2 The puzzle and challenge: Our ineffective giving

(I) Limited giving to those most in need: When faced with the “girl drowning in the pond” we are willing to sacrifice substantial wealth to save a life. However, most people don’t make large donations to the very poor (see, e.g., Meer and Priday (2020)), in spite of evidence suggesting that lives can be saved for less than 5,000 USD in expectation (and people’s apparent belief that it is even cheaper).³

(II) Inefficiency of charitable giving: There is a strong case that most donations go to charities that improve well-being far less per-dollar than others. The figure below depicts estimates of ‘cost per additional disease-adjusted-life-year (DALY)’ from the most promising health interventions.⁴

⁵

In contrast, charity-supported interventions such as providing guide dogs for the blind routinely cost over 5000 USD per dog per year, presumably yielding substantially less than a single DALY of benefit per year. Make-a-Wish ‘wishes’ for dying children cost over 11,000 USD on average; the same money may be able to save as many as three children’s lives (on average) if devoted to (e.g.) seasonal preventative anti-malarial drugs.⁶

In general, most charitable giving goes towards helping people in wealthier countries,* even though it is widely accepted that far more lives can be saved and improved when funds are devoted towards more effective interventions in poor countries (see fold).⁷

Note

This range of cost-effectiveness values draws from the growing literature on health care opportunity costs, which suggests that a figure approximating half of gross domestic product (GDP) per capita per DALY averted is a realistic level of willingness-to-pay for health care interventions in [low to middle income countries] (Ochalek, Lomas, and Claxton 2015), cited in (Bundy et al. 2017), chapter 3, page 48.

Similar calculations referring to the value of a statistical life as being proportional to GDP per capita are found throughout this volume and beyond. E.g., (a random example) (Cobiac et al. 2012) considers “a cost-effectiveness threshold of 50,000 AUSD/DALY” (just a bit below their GDP per capita in 2012.).

As of 2021 the IMF reported (wikipedia link, accessed 20 May 2021 a GDP-per-capita of over 60,000 USD in the wealthiest countries like the United States and Australia, and under 550 USD in the poorest countries, such as Madagascar and Mozambique, a ratio of roughly 100 to 1.

As Burum, Nowak, and Hoffman (2020) state: “We donate billions of dollars to charities each year, yet much of our giving is ineffective. Why are we motivated to give, but not motivated to give effectively?”

The above points (I) and (II) raises two related questions:

I. “Why don’t we give more to the most effective charities and to those most in need?”, and

II. “Why are we not more efficient with our giving choices?”

To address this, we must understand what drives giving choices, and how people react to the presentation of charity-effectiveness information.⁸

2.1 Motivation and descriptives

Individual donors, governments and firms demonstrate substantial generosity (e.g., UK charity represents 0.5-1% of GDP, US charity around 2% of GDP).⁹, ¹⁰

However, most donations go towards charities that are worthwhile but improve human well-being far less per dollar than basic medical interventions in poor countries, such as antimalarial bednets (see Givewell.org). Even within the same category, more can be achieved for less: e.g. while it costs around USD 50,000 to provide a blind person a guide dog, the equivalent amount may be able to be used to prevent many cases of visual impairment from trachoma for many people (Burton and Mabey 2009) through preventative measures and surgery, perhaps more than 50.¹¹

Social science, biology and philosophy present a range of potential theoretical explanations of how values, preferences, and biases drive this ‘inefficient altruism’. However, evidence (e.g., for ‘availability bias’, or for ‘scope insensitivity’) comes largely from small-scale experiments in domains outside of charitable-giving. It is difficult to distinguish robust, credible findings from one-off results that are vulnerable to hype, p-hacking and publication bias (echoing the ‘replication crisis’ in experimental social science (Shrout and Rodgers 2018)). Given the limited, scattered findings, we do not have a definitive picture of which factors substantially impact ‘effective giving and support for policies that reduce extreme poverty’. (We give a review of papers surveying this evidence below.)

A plain-language summary of key points, without references

Charities’ impact differs by an order of magnitude: Some charities are much more effective at saving/improving lives (and achieving other goals such as those involving animals and the environment) than others are. While it is difficult to gain precise estimate on the measures such as “cost of a life saved” there is strong evidence that there are orders of magnitude difference between different categories of charities and different interventions within these categories.

There are some very effective lifesaving charities: Some interventions seem likely to save or vastly improve individual lives at a cost in the range of USD 2000 - USD 10,000. When people are asked whether they would be willing to spend this amount or even a vastly larger amount to save a life in other contexts they typically will agree to do so.

There are two related and largely unresolved puzzles:

Why are people not more generous with the most highly effective causes? and
When they give to charity why do they not choose more effective charities?

There is some evidence on this but it is far from definitive. We do not expect there to be only a single answer to these questions; there may be a set of beliefs, biases, preferences, and underlying circumstances driving this. We would like to understand which of these are robustly supported by the evidence, and will have a sense of how important each of these are in terms of the magnitude of driving and absence of effective giving. There has been only a limited amount of research into this and it has not been systematic, coordinated, nor heavily funded.

We seek to understand because we believe that there is potential to change attitudes, beliefs, and actions (primarily charitable giving, but also political and voting behavior and workplace/career choices). Different charitable appeals, information interventions and approaches may substantially change peoples charity choices. We see potential for changing the “domain” of causes chosen (e.g., international versus US domestic) as well as the effectiveness of the charities chosen within these categories. (However, we have some disagreement over the relative potential for either of these.)

Our main ‘policy’ audience includes both effective nonprofit organisations and ‘effective altruists’. The EA movement is highly-motivated, growing, and gaining funding. However, it represents a niche audience: the ‘hyper-analytic but morally-scrupulous’. EA organisations have focused on identifying effective causes and career paths, but have pursued neither extensive outreach nor ‘market research’ on a larger audience (see Charity Science, Gates Foundation/Ideas42).

2.1.1 Descriptives of giving (US, international) and how ‘ineffective’ it is.

This subsection section needs further work, outlining the existing evidence and basic ‘world in data’ picture for the questions below.

2.2 (Lack of) previous synthesis on this

There have been some relevant prior reviews, such as Caviola et al, 2021).¹⁵

However, the current project uniquely** combines (or at least, aims to combine)

A focus on effectiveness,
considering ‘choices among charities’ as well as in isolation (see fold}
incorporating recent work and developments from the ‘EA movement’,
a rigorous, sceptical approach to evidence, and
advancing a research agenda while building tools that promote robust evidence.

¹⁶

Note

We are considering both of the questions posed in the introduction: why do people not give more to the most effective charities, and when they do give to charities why do they choose ones that are substantially less effective than others, even within similar domains?

… Evidence from cases where people are explicitly choosing among charities as well as evidence from cases where people only have a choice of whether to donate to a single charity. Both of these are relevant to the real world, although I would suggest the latter is probably more relevant for the majority of small donors.

In addressing the above questions, and getting evidence on what drives people’s generous behavior, there are advantages and disadvantages of both modes (‘choose among charities’ vs ’choose whether or not to donate to X, with between-subject variation in the presentation and incentives for X).

2.2.1 Effectiveness-specific work

outlines ultimate [rather than proximate] explanations for ineffective altruism … evolutionary perspective …; three fundamental motives—parochialism, status, and conformity

Baron and Szymanska (2011) - Heuristics and Biases in Charity: Largely conceptual, minimal survey of specific empirical/experimental papers
Gertler, Charitable Fundraising and Smart Giving (not peer-reviewed but very useful)
(Caviola, Schubert, and Nemirow 2020) “The many obstacles to effective giving” … “on how both incorrect beliefs and preferences for ineffective charities contribute to ineffective giving.”¹⁷

Caviola, Schubert, and Nemirow (2020) is highly relevant and insightful (and has some connection to the structure we present below). From their abstract:

We review the motivational and epistemic causes of (in)effective giving. Many donors view charitable giving as a matter of personal preference, which favors decisions based on emotional appeal rather than effectiveness. In addition, while many donors are motivated to give effectively, they often have misconceptions and cognitive biases that reduce effective giving.

Still, Caviola et al does not drill deeply into the strength of the evidence and the relative importance of each factor. However, this may stem from a small amount of available evidence to survey. Ideas42 (ibid) wrote:

We did not find many field-based, experimental studies on the factors that encourage people to choose thoughtfully among charities or to plan ahead to give.

In the sections below, we (intend to) compare and incorporate our outline and discussion with Caviola et al’s work, as well as the other outlines. In the folds below, we briefly compare the outlines and structure of the syntheses mentioned above.¹⁸

Comparison of other outlines: unfold each

We are working on incorporating a connection to the concepts defined in these outlines into our own sections, particularly the ‘barriers’.

Caviola, Schubert, and Nemirow (2020)

Note

The importance of effectiveness
Motivational obstacles to effectiveness

(percieved) subjectivity (of giving)
“Narrow Affective Motivation” (warm glow, empathy, etc. not necessarily tied to effectiveness)
- personal connection,
- narrow moral circle
- Scope neglect
Prioritization aversion (especially hate to choose between people)
Character and reputational benefit (people who deliberate are viewed less positively)

Epistemic Obstacles to effectiveness

Overhead myth
Quantifiability Skepticism (‘doubt that charity effectiveness can be quantified and compared’)
Innumeracy (note, our ‘quantitative biases’ include some of these, as well as (essentially) scope neglect)
Underestimation of effectiveness variance

Strategies to Increase Effective Giving

Gertler, “Charitable Fundraising and Smart Giving”

Note

Substantial motivation
A broad picture of the evidence on what motivates and can be used to giving in general
“Strategies for Effective Charities” (pp 48-59) is most relevant to the current project

Baron and Szymanska (2011) (chapter)

Note

Introduction (with problem/puzzle)
Possible Nonutilitarian Heuristics

Evaluability (focus on attributes easy to evaluate e.g., efficiency/overhead)

“instead, what is more evaluable than the lives saved per dollar of contribution is the operating cost per dollar”

Average vs. Marginal Benefit, Diversification, Prominence, Parochialism
Identifiability, Voluntary Versus Tax
Experiments
Waste, Average Cost
Diversification,
Unequal Efficiency; Unequal Efficiency, Several Projects Versus One
Nationalism
Forced Charity
Discussion: Utilitarian Models of Altruism, Maximize Total Utility, Limited Self-Sacrifice, Limited Altruism, Moral Education, Implications

2.2.1.1 Other (potentially) relevant work and examples

Gifford (2011): “seven categories of psychological barriers…” that impede “climate change mitigation and adaptation”
Stefan Schubert’s ongoing work and book in progress

2.3 Definitions - “Efficiency” versus impact

¹⁹

It is important to define the concept of talking about. What, precisely, is this ‘effectiveness’ or ‘impact’ of a charity we are focusing on? It is not trivial to get this right and there are some delicate and hotly debated questions even within the EA movement. Nonetheless, I sketch the basic idea in the math below.

Aside: ’but what about uncertainty?²⁰

Note

* This discussion abstracts away from issues of empirical uncertainty. We obviously cannot be certain state of the world ‘with or without’ one’s charitable giving. This we cannot be certain about the impact of a particular charitable gift. I aim to return to this issue in later sections. Both empirical and ‘moral uncertainty’ have been carefully considered by academic philosophers and the EA community (e.g., at the Global Priorities Institute).

For the sake of the discussion below, we might assume that either:

donors essentially only care about the expected value consequences of their choices or
to the extent that their is uncertainty over the ‘impact’ of donations and interventions, this uncertainty is similar across causes.

$G_j$: The total donations given to charity $j$ during some interval; i.e., the charity’s income.

$B_j(G_j)$: A function defining the beneficial outcome achieved by charity $j$ with the total donations $G_j$.

Here, we are referring to $B_j(G_j)$ as (the improvement to) some ultimate outcome: Lives saved (i.e., deaths averted), quality adjusted life years (QALY) added, QALY weighted by age, Disease-adjusted (DALY), future happy lives generated, sentient suffering averted, etc.²¹

The important distinction here: $B(G_j)$ does not refer to a simple intermediate ‘output’ such as ‘antimalarial nets provided’ nor ‘textbooks purchased’. We are referring to the social outcome of ultimate value; an outcome that could be valued in and of itself.

A value pluralist

Jason Schukraft:

Of course, if you’re a value pluralist, there will be multiple outcome-types that are non-instrumentally valuable, and it’s unclear how to compare different types of non-instrumental values. (e.g., It’s tempting to say that people donating to art museums are engaged in ineffective giving, but if art has non-instrumental value, then it’s difficult to straightforwardly make this case).

David Reinstein:

Perhaps. I suppose that the empirical claims I make above probably do not apply to this measure over all domains.

This naturally takes into account both the ‘technical efficiency’ in terms of how many units of output can be produced per dollar, and the rate at which each unit of this output boosts the ultimate outcomes of interest.

The ‘production function’ is (perhaps tautologically) the product of two terms:

(Total or marginal) impact per dollar = output per dollar $\times$ impact per output

²²

A donor may care about the ‘impact’ of her own donation; i.e., she may want to know the difference in outcomes that her donation achieves everything else equal. In other words, the difference in the ultimate outcome in a world with versus without her donation.

Small donor assumption: For a small donor (perhaps someone who donates less than USD 100,000), we may assume that this “rate of benefit” will be the same for both the first and the last dollar she donates.²³

Thus we consider the marginal impact, as a simplification:

$B_j^\prime (G_j)$ for the marginal donor.

I assert that $B_j^\prime(G_j)$ is the quantity that GiveWell (and perhaps other EA charity raters) are attempting to measure.

We know (evidence cited in fold):

Note

There is abundant evidence for order-of-magnitude difference in the effectiveness of charities, and the most popular ones are far from the most effective. Some further links and references:

Ord, T. (2013, March 12). “The moral imperative toward cost-effectiveness in global health. Center for Global Development.” Retrieved from www.cgdev.org/content/publications/detail/142701.

“Your dollar goes farther overseas” (Givewell, 2016).

$B_j^\prime(G_j)$ is much larger for the most impactful relative to the most popular charities.

Increased benefits could be achieved if donations were “reallocated” towards more impactful charities. An individual who gains value from her giving through it’s impact alone would naturally donate to only the one charity that has the greatest marginal impact, the charity $j$ with the greatest $B_j'(G_j)$ term. If every donor is doing this, then as an ‘equilibrium’ result, every charity receiving positive donations should have the same last-dollar marginal impact. In maths:

\[\begin{equation} B_k^\prime(G_k) = B_j^\prime(G_j)\forall j,k s.t. G_j>0, G_k>0 \end{equation}\]

Caveats (unfold):

Note

This assumes a single impact goal, essentially ‘cause neutrality’.

The idea that each donor gives only to a single charity essentially depends on the above ‘Small donor assumption’. Still, allowing that the marginal-benefit-leading-charity may vary within the range of an individuals’ donation simply implies that they should allocate among multiple charities each up to the point that the last dollar given yields the same marginal benefit as the other charities, yielding the above result. We can imagine an equilibrium in which all donors give to multiple charities, with each of these charities being virtually “tied” in their marginal effectiveness.

See also this brief (EA Forum) on the “Market efficiency of philanthropy”. I hope to extend the discussion here, considering the ‘efficient market hypothesis’ applied to charitable donations, and the limitations of this.

But we do not seem to be doing this. Again: billions are given to charity, and these charities clearly have vastly different marginal impacts, even among those that seem to target very similar outcomes.

2.4 Why (under which models) is this a puzzle?

Does the set of facts mentioned above constitute a “puzzle” for our Economics and Psychology models, or are there obvious existing explanations? In Economics terms, are donors mysteriously ‘’leaving money on the table’’ or are they simply optimizing given their their preferences and constraints? What could explain this?

We return to this in a later section, while classifying potential ‘barriers’ to effective giving.²⁴

2.4.1 Aside: Economic models of giving

²⁵

Traditionally, economic models are essentially ‘if then’ exercises meant to illustrate what factors might be driving outcomes, perhaps facilitating empirical testing. They are not, in general, claims about people’s preferences, how they behave, or what motivates them. The discussion of charity in the Economics literature has tended to focus on two extreme models, focusing on modeling assumptions that allow simple mathematical ‘if-then’ proofs. Most of these models involve an individual making choices between a private good and a homogenous charitable good so as to maximizing a “utility function” (value function), subject to their budget constraints

A adaptation of the ‘Public goods model’ (often attributed to Becker (1974)) in which individuals care about the total amount of a charitable good provided (no matter who contributes it), either because it benefits them directly or because they care about other people. (The utility of others might be subsumed into one’s own utility function). This is sometimes labeled (confusingly in my opinion) called “pure altruism”.
An extreme representation of a ‘Warm glow’ model (James Andreoni 1989) in which an individual’s utility increases (presumably at a decreasing rate) in the ’amount they sacrifice’ towards the charitable good.

It is also important to note that the ‘warm glow’ presented in the Economics literature does not map to only a single psychological mechanism or theory; it maps to many such mechanisms. There is some ambiguity as to which motivations should be subsumed in this ‘warm glow’. According to Andreoni (who coined the term):

The concept of warm-glow is only a convenient reduced-form representation for deeper and more complex considerations of givers (J. Andreoni 2006)

In fact, each of the above models is an extreme representation that was originally used to provide insight into how to think about issues such as “does government spending crowd-out private donations” and “how do individuals respond to tax-benefits for giving”? In its simplest form, each model yields predictions that are profoundly unrealistic and can easily be “rejected” by real world data.

Much is left ambiguous in each model, as well as in other fairly prominent models such as the “Impact” (Duncan 2004) or “Identification” (Atkinson 2009) models of giving, or reputation-based models (Harbaugh 1998)

For the present discussion, our focus, in considering these models, is:

(When and how) does the “actual impact of one’s gift” have an impact

into the ‘good feeling’ one gets from giving
or the thing the individual is maximizing

… in a way that drives donation choices?

Andreoni, J. 2006. Philanthropy, Handbook of Giving, Reciprocity and Altruism, SC. Kolm and j. Mercier Ythier, Eds. Amsterdam, North Holland.

Andreoni, James. 1989. “Giving with Impure Altruism: Applications to Charity and Ricardian Equivalence.” Journal of Political Economy 97 (6): 14471458.

Atkinson, A B. 2009. “Giving Overseas and Public Policy.” Journal of Public Economics 93 (5-6): 647653.

Baron, Jonathan, and Ewa Szymanska. 2011. “Heuristics and Biases in Charity.”

Becker, Gary S. 1974. “A Theory of Social Interactions.” The Journal of Political Economy 82 (6): 10631093. http://links.jstor.org/sici?sici=0022-3808(197411/12)82:6%3C1063:ATOSI%3E2.0.CO;2-W.

Bundy, Donald AP, Nilanthi de Silva, Susan Horton, Dean T. Jamison, and George C. Patton. 2017. Disease Control Priorities. Washington, DC: World Bank.

Burton, Matthew J, and David CW Mabey. 2009. “The Global Burden of Trachoma: A Review.” PLoS Neglected Tropical Diseases 3 (10): e460.

Burum, Bethany, Martin A. Nowak, and Moshe Hoffman. 2020. “An Evolutionary Explanation for Ineffective Altruism.” Nature Human Behaviour, 1–13.

“CAF World Giving Index 2018 | Research into Global Giving Behaviour.” n.d. Accessed July 9, 2020. https://www.cafonline.org/about-us/publications/2018-publications/caf-world-giving-index-2018.

Caviola, Lucius, Stefan Schubert, and Jason Nemirow. 2020. “The Many Obstacles to Effective Giving.” Judgment and Decision Making 15 (2): 159.

Cobiac, Linda J., Anne Magnus, Stephen Lim, Jan J. Barendregt, Rob Carter, and Theo Vos. 2012. “Which Interventions Offer Best Value for Money in Primary Prevention of Cardiovascular Disease?” PLoS ONE 7 (7). https://doi.org/10.1371/journal.pone.0041842.

Duncan, B. 2004. “A Theory of Impact Philanthropy.” Journal of Public Economics 88 (9-10): 21592180.

Gifford, Robert. 2011. “The Dragons of Inaction: Psychological Barriers That Limit Climate Change Mitigation and Adaptation.” American Psychologist 66 (4): 290–302. https://doi.org/10.1037/a0023566.

Greenhalgh, Caroline, and Paul Montgomery. 2020. “A Systematic Review of the Barriers to and Facilitators of the Use of Evidence by Philanthropists When Determining Which Charities (Including Health Charities or Programmes) to Fund.” Systematic Reviews 9 (1): 1–13. https://doi.org/gh3785.

Harbaugh, William T. 1998. “What Do Donations Buy? A Model of Philanthropy Based on Prestige and Warm Glow.” Journal of Public Economics 67(2) (2): 269284.

Lewr, C K. 2009. “Who Gives Charitable Donations for Overseas Development ?” 317–41. https://doi.org/10.1017/S0047279408002869.

MacAskill, William. 2016. Doing Good Better: How Effective Altruism Can Help You Help Others, Do Work That Matters, and Make Smarter Choices about Giving Back. Penguin.

Meer, Jonathan, and Benjamin A. Priday. 2020. “Generosity Across the Income and Wealth Distributions.” National Bureau of Economic Research.

Micklewright, J, and S V Schnepf. 2007. “Giving to Development: Who Gives to Overseas Causes?” WeD International Conference.

Reinstein, D. 2011. “Does One Contribution Come at the Expense of Another? Empirical Evidence on Substitution Between Charitable Donations.” The B.E. Journal of Economic Analysis and Policy (Advances) 11 (1).

Rooney, Patrick M., Mark Ottoni-Wilhelm, Xiaoyun Wang, and Xiao Han. 2019. “Dynamics of American Giving: Descriptive Evidence.” Nonprofit and Voluntary Sector Quarterly, 0899764020977661.

Shrout, Patrick E, and Joseph L Rodgers. 2018. “Psychology, Science, and Knowledge Construction: Broadening Perspectives from the Replication Crisis.” Annual Review of Psychology 69: 487–510.

Singer, Peter. 2019. The Life You Can Save: How to Do Your Part to End World Poverty. The Life You Can Save. org.

“U.S. Charitable Giving Tops $400 Billion for First Time.” n.d. Accessed July 9, 2020. https://www.cbsnews.com/news/u-s-charitable-giving-tops-400-billion-for-first-time/.

Overview of research question/problem: Why don’t people give in an evidence-based way?↩︎
For an introduction to the field of effective altruism, see this article from effectivealtruism.org, as well as here for an introductory resource list.↩︎
This does not seem to be for a lack of overall generosity. As a prominent example, US Americans give roughly 2% of their income to charities (and ‘give far more’ if we include transfers to children and other family). However, giving mostly goes to local and domestic charities which are far less effective per dollar than the ones recommended GiveWell.org. G↩︎
Many of these interventions have room for more funding from private charity. Even the upper bound of these estimates is routinely below 100 (USD) per DALY.↩︎
Figure 7.1 is from original work by The World Bank. Views and opinions expressed in the present adaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed by The World Bank.↩︎
See the bottom row of the google sheet here (accessed on 19 Nov 2021) for the cost-per-life-saved estimate.↩︎
See, e.g., evidence from a US nationally representative survey (self-reports) in Reinstein (2011), or lists of the most popular charities in wealthy countries, e.g., this YouGov poll for the UK.↩︎
Note that these two questions are not identical: the first asks about the amounts given to the most needy charities, and the second about the choice of charities conditional on giving.↩︎
(“CAF World Giving Index 2018 | Research into Global Giving Behaviour” n.d.; “U.S. Charitable Giving Tops $400 Billion for First Time” n.d.)↩︎
Jason Schukraft: Do the ‘masses of donors’ matter, or only the multimillionaire response? The average person … do small donations add up Also, knowing more about how average people to respond to analytical information (in an other regarding /social context) will inform how to influence good long term decision-making. … how to get USDA to care about animals and the government to care about the long term↩︎
The latter figure is an extrapolation from the more conservative estimates in the Givewell Blog. Note that while the effectiveness of the trachoma interventions seem to have been overstated in the past by Peter Singer and others. Still, they appear to be at least an order of magnitude more effective at prevventing harm from blindness as do guide dogs. See also Sullivan, 2013 on the cost of training a guide dog.↩︎
These numbers were gathered quickly for a timely answer. While the sources seem reputable, they should be examined more carefully.↩︎
We need to doublecheck this source. This is also consistent with a rough extrapolation from Meer and Priday (2020) appendix table 4↩︎
This is a placeholder: we should present and discuss the numbers and arguments more carefully here.↩︎
There has also been some unpublished or non-academic work: ‘Behavior and Charitable Giving’ (Ideas42, 2016), ‘Charitable Fundraising And Smart Giving’ (Gertler, 2015), and ‘The Psychology of Effective Altruism’ (Miller, 2016, slides only).↩︎
Greenhalgh and Montgomery (2020) performed ‘a systematic review of the barriers to and facilitators of the use of evidence by philanthropists when determining which charities (including health charities or programmes) to funds’ using PRISMA guidelines.↩︎
Mainly a series of MTurk experiments (and some with ‘effective altruists’ recruited through the EA Newsletter/Facebook) involving hypothetical donation choices, but also includes some literature review and conceptual breakdown↩︎
We (aim to) return to making some related comparisons, comparing our ‘breakdown of barriers’ in a later chapter to that of other papers.↩︎
Consider: The measures used are relevant to how we consider issues such as charity ‘quality ratings’ and ‘overhead aversion.’↩︎
Thanks to Jason Schukraft for bringing up this point. He noted “There’s a small pool of donors who deny that GiveWell has identified the most effective global poverty/health charities because (e.g.) GiveWell is too focused on”randomista” interventions and doesn’t give enough weight to “systematic” interventions.”↩︎
As noted, there are disagreements over how and whether we should trade off among these outcomes. Issues such as population ethics, and the importance of sentience and experience — come to the fore. We will ignore these for now.↩︎
This is obviously an oversimplification. To achieve the beneficial outcome the charity will require many intermediate inputs (or “outputs” as noted above), including ‘management’ and ‘careful targetting of programs’. Some charities may be able to acquire these inputs at better prices than others, and some may also use a more efficient mix of inputs.↩︎
See this talk/article from Owen Cotton-Barratt for more on some key economic effective altruist concepts, including heavy tailed distributions, diminishing marginal returns and comparative advantage↩︎
Economists love when we can say that something is officially a Puzzle. It is an achievement in itself to be the researcher who first discovered a Puzzle, even if we have no clue how to resolve it.↩︎
I return to this discussion in section @ref(subst-framework)↩︎