Elie Hassenfeld
About
Co-founder and CEO of GiveWell
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Claims by Elie Hassenfeld (20 of 29)
Early over-enthusiasm for data was a mistake
GiveWell's early recommendation of PSI rested on taking the organization's self-reported distribution numbers at face value—numbers that were true but not rigorously gathered—reflecting an over-enthusiasm for quantification as the path to the best answer, a mistake that pushed GiveWell toward critical evaluation of data rather than data alone.
Room for More Funding guards against oversupply
GiveWell takes seriously 'Room for More Funding'—how much money an organization can use well—and would not put an organization on its list that could only absorb $100,000 well if listing it might channel $10 million, tracking this over time and reducing support when organizations have already received large funding.
Change Our Mind contest surfaced errors and opacity
GiveWell's 'Change Our Mind' contest, offering prizes to people who found errors in its public analysis, drew more than 50 high-quality submissions including from prestigious academics, surfaced small errors across programs, and—most importantly—revealed GiveWell had been insufficiently transparent about the uncertainty inherent in its analysis.
No Lean Season: scaling failure and premature exit
GiveWell's funding of No Lean Season—a program incentivizing seasonal migration in Bangladesh based on Mobarak's small RCT—failed at scale because the small, hands-on model became harder to deliver through large microfinance institutions, and GiveWell made two mistakes: underestimating the difficulty of scaling and exiting too quickly rather than committing to a longer, iterative timeframe.
GiveWell cares about counterfactual causal impact
GiveWell cares not about what its literal dollars accomplish but about the causal impact of its work on the world; if its donation merely displaces another donor's gift to the same charity, nothing has changed, so GiveWell applies a 'Fungibility Adjustment' estimating how likely its giving is to displace others' funds and what those others would have done.
GiveWell treats root causes of poverty as unsolved
GiveWell would gladly fund programs that address the root cause of poverty if it knew how, but since it doesn't know how to alleviate the cause, it directs funds to alleviating symptoms where impact can be measured—acknowledging it would be better to fix causes than symptoms.
Mortality programs beat poverty programs on impact
When GiveWell compared poverty-reducing programs like direct cash transfers against childhood-mortality-averting programs, it generally found the mortality-averting programs offered more impact per dollar, though this rests on contestable philosophical judgments about trading off across types of good.
GiveWell's niche is empirical learnability
GiveWell deliberately directs the vast majority of funds to interventions where it can learn empirically whether it was right or wrong and improve, creating feedback loops; this leads it to avoid high-risk opportunities like funding African think tanks for pro-growth policy, not because such bets are bad but because measurable learning is GiveWell's distinctive competence.
Track record can justify funding without quantified estimate
GiveWell sometimes supports a person or program based on a strong track record—a 'good bet'—even without a quantified estimate of the good that will result, as when it funded Mushfiq Mobarak's Y-RISE center studying the science of scaling programs.
RCT results may not transfer to scale
Cost-effectiveness estimates must be qualified because randomized controlled trials are conducted under tightly monitored conditions (e.g., researchers checking daily that nets are used) and at higher baseline mortality and poverty levels than today's national-scale distributions, raising the question of how valid old study results are to current real-world deployment.
Charities lacked usable impact data in 2006
When Hassenfeld and Karnofsky sought information on what charities accomplish per dollar given, they found that detailed, useful data on charitable impact simply did not exist, with charities unable to justify claims like '$20 provides a child water for life.'
Most poor-country deaths occur in early childhood
A huge proportion of deaths in low-income countries occur in the first month, year, and five years of life; once people survive those early years, they tend to live relatively long lives, which justifies focusing on averting childhood mortality rather than treating it as merely delaying death from other causes.
Disease reduction has underappreciated developmental benefits
Reducing serious childhood illness likely yields better outcomes for the child through healthier early development plus benefits to family and community, but these effects play little role in GiveWell's actual benefit calculations even though they are important considerations.
Funding threshold set by money available and ranked opportunities
GiveWell sets its funding threshold by ranking all possible programs by cost-effectiveness measured in multiples of direct cash transfers (where giving a poor person $1,000 equals a value of 1) and funding down the ranked list as far as available money allows—currently to a multiple of 10—so the bar that top charities must clear depends on how much money GiveWell has raised.
Qualitative factors break ties near the threshold
When a program's quantified cost-effectiveness number lands near the threshold (e.g., 7 or 13 versus 10), GiveWell relies on qualitative considerations: unmodeled upside (supporting a young person/organization that could grow), confidence the organization will be transparent about what's working and failing, and the track record of the people and organization—pushing borderline decisions up or down.
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