Benefit/Cost ratio of 2 while climate-related ‘Energy Research’ has a ratio of 11. I would weight these results from meta-level research must more strongly than that from a single study. But even if we believed Hauke’s study, a benefit/cost ratio of 41.7 still lags ‘Reduce Child Malnutrition’ (ratio of 45) or ‘Expanded Immunization’ (ratio of 60). This hardly suggests that “we should consider prioritizing climate change over global development interventions.” The unconditional cash transfer benchmark that Hauke uses is a minimum and not representative of highly cost-effective interventions in global development. Using GiveWell’s estimates, deworming and malaria nets are more than 10x more cost-effective than cash. Before rushing to replace well-established priorities and interventions that are based on decades of research, we need to have substantial confidence in the new priority/intervention. This study is far from it.
Note that the Copenhagen Consensus and GiveWell results do not apply utility adjustments. If this new climate change study does so, its Benefit/Cost ratio would be distorted by improperly inflating Benefits, which make the ratio larger than it actually is.
Thanks for your response, kbog!
Animal welfare issues are plausibly getting worse and not better so I’d be less confident to assume it will not be an issue in the future. As the world develops and eats more meat, Compassion in World Farming estimates that annual factory farm land animals killed could increase by 50% over the next 30 years. Assuming people’s expanding moral circle will reverse this trend is dangerous when the animal welfare movement has progressed little over the past few decades (number of vegetarians in US have been flat; there are some animal welfare legislative victories but also setbacks like ag-gag rules). Innovations like clean meat could help but it is still early, and there are also ways technology can make things even worse. Assuming animal welfare issues remain as they currently are (neither deteriorating nor improving) seems to me a plausible and more responsible projection.
If so, for the Long Term Future EA Fund, let’s assume the Animal Welfare EA Fund “offset ratio” (to account for the meat eater problem) is the same for future generations as it is for the current generation. Based on your blog’s estimate of a nickel a day, it costs a person ~$1000 to offset a lifetime of meat consumption ($0.05/day x 365 days/year x 50 years). It seems your estimate is for people living in rich countries though, so maybe 30% of that or ~$300 is more applicable to the average human. This can be compared to the Long Term Future Fund’s expected cost effectiveness of saving a human life (for just the current generation). I’ve seen one estimate that assumes a reduction in x-risk of 1% for $70 billion dollars spent (again for the current generation only). This leads to ~$1000 per human life saved ($70 billion / 7 billion humans / 1%). If so, the meat eater problem offset ratio for the Long Term Future Fund is very roughly ~30% (~$300 offset per life saved / ~$1000 to save a life).
Let’s apply a similar logic to the Global Health EA Fund. Instead of ~$1000 to offset a lifetime of meat consumption, let’s assume 10% of that for someone living in extreme poverty, or ~$100. GiveWell estimates that AMF can save a life for ~$3000, leading to an offset ratio of ~3% (~$100 offset per life saved / ~$3000 to save a life). This is two orders of magnitude larger than your comment response (of 0.008% ~ 0.04% from $0.08 ~ $0.4 / $1000). One reason might be because you’re only accounting for one year of the meat eater problem when I’ve accounted for a lifetime’s worth of impact (which I believe is the more complete counterfactual comparison). However, I’ve not had a chance to dive into your spreadsheet so I could be mis-using your results. Any corrections or reactions are much appreciated!
Finally, I’m curious as to why you think offsetting makes little sense under utilitarianism. I’m thinking it would actually be required if one were uncertain about the conversion ratio between human and animal welfare. If we were certain about the conversion, we should just do the one intervention that’s most cost effective, in whatever domain it happens to be in (human or animal). But if we were uncertain about the conversion, we will need to ensure that one domain’s actions doesn’t inadvertently produce overall negative utility when the other domain’s consequences are summed together. In the case of saving a human life, we wouldn’t want to lower overall utility because of our underestimation of the meat eater problem. On the other hand, we wouldn’t want to just focus on animal welfare if it turns out human welfare is especially significant. Offsetting cross-domain spillover effects avoids this dilemma (I teach finance, where analogies include hedging different FX risks or asset-liability matching). For the meat eater problem, it ensures saving a human life does not lead to negative utility even if we find out that animal welfare is unexpectedly important. The offset trades one animal life for another animal life, ensuring neutral utility impact within the animal domain.
Sorry for the long reply but I’ve been worrying about the meat eater problem so found your post to be especially interesting and informative. Any response you might have would be very appreciated!
Thanks for posting this, kbog! I would be interested in your recommendation for someone donating to the EA funds. The Long Term Future and Global Development funds focus on humans and thus potentially runs into the meat eater problem. For every dollar donated to the above funds, what would be an appropriate amount to donate to the Animal Welfare Fund that is enough to offset this issue? Thanks!
A company structure to consider would be a mutual organization where all profits go to members, which in your case would be the policy holders. Profits can be retained to grow the company or policy fees can be reduced by the amounts of its profits. Mutuals have a long history and many of the most successful financial organizations in the US are mutuals (e.g. Vanguard, State Farm, Liberty Mutual, NY Life). You could develop an insurance brokerage mutual that offers products from different insurance companies. I’m not sure if there are mutuals in this space but this could be a potential structure to explore given its long history of success. Personally, I’m a huge fan of Vanguard and Jack Bogle. They’ve done tremendous good and helped millions retire with more money and fewer fees. I wish you the same success!
Hi Huwelium, thanks so much for your post! I’m also advising someone on highly cost-effective interventions, so I found your thoughtful analysis to be very interesting. My question relates to your cost effectiveness estimates vs GiveWell’s. Based on GiveWell’s spreadsheet, their modeling of DDK (2017) places that program’s cost effectiveness at 0.5x – 2.5x GiveDirectly’s. Their modeling of Bettinger et al (2017) places that program’s at 0.2x – 1.4x GiveDirectly’s. Both of these estimates are for consumption effects only and excludes non-pecuniary benefits like reduced teenage pregnancy. This seems most comparable with your document’s cost-effectiveness estimates, which are based on income effects only. However, for Pratham, you conclude its cost effectiveness is 20x − 200x GiveDirectly’s.
I’m having trouble undertanding how your estimates are one to two orders of magnitude different from GiveWell’s. I’m probably missing something important so I was wondering if you’ve attempted a reconciliation. Any clarification on assumption differences and their relative importance would be very much appreciated. Thanks so much!
I would challenge your notion that you are over-analyzing the problem and that you must make a definitive decision soon.
1. In general, better knowledge and information leads to better decision making. If you are new to the EA community or to thinking deeply about philanthropy more generally, it is very unlikely that your current notions of how to give are appropriate.
2. Once you give away money, you cannot get it back. But money you save now can always be given away later. This argues for waiting in the presence of uncertainty. For example, in the optimal stopping Secretary Problem, you should see and just reject the first 37% of all candidates before you even begin your evaluation process.
3. There are tremendous consequences to your actions so you shouldn’t take this matter lightly. Going with your gut and intuition is not the appropriate response simply because you find your dilemmas to be difficult and overwhelming. Using GiveWell’s latest model, you can expect to save a life for probably $2500 or less. Since you have several hundred thousand pounds, you could save over 100 people with what you have. You could be like Oskar Schindler. Please don’t waste this precious opportunity.