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GiveWell Incubation Grants have become an increasingly substantial part of our work, and our impression is that not everyone who follows GiveWell is familiar with this program. This blog post is intended to (a) briefly explain and outline our main goals and expectations for this work, and (b) share some updates on promising organizations that have been supported by Incubation Grants.
The goal of GiveWell Incubation Grants (previously known as GiveWell’s experimental work) is to support the development of future top charities and improve our understanding of our current top charities. We plan to do this in a few ways (not an exclusive list):
- Increasing the body of evidence around potential top charities and priority programs;
- Providing early-stage support for new organizations;
- Supporting improved monitoring and evaluation for potential or current top charities.
Good Ventures, a foundation with which we work closely, has funded the grants made as part of this work, which are listed here.
Due to the nature of this support—early-stage funding, intended to allow an organization to develop a stronger track record or to collect more evidence on a promising program—we don’t expect Incubation Grants to produce new top charities over very short time horizons. We expect there will be, in many cases, a period of multiple years between a grant and an organization or intervention being considered a potential top charity or priority program.
This post highlights grants that we don’t expect to lead to top charities before 2018. It should provide a reasonable overview of the type of grants we’re excited to recommend as part of this work. Future posts will highlight the organizations we’re closely tracking as potential 2017 top charities (No Lean Season and Zusha!).
This post will discuss Incubation Grants to:
- New Incentives
- Results for Development (R4D)
- Charity Science: Health
- Mindset engagement for cash transfers
- Incentives for immunization studies
IDinsight supports and conducts rigorous evaluations of development interventions with an explicit focus on providing useful data to inform funders and policymakers. Good Ventures made a $1.985 million grant to IDinsight for general support in June 2016 as part of GiveWell Incubation Grants.
In conversations with our network, we’ve often heard that IDinsight fills a unique gap in the development sector. There are other organizations that conduct research and advocate for evidence-based decision-making, but our impression is that IDinsight is currently the one most focused on research whose primary goal is to help decision-makers with specific decisions (in contrast to e.g. academic merit). We have seen some indications of other organizations moving in a similar direction, however. We hope that this grant allows IDinsight to grow its staff and take on more projects. IDinsight’s work has the potential to inform GiveWell’s list of top charities by increasing the body of evidence around potential priority programs and improving available monitoring and evaluation information around specific organizations.
Recently, Good Ventures made an additional grant to IDinsight to support an “embedded IDinsight team” for GiveWell top charities, i.e., a small group of IDinsight staff explicitly focused on supporting the creation of high-quality monitoring and evidence for current and future GiveWell top charities. For example, IDinsight may work with New Incentives to run an impact study, and possibly a randomized controlled trial (RCT), on its pilot program to incentivize immunization. Another possible project for the embedded team is conducting monitoring and evaluation of cataract surgery programs, which could improve our understanding of the efficacy of the program and whether we should recommend charities that work on it. Additional possible projects for the IDinsight embedded team are discussed here.
We don’t expect a new GiveWell top charity to originate from this work in 2017, but hope that it will inform our future recommendations.
We made three Incubation Grants to New Incentives for its conditional cash transfer program aimed at preventing mother-to-child transmission (PMTCT) of HIV and encouraging pregnant women to deliver in health facilities (e.g., rather than at home). We decided not to recommend New Incentives’ PMTCT and facility delivery program as a 2016 top charity due to insufficient evidence supporting the program, although we were impressed by the organization’s staff. We wrote about this decision at length in this blog post.
With our encouragement, New Incentives shifted its focus to a new program, conditional cash transfers to incentivize immunizations in Nigeria. We’re planning to follow its work on this program as a potential future top charity, although we do not consider it likely to become a GiveWell-recommended charity in 2017.
Results for Development (R4D)
Pneumonia is one of the leading killers of children worldwide, and our impression is that there is no dedicated funding stream for its treatment (as there is for other major diseases like AIDS, tuberculosis, and malaria). R4D is implementing a program to increase use of amoxicillin, the World Health Organization-recommended first-line treatment, to treat childhood pneumonia in Tanzania. In May 2016, Good Ventures provided $6.4 million to support this program as part of GiveWell Incubation Grants.
We have a positive view of R4D as an organization: its staff, evidence-driven approach, and transparency. We also believe that the use of amoxicillin to treat childhood pneumonia could be competitive with our current priority programs. Our key question around this program as a possible GiveWell top charity is monitoring and evaluation. We’re unsure whether R4D’s monitoring will lead us to feel confident that children sick with pneumonia actually receive treatment. This is due to the complex nature of the intervention, which may make it more challenging to collect high-quality monitoring data comparable with that of our current top charities.
We currently expect that R4D will have the data available to potentially qualify as a top charity in 2018 or 2019 and we hope to evaluate it then.
Charity Science: Health
Charity Science: Health was founded by members of the effective altruism community with the explicit goal of creating a GiveWell top charity. Charity Science: Health plans to send SMS text reminders for vaccinations due to the strong evidence base they see for this program in increasing immunization rates. Good Ventures made a grant of $200,000 to support the first year of the organization’s work in India.
Because we have not yet vetted the relevant evidence closely, we remain unsure about whether we would recommend SMS reminders as a priority program. Charity Science: Health has been transparent and communicative with us, and we expect to learn from its work. Charity Science: Health is also a young organization with a very short track record, and we don’t anticipate evaluating it as a top charity until 2018 or 2019.
Mindset engagement for cash transfers
GiveDirectly, one of GiveWell’s top charities, provides unconditional cash transfers to very poor individuals in East Africa. In May 2016, Good Ventures made a $350,000 grant to Innovations for Poverty Action to support an RCT—in collaboration with GiveDirectly—testing whether “mindset engagement” approaches to cash transfers, such as watching an inspirational film or meeting with a counselor, affects the outcomes for cash transfer recipients by changing the framing of the transfer and thus how it is spent. The approaches are aimed at encouraging recipients to use the transfers to pursue their goals by increasing their sense of self-efficacy and understanding of their opportunities, which—according to the researchers’ theory—may have been adversely impacted by time spent in poverty. This study could influence the work of one of our current top charities (GiveDirectly) or our understanding of cash transfers as a priority program.
Incentives for immunization studies
In 2015, Good Ventures made two $100,000 grants to support further study of whether providing incentives for immunization could increase vaccination rates. These grants were made as part of our work to grow the body of evidence around promising programs that could become potential GiveWell priority programs.
The Incubation Grants were made to the Abdul Latif Jameel Poverty Action Lab (J-PAL) at the Massachusetts Institute of Technology and Interactive Research and Development (IRD) to support high-quality replications of a promising study on the impact of providing non-cash incentives, such as grocery vouchers, for parents to vaccinate their children. The replication studies are being conducted in India and Pakistan.
We are unsure when the results of these studies will be available.
Other work to support potential future top charities
Evidence Action, the parent organization of GiveWell top charity Deworm the World Initiative as well as No Lean Season, a GiveWell Incubation Grant recipient, recently announced a call for results of RCTs and other rigorous empirical studies that demonstrated a positive impact of an intervention benefiting poor households, and is planning to fund 3-6 of these proposals for further research. We’re excited to see this announcement and expect the results may further our understanding of potential GiveWell priority programs.
Full list of GiveWell Incubation Grants
A full list of grants we’ve recommended is available at www.givewell.org/research/incubation-grants.
If you know of a strong proposal for a potential GiveWell Incubation Grant, please email firstname.lastname@example.org. We’d be particularly interested in new groups that work on promising programs for which we have not found charity implementers.Notes
 In December, we recommended a grant of $900,000 to Zusha! to scale up its road-safety programs. This grant write-up is not yet public, but notes from our initial conversations with Zusha! are available here and here.
My last post explains why I largely trust the most famous school-based deworming experiment, the report in Worms at Work about its long-term benefits. That post also gives background on the deworming debate, so please read it first. In this post, I’ll talk about the problem of generalization. If deworming in southern Busia County, Kenya, in the late 1990s permanently improved the lives of some children, what does that tell us about the impact of deworming programs today, from sub-Saharan Africa to South Asia? How safely can we generalize from this study?
I’ll take up three specific challenges to its generalizability:
- That a larger evidence base appears to show little short-term benefit from mass deworming—and if it doesn’t help much in the short run, how can it make a big difference in the long run?
- That where mass deworming is done today, typically fewer children need treatment than in the Busia experiment.
- That impact heterogeneity within the Busia sample—the same treatment bringing different results for different children—might undercut expectations of benefits beyond. For example, if examination of the Busia data revealed long-term gains only among children with schistosomiasis, that would devalue treatment for the other three parasites tracked.
In my view, none of the specific challenges I’ll consider knocks Worms at Work off its GiveWell-constructed pedestal. GiveWell’s approach to evaluating mass deworming charities starts with the long-term earnings impacts estimated in Worms at Work. Then it discounts by roughly a factor of ten for lower worm burdens in other places, and by another factor of ten out of more subjective conservatism. As in the previous post, I conclude that the GiveWell approach is reasonable.
But if I parry specific criticisms, I don’t dispel a more general one. Ideally, we wouldn’t be relying on just one study to judge a cause, no matter how compelling the study or how conservative our extrapolation therefrom. Nonprofits and governments are spending tens of millions per year on mass deworming. More research on whether and where the intervention is especially beneficial would cost only a small fraction of all those deworming campaigns, yet potentially multiply their value.
Unfortunately, the benefits that dominate our cost-effectiveness calculations manifest over the long run, as treated children grow up. And long-term research tends to take a long time. So I close by suggesting two strategies that might improve our knowledge more quickly.
Here are Stata files for the uantitative assertions and graphs presented below.Evidence suggests short-term benefits are modest
Researchers have performed several systematic reviews of the evidence on the impacts of deworming treatment. In my research, I focused on three of those reviews. Two come from institutions dedicated to producing such surveys, and find that mass deworming brings little benefit, most emphatically in the short run. But the third comes to a more optimistic answer.
The three are:
- The Cochrane review of 2015, which covers 45 trials of the drug albendazole for soil-transmitted worms (geohelminths). It concludes: “Treating children known to have worm infection may have some nutritional benefits for the individual. However, in mass treatment of all children in endemic areas, there is now substantial evidence that this does not improve average nutritional status, haemoglobin, cognition, school performance, or survival.”
- The Campbell review of 2016, which extends to 56 randomized short-term studies, in part by adding trials of praziquantel for water-transmitted schistosomiasis. “Mass deworming for soil-transmitted helminths …had little effect. For schistosomiasis, mass deworming might be effective for weight but is probably ineffective for height, cognition, and attendance.”
- The working paper by Kevin Croke, Eric Hsu, and authors of Worms at Work. The paper looks at impacts only on weight, as an indicator of recent nutrition. (Weight responds more quickly to nutrition than height.) While the paper lacks the elaborate, formal protocols of the Cochrane and Campbell reviews, it adds value in extracting more information from available studies in order to sharpen the impact estimates. It finds: “The average effect on child weight is 0.134 kg.”
Before confronting the contradiction between the first two reviews and the third, I will show you a style of reasoning in all of them. The figure below constitutes part of the Campbell review’s analysis of the impact of mass administration of albendazole (for soil-transmitted worms) on children’s weight (adapted from Figure 6 in the initial version):
Each row distills results from one experiment; the “Total” row at the bottom draws the results together. The first row, for instance, is read as follows. During a randomized trial in Uganda run by Harold Alderman and coauthors, the 14,940 children in the treatment group gained an average 2.413 kilograms while the 13,055 control kids gained 2.259 kg, for a difference in favor of the treatment group of 0.154 kg. For comparability with other studies, which report progress on weight in other ways, the difference is then re-expressed as 0.02 standard deviations, where a standard deviation is computed as a sort of average of the 7.42 and 8.01 kg figures shown for the treatment and control groups. The 95% confidence range surrounding the estimate of 0.02 is written as [–0.00, 0.04] and is in principle graphed as a horizontal black line to the right, but is too short to show up. Because of its large sample, the Alderman study receives more weight (in the statistical sense) than any other in the figure, at 21.6% of the overall number. The relatively large green square in the upper right signifies this influence.
In the lower-right of the figure, the bolded numbers and the black diamond present the meta-analytical bottom line: across these 13 trials, mass deworming increased weight by an average 0.05 standard deviations. The aggregate 95% confidence interval stretches from –0.02 to 0.11, just bracketing zero. The final version of the Campbell report expresses the result in physical units: an average gain of 0.09 kg, with a 95% confidence interval stretching from –0.09 kg to +0.28 kg. And so it concludes: “Mass deworming for soil-transmitted helminths with albendazole twice per year compared with controls probably leads to little to no improvement in weight over a period of about 12 months.”
Applying similar methods to a similar pool of studies, the Cochrane review (Analysis 4.1) produces similar numbers: an average weight gain of 0.08 kg, with a 95% confidence interval of –0.11 to 0.27. This it expresses as “For weight, overall there was no evidence of an effect.”
But Croke et al. incorporate more studies, as well as more data from the available studies, and obtain an average weight gain of 0.134 kg (95% confidence interval: 0.03 to 0.24), which they take as evidence of impact.
How do we reconcile the contradiction between Croke et al. and the other two? We don’t. For no reconciliation is needed, as is made obvious by this depiction of the three estimates of the impact of mass treatment for soil-transmitted worms on children’s weight:
Each band depicts one of the confidence intervals I just cited. The varied shading reminds us that within each band, confidence is highest near the center. The bands greatly overlap, meaning that the three reviews hardly disagree. Switching from graphs to numerical calculations, I find that the Cochrane results reject the central Croke et al. estimate of 0.134 kg at p = 0.58 (two-tailed Z-test), which is to say, they do not reject with any strength. For Croke et al. vs. Campbell, p = 0.64. So the Croke et al. estimate does not contradict the others; it is merely more precise. The three reviews are best seen as converging to a central impact estimate of about 0.1 kg of weight gain. Certainly 0.1 kg fits the evidence better than 0.0 kg.
If wide confidence intervals in the Cochrane and Campbell reviews are obscuring real impact on weight, perhaps the same happening with other outcomes, including height, hemoglobin, cognition, and mortality. Discouragingly, when I scan the Cochrane review’s “Summary of findings for the main comparison” and Campbell’s corresponding tables, confidence intervals for outcomes other than weight look more firmly centered on zero. That in turn raises the worry that by looking only at weight, Croke et al. make a selective case on behalf of deworming.
On the other hand, when we shift our attention from trials of mass deworming to trials restricted to children known to be infected—which have more power to detect impacts—it becomes clear that the boost to weight is not a one-off. The Cochrane review estimates that targeting treatment at kids with soil-transmitted worms increased weight by 0.75 kilograms, height by 0.25 centimeters, mid-upper arm circumference by 0.49 centimeters, and triceps skin fold thickness by 1.34 millimeters, all significant at p = 0.05. Treatment did not, however, increase hemoglobin (Cochrane review, “Data and Analyses,” Comparison 1).
In this light, the simplest theory that is compatible with the evidence arrayed so far is that deworming does improve nutrition in infected children while leaving uninfected children unchanged; and that available studies of mass deworming tend to lack the statistical power to detect the diluted benefits of mass deworming, even when combined in a (random effects) meta-analysis. The compatibility of that theory with the evidence, by the way, exposes a logical fallacy in the Cochrane authors’ conclusion that “there is now substantial evidence” that mass treatment has zero effect on the outcomes of interest. Lack of compelling evidence is not compelling evidence of lack.
Yet the Cochrane authors might be right in spirit. If the benefit of mass deworming is almost too small to detect, it might be almost too small to matter. Return to the case of weight: is ~0.1 kg a lot? Croke et al. contend that it is. They point out that “only between 2 and 16 percent of the population experience moderate to severe intensity infections in the studies in our sample that report this information,” so their central estimate of 0.134 could indicate, say, a tenth of children gaining 1.34 kg (3 pounds). This would cohere with Cochrane’s finding of an average 0.75 kilogram gain in trials that targeted infected children. In a separate line of argument, Croke et al. calculate that even at 0.134, deworming more cost-effectively raises children’s weight than school feeding programs do.
But neither defense gets at what matters most for GiveWell, which is whether small short-term benefits make big long-term earnings gains implausible. Is 0.134 kg in weight gain compatible with 15% income gain 10 years later reported in Worms at Work?
More so than it may at first appear, once we take account of two discrepancies embedded in that comparison. First, more kids had worms in Busia. I calculate that 27% of children in the Worms sample had moderate or serious infections, going by World Health Organization (WHO) guidelines, which can be viewed conservatively as double the 2–16% Croke et al. cite as average for the kids behind that 0.134 kg number. So in a Worms-like setting, we should expect twice as many children to have benefited, doubling the average weight gain from 0.134 to 0.268 kg. Second, at 13.25 years, the Worms children were far older than most of the children in the studies surveyed by Croke et al. Subjects averaged 9 months of age in the Awasthi 2001 study, 12–18 months in Joseph 2015, 24 months in Ndibazza 2012, 36 months in Willett 1979, and 2–5 years in Sur 2005. 0.268 kg means more for such small people. As Croke et al. point out, an additional 0.268 kg nearly suffices to lift a toddler from the 25th to the 50th percentile for weight gain between months 18 and 24 of life (girls, boys).
In sum, the statistical consensus on short-term impacts on nutritional status does not render implausible the long-term benefits reported out of Busia. The verdict of Garner, Taylor-Robinson, and Sachdev—“no effect for the main biomedical outcomes…, making the broader societal benefits on economic development barely credible”—overreaches.In many places, fewer kids have worms than in Busia in 1998–99
If we accept the long-term impact estimates from Worms at Work, we can still question whether those results carry over to other settings. This is precisely why GiveWell deflates the earnings impact by two orders of magnitude in estimating the cost-effectiveness of deworming charities. One of those orders of magnitude arises from the fact that school-age children in Busia carried especially heavy parasite loads. Where loads are lighter, mass deworming will probably do less good. (The other order of magnitude reflects a more subjective worry that if Worms at Work were replicated in other places with similar parasite loads, it would fail to show any benefits there, a theme to which I will return at the end.)
GiveWell’s cost-effectiveness spreadsheet does adjust for difference in worm loads between Worms and places where recommended charities support mass deworming today. So I spent some time scrutinizing this discount—more precisely, the discounts of individual GiveWell staffers. I worried in particular that the ways we measure worm loads could lead my colleagues to overestimate the need for and benefit from mass deworming.
As a starting point, I selected a few data points from one of the metrics GiveWell has gathered, the fraction of kids who test positive for worms. This table shows the prevalence of worm infection, by type, in Busia, 1998–99, before treatment, and in program areas of two GiveWell-recommended charities:
The first row, computed from the public Worms data set, reports that before receiving any treatment from the experiment, 81% of tested children in Busia were positive for hookworm, 51% for roundworm, 62% for whipworm, and 36% for schistosomiasis. 94% tested positive for at least one of those parasites. On average, each child carried 2.3 distinct types of worm. Then, from the GiveWell cost-effectiveness spreadsheet, come corresponding numbers for areas served by programs linked to the Schistosomiasis Control Initiative (SCI) and Deworm the World. Though approximate, the numbers suffice to demonstrate that far fewer children served by these charities have worms than in the Worms experiment. For example, the hookworm rate for Deworm the World is estimated at 24%, which is 30% of the rate of Busia in 1998–99. Facing less need, we should expect these charities’ activities to do less good than is found in Worms at Work.
But that comparison would misrepresent the value of deworming today if the proportion of serious infections is even lower today relative to Busia. To get at the possibility, I made a second table for the other indicator available to GiveWell, which is the intensity of infection, measured in eggs per gram of stool:
Indeed, this comparison widens the apparent gap between Busia of 1998–99 and charities of today. For example, hookworm prevalence in Deworm the World service areas was 30% of the Busia rate (24 vs. 81 out of every 100 of kids), while intensity was only 20% (115 vs. 568 eggs/gram).
After viewing these sorts of numbers, the median GiveWell staffer multiplies the Worms at Work impact estimate by 14%—that is, divides it by seven. In aggregate, I think my coworkers blend the discounts implied by the prevalence and intensity perspectives.
To determine the best discount, we’d need to know precisely what characterized the children within the Worms experiment who most benefited over the long term—perhaps lower weight, or greater infection with a particular parasite species. As I will discuss below, such insight is easier imagined than attained. Then, if we had it, we would need to know the number of children in today’s deworming program areas with similar profiles. Obtaining that data could be a tall order in itself.
To think more systematically about how to discount for differences in worm loads, within the limits of the evidence, I looked to some recent research that models how deworming affects parasite populations. Nathan Lo and Jason Andrews led the work (2015, 2016). With Lo’s help, I copied their approach in order to estimate how the prevalence of serious infection varies with the two indicators at GiveWell’s fingertips.
For my purposes, the approach introduces two key ideas. First, data gathered from many locales shows how, for each worm type, the average intensity of infection tends to rise as prevalence increases. Not surprisingly, where worm infection is more common, average severity tends to be higher too—and Lo and colleagues estimate how much so. Second is the use a particular mathematical family of curves to represent the distribution of children by intensity levels—how many have no infection, how many have 1-100 eggs/gram, how many are above 100 eggs/gram, etc. (The family, the negative binomial, is an accepted model for the prevalence of infectious diseases.) If we know two things about the pattern of infection, such as the fraction of kids who have it and their average intensity, we can mathematically identify a unique member of the family. And once a member is chosen, we can estimate the share of children with, for example, hookworm infections exceeding 2,000 eggs/gram, which is the WHO’s suggested minimum for moderate or heavy infection.
The next two graphs examine how, under these modeling assumptions, the fraction of children with moderate/heavy infections varies in tandem with the two indicators at GiveWell’s disposal, which are prevalence of infection and average infection intensity:
The important thing to notice is that the curves are much curvier in the first graph. There, for example, as the orange hookworm curve descends, it converges to the left edge just below 40%. This suggests that if a community has half as many kids with hookworm as in Busia—40% instead of about 80%—then it could have far less than half as many kids with serious infections—indeed, almost none. But the straighter lines in the second graph mean that a 50% drop in intensity (eggs/gram) corresponds to a 50% drop in the number of children with serious disease.
While we don’t know exactly what defines a serious infection, in the sense of offering hope that treatment could permanently lift children’s trajectories, these simulations imply that it is reasonable for GiveWell to extrapolate from Worms at Work on the basis of intensity (eggs/gram).
Returning to the intensity table above, I find that the Deworm the World egg counts, by worm type, average 16% of those in Busia. For the Schistosomiasis Control Initiative, the average ratio is 7% (and is 6% just for SCI’s namesake disease). These numbers say—as far as this sort of analysis can take us—that GiveWell’s 14% discounts are about right for Deworm the World, and perhaps ought to be halved for SCI. Halving is not as big a big change as it may seem; GiveWell has no illusions about the precision of its estimates, and performs them only to sense the order of magnitude of expected impact.Impact heterogeneity in the Worms experiment
Having confronted two challenges to the generalizability of Worms at Work—that short-term non-impacts make long-term impacts implausible, and that worm loads are lower in most places today than they were in Busia in 1998–99—I turned to one more. Might there be patterns within the Worms at Works data that would douse hopes for impact beyond? For example, if only children with schistosomiasis experienced those big benefits, that would call into question the value of treating geohelminths (hookworm, roundworm, whipworm).
Returning to the Worms at Work data, I searched for—and perhaps found—signs of heterogeneity in impact. I gained two insights thereby. The first, as it happens, is more evidence that is easier-explained if we assume that the Worms experiment largely worked, the theme of the last post. The second is a keener sense that there is no such thing as the “the” impact of an intervention, since it varies by person, time, and place. That heightened my nervousness about extrapolating from a single study. Beyond that general concern, I did not find specific evidence that would explicitly cast grave doubt on whole deworming campaigns.
My hunt for heterogeneity went through two phases. In the first, motivated by a particular theory, I brought a narrow set of hypotheses to the data. In the second, I threw about 20 hypotheses at the data and watched what stuck: Did impact vary by sex or age? By proximity to Lake Victoria, where live the snails that carry Schistosoma mansoni? As statisticians put it, I mined the data. The problem with that is that since I tested about 20 hypotheses, I should expect about one to manifest as statistically significant just by chance (at p = 0.05). So the pattern I unearthed in the second phase should perhaps not be viewed as proof of anything, but as the basis for a hypothesis that, for a proper test, requires fresh data from another setting.Introducing elevation
My search began this way. In my previous post, I entertained an alternative theory for Owen Ozier‘s finding that deworming indirectly benefited babies born right around the time of the original Worms experiment. Maybe, I thought, the 1997–98 El Nino, which brought heavy flooding to Kenya, exacerbated the conditions for the spread of worms, especially at low elevations. And perhaps by chance the treatment schools were situated disproportionately at high elevations, so their kids fared better. This could explain all the results in Worms and its follow-ups, including Ozier’s paper. But the second link in that theory proved weak, especially when defining the treatment group as groups 1 and 2 together, as done in Worms at Work. (Group 1 received treatment starting in 1998, group 2 in 1999, and group 3 in 2001, after the experiment ended.) Average elevation was essentially indistinguishable between the Worms at Work treatment and control groups.
Nevertheless, my investigation of the first link in the theory led me to some interesting discoveries. To start, I directly tested the hypothesis that elevation mattered for impact by “interacting” elevation with the treatment indicator in a key Worms at Work regression. In the original regression, deworming is found to increase the logarithm of wage earnings by 0.269, meaning that deworming increased wage earnings by 30.8%. In the modified regression, the impact could vary with elevation in a straight-line way, as shown in this graph of the impact of deworming in childhood on log wage earnings in early adulthood as a function of school elevation:
The grey bands around the central line show confidence intervals rather as in the earlier graph on weight gains. The black dots along the bottom show the distribution of schools by elevation.
I was struck to find the impact confined to low schools. Yet it could be explained. Low schools are closer to Lake Victoria and the rivers that feed it; and their children therefore were more afflicted by schistosomiasis. In addition, geohelminths (soil-transmitted worms) might have spread more easily in the low, flat lands, especially after El Nino–driven floods. So lower schools may have had higher worm loads.
To fit the data more flexibly, I estimated the relationship semi-parametrically, with locally weighted regressions. This involved analyzing whether among schools around 1140 meters, deworming raised wages; then the same around 1150 meters, and so on. That produced this Lowess-smoothed graph of the impact of deworming on log wage earnings:
This version suggests that the big earnings impact occurred in schools below about 1180 meters, and possibly among schools at around 1250. (For legibility, I truncated the fit at 1270 meters; beyond which the confidence intervals explode for lack of much data.)
Motivated by the theory that elevation mattered for impact because of differences in pre-experiment infection rates, I then graphed how those infections varied with elevation, among the subset of schools with the needed data. Miguel and Kremer measure worm burdens in three ways: prevalence of any infection, prevalence of moderate or heavy infection, and intensity (eggs/gram). So I did as well. First, this graph shows infection prevalence versus school elevation, again in a locally smoothed way:
Like the first table in this post, this graph shows that hookworms lived in nearly all the children, while roundworm and whipworm were each in about half. Not evident before is that schistosomiasis was common at low elevations, but faded higher up. Roundworm and whipworm also appear to fall as one scans from left to right, but then rebound around 1260 meters.
The next graph is the same except that it only counts infections that are moderate or heavy according to WHO definitions:
Interestingly, restricting to serious cases enhances the similarity between the infection curves, just above, and the earlier semi-parametric graph of earnings impact versus elevation. The “Total” curve starts high, declines until 1200 meters or so, then peaks again around 1260. Last, I graphed Miguel and Kremer’s third measure of worm burden, intensity, against elevation. Those images resemble the graph above, and I relegate them to a footnote for concision.
These elevation-stratified plots teach three lessons. First, the similarity between the prevalence contours and the earnings impact contour shown earlier—high at the low elevations and then again around 1260 meters—constitutes circumstantial evidence for a sensible theory: children with the greatest worm burdens benefited most from treatment. Second, that measuring worm load to reflect intensity—moving to the graph just above from the one before—strengthens this resemblance and reinforces the notion of extrapolating from Worms at Work on the basis of intensity (average eggs/gram, not how many kids have any infection).
Finally, these patterns buttress the conclusion of my last post, that the Worms experiment mostly worked. If we grant that deworming probably boosted long-term earnings of children in Busia, then it becomes unsurprising that it did so more where children had more worms. But if we doubt the Worms experiments, then these results become more coincidental. For example, if we hypothesize that flawed randomization put schools whose children were destined to earn more in adulthood disproportionately in the treatment group, then we need another story to explain why this asymmetry only occurred among the schools with the heaviest worm loads. And all else equal, per Occam’s razor, more-complicated theories are less credible.
As I say, the evidence is circumstantial: two quantities of primary interest—initial worm burden and subsequent impact—relate to elevation in about the same way. Unfortunately, it is almost impossible to directly assess the relationship between those two quantities, to ask whether impact covaried with need. The Worms team did not test kids until their schools were about to receive deworming treatment “since it was not considered ethical to collect detailed health information from pupils who were not scheduled to receive medical treatment in that year.” My infection graphs are based on data collected at treatment-group schools only, just before they began receiving deworming in 1998 or 1999. Absent test results for control-group kids, I can’t run the needed comparison.
Contemplating the exploration to this point, I was struck to appreciate that while elevation might not directly matter for the impacts of deworming, like a saw through a log, introducing it exposed the grain of the data. It gave me insight into a relationship that I could not access directly, between initial worm load and subsequent benefit.Mining in space
After I confronted the impossibility of directly testing whether initial worm burden influenced impact, I thought of one more angle from which to attack the question, if obliquely. This led me, unplanned, to explore the data spatially.
As we saw, nearly all children had geohelminths. So all schools were put on albendazole, whether during the experiment (for treatment groups) or after (control group). In addition, the pervasiveness of schistosomiasis in some areas called for a second drug, praziquantel. I sought to check whether the experiment raised earnings more for children in those areas. Such a finding could be read to say that schistosomiasis is an especially damaging parasite, making treatment for it especially valuable. Or, since the low-elevation schistosomiasis schools tended to have the highest overall worm burdens, it could be taken as a sign that higher parasite loads in general lead to higher benefit from deworming.
Performing the check first required some educated guess work. The Worms data set documents which of the 50 schools in the treatment groups needed and received praziquantel, but not which of the 25 control group schools would have needed it in 1998–99. To fill in these blanks, I mapped the schools by treatment group and praziquantel status. Group 1 schools, treated starting in 1998, are green. Group 2 schools, treated starting in 1999, are yellow. And group 3 (schools not treated till 2001) are red. The white 0’s and 1’s next to the group 1 and 2 markers show which were deemed to need praziquantel, with 1 indicating need:
Most of the 1’s appear in the southern delta and along the shore of Lake Victoria. By eyeballing the map, I could largely determine which group 3 schools also needed praziquantel. For example, those in the delta to the extreme southwest probably needed it since all their neighbors did. I was least certain about the pair to the southeast, which lived in a mixed neighborhood, as it were; I arbitrarily marked one for praziquantel and one not.
Returning to the Worms at Work wage earnings regression and interacting treatment with this new dummy for praziquantel need revealed no difference in impact between schools where only albendazole was deemed needed and given, and schools where both drugs were needed and given:
Evidently, treatment for geohelminths and schistosomiasis, where both were needed, did not help future earnings much more or less than treatment for geohelminths, where only that was warranted. So the comparison generates no strong distinction between the worm types.
After I mapped the schools, it hit me: I could make two-dimensional versions of my earlier graphs, slicing the data not by elevation, but by longitude and latitude.
To start, I fed the elevations of the 75 schools, marked below with white dots, into my statistics software, Stata, and had it estimate the topography that best fit. This produced a depiction of the contours of the land in southern Busia County, with the brightest reds indicating the highest areas:
(Click image for a larger version.) I next graphed the impact of deworming on log wage earnings. Where before I ran the Worms at Work wage earnings regression centering on 1140 meters, then 1150, etc., now I ran the regression repeatedly across a grid, each time giving the most weight to the nearest schools :
Two valleys of low impact dimly emerge, one toward the Lake in the south, one in the north where schools are higher up. Possibly these two troughs are linked to the undulations in my earlier, elevation-stratified graphs.
Next, I made graphs like these for all 21 baseline variables that Worms checks for balance—such as fraction of students who are girls and average age. All the graphs are here. Now I wonder if this was a mistake. None of the graphs fit the one above like a key in lock, so I found myself staring at blobs and wondering which vaguely resembled the pattern I sought. I had no formal, pre-specified measure of fit, which increased uncertainty and discretion. Perhaps it was just a self-administered Rorschach test. Yet the data mining had the power to dilute any p values from subsequent formal tests.
In the end, one variable caught my eye when mapped, and then appeared to be an important mediator of impact when entered into the wage earnings regression. It is: a child’s initial weight-for-age Z-score (WAZ), which measures a child’s weight relative to his or her age peers. Here is the WAZ spatial plot side by side with the impact plot I just showed you. To my eye, where WAZ was high, subsequent impact was generally lower:
(Since most children in this sample fell below the reference median, their weight-to-age Z-scores were negative, so in here average WAZ ranges between –1.3 and about –1.5.)
Going back to two dimensions, this graph more directly checks the relationship I glimpsed above, by showing how the impact of deworming on wage earnings varied with children’s pre-treatment weight-to-age Z-score:
It appears that only children below –2, which is the standard definition of “underweight,” benefited enough from deworming treatment that it permanently lifted their developmental trajectories.
If the pattern is real, two dynamics could explain it. Children who were light for their age may have been so precisely because they carried more parasites, and were in deep need of treatment. Or perhaps other health problems made them small, which also rendered them less resilient to infection, and again more needful of treatment. The lack of baseline infection data for the control group prevents me from distinguishing between these theories.
Struck by this suggestion that low initial weight predicted impact, and mindful of the meta-analytic consensus that deworming affects weight, I doubled back to the original Worms study to ask a final question. Were any short-term weight gains in Busia concentrated among kids who started out the most underweight? This could link short-term impacts on weight with long-term impacts on earnings, making both more credible. I made this graph of the one-year impact of deworming treatment on weight-for-age Z-score versus weight-for-age Z-score before treatment (1998):
But there is a puzzling twist. While treatment raised weight among the most severely underweight children, it apparently reduced the weight of the heaviest children. (Bear in mind that in registering just above 0, the highest-WAZ children in Busia were merely surpassing 50th percentile in the global reference population.) Conceivably, certain worm infections cause weight gain, which is reversed by treatment; but here I am speculating. Statisticians might wonder if this graph reveals regression toward the mean. Just as the temperature must rise after the coldest day of the year and fall after the hottest, we could expect that the children who started the experiment the most underweight would become less so, and vice versa. But since the graph compares treatment and control schools, regression toward the mean only works as a theory if it occurred more in the treatment group. That would require a failure of randomization. The previous post argued that the imperfections in the Worms randomization were probably not driving the main results; but possibly they are playing a larger role in these second-order findings about heterogeneity of impact.
Because of these doubts, and because I checked many hypotheses before gravitating to weight-for-age as a mediator of impact, I am not confident that physical health was a good predictor of the long-run impact of deworming on earnings. I view the implications of the last two graphs—that deworming increased weight in the short run and earnings in the long run only among the worst-off children—merely as intriguing. As an indicator of heavy worm burden or poor general health, low weight may have predicted impact. That hypotheses ought to probed afresh in other data, this time with pre-registered transparency. The results from such replication could then sharpen our understanding of how to generalize from Worms at Work.
But I emphasize that my earlier findings revolving around elevation are more confident, because they came out of a small and theoretically motivated set of hypotheses. At elevations where worms were more prevalent, deworming did more long-term good.Conclusions
I glean these facts:
- Treatment of children known to carry worms improves their nutritional status, as measured by weight and height.
- Typically, a minority of children in today’s deworming settings are infected, so impacts from mass deworming are smaller and harder to detect.
- In meta-analyses, 95% confidence intervals for the impacts of mass deworming tend to contain zero.
- In the case of weight—which is among the best-studied outcomes and more likely to respond to treatment in the short run—Croke et al. improve the precision of meta-analysis. Their results are compatible with others’ estimates, yet make it appear unlikely that average short-term impact of mass deworming is zero or negative.
- Though the consensus estimate of about 0.1 kg for weight gain looks small, once one accounts for the youth and low infection rates of the children behind the number, it does not sit implausibly with the big long-term earnings benefit found in Worms at Work.
- Extrapolating the Worms at Work results to other settings in proportion to infection intensity (eggs/gram) looks reasonable. This will adjust for the likelihood that as prevalence of infection falls, prevalence of serious infection falls faster. Extrapolating this way might leave GiveWell’s cost-effectiveness rating for the Deworm the World unchanged while halving that for the Schistosomiasis Control Initiative (which is not a lot in calculations that already contain large margins of error).
- Within Busia, 1998–99, evidence suggests that the benefits of deworming were confined to children who were the worst off, e.g., who were more numerous at elevations with the most worm infections.
- To speak to the theme of the previous post, this hint of heterogeneity is harder to explain if we believe randomization failure caused the Worms at Work results.
- I did not find heterogeneity that could radically alter our appraisal of charities, such as signs that only treatment of schistosomiasis had long-term benefits.
This recitation of facts makes GiveWell’s estimate of the expected value of deworming charities look reasonable.
Yet, it is also unsatisfying. It is entirely possible that today’s deworming programs do much less, or much more, good than implied by the most thoughtful extrapolation from Worms at Work. Worms, humans, institutions, and settings are diverse, so impacts probably are too. And given the stakes in wealth and health, we ideally would not be in the position of relying so much on one study, which could be flawed or unrepresentative, my defenses notwithstanding. Only more research can make us more sure. If donors and governments are willing to spend nine-figure sums on deworming, they ought to devote a small percentage of that flow to research that could inform how best to spend that money.
Unfortunately, research on long-term impacts can take a long time. In the hope of bringing relevant knowledge to light faster, here are two suggestions. All reasonable effort should be made to:
- Gather and revisit underlying data (“microdata”) from existing high-quality trials, so that certain potential mediators of impact, such as initial worm load and weight, can be studied. This information could influence how we extrapolate from the studies we have to the contexts where mass deworming may be undertaken today. As a general matter, it cannot be optimal that only the original authors can test hypotheses against their data, as is so often the case. In practice, different authors test different outcomes measured different ways, reducing comparability across studies and eroding the statistical power of meta-analysis. Opportunities for learning left unexploited are a waste potentially measured in the health of children.
- Turn past short-term studies into long-term ones by tracking down the subjects and resurvey them. This is easier said than done, but that does not mean a priori that it would be a waste to push harder against this margin. Then, long-term research might not take quite so long.
 Croke et al. do motivate their focus on weight in a footnote. Only three outcomes are covered by more than three studies in the Cochrane review’s meta-analyses: weight, height, and hemoglobin. Height responds less to recent health changes than weight, so analysis of impacts on height should have lower power. Hemoglobin destruction occurs most with hookworm, yet only one of the hemoglobin studies in the Cochrane review took place in a setting with significant hookworm prevalence.
 I thank Kevin Croke for pointing out the need for this adjustment.
 Columns S–W of the Parameters tab suggest several choices based on prevalence, intensity, or a mix. Columns Y–AC provide explanations. GiveWell staff may then pick from suggested values or introduce their own.
 Lo et al. 2016 fit quadratic curves for the relationship between average infection intensity among the infected (in eggs/gram) and prevalence of any infection. The coefficients are in Table A2. If we then assume that the distribution of infection intensity is in the (two-parameter) negative binomial family, fixing two statistics—prevalence and average intensity as implied by its quadratic relationship with prevalence—suffices to determine the distribution. We can then compute the number of people whose infection intensity exceeds a given standard. In the usual conceptual framework of the negative binomial distribution, each egg per gram is considered a “success.” A fact about the negative binomial distribution that helps us determine the parameters is P = 1–(1 + M/r)^(–r), where M is average eggs/gram for the entire population, including the uninfected; r is the dispersion parameter, i.e., the number of failures before the trials stop; and P is prevalence of any infection, i.e., the probability of at least one success before the requisite number of failures. One conceptual problem in this approach is that intensity in eggs/gram is not a natural count variable despite being modeled as such. Changing the unit of mass in denominator, such as to 100 mg, will somewhat change the simulation results. In the graphs presented here, I work with 1000/24 = 41.67 grams as the denominator since that is a typical mass on the slide of a Kato-Katz test and 24 is thus a standard multiplier when performing the test.
 I also experimented with higher-order polynomials in elevation. This hardly changed the results.
 I rerun the Worms at Work regression repeatedly while introducing weights centered around elevations 1140, 1150, …, etc. meters. Following the default in Stata’s lowess command, the kernel is Cleveland’s bicube. The bandwidth is 50% of the sample elevation span.
 The Worms research team tested random subsets of children at treatment schools just before they were treated, meaning that pre-treatment infection data are available for a third of schools (group 1) for early 1998 and another third (group 2) for early 1999. To maximize statistical power, I merge these pre-treatment samples. Ecological conditions changed between those two collection times, as the El Nino passed, which may well have affected worm loads. But pooling them should not cause bias if schools are reasonably well mixed in elevation, as they appear to be. Averages adjust for the stratification in the sampling of students for testing: 15 students were chosen for each school and grade.
 Miguel and Kremer modify the World Health Organization’s suggested standards for moderate infection, stated with reference to eggs per gram of stool. To minimize my discretion, I follow the WHO standards exactly.
 There are separate graphs for hookworm, roundworm, whipworm, and schistosomiasis. Here, the shades of grey do not signify levels of confidence about the true average value. Rather, they indicate the 10th, 20th, …, 90th percentiles in eggs per gram, while the black lines show medians (50th percentiles).
 Among the group 3 schools, I marked those which school identifiers 108, 218, 205, 202, 189, 167, 212, 211 as warranting praziquantel.
 The spatially smoothed impact regressions, and the spatially smoothed averages of baseline variables graphed next, are plotted using the same bandwidth and kernel as before, except that now distance is measured in degrees, in two dimensions. Since Busia is very close to the equator, latitude and longitude degrees correspond to the same distances. Locally weighted averages are computed at a 21×21 grid of points within the latitude and longitude spans of the schools. Points more than .05 degrees from all schools are excluded. Stata’s thin-plate-spline interpolation then fills in the contours.
 Weight-for-age z scores are expressed relative to the median of a reference distribution, which I believe comes from samples of American children from about 50 years ago. The WHO and CDC provide reference tables.
 The regressions behind the following two graphs incorporate all controls from the Baird et al. low wage earnings regression that are meaningful in this shorter-term context: all interactions of sex and standard (grade) dummies, zone dummies, and initial pupil population.
The post How thin the reed? Generalizing from “Worms at Work” appeared first on The GiveWell Blog.
There are only a few days left to give to charity this calendar year.
The majority of donors who support GiveWell’s recommendations choose to make their gifts in December, for tax reasons or due to the holiday season.
This blog post contains quick tips and information about donating to GiveWell’s recommended charities.
But first, to everyone who supported our charities or followed our work in 2016: Thank you!Donate Here
Will my donation be tax-deductible?
Donors in many countries can make tax-deductible donations to GiveWell’s recommended charities.
- Click here to view this information by country; scroll down to see this information listed by charity.
What’s the best way for me to donate?
Please don’t hesitate to reach out to email@example.com if you have any questions about donation logistics. We’re happy to talk with you about questions about our research or recommendations, too.
I’ve decided to give a little more than double what I normally give to charity this year, and skip giving next year. I see many reasons to give a larger-than-normal gift this year, and no countervailing reasons. If it weren’t for some idiosyncratic factors in my situation, I would roll my next three years of giving into this year’s gift.
I decided to write up my reasoning in the hopes of prompting others to consider whether they should be doing similarly. That said, everyone’s financial situation is different, and it may be a good idea to consult with a tax lawyer for personalized advice.Tax policy
The issue that originally prompted me to consider a larger-than-usual gift was the prospect of changing tax policy due to the new administration, which could result in lower tax benefits for charitable giving in 2017 vs. 2016. A quick summary of my thinking follows; this should not be taken as tax advice, merely as my own personal guesswork and reasoning behind my own giving.
President-elect Trump’s public tax plan has three important features that could affect tax benefits for charitable giving:
- Reducing tax rates “across-the-board.”* The proposal looks similar in this respect to the 2016 House Republican Tax Reform Plan. Depending on one’s tax bracket, this could mean that the benefit for charitable giving falls by a few percentage points, so giving this year could save more money on taxes than giving next year.
- Raising the standard deduction significantly (more than doubling it). The proposal looks similar in this respect to the 2016 House Republican Tax Reform Plan. Charitable deductions are only beneficial insofar as total itemized deductions exceed the standard deduction; depending on how else treatment of itemized deductions changes, and on a taxpayer’s specific situation, this could reduce the amount of charitable giving that is effectively deductible by several thousand dollars per year, or not at all. It could also strengthen the case for giving less frequently than once per year.
- Capping total itemized deductions at $100k for singles/$200k for couples. If this happened as stated, it could effectively eliminate the tax benefit of charitable giving for many people (most of them earning very high amounts, giving very high amounts, or both). The 2016 House Republican Tax Reform Plan does not have a similar provision, and I consider this change less likely than the above two.
GiveWell’s top charities look strong this year and have very large amounts of room for more funding. It’s reasonably likely that this will be true again in the next few years, but I don’t know that it will be, and it’s hard to imagine the giving opportunities on this front getting much better in the near term.
I also see a fair amount of appeal in the option I mentioned in the staff personal giving post:
I thought about reallocating my giving to another individual, someone who is quite value-aligned with me and quite knowledgeable, and thinks differently enough that they might see opportunities I don’t.
Right now, I can think of more than one individual in this category, and some of the giving opportunities they’re interested in are not a fit for Good Ventures. In future years, I hope that the Open Philanthropy Project makes connections with more donors and effective philanthropy rises generally, and this could mean that more money flows to opportunities in this category (opportunities that I don’t see and/or that aren’t a good fit for Good Ventures). This is another case where it seems like giving opportunities may get weaker, but are unlikely to get stronger.What I’m doing
I’m planning to give an amount equivalent to my next two years’ worth of charitable giving, taking the likely trajectory of my salary into account. If not for some idiosyncratic aspects of my situation, I would have gone with three years. I don’t want to plan beyond three years because I think there are a lot of difficult-to-anticipate changes that could take place in that time.
Note that there are limits on the total proportion of income that can be deducted in a year, and one should check these before deciding to make a multi-year gift this year.
* Though as written, the tax plan would appear to constitute a major tax increase for many single filers, based on this statement: “Brackets for single filers are ½ of these amounts.” I’ve chosen not to focus on this issue, partly because there is no similar change in the 2016 House Republican Tax Reform Plan.
I’m the Director of Operations at GiveWell, and I’m spreading the word about two openings on my team for experienced professionals.
We’re hiring a Donations Manager to lead the team that processes donations to GiveWell for the support of our recommended charities and our operating support. We’re also hiring an Operations and Legal Program Manager to lead complex, multi-disciplinary projects and assist with compliance and legal matters.
If you’re interested in applying, we’d love to hear from you! Links to the application forms are included in the job descriptions linked above.
If you’re currently trying to figure out where you’ll give this year and think it might be helpful to talk to us about your decision, please feel free to contact us at firstname.lastname@example.org or via this form. We’d be happy to set up a phone call or answer questions over email.
We’ve spoken with many individuals who use our research over the past year. Our impression is that these conversations can be useful for donors and potential donors because (1) we publish a lot of information on our website, and it can be challenging for a time-constrained individual to find, read, and analyze all of it; and (2) different donors have different values and intuitions, and we believe it can be helpful to talk through the strengths and weaknesses of, and other considerations related to, the organizations we recommend. Conversations like these also help us understand how people use our research and what questions they have.
Due to limited staff capacity, it’s possible we won’t be able to speak with everyone who requests a call, although based on past experience we hope to be able to connect with anyone who gets in touch.
We look forward to hearing from you!
A few weeks ago, we wrote:
[…] we are recommending that donors split their gift, with 75% going to [the Against Malaria Foundation (AMF)] and 25% going to [the Schistosomiasis Control Initiative (SCI)], or give to GiveWell for making grants at our discretion and we will use the funds to fill in the next highest priority gaps.
We’ve gotten some questions about what the difference is between giving according to our recommended allocation (75% to AMF and 25% to SCI) and giving to GiveWell for making grants at our discretion. This post explains the difference.
How we will use discretionary grant funds
In the past, we allocated grants to top charities either in line with our most recent recommendation to individual donors or, if we had tracked enough funding to hit the targeted amounts recommended to individual donors, we would allocate grants to top charities where we judged them to be most needed. (See this post for a more in-depth description of this process.)
For the next set of grants we will make with discretionary funds, in February or March 2017, we plan to:
- Ask top charities for an update on their total revenues from all sources; and
- Use this information to update our views on which remaining funding gaps are most valuable to fill, and grant the funds to that gap.
AMF currently has our highest-ranked funding gap for individuals, followed by SCI. (We are recommending that individuals give 75% of their donation to AMF and 25% to SCI, instead of 100% to AMF, because we expect donors following our recommendation to give more than it would take to fill AMF’s highest priority gap and it would be difficult for us to coordinate a quick change in our recommended allocation as soon as AMF’s highest-ranked funding gap was filled.)
Note that, using the plan described above, we would likely not allocate exactly 75% of our grant to AMF and 25% to SCI. If the AMF funding gap we are prioritizing is still sufficiently large in February or March when taking all of AMF’s revenues from all sources into account, it’s likely that we would allocate 100% of the grant to AMF. If AMF’s gap were already filled (or could be filled with only part of the grant), we may allocate the funds to SCI or another top charity that we judge to have the most valuable funding gap.
We are uncertain whether we will continue to use this full process for other grants we make in the future. If we decide to not reassess charities’ funding gaps before making a grant, we will plan to allocate the grant according to our last public recommendation to individual donors.
What implications does our approach have for donor agency
It is almost always the case in charitable giving that donors that give after you will be affected, in expectation, by your gift and may reduce their gift to the organization of your choice as a result. There are some specific ways in which that dynamic plays out as a result of the allocation decisions we have made:
- For donors who give to our top charities, but not the one(s) that we recommend on the margin, those gifts will affect how much funding we expect those organizations to get next year. The funds may also affect how quickly the organization is able to scale in the next year, which could increase how much we think they can use productively in the following year. Both these factors (working in opposite directions) could affect how much funding we recommend donors give to them next year. (See our review of Deworm the World for an example of how we calculate room for more funding based on past revenue.)
- For donors who give to the charities we recommend on the margin (AMF and SCI currently), their gifts increase the chance that the funding gaps we have prioritized are filled and that we reallocate funds to other charities. The reallocation could happen as soon as February/March, when we plan to make our next round of grants.
We would guess that many of our donors would be happy to learn that these decisions allow us to play a “coordinating” role, in which we direct some additional funding to where we believe it’s needed most. However, donors who disagree with us to some degree may decide to give to top charities we haven’t prioritized on the margin. For example, donors who feel strongly about giving to deworming over malaria prevention (because, say, they disagree with how steeply we’ve discounted the evidence for deworming or because they value lives improved over deaths averted more than we do), may choose to give to the END Fund, whose funding gap is GiveWell’s highest priority deworming gap that is unlikely to be filled, rather than SCI. Donors who feel strongly about supporting malaria prevention over deworming, they may decide to give to Malaria Consortium over AMF, for the same reason.
For a full list of the funding gaps we seek to fill and in what order, see this spreadsheet.
The post Discretionary grant making and implications for donor agency appeared first on The GiveWell Blog.
Our goal with hosting quarterly open threads is to give blog readers an opportunity to publicly raise comments or questions about GiveWell or related topics (in the comments section below). As always, you’re also welcome to email us at email@example.com or to request a call with GiveWell staff if you have feedback or questions you’d prefer to discuss privately. We’ll try to respond promptly to questions or comments.
If you have questions related to the Open Philanthropy Project, you can post those in the Open Philanthropy Project’s most recent open thread.
You can view our September 2016 open thread here.
One important input we consider in our top charity recommendations is our quantitative cost-effectiveness estimate of each charity: an estimate of how much good the charity accomplishes per dollar. We have written before about the many challenges of constructing and interpreting such cost-effectiveness estimates. One such challenge is the problem of how to assign a numerical “score” to various good outcomes such as averted deaths or increased incomes, so that they can all be compared on the same scale. This involves answering questions such as:
- What would I do if I had a choice between doubling the income of three individuals in extreme poverty for a year and averting the death of a child under the age of 5?
- How many deaths of children under the age of 5 would I need to avert to accomplish as much good as averting the death of one adult?
GiveWell staff members enter their estimates for such values-based comparisons in our cost-effectiveness analysis (CEA). Along with empirical estimates of program costs and outcomes, this forms the basis for the numerical comparisons we make between our top charities.
Recently, a post on the Effective Altruism Forum raised concerns about our recommendation of AMF specifically. It argues that the value of averting deaths for children under five depends on one’s view of population ethics – a branch of philosophy that asks questions like “Is it good to avert a death if it has no long-run impact on the total population?” – and that some approaches to population ethics would imply a substantial discount to our cost-effectiveness estimates. We’ve chosen to respond to this argument at length because we think it is interesting, serves as a good example of the thorny issues we grapple with in estimating cost-effectiveness, and gives the opportunity to explain some aspects of our 2016 cost-effectiveness model that are not widely understood.
In this post, I will
- Explain basic concepts in population ethics and how they inform the way people think about the value of averting death
- Summarize arguments from the post mentioned above, which argues that people with certain views on population ethics should substantially discount our cost-effectiveness estimate of the Against Malaria Foundation (AMF) to better reflect their values
- Walk through the reasoning behind our current estimate of AMF’s cost-effectiveness and explain why I believe it’s compatible with most plausible accounts of population ethics, so discounts as aggressive as those suggested in the linked post are likely inappropriate.
Population ethics is a branch of philosophy which outlines some major considerations that influence how people value averting deaths. It is defined in the link as “the theory of when one state of affairs is better than another, where the states of affairs may differ over the number of people who ever live.” Population ethics deals with questions such as:
- Can it be morally good or bad to create new people who would not have otherwise lived, or is creating people always morally neutral (assuming they do not affect people who are already living)?
- Is the badness of death lessened if someone else will be born to “replace” the person who died?
- What makes (premature) death bad? Is it because the individual misses out on the years of happy life they would have otherwise lived? Is it because they had a strong preference to live that was violated? Is it because surviving loved ones will grieve? Is it some combination of these?
- Is death worse at some ages than at others?
A population ethics stance is a set of beliefs that inform how to compare different states of the world by answering these and similar questions. Staff members’ implicit or explicit stance on population ethics guides the way they quantify the value of averting death in the CEA. This in turn can greatly affect how the cost-effectiveness of life-saving charities such as the Against Malaria Foundation (AMF) and Malaria Consortium’s seasonal malaria chemoprevention program compares to the cost-effectiveness of life-improving charities such as GiveDirectly and charities implementing mass deworming programs.
- Total hedonic utilitarianism: The stance that the best state of the world is the one with the most total happiness or fulfillment. Under total hedonic utilitarianism, averting someone’s death is only good to the extent that it results in more overall happiness experienced in the world (regardless of who experiences it). Thus, if a child dies, and as a result the family has another child they would not otherwise have had, the badness of the child’s death comes only from the gap between their death and the new child’s birth and the grief and other negative effects experienced by family members. (This is assuming the child who died and the new child who is born as a result would live similarly happy lives.) This counterintuitive result is referred to as “the replacement problem” in this post. Michael argues that a total utilitarian should substantially discount GiveWell’s cost-effectiveness estimate for AMF to account for the replacement problem.
- The deprivation stance: According to the deprivation stance, the badness of death is equal to the number of years of life that the individual who died misses out on as a result of their death: in other words, their life expectancy at the time of death. Unlike in total hedonic utilitarianism, this badness is not lessened even if someone else is immediately born as a direct result of the death. Michael argues that this is the point of view which is most favorable to AMF, but those who hold this view should still be more interested in other interventions, such as life extension.
- The time-relative interest stance: Quoting from Michael’s original post,
[The time-relative interest account] holds the badness of death depends, roughly, on the extent to which it frustrates the person’s interests in continuing to live. This captures the [intuition] many people have that it’s much more important to save a 20-year old than a 1-minute old foetus because, in essence, that 1-minute old foetus hasn’t developed enough to miss out on life.
Note: the time-relative interest stance, unlike the previous two stances on population ethics, doesn’t suggest a simple formula that would capture most of the “badness” of death if all the empirical facts were known. Michael argues that those who subscribe to the time-relative interest stance should “reduce [GiveWell’s] estimate of AMF’s effectiveness by however much [they] discount child deaths compared to adult ones.”
- Epicureanism: This stance holds that death in itself cannot be bad for the individual who died, because as soon as they die, they cease to have morally relevant interests. Thus, a given death is only bad due to suffering in the process of death, and the grief and other negative effects it has on survivors. Epicureanism also does not immediately suggest a simple formula that would estimate the badness of death given all the empirical facts.
I believe Michael suggests discounts on our cost-effectiveness estimate of AMF that would be too aggressive for most people’s value systems. I explain this further in the rest of the post, but I want to first make a general point: if you are concerned your stance on population ethics does not align with the GiveWell median, I believe it would be better to download an editable copy of GiveWell’s CEA and input your own values for rows 7, 53, 63, and 64 on the “Parameters” sheet than to attempt to multiply GiveWell’s bottom-line cost-effectiveness estimate by some factor to account for expected differences in population ethics. This is because the cost-effectiveness model is complex and in parts unintuitive, and I don’t believe it will be straightforward to guess how your stance on population ethics differs from the implicit stance of the median GiveWell staff member.Summary of considerations against discounting our cost-effectiveness estimate
Here are the key reasons why I believe most people should likely not reduce GiveWell’s stated cost-effectiveness estimate of AMF as much as Michael suggests:
- GiveWell’s 2016 cost-effectiveness model suggests that the median GiveWell staff member believes that averting the death of a young child averts ~8 disability-adjusted life-years, or DALYs (“Parameters”, C14), whereas last year the GiveWell median opinion was ~35 DALYs averted, as Michael states in the post. While GiveWell does not take an official stance on population ethics as an organization, I believe this change is a result of some staff members leaning away from an explicit “discounted age-weighted expected years of life lost” model of value to a more complex and less precise model of value that does not map intuitively to the concept of DALYs as used in health economics. (More.)
- GiveWell’s current cost-effectiveness estimate does not predict that a $3500 donation to AMF will, on expectation, prevent the death of one young child. It predicts that a $3500 donation to AMF will on expectation cause a combination of outcomes that are equivalent in value to saving the life of one young child, according to the median staff member. (More.) According to staff median estimates, the benefits of AMF are driven
- ~27% by preventing deaths of children under the age of 5
- ~36% by preventing deaths of people age 5 and over
- ~37% by providing future financial benefits due to improved young-childhood development, similar to deworming charities.
- It is not clear that the “replacement problem” described in the original post fully applies to AMF, and if it does, it’s not clear that conservative staff estimates of value (see A) don’t already account for it in the 2016 model. (More). Because of this, a total utilitarian may end up concluding that AMF is more cost-effective than we suggest. (More.)
- I don’t believe it’s obvious than even an Epicurean would consider AMF to be dramatically less cost-effective than the GiveWell CEA suggests — although it is likely that they would discount our cost-effectiveness estimate somewhat. (More.)
I provide more detail on these four points in the rest of the post. I’m going to reference specific cell numbers and sheets in our November 2016 CEA throughout.DALYs are a flexible, non-literal measure in our CEA
From Michael’s original post:
[GiveWell claims that saving a child’s life is worth] 35 ‘QALYs’ (Quality-Adjusted Life Years), which is [a] more technical way of saying it creates 35 years of healthy life for the beneficiary.
As mentioned above, the new median value is 8 DALYs per under-5 life saved. (See the “YLL per Death” sheet in our CEA for examples of calculations that take a more explicit deprivation or time-relative interest stance, which were more common in the past.)
More generally, in the context of our CEA, I don’t think it makes sense to treat “DALYs” as straightforwardly denoting “years of life lost by the person with the disease + years lived with disability due to the disease.” Particularly in 2016, staff members interpreted the “DALYs averted per death of an under-5 averted—AMF” parameter (“Parameters”, B63) as an opportunity to quantify how holistically “bad” the death of a young child is. This interpretation can take into account broader considerations such as:
- For total utilitarians, concerns of replacement
- Harm caused by parental grief
- Potential economic harm caused by sickness and death
- Time-relative interest considerations which weigh young children less highly than older people
- Considerations around the loss of the child’s individual identity and the child’s desire for life
Note: I’m not sure what considerations different staff members actually did take into account; I’m just observing that our interpretation of the DALY unit is broad enough to allow for a range of such considerations.
Because DALYs have been somewhat divorced from their rigid, concrete meaning, I think comparing how a given staff member filled in row 7, row 53, row 63, and row 64 in the “Parameters” sheet (all of which are value inputs) is more informative for understanding their values than looking at any one of those inputs in isolation. For example, staff member Sophie included comments on her inputs in O7, O63, and O64.
Also because of this ambiguity, if you want to make adjustments for your beliefs on population ethics, I believe it would be better to download an editable copy of GiveWell’s CEA and input your own values for rows 7, 53, 63, and 64 in the “Parameters” sheet than to attempt to discount GiveWell’s bottom-line cost-effectiveness estimate by some factor to account for expected differences in population ethics.
In particular, I think that with certain reasonable assumptions about replacement rate (calculated here), a total utilitarian or someone with a time-relative interest account of value may end up concluding that AMF is more cost-effective than our estimates suggest (see below).Over 65% of AMF’s expected benefits are not driven by saving young children
From Michael’s original post:
GiveWell estimate[s], although this is not to be taken too seriously, [that] $3,500 to AMF saves a child’s life.
However, the cost-effectiveness estimate for AMF produced by our staff median parameters (B74) is around $3400 per young life saved-equivalent, not $3400 per young life saved.
That is, our cost-effectiveness estimate does not predict that if you give $3400 to AMF you will, on expectation, prevent the death of one young child. It predicts that if you give $3400 to AMF, you will on expectation cause a combination of outcomes that, according to the values of the median GiveWell staff member, are morally equivalent to saving the life of one young child. Specifically, the expected benefits of AMF are split between:
- Preventing the deaths of children under 5
- Preventing the deaths of people 5 or older
- Improving the expected future income of young children, similar to deworming
Note that benefits in the 2015 model were also not exclusively driven by preventing deaths of children under the age of 5: cell M20 in the “GW medians” sheet of the 2015-2016 CEA implies that the median staff member believed that one year of bed net coverage was almost as effective as one year of deworming for improving expected future income of children. The primary new addition this year is accounting for the deaths of older people.Example calculation
This section walks through the math of how we achieved our “cost per young life saved”-equivalent figure; please consider this section especially optional.
According to our median cost-effectiveness estimates (all cell numbers taken from the “Bed Nets” sheet of our CEA):
- For every ~$9,161 spent, on expectation one marginal death of a child under the age of 5 is averted (cell B55). Each under-5 death prevented gets a weight of one “young life equivalent” unit. According to the median GiveWell staff member, averting the death of a child under 5 averts about 8 DALYs (“Bed Nets”, B57).
- For every ~$37,391 spent, on expectation one marginal death of a person age 5 or over is averted (multiply cells B61 and B63 to get the ratio of 5-or-over deaths averted / under-5 death averted, and then divide the cost per under-5 death above by this value). According to the median GiveWell staff member, each 5-or-over death prevented gets a weight of 4 “young life equivalent” units (“Bed Nets”, B62).
- For every ~$500 spent, on expectation you have produced financial utility equivalent to increasing an individual’s ln(consumption) by one unit for one year (“Bed Nets”, B69, multiplied by 500)—which means ~doubling their income for a year. According to the median GiveWell staff member, averting 1 DALY is equivalent to increasing ln(consumption) by one unit for three years (“Bed Nets”, B72). Combining that with the value of 8 DALYs per death of a young child above, this means that each unit increase of ln(income) gets a weight of 1 / 24 “young life equivalent” units.
This means that a $37,391 donation to AMF would, in expectation:
- Prevent the deaths of ~4.08 children under the age of 5, i.e. ~4.08 young-life equivalent units
- Prevent the death of ~1 person over the age of 5, i.e. ~4 young-life equivalent units.
- Have a financial benefit equivalent to increasing the ln(income) of 37.391 * 2 = 74.782 people by one unit, i.e. 3.12 young-life equivalent units.
Putting that together we have $37,391 / (4.08 + 4 + 3.12) = $3338 per young life saved-equivalent. (Note: this is not exactly equivalent to the value in “Results”, R4, likely because of a combination of rounding and small adjustments that are not accounted for here.)Do parents have additional children to replace those who die at a young age?
In 2014, GiveWell commissioned David Roodman to write a report on the possible causal link between mortality and fertility, which is linked in Michael’s post:
By GiveWell’s own estimates, the effect of AMF is that it leaves total population numbers largely unchanged. I call this the ‘replacement problem’ for total utilitarians because, in these replacement cases, they can’t say there’s much (or any) value in saving lives apart from the effects of bereavement on the parents.
However, I think the picture presented in David Roodman’s analysis is more nuanced than this. From the conclusion of that report:
As mentioned at the outset, we should expect that where fertility is most controlled, typically indicated by total fertility of about 2 births/woman or less, that the volitional replacement effect is large—that for every child’s life saved, parents avert one birth. That births/woman averaged 2.7 in developing countries as a whole in 2005–10, and that the number has probably fallen more since, suggest that most couples today are engaging in family planning. Meanwhile, where the fertility transition does not yet appear to have occurred the replacement effect is likely much smaller. The studies I find most informative tend to corroborate this theory, indicating near-full replacement among a group of relatively affluent countries; partial replacement in a context where fertility had begun to decline but still had far to go (Uttar Pradesh); and no replacement in an area of continuing high fertility (Northern Ghana).
I spent a little bit of time trying to come up with a reasonable range of estimates for the fertility replacement rate in the areas AMF operates in, primarily informed by whether it seems like those areas are converging to a fertility rate of 2 births per woman. This should definitely not be considered the final word on the complicated topic of replacement rates, nor should it be considered an official GiveWell estimate. However, I found it to be helpful as a personal exercise, and it may be valuable to some people to see my reasoning:
According to GiveWell’s latest update on top charities, marginal funds from GiveWell-influenced donors will go toward AMF’s Execution Level 1 gap. We believe at Execution Level 1, AMF will likely use marginal funds to do more work in the places it had worked previously: primarily Malawi, Ghana, and Uganda. According to this tool, estimates for fertility 2010-2015 for these countries were:
- ~5.25 lifetime births per woman in Malawi
- ~5.91 births per woman in Uganda
- ~4.25 births per woman in Ghana
(To get the above estimates, I chose the following options in the linked tool, in order: “Total fertility (children per woman)”, “Malawi, Ghana, Uganda”, “2010-2015”.)
I also calculated the average fertility rate across these three countries (weighted by population) to be approximately 5.2 in this spreadsheet (see sheet “AMF Countries”).
Furthermore, according to the United Nations’ probabilistic predictions as shown in this tool, it seems none of these three countries are likely to converge to two births per woman until past 2050. Based on David’s analysis, this would imply that family planning is not pervasive in the countries where AMF will likely operate over the next couple of years, and so the death of a child would lead to significantly less than one additional expected birth in the family. After a very quick scan of the table in the Conclusions section of the mortality-fertility report, one study struck out as potentially most informative for estimating replacement rates in such countries: Bhalotra and van Soest 2008.
Bhalotra and van Soest 2008 was a study in Uttar Pradesh, India, which used data from 1963-1999 to estimate that the death of a child under one month old is followed by 0.37-0.52 extra births. In the “India” sheet of my spreadsheet, I calculate that average fertility over the period of 1960-2000 was approximately 4.78. Assuming fertility in Uttar Pradesh 1963-1999 was similar, it seems the average fertility today in Ghana, Malawi, and Uganda is slightly higher than in the population studied in Bhalotra and van Soest 2008 (5.2 vs 4.78). This implies the expected replacement rate should potentially be even lower than 0.37-0.52.
I want to emphasize that these calculations are extremely rough, but they support my general impression that it is not clear that replacement is close to 1 in countries where AMF is likely to use its marginal funds in 2016.A total utilitarian may consider AMF more cost-effective than GiveWell does
From Michael’s original post:
I should note it’s not particularly important what the exact replacement ratio is. If it turns out AMF causes parents to have 0.5 fewer children for every 1 life it saves, the total utilitarian should still [halve] AMF’s effectiveness.
This doesn’t seem obvious to me; there are a couple of flavors of total utilitarian I could imagine, and in general they don’t seem to assign a substantially lower value of DALYs averted per death of a young child averted than the GiveWell median, even assuming a 75% replacement rate (which is significantly higher than my best guess). The values used below come from the results of modifying the following values on the “YLL per death” sheet of our CEA: “Discount rate” (C6), “Age-weight parameter beta” (C7), and life expectancy at age 5 (I8 and J8).
- You could count additional years of life created without discounting over time or weighting by age. After the deprivation stance, this is perhaps the view of population ethics that is most favorable to AMF. Assuming a life expectancy of ~65 years at age 5, preventing the death of a five-year-old in Malawi or Papua New Guinea would avert 65 * 0.25 = ~16 DALYs if there is a 75% replacement rate.
- You could discount by time and weight by age. This is the sub-type of the total utilitarian viewpoint that is least favorable to AMF. Suppose the “Discount rate” is set to 0.03 and the “Age-weight parameter beta” is set to 0.04 (giving the highest weight to a year of experience as a 25 year old and a lower weight to a year of experience as a young child). If life expectancy at age five is still 65, then causing an additional five-year-old to exist would produce the equivalent ~36 years of healthy life for a 25 year old. Adjusting for replacement, preventing the death of a five-year-old in Malawi or Papua New Guinea would avert 36 * 0.25 = 9 DALYs, similar to the GiveWell median of 8.
- You could have an in-between view (for example, weighting by age without discounting or vice versa), which would produce in-between estimates for DALYs per young death prevented.
It seems that because GiveWell staff members’ median estimates for the value of averting the death of a young child are already relatively conservative from a pure total utilitarian standpoint, it may be too aggressive for most total utilitarians to discount further due to the replacement problem, unless they believe replacement rates to be close to 1.
I believe broadly similar considerations would apply to certain kinds of time-relative interest viewpoints.An Epicurean may still believe AMF produces substantial value per dollar
From Michael’s original post:
The fourth option is the Epicurean view, named after Greek philosopher Epicurus. It holds that there’s nothing good about creating someone and that death doesn’t harm anyone: once someone is dead, there is no them for anything to be bad. Obviously the process of dying can be painful. The point Epicureans make is that nothing is good or bad for you once you’re dead. On this account, the badness of death consists only in the suffering felt by the living.
For Epicureans, the value of their $3,500 donation to AMF is that it stops a family from having to grieve for a lost child.
I think there are significant costs for survivors beyond the emotional cost of grief associated with the deaths prevented by AMF:
- For the deaths of children under the age of 5: if parents “replace” their lost child by having another, we must include in the DALY burden estimate:
- The monetary costs of having a new baby and raising that baby to the age the deceased child was at the time of death, which may significantly impact the consumption or savings of a poor family
- The strain on the mother’s health and productivity associated with the course of a normal pregnancy
- The expected harm due to the possibility of serious complications in pregnancy — if this results in the mother’s death, it could result in serious permanent harm to her surviving dependents (see the next bullet point)
The above costs are higher the higher you believe the replacement rate is.
- For the deaths of people over the age of 5, particularly if they are parents or heads of households, we must account for:
- The loss of the productive income they provide to the family
- Other harm caused to dependents due to the loss of their care and guidance — it’s plausible this is very long-lasting
- For all malaria deaths, we must account for the costs of seeking medical care to attempt to prevent the death and the costs of funeral rites, both of which might be a large strain on a poor family’s income
In the comments section of the original post, Michael also suggests that it’s implausible that grief alone could impose a significant welfare cost here:
[Replacement] doesn’t say anything about the parents. Total utils should account for that too, but note how much of the value of the intervention replacement removes. You thought you were giving a child 35+ years of life and preventing parental suffering, but now you’re just (in effect) doing the [latter]. If parental suffering is equivalent to taking away 1 year of happy life away from each parent (IMO, v unlikely), then AMF is equivalent to 2 happy years rather than 37+.
I run through some calculations here http://www.plantinghappiness.co.uk/the-questionable-importance-of-saving-lives/
It’s not clear to me that the non-tangible costs to the parents should be assigned a value of less than 1 DALY total, particularly under the broad and flexible conception of DALYs that is used in the GiveWell CEA. I think there are two plausible ways we could try to quantify this from the parents’ perspective:
- Preference utilitarianism: How many years of their lives would parents trade away to prevent their young child from dying?
- Hedonic utilitarianism: How long do parents grieve after the death of their young child, and how intensely on average do they experience this grief?
If you have a preference utilitarian theory of value, then it seems plausible that averting the death of a child could be equivalent to averting multiple DALYs to a parent. One parent has replied to the linked comment saying they would likely trade multiple years of their life to prevent the death of their two-year-old child, and this doesn’t strike me as a very unusual sentiment. Parents have also been known to sacrifice their life or take large risks to save their child.
However, Michael’s theory of value appears to be hedonic utilitarianism, as explained in this comment:
I’m thinking hedonically and am leaning on the literature on hedonic adaptation….few [life] events have a long term impact on happiness, either positive or negative.
I am not familiar enough with the literature on bereavement, subjective well-being and hedonic adaptation to have an informed view on how long parents typically grieve for the death of a young child, or a good sense of how intense the subjective experience of grief is. I find it plausible that the negative effects on parents’ subjective well-being could be relatively moderate and short-lived, and I also find it plausible that these effects could be extreme and long-lasting.
My colleague Luke Muehlhauser studied the literature on subjective well-being several months ago, so I asked him for his impressions. He replied:
It’s hard to say. First, the strongest designs used for studies of subjective well-being (SWB) and life events are panel studies (for a review see Luhmann et al. 2012), which makes causal inference quite tricky, even given recent econometric innovations. Second, the outcome measures typically used in SWB studies are not as well-validated as (e.g.) patient-reported outcomes used in health care (PROMIS) or the measures typically used in educational testing, especially for use across cultures and over long periods of time (as in studies of SWB and life events).
That said, if we cross our fingers and hope that the available panel studies are very roughly capturing what’s going on, we can make some guesses. I haven’t seen panel studies on SWB and the loss of a child, but perhaps we should expect the SWB effects to be similar as with the loss of a spouse, or perhaps somewhat smaller than that, especially in areas with a high rate of under-5 mortality. The Luhmann et al. meta-analysis of prospective panel studies on SWB and the loss of a spouse says (p. 605), roughly, that loss of a spouse is indeed quite bad for SWB initially, that pre-event levels of “cognitive well-being” (cognitively-assessed life satisfaction) are typically achieved within a couple years, and that adaptation is surprisingly rapid for “affective well-being” (feelings of happiness/sadness), with pre-event levels achieved within a couple months. So loss of a spouse is bad, but (according to Luhmann et al.) less bad than (e.g.) unemployment. That said, I should add that I don’t personally trust these underlying studies, nor Luhmann et al.’s method of combining them.
All told, I would guess an Epicurean would likely choose a lower value for “DALYs averted per death of an under-5 averted” than the GiveWell median of 8 (“Parameters”, C63), but I am unsure whether it would be a dramatic downward adjustment, particularly if the Epicurean places relatively more weight on parents’ stated or revealed preferences compared to their subjective experiences, or if they are relatively skeptical of academic research on subjective well-being. For example, my value of 4 DALYs per under-5 death averted (“Parameters”, E63) seems within the realm of plausibility for an Epicurean. This along with my other inputs suggests that I should believe AMF is approximately as cost-effective as GiveDirectly (“Results”, D12).Conclusion
If you have strong beliefs about population ethics, and are interested in donating to organizations serving the global poor that meet our criteria, I think it would be valuable to download an editable copy of our CEA, override cells C7, C53, C63, and C64 in the “Parameters” sheet with your own values, and then view column R in the “Results” sheet to see what charity cost-effectiveness estimates and rankings that would imply. I’ve outlined some considerations that may apply to total utilitarians and Epicureans above.
If you don’t have a strong stance on population ethics and are wondering what the original critique should imply about whether your giving decisions, two main things are worth keeping in mind:
- According to the median GiveWell staff member, over half of the benefits of AMF are driven by saving the life of people aged 5 and older or by improving future incomes for young children, rather than saving the lives of children under 5 (“Bed Nets”, B78-80). If you otherwise agreed with the median staff member’s values but believed that averting a young child’s death averts ~0 DALYs, AMF’s cost-effectiveness would be reduced ~37%. If you believed that averting death in general has ~0 value but agreed with the median staff member’s empirical and moral beliefs about improving future incomes, AMF’s cost-effectiveness would be reduced ~73%.
- The median staff member’s estimate of 8 DALYs averted per young death averted appears to be within the realm of plausibility for multiple stances on population ethics, including total utilitarianism (where it seems to be more on the low end) and Epicureanism (where it seems to be on the high end). I would guess that it is also within the range of many interpretations of the time-relative interest theory of value.
For this post, GiveWell staff members wrote up the thinking behind their personal donations for the year. We made similar posts in 2013, 2014, and 2015. After Elie and Holden, staff are listed in order of their start dates at GiveWell.
For my year-end donation, I’m planning to give to GiveWell for regranting.
I already gave a significant portion of this year’s donation to a political campaign, so I’m planning to give less at the end of this year than I have in previous years.
I spend most of my time working on GiveWell’s research, so it’s likely not surprising that I plan to follow our recommendation. I think the quality of the research our team produced this year was higher than it has ever been. In particular:
- We significantly increased our focus on organizations’ funding gaps and have a better picture of how GiveWell-directed funds could interact with other funders than we had in the past.
- Our cost-effectiveness analysis was subject to significantly more staff debate than it was in the past, leading to several important changes that, I believe, improved the model.
- David Roodman is in the midst of a deep investigation of the evidence for deworming. His analysis of that intervention has significantly improved our understanding of the strengths and weaknesses of this evidence.
The option I considered most seriously instead of following GiveWell’s recommendation was supporting organizations I know about through the Open Philanthropy Project’s work on biosecurity and pandemic preparedness, which I’m very involved in. The two options I considered seriously are iGEM and the Center for Health Security. As far as I know, these are both extraordinary organizations that I would be excited to support in Open Philanthropy’s absence. Given Open Philanthropy’s work in this area, I’m uncertain about the impact of additional funds.
I also considered saving to give later. My intuition is that there are few opportunities that I would personally decide I wanted to give to in the future that I would be unable to convince someone else to give to instead. That led me to decide to give now instead of later.
My personal giving is very small compared to the giving I advise on. I’ve chosen to focus my personal giving on: (a) things that larger donors I advise can’t or won’t do; (b) checking boxes I want to check for considering myself a personally moral/ethical person, which is related but not identical to trying for maximum expected positive impact on the world.
Earlier this year, I gave to a political campaign that I considered important and high-impact per dollar. This falls under (a) because there are per-individual contribution limits.
I expect that I will see future opportunities in category (a) as well, but I don’t see any at the moment that seem a good match for my level of giving, so I considered a few possibilities:
- I thought about simply saving my money for future opportunities.
- I thought about participating in the donor lottery mentioned by Tim, Ajeya and Helen—I think it’s a very interesting idea and I am on board with the arguments for how it can be beneficial.
- I thought about reallocating my giving to another individual, someone who is quite value-aligned with me and quite knowledgeable, and thinks differently enough that they might see opportunities I don’t. As a general point, I think reallocating to others addresses a similar issue to the donor lottery—trying to consolidate donations so that a smaller number of people can put in a greater amount of effort – and it seems to me that it is a better way of doing so when one has a person in mind they’re comfortable reallocating to. (Of course, hybrid approaches are possible too —one could reallocate to a person who then plays the lottery, with the winner of the lottery considering reallocation as well.)
I haven’t finalized my decision yet, but I am leaning toward the last option. The “EA Giving Group” DAF mentioned by Nick is one possibility, and there are others as well.
Regarding (b): every year, I want to give a significant amount to “charity” as conventionally construed, straightforwardly helping the less fortunate. I generally believe in trying to be an ethical person by a wide variety of different ethical standards (not all of which are consequentialist). And I wouldn’t feel that I were meeting this standard if I were giving nothing (or a trivial amount) to known, outstanding opportunities to help the less fortunate, for purposes of saving as much money as possible for adversarial projects (such as political campaigns) and/or more speculative projects (such as work related to artificial intelligence). I think the best giving opportunities in this category are GiveWell’s top charities, so I will be giving a portion of this year’s donation there, following the recommended allocation.
One more comment: this year I am considering donating an unusually large amount because I think tax rates are likely to fall soon.
I continue to believe that GiveWell top charities are the best option for impact-focused giving for individuals and I plan to give most of my annual gift this year to GiveWell for regranting at its discretion to top charities. I am grateful for all the work, thoughtfulness, and hours of debate that my colleagues put into the recommendations, and I believe that the recommendations are as strong as they’ve ever been. I am excited to support the most effective charities I know of.
At a smaller scale, I’m planning to make a number of “good citizenship” donations. I’m distressed by the growth of illiberalism and disregard for the truth in our society. I don’t expect to be able to make an impact against these threats with my dollars, but hope that setting aside a small portion of my charitable budget for this cause leads me to think seriously about the issues involved and have conversations with people who know more than I do about this. I feel that it’s an important time to be an engaged and alert citizen and it’s important to me to have some skin in the game.
I’m planning to give 80% of my charitable contributions this year according to the GiveWell recommended split. I think the main updates on our top charities since last year are positive, and I continue to be very excited about giving to them. Additionally, I wasn’t really involved in the GiveWell top charity selection process at all this year, and at this point I don’t see any grounds for differing with my colleagues on their recommended split.
That said, I feel mildly less urgent about these opportunities than I did last year, because I think they may be available longer than I had suggested then and because I’ve become somewhat more optimistic about the possibility that the Open Philanthropy Project will find considerably more impactful opportunities in the future. (These considerations are mainly why I recommended that Good Ventures not significantly increase its contributions to top charities this year, but I’m not reducing my giving to save more for the future because I think it’s good for me to be in the habit of giving meaningfully each year.)
With the other 20% of my giving:
- As with last year, I’m planning to give 5% to GiveWell for operating expenses. At this point, I value the top charities research but am primarily a consumer rather than a producer of it, and I think it’s totally appropriate for me to contribute to pay for it. This decision probably doesn’t matter this year because I think GiveWell is likely to hit its excess assets policy in the coming year due to the separation of the Open Philanthropy Project, so the marginal contributions to GiveWell’s operating expenses will just be passed on to top charities. But I hope that I can save my colleagues some time on fundraising efforts and promote the idea that it’s a good choice for donors who use GiveWell’s research to direct some portion of their giving to it.
- Also in line with last year, I’m giving 5% to GiveDirectly. As I said then, “I continue to feel that they are a uniquely outstanding organization and add a huge amount of value by serving as a benchmark against which other organizations and interventions can be compared… I don’t think that my contribution will add as much value with GiveDirectly as it would elsewhere, but if I didn’t give significantly to GiveDirectly this year, I’d want to again within the next few years to renew my claim to being a ‘supporter’ and because I find them a particularly valuable organization to be able to discuss when making the case for giving a lot.”
- For the first time this year, I’m planning to give 10% of my giving to organizations focused on farm animals. I still don’t feel like I have any real grasp on how to weigh animal organizations against those focused on humans, but I believe that animal suffering is worthy of some moral concern, and having seen Lewis’ work up close, I no longer feel that 0% is the right portion of my portfolio to be allocating to these issues. That said, I’m not sure if this will prove a sustainable level for me: at this point the case for me here is almost entirely intellectual rather than emotional, and I’m hoping that starting to give in this area might help me begin to feel more emotionally motivated by the cause.
As in past years, I considered and ultimately decided against devoting my annual giving to one of the organizations we’ve come across at Open Phil. I continue to spend the bulk of my working hours on Open Phil’s policy efforts, and see supporting other organizations with my charitable contributions as an attractive form of diversification (even though I don’t generally think it’s useful to diversify in charity).
Giving to GiveWell’s recommendation is not quite as attractive to me this year as it has been in past years because my expectation that my donations might reach better opportunities elsewhere has increased. Even if I am able to find opportunities that I am more excited about, I expect GiveWell’s recommendation to remain my primary suggestion for family, friends, and others who aren’t planning to spend a lot of their own time on the process.
I expect that seriously considering non-GiveWell opportunities would take a substantial amount of time, so I have signed up for a donation lottery to save that time in expectation, and justify spending more time in the 5% chance that I win.
Options that I already know I would want to investigate if I won the lottery:
- Giving the money to people who I believe would make the decision at least as well as I would (as measured by my values)
- Trying to influence very long-term outcomes
- Improving animal welfare
- Capitalizing on unique political opportunities
- Speculating on small projects that I believe I have a comparative advantage in discovering or evaluating
- Regranting to top charities by giving to GiveWell
I would also like to think more about ideal donor behavior in a community of donors that want to cooperate and have overlapping but non-identical values and beliefs, and substantial uncertainty. For example, I would like to consider when and how the following are appropriate:
- Spreading gifts among plausibly-ideal opportunities versus highest expected value only
- ‘Coordinating’ with Open Phil as an individual donor in its priority cause areas
- Giving to one’s employer
- Aggregating donations, such as donation lotteries and passing donations to other donors
- Saving for later giving
I continue to make donations to some organizations that provide services I value, on the expectation that this is a good practice for people to follow generally in order to offset the cost of providing those services and signal its value. The vast majority of such donations this year went to CFAR; I particularly benefitted from attending a workshop in January.
I plan to give 65% of my charitable budget to GiveWell for regranting to top charities. I generally follow GiveWell’s recommended allocation for my global poverty-related giving unless I have very strong reason not to. This helps to ensure that I debate any of my personal disagreements with the recommended allocation with my coworkers, which could ultimately influence much more funding than my personal donation. And, if my arguments don’t succeed, it ensures that I factor in my coworkers’ knowledge and values to my giving decision. Ultimately, I feel confident in our recommended allocation this year and am excited to support it.
That said, there were many difficult judgment calls that went into our final recommendation. The allocation decision we made that I was most unsure about was not prioritizing some further funding to Malaria Consortium’s seasonal malaria chemoprevention (SMC) work above part of the Against Malaria Foundation’s (AMF) and the Schistosomiasis Control Initiative’s (SCI) remaining gaps. I think there is a strong argument that Malaria Consortium is roughly as cost-effective as these other opportunities, and it seems unlikely that Malaria Consortium would hit diminishing returns at the level of funding it received ($5 million). Also, I think that Malaria Consortium may be a stronger option than AMF for donors who put especially high weight on strength of evidence and cost-effectiveness. (This year, for most staff members, about 60%+ of the benefits of AMF in our cost-effectiveness analysis came from averting adult malaria mortality and improving childhood development, but the evidence base for both of these impacts is relatively limited.) However, I ultimately felt that it was reasonable to prioritize the next tier of AMF and SCI funding gaps above Malaria Consortium since those gaps had similar cost-effectiveness and have been more thoroughly vetted. (For full disclosure, I was the main researcher who worked on reviewing Malaria Consortium—it’s possible that those who interact directly with a charity are more often biased in favor of it.)
I already gave 30% of my charitable budget to a political campaign earlier this year because I believed that it was among the most cost-effective uses of my money.
With the final 5% of my charitable budget, I plan to give to charities that promote farm animal welfare. I have not yet fully worked out my view on the cost-effectiveness of these charities, but I’m convinced enough that they could be outstanding opportunities that I want to provide some funding to them.
Other options that I considered were organizations that work on reducing the likelihood of global catastrophic risks and organizations that work against authoritarianism. However, I wasn’t able to feel confident enough in any particular organization to be willing to donate to it. I hope to learn more about these types of organizations in the future and hope to see more public debate about the best groups to donate to in these causes.
Another factor in deciding against additional donations to relatively speculative giving opportunities (beyond my political donation) was that I have a strong desire to tangibly help people in the near term. Even if I give a larger portion of my donations to more speculative causes in the future, I always want to make sure that I’m doing my part to provide significant support to those who are worst off.
I have not yet decided where I am going to give this year; this post only represents my preliminary thoughts. I do not plan to spend a large amount time thinking about my donation this year, given its relatively small size, so the conclusions I reach are probably close to those I will ultimately act upon.
I probably won’t give to a GiveWell top charity this year. But, if I were going to, I would likely give to SCI, based on my estimates of GiveWell’s top charities’ cost-effectiveness and room for more funding. This year, differences between staff members’ conclusions in GiveWell’s cost-effectiveness analysis (CEA) were largely driven by the inputs related to:
- Value judgments (e.g. how to weigh improving a life against preventing a death).
- Our confidence in the evidence for the developmental effects from deworming and bed nets.
You can find my inputs and other staff members’ in the CEA linked above.
I think it makes sense to adjust my views towards the median staff views on parameters related to evidence. I’m not sure whether or not it makes sense to adjust my value judgments towards the staff medians, but right now I lean against doing so. If I change our CEA so that everyone shares my value judgments, then AMF and the Malaria Consortium appear to be as cost-effective as GiveDirectly. Sightsavers comes out as about 5x as cost-effective as GiveDirectly, SCI 8x, and Deworm the World 10x. Deworm the World is already fully funded through its “Execution Level 2” gap and I doubt that more funding next year would significantly affect its plans, given the slow rate at which it has used funding in the past. SCI, however, is only funded partially through its Execution Level 2 gap. So, SCI would be my first pick.
However, I suspect I won’t give to any of GiveWell’s top charities this year, because I think there are other giving opportunities that better match my values. For example, I value animal welfare, and, although I haven’t looked into the calculations closely, my understanding is that the number of animals you’d have to be willing to trade off against a human to make GiveWell’s recommended charities look better than farm animal welfare charities is high. (For an example of a rough estimate of the cost-effectiveness of corporate campaigns, see here. Although note that corporate campaigns may be significantly more cost-effective than other animal welfare interventions). Therefore, I may choose to give to Animal Charity Evaluators, one of their recommended charities, or organizations that Lewis recommends. I’m also pretty excited about reducing global catastrophic risks, but I have even less certainty about which organizations are doing great work in this space.
I have considered giving to the options Nick and Ajeya describe, but these choices are somewhat logistically challenging for me this year. It’s likely that I’ll give to opportunities like these in the future.
This year I am donating to the “EA Giving Group” DAF (donor-advised fund). Since 2012, one of my side projects has been working with a private individual (who has provided the vast majority of the funds and prefers to remain anonymous) to make donations to organizations working in the effective altruism space and organizations working on mitigating global catastrophic risks (especially potential risks from advanced AI). We meet every three weeks to discuss potential donation opportunities and make decisions, and we both keep up with activities in the space through relationships we’ve built up over time. The DAF is jointly controlled by me and this partner.
A list of donations we’ve made in the past (without dollar amounts) is available here (arranged by year and decreasing order of grant size). The organizations that received the most funding were the Centre for Effective Altruism (CEA), the Future of Life Institute, 80,000 Hours (part of CEA), and Founders Pledge. I think these grants have gone well overall, as has our support for Charity Entrepreneurship and the Cambridge Centre for the Study of Existential Risk. In most cases, we supported these organizations relatively early in their existence, and we’ve mainly supported them when they were new or relatively young.
Over the last year, Open Phil has also made grants in these areas based on my recommendations. I anticipate that there will be some cases where a grant would be a good fit for this DAF but not Open Phil. However, with Open Phil as a funder in this space it has been harder to find opportunities that are as promising and neglected as we were able to find previously.
I don’t yet know what this DAF will support in the coming year, but it will probably have a similar flavor to what was supported in the past.
I am making this donation instead of a donation to GiveWell’s top charities primarily because (i) I think this is more optimized for influencing long-term outcomes for the world (which is my primary altruistic objective—reasoning here) and secondarily because (ii) I think we have a good chance of getting a “multiplier effect” where support of the effective altruist community eventually results in more total donations to GiveWell’s top charities and other things I find comparably good.
If you want to make a contribution to this DAF, then fill out this form.
This might be a good fit for people who have some combination of the following properties: interest in effective altruism and/or global catastrophic risks, context needed to assess our (still early) track record, trust in my judgment and/or my partner’s judgment, limited time/context available to make donation decisions themselves. We update contributors on grants made a couple of times per year.
This year, I am giving to the donor lottery set up by Carl Shulman and Paul Christiano. My reasons are largely similar to those described in the linked post and by Ajeya elsewhere in this post: creating a chance that my donation will be large enough to significantly affect the recipient organization, and reducing the time I spend thinking about where to donate unless my donation is that size. In keeping with the latter point, I haven’t thought hard about where I would give if I ended up winning the donor lottery. Some organizations and areas I would want to consider include the Machine Intelligence Research Institute, Animal Charity Evaluators, the International Refugee Assistance Project, and organizations working against populism/authoritarianism/nationalism.
While I believe that all of GiveWell’s recommended charities are excellent giving opportunities, I plan to deviate from GiveWell’s recommendation and give 100% to GiveDirectly this year.
GiveWell’s recommendation is informed by our estimation of the comparative cost-effectiveness of donations to our top-recommended charities. This model is heavily influenced by a small number of inputs related to the trade-off between the value of consumption benefits and the value of preventing deaths, especially of very young children (see “Parameters” tab rows 7, 53, 63, and 64). Since last year, it has become increasingly clear to me that my values differ somewhat from GiveWell’s as a whole. I am uncertain about the stability of my own values, and very uncertain about the values of those I aim to benefit and whether these last are likely to be closer to my values or to GiveWell’s values.
My values differ from GiveWell’s in the following ways:
- I value increasing household consumption comparatively more highly than averting deaths of very young children. For more, see the comments on rows 7, 53, and 63 of the “Parameters” tab of GiveWell’s cost-effectiveness model.
- I am more skeptical that deworming has effects similar to those described in Baird et al. 2015 in the contexts where GiveWell-recommended charities work. I have not seen David Roodman’s forthcoming blog post on this topic, but he has written, “My confidence fell in the generalizability of that finding to other settings, as discussed in the next post.”
These values lead me to believe that GiveWell’s top-recommended charities are roughly similar in cost-effectiveness. (My results for charities range from 0.3x-2.2x as cost-effective as GiveDirectly, which is well within my margin of uncertainty for our model accuracy and for my value judgements.) However, based on our collective values, GiveWell has prioritized the top-tier funding gaps for every other top charity above the top-tier funding gap for GiveDirectly. Thus, we expect that GiveDirectly will be constrained by funding this year and will downsize somewhat. Because I believe that GiveDirectly’s cost-effectiveness is similar to that of other top charities, and that GiveDirectly is strong or strongest on other key considerations such as evidence of impact and transparency, yet GiveDirectly will be underfunded compared to other top charities, I believe that additional contributions are best given to GiveDirectly.
Though I have not yet finalized my giving decisions for this year, they will likely be similar to last year’s, and for similar reasons. I will give a portion to GiveWell top charities for regranting, and a portion specifically to GiveDirectly. I will also give a portion to social justice, advocacy and human rights organizations. One of these organizations will be Northwest Health Law Advocates (NoHLA), an underfunded healthcare consumer advocacy organization whose work and impact I understand well due to a personal connection. I believe their work will be especially critical in fighting cutbacks to programs that provide access to healthcare. More research on charities in the abovementioned categories remains to be done before I make my final decisions.
I will also likely give to Strong Minds after doing a little more research, for reasons similar to Chelsea’s and Isabel’s.
I plan to give the majority of my year-end donation to GiveWell’s recommended charities. Among GiveWell’s top charities, I plan to give 75% of my donation to the Against Malaria Foundation, in line with GiveWell’s recommended allocation. I spent a significant amount of time with the GiveWell research team this year and feel more confident in GiveWell’s year-end recommendations as a result; reading my colleagues’ contributions to this post is a reminder of why I value the recommendations put forward by this group.
I plan to deviate from GiveWell’s remaining recommended allocation (25% to the Schistosomiasis Control Initiative) and provide the remaining 25% of my donation to the Malaria Consortium for its work on seasonal malaria chemoprevention. I value improving health outcomes highly (and relative to income-improving interventions), although like Sophie I am uncertain about the stability of my values, as I remain relatively early in my charitable giving.
I am planning to make a smaller number of donations to charities working to support domestic causes and social justice, in addition to my gifts to GiveWell’s recommended charities, to fulfill what I see as my civic responsibility.
This year, I’m planning on following GiveWell’s recommended allocation of donations to top charities: 75% to the Against Malaria Foundation (AMF) and 25% to the Schistosomiasis Control Initiative (SCI). I agree with my colleagues that, taking grants GiveWell recommended to Good Ventures into account, an allocation of 75% to AMF and 25% to SCI is best for contributing towards filling the most valuable remaining funding gaps among our top charities.
Before deciding on donating according to GiveWell’s recommended allocation, I considered several other donation options:
- Giving part of my donation to covering GiveWell’s operational costs: As a GiveWell employee, I don’t think I’m in the best position the diversify the donor base for covering GiveWell’s operating expenses, though I think it’s valuable and reasonable for others who use GiveWell’s research to allocate some of their donation to do so.
- Giving part of my donation to GiveDirectly: Last year, I allocated 10% of my donation to GiveDirectly, mostly because the idea of allocating some of my donation to a “low-risk” opportunity (i.e., I was highly confident that it would do some significant good) appealed to me as a donor. After more intensive engagement with our cost-effectiveness analysis this year, I don’t find this consideration as salient as I did last year.
- Giving part of my donation to Malaria Consortium for its work on seasonal malaria chemoprevention: I have wavered between allocating 25% of my donation to Malaria Consortium or SCI. My best guess is that additional donations to Malaria Consortium would be highly valuable, since I think that seasonal malaria chemoprevention is roughly as cost-effective as bed nets, and since GiveWell capped its recommended grant from Good Ventures to Malaria Consortium at $5 million, since we know significantly less about the organization and the intervention than we do for our other recommendations. Additionally, the amount of funding that GiveWell recommended Good Ventures grant to SCI would fill all of SCI’s execution level 1 funding gap and half of its execution level 2 funding gap, but it seems likely that Malaria Consortium still has a large unfilled execution level 1 funding gap (see this blog post for definitions for these terms). However, given our relatively limited knowledge of seasonal malaria chemoprevention and Malaria Consortium, and given my uncertainty about whether to interpret the differences between the cost-effectiveness of bed nets, seasonal malaria chemoprevention, and deworming given my values and inputs into our cost-effectiveness analysis as meaningful, I’ve decided to default to my colleagues’ wisdom and follow GiveWell’s recommended allocation.
I will again give exclusively to farm animal welfare groups for similar reasons to last year:
- Farm animal welfare is important: Roughly six billion caged layer hens, 15 billion broiler chickens, and 80 billion fish are confined globally at any time, and many suffer from mutilations and inhumane slaughter.
- Farm animal welfare is still neglected, though less so than before: The Open Philanthropy Project and other new donors have brought much-needed funds to the field, but farm animal welfare philanthropy remains tiny compared to the problem’s scale.
- Farm animal welfare is more tractable than ever: The unexpectedly rapid success of corporate cage-free campaigns has created a window of opportunity to push for further reforms—especially global cage-free, broiler chicken, and farmed fish welfare policies.
I was torn this year on my recommendations. The Open Philanthropy Project’s farm animal welfare grants have significantly reduced the short-term need for more funding at the most effective groups, so my dollar might go further at groups we’re not funding. In particular, my dollar would probably go furthest at smaller groups that we’re unlikely to fund soon due to the time required to investigate them. But I also didn’t want to devote time to investigating these groups for my personal donations, or to donate blindly to groups I haven’t investigated yet. (After discussing a draft of this post with my coworkers, we’re now going to put more thought into whether there might be an efficient way to get some of these groups funded, such as by finding a re-grantor.) So I’ve decided to support the groups I’m already most excited about, most of which are grantees:
- I plan to support the five advocacy groups that I believe are primarily responsible for the major recent US and international corporate wins for layer hens and broiler chickens: The Humane Society of the US Farm Animal Protection Campaign, The Humane League, Mercy for Animals, Humane Society International, and Compassion in World Farming USA.
- I also plan to donate to Animal Charity Evaluators, which I’ve recently become more positive on and believe (like the groups above) would benefit from a broader donor support base even if we do fund it.
- I also plan to donate to the Good Food Institute, which I believe is the most effective non-profit working in the important space of promoting technological alternatives to animal products.
- I donated earlier this year to Citizens for Farm Animal Protection, the group that ran the successful Massachusetts farm animal welfare ballot measure, because I believe ballot measure campaigns are important and benefit from a broad base of support.
I plan to split my gift this year between Malaria Consortium’s seasonal malaria chemoprevention program and Strong Minds, a charity that treats women in Uganda with depression through talk therapy groups led by community workers. Strong Minds’ program has randomized evidence of effectiveness, is in my assessment potentially highly cost-effective, and is supported by monitoring published online.
I plan to allocate 50% of my giving to Malaria Consortium because on my values and assumptions (as entered in GiveWell’s publicly available 2016 cost-effectiveness analysis) seasonal malaria chemoprevention is the most cost-effective, evidence-backed giving opportunity I am aware of. My decision to differ from GiveWell’s recommended allocation for giving season 2016 relies on the difference between my personal values and those of the median GiveWell staffer. As some readers are aware, GiveWell’s assessment of the relative cost-effectiveness of its recommended organizations rests, among other things, on how the median GiveWell staffer makes two controversial philosophical tradeoffs. The first is how many years of roughly doubling a person’s income is as valuable an additional year of healthy life. The second is how many child lives are equal to the value of one adult life. Relative to the values underlying our recommendations, I value improving health more highly than increasing wealth, and I consider the value of saving the lives of young children to be much closer to the value of saving the lives of adults.
My decision to allocate 50% of my giving to Strong Minds is both more speculative and more personal. I have decided to give 50% to Strong Minds over GiveWell charities for three reasons. Firstly, I think mental health is one of the most neglected areas in global health funding and innovation, and I want to incentivise and celebrate early-stage, evidence-driven, transparent organizations like Strong Minds. Second, I believe that the suffering experienced by adults with moderate to severe mental illness and their dependants and loved ones is often underestimated. It is plausible to me that giving to Strong Minds may improve well-being as much as GiveWell’s most effective charities. Finally, mental health treatment has fundamentally changed my life and the lives of many of my loved ones. For that reason, I consider it a personal privilege to donate to an organization making effective health services available to women facing stigma and difficult circumstances without comparison in the developed world.
I gave most of my charitable giving budget for this year to GiveWell (for grants to recommended charities at its discretion, so to AMF) in January, shortly before I left my prior job. I timed my donation this way to take advantage of my former employer’s generous charitable donation matching program.
I haven’t settled on final proportions yet, but I will probably give about half of the remainder of my annual contributions to GiveDirectly and the other half to nonprofits working to preserve political freedom and the rule of law in democratic societies. I spent very little time engaging in our top charities selection process this year, and was torn between donating to SCI to align with our updated recommendations and donating to GiveDirectly. Like Alexander, I value GiveDirectly as a benchmark for other organizations and interventions. This, along with my lack of deep engagement in our charity evaluation process, skepticism about the cost-effectiveness of deworming (for reasons similar to Sophie’s), and desire to limit the amount of time I spend thinking about where to give, led me to settle on GiveDirectly.
My largest donations during the past ten years or more have been to several Universities where I was a student or a faculty member. They are all public Universities and are always starved for funding. In 2016 my largest gift by far was to UC Berkeley, which currently obtains only 12% of its financial support from the State of California and is running a large deficit. In contrast to the other Universities ranked in the top six in the world, all of which are private, UC Berkeley is public and about 40% of the Berkeley undergraduates are from low income families and are eligible for Pell grants from the federal government. Thus, Berkeley is a particularly powerful engine for upward social mobility. My family was poor but I was able to earn enough working nights and summers to pay my way through a public University. I am grateful to the excellent education I received and want to pass that opportunity along. (My second largest gifts have been to pay for the tuition and living expenses of several relatives).
This year, I gave approximately 55% of my donation budget to a political campaign in mid-October. I plan to give the remaining 45% to the donor lottery set up by Carl Shulman and Paul Christiano. This lottery allows me to contribute a certain amount of money to a common fund, in exchange for a probability of deciding the allocation of the whole fund that is proportional to the amount of money I put in. There are three main reasons I prefer giving to this lottery over donating to a charity directly:
- I can stop worrying about where to give unless I win. If I win, I will control several times as much money as I put in, so then I can justify spending much more time and energy optimizing this decision than I could if I were allocating my individual contribution.
- Even if I were fairly confident about what charity I would give to if I were to win the lottery, giving a large chunk of money at once would allow me to have influence and access to that charity which I wouldn’t have been able to achieve with a smaller donation: for example, my donation might enable the creation of an entirely new sub-program in that charity.
- I think the lottery is an interesting innovation in how people give, and I want to contribute to it having a healthy launch and signal my support for the idea of experimenting with giving in this way.
In the spirit of the lottery, I haven’t thought very much at all about where I would give if I won—but currently, I am weakly leaning toward Animal Charity Evaluators, based largely on a single conversation with Lewis.
I plan to contribute 80% of my year-end giving to the END Fund. When my personal views are accounted for in our cost-effectiveness analysis, deworming charities have the highest expected value. I chose the END Fund over our other deworming charities based on its room for more funding.
Although I am giving to the END Fund, I would not be comfortable broadly recommending deworming organizations to all my friends and family members wondering where to give. It’s possible that our deworming charities accomplish very little, and not everyone shares the view that the best charities to give to are the ones where contributions have the highest expected value.
I intend to split the last 20% of my annual donations among GiveWell’s other top charities and a few organizations that don’t fit into GiveWell’s evaluation framework.
I won’t be contributing any portion of my donations to GiveWell’s operating costs. I would be uncomfortable donating to my employer, and I would prefer that donating to GiveWell does not become a norm among staff.
GiveWell’s recommendations represent the most convincing estimate I’ve seen of where to give money in order to do the most good per dollar, in terms of averting deaths and improving lives, and they are also reasonably well-aligned with my values. For that reason, I’ll be giving the bulk of my year-end donation (two-thirds) to GiveWell top charities according to our headline recommendation.
I find some of my coworkers’ arguments for deviating from our headline recommendation compelling. For example, I think it’s plausible that Malaria Consortium has an unfilled funding gap on par with AMF or SCI’s. However, I’ll be following the standard GiveWell recommendation, giving 75% of what I’ve allocated for top charities to AMF and the remaining 25% to SCI. At this point in time, I don’t believe I have any insights that would make me confident in deviating from the collective wisdom of the GiveWell research team. While I’m uncertain about the impact of deworming, my best-guess inputs to our cost-effectiveness analysis find it to be more cost-effective in estimation than most of my coworkers’ do, and so in the case of SCI, I’m comfortable giving to an opportunity that I view as risky but with a nontrivial chance of high impact per dollar.
The remaining one-third of my year-end giving will go to causes that are less evidence-backed in some cases but highly-aligned with my values, namely in terms of furthering social justice and my own civic engagement. Similarly to Holden, I believe in trying to be ethical according to a variety of ethical standards, including nonconsequentialist ones. I also view financial support as a means of engaging with causes I care about and signaling my support for organizations that do work I’d like to see more of. Prior to finalizing how I will allocate this portion of my giving, I hope to continue to discuss promising giving opportunities with coworkers and friends. As of now, I tentatively plan to support:
- Causa Justa :: Just Cause: As a resident of a predominantly Latinx, predominantly working-class neighborhood in Oakland, I see supporting Causa Justa :: Just Cause—a Bay Area-based grassroots organization supporting housing rights, immigrant rights, and racial justice—as a means of supporting the community in which I live.
- Planned Parenthood: I want to support an organization that provides and advocates for reproductive health services in the United States, and Planned Parenthood is one I am familiar with and trust. I haven’t yet decided whether I will give to Planned Parenthood or to the Planned Parenthood Action Fund.
- International Refugee Assistance Project (IRAP): Refugee resettlement is a cause I feel personally drawn to, and I admire IRAP’s focus on systemic advocacy.
- Strong Minds: Mental health is another cause I feel passionate about supporting. My colleague Chelsea brought this organization to my attention, and I’m excited about the opportunity to support a program that is providing much-needed mental health care to women in Uganda. In my opinion, mental health is severely underfunded worldwide, and I would like to see Strong Minds scale up and possibly inspire the creation of more evidence-based mental health programs around the world.
- GiveDirectly: This is a bit different from the other organizations in this list, in that it is one of GiveWell’s evidence-backed, underfunded top charities. While I believe GiveDirectly is most likely less cost-effective than AMF and SCI, I want to support its work as an organization that is empowering people living in extreme poverty, carrying out programs that allow for autonomy, and doing rigorous research on its programs. I’ve placed it in this category because I’m donating to it primarily because I see it as supporting a model of distributive justice that I would like to see further developed, rather than because I believe it is the most cost-effective giving opportunity available to me this year.
I considered supporting a variety of other causes with this portion of my giving, including environmental justice, animal welfare, criminal justice reform, preventing homelessness in the Bay Area, and justice for Native people in the U.S. In the end, I limited myself to the five causes above in order to keep each donation large enough to feel meaningful. I imagine I might make a significant contribution to political advocacy in the first half of 2017, but I’m currently uncertain what form that might take.
I briefly considered but decided against donating to GiveWell unrestricted to support our operating costs. I believe it makes sense for donors who value our research and recommendations to support GiveWell financially, but as an employee, I—like many of my colleagues—don’t believe I’m in a good position to diversify our donor base.
The post Staff members’ personal donations for giving season 2016 appeared first on The GiveWell Blog.
The following statements are true:
- “GiveWell is a nonprofit dedicated to finding outstanding giving opportunities through in-depth analysis. Thousands of hours of research have gone into finding our top-rated charities.”
- GiveWell recommends four deworming charities as having outstanding expected value. Why? Hundreds of millions of kids harbor parasitic worms in their guts. Treatment is safe, effective, and cheap, so much so that where the worms are common, the World Health Organization recommends administering pills once or twice a year to all children without incurring the cost of determining who is infected.
- Two respected organizations, Cochrane and the Campbell Collaboration, have systematically reviewed the relevant studies and found little reliable evidence that mass deworming does good.
That list reads like a logic puzzle. GiveWell relies on evidence. GiveWell recommends mass-deworming charities. The evidence says mass deworming doesn’t work. How is that possible? Most studies of mass deworming track impact over a few years. The handful that look longer term find big benefits, including one in Kenya that reports higher earnings in adulthood. So great is that benefit that even when GiveWell discounts it by some 99% out of doubts about generalizability, deworming charities look like promising bets.
Still, as my colleagues have written, the evidence on deworming is complicated and ambiguous. And GiveWell takes seriously the questions raised by the Cochrane and Campbell evidence reviews. Maybe the best discount is not 99% but 100%. That would make all the difference for our assessment. This is why, starting in October, I delved into deworming. In this post and the next, I will share what I learned.
In brief, my confidence rose in that Kenya study’s finding of higher earnings in adulthood. I will explain why below. My confidence fell in the generalizability of that finding to other settings, as discussed in the next post.
As with all the recommendations we make, our calculations may be wrong. But I believe they are reasonable and quite possibly conservative. And notice that they do not imply that the odds are 1 in 100 that deworming does great good everywhere and 99 in 100 that it does no good anywhere. It can instead imply that kids receiving mass deworming today need it less than those in the Kenya study, because today’s children have fewer worms or because they are healthy enough in other respects to thrive despite the worms.
Unsurprisingly, I do not know whether 99% overshoots or undershoots. I wish we had more research on the long-term impacts of deworming in other settings, so that we could generalize with more nuance and confidence.
In this post, I will first orient you with some conceptual and historical background. Then I’ll think through two concerns about the evidence base we’re standing on: that the long-term studies lack design features that would add credibility; and that the key experiment in Kenya was not randomized, as that term is generally understood.Background Conclusions vs. decisions
There’s a deeper explanation for the paradox that opens this post. Back in 1955, the great statistician John Tukey gave an after-dinner talk called “Conclusions vs Decisions,” in which he meditated on the distinction between judging what is true—or might be true with some probability—and deciding what to do with such information. Modern gurus of evidence synthesis retain that distinction. The Cochrane Handbook, which guides the Cochrane and Campbell deworming reviews, is emphatic: “Authors of Cochrane reviews should not make recommendations.” Indeed, researchers arguing today over the impact of mass deworming are mostly arguing about conclusions. Does treatment for worms help? How much and under what circumstances? How confident are we in our answers? We at GiveWell—and you, if you’re considering our charity recommendations—have to make decisions.
The guidelines for the GRADE system for rating the quality of studies nicely illustrates how reaching conclusions, as hard and complicated as it is, still leaves you several logical steps short of choosing action. Under the heading, “A particular quality of evidence does not necessarily imply a particular strength of recommendation,” we read:
For instance, consider the decision to administer aspirin or acetaminophen to children with chicken pox. Observational studies have observed an association between aspirin administration and Reye’s syndrome. Because aspirin and acetaminophen are similar in their analgesic and antipyretic effects, the low-quality evidence regarding the potential harms of aspirin does not preclude a strong recommendation for acetaminophen.
Similarly, high-quality evidence does not necessarily imply strong recommendations. For example, faced with a first deep venous thrombosis (DVT) with no obvious provoking factor patients must, after the first months of anticoagulation, decide whether to continue taking warfarin long term. High-quality randomized controlled trials show that continuous warfarin will decrease the risk of recurrent thrombosis but at the cost of increased risk of bleeding and inconvenience preferences. Because patients with varying values and preferences are likely to make different choices, guideline panels addressing whether patients should continue or terminate warfarin may—despite the high-quality evidence—offer a weak recommendation.
I think some of the recent deworming debate has nearly equated the empirical question of whether mass deworming “works” with the practical question of whether it should be done. More than many participants in the conversation, GiveWell has seriously analyzed the logical terrain between the two questions, with a explicit decision framework that allows and forces us to estimate a dozen relevant parameters. We have found the decision process no more straightforward than the research process appears to be. You can argue with how GiveWell has made its calls (and we hope you will, with specificity), and such argument will probably further expose the trickiness of going from conclusion to decision.
The rest of this post is about the “conclusions” side of the Tukey dichotomy. But having spent time with our spreadsheet helped me approach the research with a more discerning eye, for example, by sensitizing me to the crucial question of how to generalize from the few studies we have.The research on the long-term impacts of deworming
Two studies form the spine of GiveWell’s support for deworming. Ted Miguel and Michael Kremer’s seminal Worms paper reported that after school-based mass deworming in southern Busia county, Kenya, in the late 1990s, kids came to school more. And there were “spillovers”: even kids at the treated schools who didn’t take the pills saw gains, as did kids at nearby schools that didn’t get deworming. However, children did not better on standardized tests. In all treatment schools, children were given albendazole for soil-transmitted worms—hookworm, roundworm, whipworm. In addition, where warranted, treatment schools received praziquantel for schistosomiasis, which is transmitted through contact with water and was common near Lake Victoria and the rivers that feed it.
Worms at Work, the sequel written with Sarah Baird and Joan Hamory Hicks, tracked down the (former) kids 10 years later. It found that the average 2.4 years of extra deworming given to treatment group children led to 15% higher non-agricultural earnings, while hours devoted to farm work did not change. The earnings gain appeared concentrated in wages (as distinct from self-employment income), which rose 31%. That’s a huge benefit for a few dollars of deworming, especially if it accrued for years, and is what drives GiveWell’s recommendations of deworming charities.
Four more studies track impacts of mass deworming over the long run:
- In 2009–10, Owen Ozier surveyed children in Busia who were too young to have participated in the Kenya experiment, since they were not in school yet, but who might have benefited through the deworming of their school-age siblings and neighbors. (If your big sister and her friends don’t have worms, you’re less likely to get them too.) Ozier found that kids born right around the time of the experiment scored higher on cognitive tests years later.
- The Worms team confidentially shared initial results from the latest follow-up on the original experiment, based on surveys fields in 2011–14. Many of those former schoolchildren now have children of their own. The results shared are limited and preliminary, and I advised my colleagues to wait before updating their views based on this research.
- Kevin Croke followed up on a deworming experiment that took place across the border in Uganda in 2000–03. (GiveWell summary here.) Dispensing albendazole (for soil-transmitted worms) boosted children’s scores on basic tests of numeracy and literacy administered years later, in 2010 and 2011. I am exploring and discussing the findings with Kevin Croke, and don’t have anything to report yet.
- In a remarkable act of historical scholarship, Hoyt Bleakley tracked the impacts of the hookworm eradication campaign initiated by the Rockefeller Foundation in the American South a century ago. Though not a randomized experiment, his analysis indicates that children who benefited from the campaign went on to earn more in adulthood.
These studies have increased GiveWell’s confidence in generalizing from Worms at Work—but perhaps only a little. Two of the four follow-up on the original Worms experiment, so they do not constitute fully independent checks. One other is not experimental. For now, the case for mass deworming largely stands or falls with the Worms and Worms at Work studies. So I will focus on them.Worm Wars
A few years ago, the International Initiative for Impact Evaluation (3ie) funded British epidemiologists Alexander Aiken and Calum Davey to replicate Worms. (I served on 3ie’s board around this time.) With coauthors, the researchers first exactly replicated the study using the original data and computer code. Then they analyzed the data afresh with their preferred methods. The deeply critical write-ups appeared in the International Journal of Epidemiology in the summer of 2015. The next day, Cochrane (which our Open Philanthropy Project has funded) updated its review of the deworming literature, finding “quite substantial evidence that deworming programmes do not show benefit.” And so, on the dreary plains of academia, did the great worm wars begin.
I read through the blogospheric explosion of debate. Much of it is secondary for GiveWell, because it is about the reported bump-up in school attendance after deworming. That matters less to us than the long-term impact on earnings. Getting kids to school is only a means to other ends—at best. Similarly, much debate centers on those spillovers: all sides agree that the original Worms paper overestimated their geographic reach. But that is not so important when assessing charities that aim to deworm all (school-age) children in a region rather than a subset as in the experiment.
I think GiveWell should focus on these three criticisms aired in the debate:
- The Worms experiment and the long-term follow-ups lack certain design features that are common in epidemiology, with good reason, yet are rare in economics. For example, the kids in the study were not “blinded” through use of placebos to whether they were in a treatment or control group. Maybe they behaved differently merely because they knew they were being treated and observed.
- The Worms experiment wasn’t randomized, as that term is usually meant.
- Against the handful of promising (if imperfect) long-term studies are several dozen short-term studies, which in aggregate find little or no benefit for outcomes such as survival, height, weight, hemoglobin, cognition, and school performance. The surer we are that the short-term impacts are small, the harder it is to believe that the long-term impacts are big.
I will discuss the first two criticisms in this post and the third in the next.“High risk of bias”: Addressing the critique from epidemiology
Perhaps the most alarming charge against Worms and its brethren has been that they are at “high risk of bias” (Cochrane, Campbell, Aiken et al., Davey et al.). This phrase comes out of a method in epidemiology for assessing the reliability of studies. It is worth understanding exactly what it means.
Within development economics, Worms is seminal because when it circulated in draft in 1999, it launched the field experimentation movement. But it is not as if development economists invented randomized trials. Long before the “randomistas” appeared, epidemiologists were running experiments to evaluate countless drugs, devices, and therapies in countries rich and poor. Through this experience, they developed norms about how to run an experiment to minimize misleading results. Some are codified in the Cochrane Handbook, the bible of meta-analysis, which is the process of systematically synthesizing the available evidence on such questions as whether breast cancer screening saves lives.
The norms make sense. An experimental study is more reliable when there is:
- Good sequence generation: The experiment is randomized.
- Sequence concealment: No one knows before subjects enter the study who will be assigned to treatment and who to control. This prevents, for example, cancer patients from dropping out of a trial of a new chemotherapy when they or their doctors learn they’ve been put in the control group.
- Blinding: During the experiment, assignment remains hidden from subjects, nurses, and others who deliver or sustain treatment, so that they cannot adjust their behavior or survey responses, consciously or otherwise. Sometimes this requires giving people in the control group fake treatment (placebos).
- Double-blinding: The people who measure outcomes—who take blood pressure, or count the kids showing up for school—are also kept in the dark about who is treatment and who is control.
- Minimized incomplete outcome data (in economics, “attrition”): If some patients on an experimental drug fare so poorly that they miss follow-up appointments and drop out of a study, they could make the retained patients look misleadingly well-off.
- No selective outcome reporting: Impacts on all outcomes measured are reported—for otherwise we should become suspicious of omissions. Are the researchers hiding contrary findings, or mining for statistically significant impacts? One way researchers can reduce selective reporting and the appearance thereof is to pre-register their analytical plans on a website outside their control.
Especially when gathering studies for meta-analysis, epidemiologists prize these features, as well as clear reporting of their presence or absence.
Yet most of those features are scarce in economics research. Partly that is because economics is not medicine: in a housing experiment, to paraphrase Macartan Humphreys, an agency can’t give you a placebo housing voucher that leaves you sleeping in your car without your realizing it. Partly it is because these desirable features come with trade-offs: the flexibility to test un-registered hypotheses can let you find new facts; sometimes the hospital that would implement your experiment has its own views on how things should be done. And partly the gap between ideal and reality is a sign that economists can and should do better.
I can imagine that, if becoming an epidemiologist involves studying examples of how the absence of such design features can mislead—even kill—people, then this batch of unblinded, un-pre-registered, and even un-randomized deworming studies out of economics might look passing strange. So might GiveWell’s reliance upon them.
The scary but vague term of art, “high risk of bias,” captures such worries. The term arises from the Cochrane Handbook, which, as I’ve mentioned, is the authoritative guide for the process of systematically synthesizing available research on a health-related question. The Handbook, like meta-analysis in general, strives for an approach that is mechanical in its objectivity. Studies are to be sifted, sorted, and assessed on observable traits, such as whether they are blinded. In providing guidance to such work, the Handbook distinguishes credibility from quality. “Quality” could encompass such traits as whether proper ethical review was obtained. Since Cochrane focuses on credibility, the handbook authors excluded “quality” from their nomenclature for study design issues. They settled on “risk of bias” as a core term, it being the logical antithesis of credibility.
Meanwhile, while some epidemiologists have devised scoring systems to measure risk of bias—plus 1 point for blinding, minus 2 for lack of pre-registration, etc.—the Cochrane Handbook says that such scoring is “is not supported by empirical evidence.” So, out of a sort of humility, the Handbook recommends something simpler: run down a checklist of design features, and for each one, just judge whether a study has it or not. If it does, label it as having “low risk of bias” in that domain. Otherwise, mark it “high risk of bias.” If you can’t tell, call it “unclear risk of bias.”
Thus, when a study earns the “high risk of bias” label, that means that it lacks certain design features that all concerned agree are desirable. Full stop.
So while the Handbook’s checklist brings healthy objectivity to evidence synthesis, it also brings limitations, especially in our context:
- Those unversed in statistics, including many decision-makers, may not appreciate that “bias” carries a technical meeting that is less pejorative than the everyday one. It doesn’t mean “prejudiced.” It means “gives an answer different from the true answer, on average.” So, especially in debates that extend outside of academia, its use tends to sow confusion and inflame emotions.
- The binaristic label “high risk of bias” may be humble in origins, but it does not come off as humble in use. At least to non-experts the pronouncement, “the study is at high risk of bias,” seem confident. But how big is the potential bias and how great the risk? More precisely, what is the probability distribution for the bias? No one knows.
- While useful when distilling knowledge from reams of research, the objectivity of the checklist comes at a price in superficiality. And the trade-off becomes less warranted when examining five studies instead of 50. As members of the Worms team point out, some Cochrane-based criticisms of their work make less sense on closer inspection. For example, the lack of blinding in Worms “cannot explain why untreated pupils in a treatment school experienced sharply reduced worm infections.” As we will see, by probing beneath the surface of a study—engaging with its specifics, examining its data and code—one can learn much that can enhance or degrade credibility.
- The checklist is incomplete. E.g., with an assist from Ben Bernanke, economics is getting better at transparency. Perhaps we should brand all studies for which data and code have not been publicly shared as being at “high risk of bias” for opacity. The controversy that ensued after the 3ie-funded replication of Worms generated a lot of heat, but light too. There were points of agreement. New analysts brought new insights. Speaking personally, exploring the public data and code for Worms and Worms at Work ultimately raised my trust in those studies, as I will explain. If it had done opposite, that too would have raised my confidence in whatever conclusion I extracted. Arguably, Worms is now the most credible deworming study, for no other has survived such scrutiny.
So what is a decisionmaker to do with a report of “high risk of bias”? If the choice is between relying on “low risk” studies and “high risk” studies, all else equal, then the choice is clear: favor the “low risk” studies. But what if all the studies before you contain “high risk of bias”?
That question may seem to lead us to an analytical cul-de-sac. But some researchers have pushed through it, with meta-epidemiology. A 1995 article (hat tip: Paul Garner) drew together 250 studies from 33 meta-analyses of certain interventions relating to pregnancy, labor, and delivery. They asked: do studies lacking blinding or other good features report bigger impacts? The answers were “yes” for sequence concealment and double-blinding and “not so much” for randomization and attrition. More studies have been done like that. And researchers have even aggregated those, which I suppose is meta-meta-epidemiology. (OK, not really.) One example cited by the Cochrane Handbook finds that lack of sequence concealment is associated with an average impact exaggeration of 10%, and, separately, that lack of double-blinding is associated with exaggeration by 22%.
To operationalize “high risk of bias,” we might discount the reported long-term benefits from deworming by such factors. No one knows if those discounts would be right. But they would make GiveWell’s ~99% discount—which can compensate for 100-fold (10000%) exaggeration—look conservative.
The epidemiological perspective should alert economists to ways they can improve. And it has helped GiveWell appreciate limitations in deworming studies. But the healthy challenge from epidemiologists has not undermined the long-term deworming evidence as completely as it may at first appear.Why I pretty much trust the Worms experiment
I happened to attend a conference on “What Works in Development” at the Brookings Institution in 2008. As economists enjoyed a free lunch, the speaker, Angus Deaton, launched a broadside against the randomization movement. He made many points. Some were so deep I still haven’t fully grasped them. I remember best two less profound things he said. He suggested that Abhijit Banerjee and Esther Duflo flip a coin and jump out of an airplane, the lucky one with a parachute, in order to perform a much-needed randomized controlled trial of this injury-prevention technology. And he pointed out that the poster child of the randomization movement, Miguel and Kremer’s Worms, wasn’t actually randomized—at least not as most people understood that term.
It appears that that the charity that carried out the deworming for Miguel and Kremer would not allow schools to be assigned to treatment or control via rolls of a die or the computer equivalent. Instead, Deaton said, the 75 schools were listed alphabetically. Then they were assigned cyclically to three groups: the first school went to group 1, the second to group 2, the third to group 3, the fourth to group 1, and so on. Group 1 started receiving deworming treatment in 1998; group 2 in 1999; and group 3, the control, not until after the experiment ended in 2000. During the Q&A that day at Brookings, Michael Kremer politely argued that he could think of no good theory for why this assignment system would generate false results—why it would cause, say, group 1 students to attend school more for some reason other than deworming. I think Deaton replied by citing the example of a study that was widely thought to be well randomized until someone showed that it wasn’t. His point was that unless an experiment is randomized, you just can’t sure be that no causal demons lurk within.
This exchange came to mind when I began reading about deworming. As I say, GiveWell is less interested in whether treatment for worms raised school attendance in the short run than whether it raised earnings in the long run. But those long-term results, in Worms at Work, depend on the same experiment for credibility. In contrast with the meta-analytic response to this concern, which is to affix the label “high risk of bias for sequence generation” and move on, I dug into the study’s data. What I attacked hardest was the premise that before the experiment began, the three school groups were statistically similar, or “balanced.”
Mostly the premise won.Yes, there are reasons to doubt the Worms experiment…
If I were the prosecutor in Statistical balance police v. Miguel and Kremer, I’d point out that:
- Deaton had it wrong: schools were not alphabetized. It was worse than that, in principle. The 75 schools were sorted alphabetically by division and zone (units of local geography in Kenya) and within zones by enrollment. Thus, you could say, a study famous for finding more kids in school after deworming formed its treatment groups on how many kids were in school before deworming. That is not ideal. In the worst case, the 75 schools would have been situated in 25 zones, each with three schools. The cyclic algorithm would then have always put the smallest school in group 1, the middle in group 2, and the largest in group 3. And if the groups started out differing in size, they would probably have differed in other respects too, spoiling credibility. (In defense of Deaton, I should say that the authors’ description of the cyclical procedure changed between 2007 and 2014.)
- Worms reports that the experimental groups did start out different in some respects, with statistical significance: “Treatment schools were initially somewhat worse off. Group 1 pupils had significantly more self-reported blood in stool (a symptom of schistosomiasis infection), reported being sick more often than Group 3 pupils, and were not as clean as Group 2 and Group 3 pupils (as observed by NGO field workers).” Now, in checking balance, Table I of Worms makes 42 comparisons: group 1 vs. group 3 and group 2 vs. group 3 for 21 variables. Even if balance were perfect, when imposing a p = 0.05 significance threshold, one should expect about 5% of the tests to show up as significant, or about two of 42. In the event, five show up that way. I confirmed with formal tests that these differences were unexpected in aggregate if the groups were balanced.
- Moreover, the groups differed before the experiment in a way not previously reported: in school attendance. Again, this looks very bad, at least on the surface, since attendance is a major focus of Worms. According to school registers, attendance in grades 3–8 in early 1998 averaged 97.3%, 96.3%, and 96.9% in groups 1, 2, and 3 respectively. Notice that group 3’s rate put it between the two others. This explains why, when Worms separately compares groups 1 and 2 to 3, it does not find terribly significant differences (p = 0.4, 0.12). But the distance from group 1 to 2—which is not checked—is more significant (p = 0.02), as is that from group 1 to 2 and 3 averaged together (p = 0.06). In the first year of the experiment, only group 1 was treated. So if it started out with higher attendance, can we confidently attribute the higher attendance over the following year to deworming?
Miguel and Kremer point out that school registers, from which those attendance rates come, “are not considered reliable in Kenya.” Indeed, at about 97%, the rates converge rather implausibly toward perfection. This is why the researchers measured attendance by independently sending enumerators on surprise visits to schools. They found attendance around 68–76% in the 1998 control group schools (bottom of Table VI). So should we worry about a tiny imbalance in nearly meaningless school-reported attendance? Perhaps so. I find that at the beginning of the experiment the school- and researcher-reported attendance correlated positively. Each 1% increase in a school’s self-reported attendance—equivalent to moving from group 2 to group 1—predicted a 3% increase in researcher-recorded attendance (p = 0.008), making the starting difference superficially capable of explaining roughly half the direct impact found in Worms.
To start with, in response to the points above:
- Joan Hamory Hicks, who manages much of the ongoing Worms follow-up project, sent me the spreadsheet used to assign the 75 schools to the three groups back in 1997. Its contents do not approximate the worst case I described, with three schools in each zone. There are eight zones, and their school counts range from four to 15. Thus, cyclical assignment did introduce substantial arbitrariness with respect to initial school enrollment. In some zones the first and smallest school went into group 1, in others group 2, in others group 3.
- As for the documented imbalances, such as kids in group 1 schools being sick more often, Worms points out that these should probably make the study conservative: the groups that ultimately fared better started out worse off.
- The Worms team began collecting attendance data in all three groups, in early 1998 before the first deworming visits took place. Those more-accurate numbers do not suggest imbalance across the three groups (p = 0.43). And the correlation of school-recorded attendance, which is not balanced, and researcher-recorded attendance, which is, is not especially dispositive. If you looked across a representative 75 New York City schools at two arbitrarily chosen variables were—say, fraction of students who qualify for free meals and average class size—they could easily be correlated too. Finally, when I modify a basic Miguel and Kremer attendance regression (Table IX, col. 1) to control for the imbalanced school-recorded attendance variable, it hardly perturbs the results (except by restricting the sample because of missing observations for this variable). If initial treatment-control differences in school-recorded attendance were a major factor in the celebrated impact estimates, we would expect that controlling for the former would affect the latter
In addition, three observations more powerfully bolster the Worms experiment.
First, I managed to identify the 75 schools and link them to a public database of primary schools in Kenya. (In email, Ted Miguel expressed concern for the privacy of the study subjects, so I will not explain how I did this nor share the school-level information I gained thereby, except the elevations discussed just below.) This gave me fresh school-level variables on which to test the balance of the Worms experiment, such as institution type (religious, central government, etc.) and precise latitude and longitude. I found little suggestion of imbalance on the new variables as a group (p= 0.7, 0.2 for overall differences between group 1 or 2 and group 3; p = 0.54 for a difference between groups 1 and 2 together and group 3, which is the split in Worms at Work). Then, with a Python program I wrote, I used the geo-coordinates of the schools to query Google for their elevations in meters above sea level. The hypothesis that the groups differed on elevation is rejected at p = 0.36, meaning once more that a hypothesis of balance on a new variable is not strongly rejected. And if we aggregate groups 1 and 2 into a single treatment group as in Worms at Work, p = 0.97.
Second, after the Worms experiment finished in 2000—and all 75 schools were receiving deworming—Miguel and Kremer launched a second, truly randomized experiment in the same setting. With respect to earnings in early adulthood (our main interest), the new experiment generates similar, if less precise, results. The experiment took on a hot topic of 2001: whether to charge poor people for basic services such as schooling and health care, in order to make service provision more financially sustainable as well as more accountable to clients. The researchers took the 50 group 1 and group 2 schools from the first experiment and randomly split them into two new groups. In the new control group, children continued to receive deworming for free. In the new treatment group, for the duration of 2001, families were charged 30 shillings ($0.40) for albendazole, for soil-transmitted worms, and another 70 shillings ($0.90) for praziquantel, where warranted for schistosomiasis. In response to the “user fees,” take-up of deworming medication fell 80% in the treatment group (which therefore, ironically, received less treatment). In effect, a second and less impeachable deworming experiment had begun.
Like the original, this new experiment sent ripples into the data that the Worms team collected as it tracked the former schoolchildren into adulthood. Because the user fee trial affected a smaller group—50 instead of 75 schools—for a shorter time—one year instead of an average 2.4 in the original experiment—it did not generate deworming impact estimates of the same precision. This is probably why Worms at Work gives those impact estimates less space than the ones derived from the original experiment.
But they are there. And they tend to corroborate the main results. The regression that has anchored GiveWell’s cost-effectiveness analysis puts the impact of the first experiment’s 2.4 years of deworming on later wage earnings at +31% (p = 0.002). If you run the publicly available code on the publicly available data, you discover that the same regression estimates that being in the treatment arm of the second experiment cut wage earnings by 14% (albeit with less confidence: p = 0.08). The hypothesis that the two implied rates of impact are equal—31% per 2.4 years and 14% per 80% x 1 year—fits the data (p = 0.44). More generally, Worms at Work states that among 30 outcomes checked, in domains ranging from labor to health to education, the estimated long-term impacts of the two experiments agree in sign in 23 cases. The odds of that happening by chance alone are 1 in 383.
The third source of reinforcement for the Worms experiment is Owen Ozier’s follow-up. In 2009 and 2010, he and his assistants surveyed 2400 children in the Worms study area who were born between about 1995 and 2001. I say “about” because their birth dates were estimated by asking them how many years old they were, and if a child said in August 2009 that she was eight, that meant that she was born in 2000 or 2001. By design, the survey covered children who were too young to have been in school during the original Worms experiment, but who might have benefited indirectly, through the deworming of their older siblings and neighbors. The survey included several cognitive tests, among them Raven’s Matrices, which are best understood by looking at an example.
This graph from the Ozier working paper shows the impact of Miguel and Kremer’s 1998–2000 deworming experiment on Raven’s Matrix scores of younger children, by approximate year of birth:
To understand the graph, look at the right end first. The white bars extending slightly below zero say that among children born in 2001 (or maybe really 2002) those linked by siblings and neighbors to group 1 or group 2 schools scored slightly lower than those linked to group 3 schools—but not with any statistical significance. The effective lack of difference is easy to explain since by 2001, schools in all three groups were or had been receiving deworming. (Though there was that user fee experiment in 2001….) For children in the 2000 birth cohort, no comparisons are made, because of the ambiguity over whether those linked to group 3 were born in 2000, when group 3 didn’t receive deworming, or 2001, when it did. Moving to 1999, we find more statistically significant cognitive benefits for kids linked to the group 1 and 2 schools, which indeed received deworming in 1999–2000. Something similar goes for 1998. Pushing farther back, to children born before the experiment, we again find little impact, even though a few years after birth some would have had deworming-treated siblings and neighbors and some not. This suggests that the knock-on benefit for younger children was largely to confined to their first year of life.
The evidence that health problems in infancy can take a long-term toll is interesting in itself. But it matters for us in another way too. Suppose you think that because the Worms experiment’s quasi-randomization failed to achieve balance, initial cross-group differences in some factor, visible or hidden, generated the Worms at Work results. Then, essentially, you must explain why that factor caused long-term gains in cognitive scores only among kids born during the experiment. If, say, children at group 1 schools were less poor at the onset of the experiment, creating the illusion of impact, we’d expect the kids at those schools to be less poor a few years before and after too.
It’s not impossible to do meet this challenge. I conjectured that the Worms groups were imbalanced on elevation, which differentially exposed them to the destructive flooding caused by the strong 1997–98 El Nino. But my theory foundered on the lack of convincing evidence of imbalance on elevation, which I described above.
At any rate, the relevant question is not whether it is possible to construct a story for how poor randomization could falsely generate all the short- and long-term impacts found from the Worms experiment. It is how plausible those explanations would be. The more strained the alternative theories, the more credible does the straightforward explanation become, that giving kids deworming pills measurably helped them.
One caveat: GiveWell has not obtained Ozier’s data and code, so we have not vetted this study as much as we have Worms and Worms at Work.Summary
I came to this investigation with some reason to doubt Worms and found more when I arrived. But in the end, the defenses persuade me more than the attacks. I find that:
- The charge of “high risk of bias” is legitimate but vague.
- Under a barrage of tests, the statistical balance of the experiment mostly survives.
- The original experimented is corroborated by a second, randomized one.
- There is evidence that long-term cognitive benefits are confined to children born right around the time of the experiment, a pattern that is hard to explain except as impacts of deworming.
In addition, I plan to present some fresh findings in my next post that, like Ozier’s, seem to make alternative theories harder to fashion.
When there are both reasons to doubt and reasons to trust an experiment, the right response is not to shrug one’s shoulders, or give each point pro and con a vote, or zoom out and ponder whether to side with economists or epidemiologists. The right response is to ask: what is the most plausible theory that is compatible with the entire sweep of the evidence? For me, an important criterion for plausibility is Occam’s razor: simplicity.
As I see it now, the explanation that best blends simplicity and compatibility-with-evidence runs this way: the imbalances in the Worms experiment are real but small, are unlikely to explain the results, and if anything make those results conservative; thus, the reported impacts are indeed largely impacts. If one instead assumes the Worms results are artifacts of flawed experimental design, execution, and analysis, then one has to construct a complicated theory for why, e.g. the user fee experiment produces similar results, and why the benefits for non-school-age children appear confined to those born in the treatment groups around the time of differential treatment.
I hope that anyone who disagrees will prove me wrong by constructing an alternative yet simple theory that explains the evidence before us.
I’m less confident when it comes to generalizing from these experiments. Worms, Worms at Work, and Ozier tell us something about what happened after kids in one time and place were treated for intestinal helminths. What do those studies tell us about the effectiveness of deworming campaigns today, from Liberia to India? I’ll explore that next.Notes
The WHO estimates that 2 billion people carry soil-transmitted “geohelminths,” including hookworm, roundworm, and whipworm. Separately, it reports that 258 million people needed treatment for schistosomiasis which is transmitted by contact with fresh water. Children are disproportionately affected because of their play patterns and poorer hygiene.
For an overview, I recommend Tim Harford’s graceful take. To dig in more, see the Worms authors’ reply and the posts by Berk Ozler, Chris Blattman, and my former colleagues Michael Clemens and Justin Sandefur. To really delve, read Macartan Humphreys and the reply thereto.
Figures obtained by dividing the “total” point estimates from the linked figures into 1. The study expresses higher benefits as lower risk estimates, in the sense that risk of bad outcomes is reduced.
For the cumulative distribution function of the binomial distribution, F(30,7,0.5) = .00261.
We have refreshed our top charity rankings and recommendations. We now have seven top charities: our four top charities from last year and three new additions. We have also added two new organizations to our list of charities that we think deserve special recognition (previously called “standout” charities).
Instead of ranking organizations, we rank funding gaps, which take into account both charities’ overall quality and cost-effectiveness and what more funding would enable them to do. We also account for our expectation that Good Ventures, a foundation we work closely with, will provide significant support to our top charities ($50 million in total). Our recommendation to donors is based on the relative value of remaining gaps once Good Ventures’ expected giving is taken into account. We believe that the remaining funding gaps offer donors outstanding opportunities to accomplish good with their donations.Our top charities and recommendations for donors, in brief
We are continuing to recommend the four top charities we did last year and have added three new top charities:
- Against Malaria Foundation (AMF)
- Schistosomiasis Control Initiative (SCI)
- END Fund for work on deworming (added this year)
- Malaria Consortium for work on seasonal malaria chemoprevention (added this year)
- Sightsavers for work on deworming (added this year)
- Deworm the World Initiative, led by Evidence Action
We have ranked our top charities based on what we see as the value of filling their remaining funding gaps. We do not feel a particular need for individuals to divide their allocation across all of the charities, since we are expecting Good Ventures will provide significant support to each. For those seeking our recommended allocation, we recommend giving 75% to the Against Malaria Foundation and 25% to the Schistosomiasis Control Initiative, which we believe to have the most valuable unfilled funding gaps.
Our recommendation takes into account the amount of funding we think Good Ventures will grant to our top charities, as well as accounting for charities’ existing cash on hand, and expected fundraising (before gifts from donors who follow our recommendations). We recommend charities according to how much good additional donations (beyond these sources of funds) can do.
Other Charities Worthy of Special Recognition
As with last year, we also provide a list of charities that we believe are worthy of recognition, though not at the same level (in terms of likely good accomplished per dollar) as our top charities (we previously called these organizations “standouts”). They are not ranked, and are listed in alphabetical order.
- Development Media International (DMI)
- Food Fortification Initiative (FFI). FFI is a new addition to the list.
- The Global Alliance for Improved Nutrition’s Universal Salt Iodization program (GAIN – USI)
- Iodine Global Network (IGN)
- Living Goods
- Project Healthy Children (PHC). PHC is a new addition to the list.
Below, we provide:
- An explanation of major changes in the past year that are not specific to any one charity. More
- A discussion of our approach to room for more funding and our ranking of charities’ funding gaps. More
- Summary of key considerations for top charities. More
- Detail on each of our new top charities, including an overview of what we know about their work and our understanding of each organization’s room for more funding. More
- Detail on each of the top charities we are continuing to recommend, including an overview of their work, major changes over the past year and our understanding of each organization’s room for more funding. More
- The process we followed that led to these recommendations. More
- A brief update on giving to support GiveWell’s operations vs. giving to our top charities. More
Conference call to discuss recommendations
We are planning to hold a conference call at 5:30pm ET/2:30pm PT on Thursday, December 1 to discuss our recommendations and answer questions.
If you’d like to join the call, please register using this online form. If you can’t make this date but would be interested in joining another call at a later date, please indicate this on the registration form.
Below, we summarize the major causes of changes to our recommendations (since last year).
Most important changes in the last year:
- We engaged with more new potential top charities this year than we have in several years (including both inviting organizations to participate in our process and responding to organizations that reached out to us). This work led to three additional top charities. We believe our new top charities are outstanding giving opportunities, though we note that we are relatively less confident in these organizations than in our other top charities—we have followed each of the top charities we are continuing to recommend for five or more years and have only began following the new organizations in the last year or two.
- Overall, our top charities have more room for more funding than they did last year. We now believe that AMF, SCI, Deworm the World, and GiveDirectly have strong track records of scaling their programs. Our new top charities add additional room for more funding and we believe that the END Fund and Malaria Consortium, in particular, could absorb large amounts of funding in the next year. We expect some high-value opportunities to go unfilled this year.
- Last year, we wrote about the tradeoff between Good Ventures accomplishing more short-term good by filling GiveWell’s top charities’ funding gaps and the long-term good of saving money for other opportunities (as well as the good of not crowding out other donors, who, by nature of their smaller scale of giving, may have fewer strong opportunities). Due to the growth of the Open Philanthropy Project this year and its increased expectation of the size and value of the opportunities it may have in the future, we expect Good Ventures to set a budget of $50 million for its contributions to GiveWell top charities. The Open Philanthropy Project plans to write more about this in a future post on its blog.
Types of funding gaps
We’ve previously outlined how we categorize charities’ funding gaps into incentives, capacity-relevant funding, and execution levels 1, 2, and 3. In short:
- Incentive funding: We seek to ensure that each top charity receives a significant amount of funding (and to a lesser extent, that charities worthy of special recognition receive funding as well). We think this is important for long-run incentives to encourage other organizations to seek to meet these criteria. This year, we are increasing the top charity incentive from $1 million to $2.5 million.
- Capacity-relevant funding: Funding that we believe has the potential to create a significantly better giving opportunity in the future. With one exception, we don’t believe that any of our top charities have capacity-relevant gaps this year. We have designated the first $2 million of Sightsavers’ room for more funding as capacity-relevant because seeing results from a small number of Sightsavers deworming programs would significantly expand the evidence base for its deworming work and has the potential to lead us to want to support Sightsavers at a much higher level in the future (more).
- Execution funding: Funding that allows charities to implement more of their core programs. We separated this funding into three levels: level 1 is the amount at which we think there is a 50% chance that the charity will be bottlenecked by funding; level 2 is a 20% chance of being bottlenecked by funding, and level 3 is a 5% chance.
Ranking funding gaps
The first million dollars to a charity can have a very different impact from, e.g., the 20th millionth dollar. Accordingly, we have created a ranking of individual funding gaps that accounts for both (a) the quality of the charity and the good accomplished by its program per dollar, and (b) whether a given level of funding is capacity-relevant and whether it is highly or only marginally likely to be needed in the coming year.
The below table lays out our ranking of funding gaps. When gaps have the same “Priority,” this indicates that they are tied. When gaps are tied, we recommend filling them by giving each equal dollar amounts until one is filled, and then following the same procedure with the remaining tied gaps. See footnote for more.*
The table below includes the amount we expect Good Ventures to give to our top charities. For reasons the Open Philanthropy Project will lay out in another post, we expect that Good Ventures will cap its giving to GiveWell’s top charities this year at $50 million. We expect that Good Ventures will start with funding the highest-rated gaps and work its way down, in order to accomplish as much good as possible.
Note that we do not always place a charity’s full execution level at the same rank and in some cases rank the first portion of a given charity’s execution level ahead of the remainder. This is because many of our top charities are relatively close to each other in terms of their estimated cost-effectiveness (and thus, the value of their execution funding). For reasons we’ve written about in the past, we believe it is inappropriate to put too much weight on relatively small differences in explicit cost-effectiveness estimates. Because we expect that there are diminishing returns to funding, we would guess that the cost-effectiveness of a charity’s funding gap falls as it receives more funding.
Priority Charity Amount, in millions USD (of which, expected from Good Ventures*) Type Comment 1 Deworm the World $2.5 (all) Incentive – 1 SCI $2.5 (all) Incentive – 1 Sightsavers $2.5 (all) Incentive – 1 AMF $2.5 (all) Incentive – 1 GiveDirectly $2.5 (all) Incentive – 1 END Fund $2.5 (all) Incentive – 1 Malaria Consortium $2.5 (all) Incentive – 1 Other charities worthy of special recognition $1.5 (all) Incentive $250,000 each for six charities 3 SCI $6.5 (all) Fills rest of execution level 1 Highest cost-effectiveness of remaining level 1 gaps 4 AMF $8.5 (all) First part of execution level 1 Similar cost-effectiveness to END Fund and Sightsavers and greater understanding of the organization. Expect declining cost-effectiveness within Level 1, and see other benefits (incentives) to switching to END Fund and Sightsavers after this point. 5 END Fund $2.5 (all) Middle part of execution level 1 Given relatively limited knowledge of charity, capping total recommendation at $5 million 6 Sightsavers $0.5 (all) Fills rest of execution level 1 Similar cost-effectiveness to AMF and the END Fund 7 Deworm the World $2.0 (all) Fills execution level 2 Highest-ranked level 2 gap. Highest cost-effectiveness and confidence in organization 8 SCI $4.5 (all) First part of execution level 2 Highest cost-effectiveness of remaining level 2 gaps 9 Malaria Consortium $2.5 (all) Part of execution level 1 Given relatively limited knowledge of charity, capping total recommendation at $5 million 10 AMF $18.6 ($5.1) Part of execution level 1 Expect declining cost-effectiveness within level 1; ranked other gaps higher due to this and incentive effects 11 SCI $4.5 ($0) Fills execution level 2 Roughly expected to be more cost-effective than the remaining $49 million of AMF level 1
* Also includes $1 million that GiveWell holds for grants to top charities. More below.
The table below summarizes the key considerations for our seven top charities. More detail is provided below as well as in the charity reviews.Consideration AMF Malaria Consortium Deworm the World END Fund SCI Sightsavers GiveDirectly Estimated cost-effectiveness (relative to cash transfers) ~4x ~4x ~10x ~4x ~8x ~5x Baseline Our level of knowledge about the organization High Relatively low High Relatively low High Relatively low High Primary benefits of the intervention Under-5 deaths averted and possible increased income in adulthood Possible increased income in adulthood Immediate increase in consumption and assets Ease of communication Moderate Strong Strong Strong Moderate Moderate Strongest Ongoing monitoring and likelihood of detecting future problems Moderate Moderate Strong Moderate Moderate Moderate Strongest Room for more funding, after expected funding from Good Ventures and donors who give independently of our recommendation High: less than half of Execution Level 1 filled High: not quantified, but could likely use significantly more funding Low: Execution Levels 1 and 2 filled High: half of Execution Level 1 filled Moderate: Execution Level 1 and some of Level 2 filled Moderate: Execution Level 1 filled Very high: less than 15% of Execution Level 1 filled
If Good Ventures uses a budget of $50 million to top charities and follows our prioritization of funding gaps, it will make the following grants (in millions of dollars, rounded to one decimal place):
- AMF: $15.1
- Deworm the World: $4.5
- END Fund: $5.0
- GiveDirectly: $2.5
- Malaria Consortium: $5.0
- SCI: $13.5
- Sightsavers: $3.0
- Grants to other charities worthy of special recognition: $1.5
We also hold about $1 million that is restricted to granting out to top charities. We plan to use this to make a grant to AMF, which is the next funding gap on the list after the expected grants from Good Ventures.
We estimate that non-Good Ventures donors will give approximately $27 million between now and the start of June 2017; we expect to refresh our recommendations to donors in mid-June. Of this, we expect $18 million will be allocated according to our recommendation for marginal donations, while $9 million will be given based on our top charity list—this $9 million is considered ‘expected funding’ for each charity and therefore subtracted from their room for more funding.
$18 million spans two gaps in our prioritized list, so we are recommending that donors split their gift, with 75% going to AMF and 25% going to SCI, or give to GiveWell for making grants at our discretion and we will use the funds to fill in the next highest priority gaps.
Before this year, our top charity list had remained nearly the same for several years. This means that we have spent hundreds of hours talking to these groups, reading their documents, visiting their work in the field, and modeling their cost-effectiveness. We have spent considerably less time on our new top charities, particularly Malaria Consortium, and have not visited their work in the field (though we met with Sightsavers’ team in Ghana). We believe our new top charities are outstanding giving opportunities, though we think there is a higher risk that further investigation will lead to changes in our views about these groups.
A note about deworming
Four of our top charities, including two new top charities, support programs that treat schistosomiasis and soil-transmitted helminthiasis (STH) (“deworming”). We estimate that SCI and Deworm the World’s deworming programs are more cost effective than mass bednet campaigns, but our estimates are subject to substantial uncertainty. For Sightsavers and END Fund, our greater uncertainty about cost per treatment and prevalence of infection in the areas where they work leads us to the conclusion that the cost-effectiveness of their work is on par with that of bednets. It’s important to note that we view deworming as high expected value, but this is due to a relatively low probability of very high impact. Our cost-effectiveness model implies that most staff members believe you should use a multiplier of less than 1% compared to the impact (increased income in adulthood) found in the original trials—this could be thought of as assigning some chance that deworming programs have no impact, and some chance that the impact exists but will be smaller than was measured in those trials. Full discussion in this blog post. Our 2016 cost-effectiveness analysis is here.
This year, David Roodman conducted an investigation into the evidence for deworming’s impact on long-term life outcomes. David will write more about this in a future post, but in short, we think the strength of the case for deworming is similar to last year’s, with some evidence looking weaker, new evidence that was shared with us in an early form this year being too preliminary to incorporate, and a key piece of evidence standing up to additional scrutiny.
END Fund (for work on deworming)
Our full review of END Fund is here.
The END Fund (end.org) manages grants, provides technical assistance, and raises funding for controlling and eliminating neglected tropical diseases (NTDs). We have focused our review on its support for deworming.
About 60% of the treatments the END Fund has supported have been deworming treatments, while the rest have been for other NTDs. The END Fund has funded SCI, Deworm the World, and Sightsavers. We see the END Fund’s value-add as a GiveWell top charity as identifying and providing assistance to programs run by organizations other than those we separately recommend, and our review of the END Fund has excluded results from charities on our top charity list.
We have not yet seen monitoring results on the number of children reached in END Fund-supported programs. The END Fund has instituted a requirement that grantees conduct coverage surveys and the first results will be available in early 2017. While we generally put little weight on plans for future monitoring, we feel that the END Fund’s commitment is unusually credible because surveys are already underway or upcoming in the next few months, we are familiar enough with the type of survey being used (from research on other deworming groups) that we were able to ask critical questions, and the END Fund provided specific answers to our questions.
We have more limited information on some questions for the END Fund than we do for the top charities we have recommended for several years. We do not have a robust cost per treatment figure, and also have limited information on infection prevalence and intensity.
We estimate that the END Fund could productively use between $10 million (50% confidence) and $22 million (5% confidence) in the next year to expand its work on deworming. By our estimation, about a third of this would be used to fund other NTD programs.
This estimate is based on (a) a list of deworming funding opportunities that the END Fund had identified as of October and its expectation of identifying additional opportunities over the course of the year (excluding opportunities to grant funding to Deworm the World, SCI, or Sightsavers, which we count in those organizations’ room for more funding); and (b) our rough estimate of how much funding the END Fund will raise. The END Fund is a fairly new organization whose revenue comes primarily from a small number of major donors so it is hard to predict how much funding it will raise.
The END Fund’s list of identified opportunities includes both programs that END Fund has supported in past years and opportunities to get new programs off the ground.
Sightsavers (for work on deworming)
Our full review of Sightsavers is here.
Sightsavers (sightsavers.org) is a large organization with multiple program areas that focuses on preventing avoidable blindness and supporting people with impaired vision. Our review focuses on Sightsavers’ work to prevent and treat neglected tropical diseases (NTDs) and, more specifically, advocating for, funding, and monitoring deworming programs. Deworming is a fairly new addition to Sightsavers’ portfolio; in 2011, it began delivering some deworming treatments through NTD programs that had been originally set up to treat other infections.
We believe that deworming is a highly cost-effective program and that there is moderately strong evidence that Sightsavers has succeeded in achieving fairly high coverage rates for some of its past NTD programs. We feel that the monitoring data we have from SCI and Deworm the World is somewhat stronger than what we have from Sightsavers—in particular, the coverage surveys that Sightsavers has done to date were on NTD programs that largely did not include deworming. Sightsavers plans to do annual coverage surveys on programs that are supported by GiveWell-influenced funding.
We have more limited information on some questions for Sightsavers than we do for the top charities we have recommended for several years. We do not have a robust cost-per-treatment figure, though the information we have suggests that it is in the same range as the cost-per-treatment figures for SCI and Deworm the World. We also have limited information on infection prevalence and intensity in the places Sightsavers works. This limits our ability to robustly compare Sightsavers’ cost effectiveness to other top charities, but our best guess is that the cost-effectiveness of the deworming charities we recommend is similar.
We believe Sightsavers could productively use or commit between $3.0 million (50% confidence) and $10.1 million (5% confidence) in funding restricted to programs with a deworming component in 2017.
This estimate is based on (a) a list of deworming funding opportunities that Sightsavers created for us; and (b) our understanding that Sightsavers would not allocate much unrestricted funding to these opportunities in the absence of GiveWell funding. It’s difficult to know whether other funders might step in to fund this work, but Sightsavers believes that is unlikely and deworming has not been a major priority for Sightsavers to date.
Sightsavers’ list of opportunities includes both adding deworming to existing NTD mass distribution programs and establishing new integrated NTD programs that would include deworming and spans work in Nigeria, Guinea-Bissau, Democratic Republic of Congo, Guinea, Cameroon, Cote d’Ivoire, and possibly South Sudan.
Malaria Consortium (for work on seasonal malaria chemoprevention)
Our full review of Malaria Consortium is here.
Malaria Consortium (malariaconsortium.org) works on preventing, controlling, and treating malaria and other communicable diseases in Africa and Asia. Our review has focused exclusively on its seasonal malaria chemoprevention (SMC) programs, which distribute preventive anti-malarial drugs to children 3-months to 59-months old in order to prevent illness and death from malaria.
The evidence for SMC appears strong (stronger than deworming and not quite as strong as bednets), but we have not yet examined the intervention at nearly the same level that we have for bednets, deworming, unconditional cash transfers, or other priority programs. The randomized controlled trials on SMC that we considered showed a decrease in cases of clinical malaria but were not adequately powered to find an impact on mortality.
Malaria Consortium and its partners have conducted studies in most of the countries where it has worked to determine whether its programs have reached a large proportion of children targeted. These studies have generally found positive results, but leave us with some remaining questions about the program’s impact.
Overall, we have more limited information on some questions for Malaria Consortium than we do for the top charities we have recommended for several years. We have remaining questions on cost per child per year and on offsetting effects from possible drug resistance and disease rebound.
We have not yet attempted to estimate Malaria Consortium’s maximum room for more funding. We would guess that Malaria Consortium could productively use at least an additional $30 million to scale up its SMC activities over the next three to four years. We have a general understanding of where additional funds would be used but have not yet asked for a high level of detail on potential bottlenecks to scaling up.
We do not believe Malaria Consortium has substantial unrestricted funding available for scaling up its support of SMC programs and expect its restricted funding for SMC to remain steady or decrease in the next few years.
Against Malaria Foundation (AMF)
Our full review of AMF is here.
AMF (againstmalaria.com) provides funding for long-lasting insecticide-treated net distributions (for protection against malaria) in developing countries. There is strong evidence that distributing nets reduces child mortality and malaria cases.
AMF provides a level of public disclosure and tracking of distributions that we have not seen from any other net distribution charity.
We estimate that AMF’s program is roughly 4 times as cost effective as cash transfers (see our cost-effectiveness analysis). This estimate seeks to incorporate many highly uncertain inputs, such as the effect of mosquito resistance to the insecticides used in nets on how effective they are at protecting against malaria, how differences in malaria burden affect the impact of nets, and how to discount for displacing funding from other funders, among many others.
Important changes in the last 12 months
In 2016, AMF significantly increased the number and size of distributions it committed funding to. Prior to 2015, it had completed (large-scale) distributions in two countries, Malawi and Democratic Republic of Congo (DRC). In 2016, it completed a distribution in Ghana and committed to supporting distributions in an additional three countries, including an agreement to contribute $28 million to a campaign in Uganda, its largest agreement to date by far.
AMF has continued to collect and share information on its past large-scale distributions. This includes both data from registering households to receive nets (and, in some cases, data on the number of nets each household received) and follow-up surveys to determine whether nets are in place and in use. Our research in 2016 has led us to moderately weaken our assessment of the quality of AMF’s follow up surveys. In short, we learned that the surveys in Malawi have not used fully randomized selection of households and that the first two surveys in DRC were not reliable (full discussion in this blog post). We expect to see follow-up surveys from Ghana and DRC in the next few months that could expand AMF’s track record of collecting this type of data. We also learned that AMF has not been carrying out data audits in the way we believed it was (though this was not a major surprise as we had not asked AMF for details of the auditing process previously).
AMF has generally been communicative and open with us. We noted in our mid-year update that AMF had been slower to share documentation for some distributions; however, we haven’t had concerns about this in the second half of the year.
In August 2016, four GiveWell staff visited Ghana where an AMF-funded distribution had recently been completed. We met with AMF’s program manager, partner organizations, and government representatives and visited households in semi-urban and rural areas (notes and photos from our trip).
Our estimate of the cost-effectiveness of nets has fallen relative to cash transfers since our mid-year update. At that point, we estimated that nets were ~10x as cost-effective as cash transfers, and now we estimate that they are ~4x as cost-effective as cash transfers. This change was partially driven by changes in GiveWell staff’s judgments on the tradeoff between saving lives of children under five and improving lives (through increased income and consumption) in our model, and partially driven by AMF beginning to fund bed net distributions in countries with lower malaria burdens than Malawi or DRC.
AMF currently holds $17.8 million, and expects to commit $12.9 million of this soon. We estimate it will receive an additional $4 million by June 2017 ($2 million from donors not influenced by GiveWell and $2 million from donors who give based on our top charity list) that it could use for future distributions. Together, we expect that AMF will have about $9 million for new spending and commitments in 2017.
We estimate that AMF could productively use or commit between $87 million (50% confidence) and $200 million (5% confidence) in the next year. We arrived at this estimate from a rough estimate of the total Africa-wide funding gap for nets in the next three years (from the African Leaders Malaria Alliance)—estimated at $125 million per year. The estimate is rough in large part because the Global Fund to Fight AIDS, Tuberculosis and Malaria, the largest funder of LLINs, works on three-year cycles and has not yet determined how much funding it will allocate for LLINs for 2018-2020. We talked to people involved in country-level planning of mass net distributions and the Global Fund, who agreed with the general conclusion that there were likely to be large funding gaps in the next few years. In mid-2016, AMF had to put some plans on hold due to lack of funding.
We now believe that AMF has a strong track record of finding distribution partners to work with and coming to agreements with governments, and we do not expect that to be a limiting factor for AMF. The main risks we see to AMF’s ability to scale are the possibility that funding from other funders is sufficient (since our estimate of the gap is quite rough), the likelihood that government actors have limited capacity for discussions with AMF during a year in which they are applying for Global Fund funding, AMF’s staff capacity to manage discussions with additional countries (it has only a few staff members), and whether gaps will be spread across many countries or located in difficult operating environments. We believe the probability of any specific one of these things impeding AMF’s progress is low.
We believe there are differences in cost-effectiveness within execution level 1 and believe the value of filling the first part of AMF’s gap may be higher than additional funding at higher levels. This is because AMF’s priorities include committing to large distributions in the second half of 2019 and 2020, which increases the uncertainty about whether funding would have been available from another source.
We and AMF have discussed a few possibilities for how AMF might fill funding gaps. AMF favors an approach where it purchases a large number of nets for a small number of countries. This approach has some advantages including efficiency for AMF and leverage in influencing how distributions are carried out. Our view is that the risk of displacing a large amount of funding from other funders using this approach outweighs the benefits. If AMF did displace a large amount of funding which would otherwise have gone to nets, that could make donations applied to these distributions considerably less cost-effective. More details on our assessment of AMF’s funding gap are in our full review.
Deworm the World Initiative, led by Evidence Action
Our full review of Deworm the World is here.
Deworm the World (evidenceaction.org/#deworm-the-world), led by Evidence Action, advocates for, supports, and evaluates deworming programs. It has worked in India and Kenya for several years and has recently expanded to Nigeria, Vietnam, and Ethiopia.
Deworm the World retains or hires monitors who visit schools during and following deworming campaigns. We believe its monitoring is the strongest we have seen from any organization working on deworming. Monitors have generally found high coverage rates and good performance on other measures of quality.
As noted above, we believe that Deworm the World is slightly more cost-effective than SCI, more cost-effective than AMF and the other deworming charities, and about 10 times as cost-effective as cash transfers.
Important changes in the last 12 months
Deworm the World has made somewhat slower progress than expected in expanding to new countries. In late 2015, Good Ventures, on GiveWell’s recommendation, made a grant of $10.8 million to Deworm the World to fund its execution level 1 and 2 gaps. Execution level 1 funding was to give Deworm the World sufficient resources to expand into Pakistan and another country. Deworm the World has funded a prevalence survey in Pakistan, which is a precursor to funding treatments in the country. It has not expanded into a further country that it was not already expecting to work in. As a result, we believe that Deworm the World has somewhat limited room for more funding this year.
Overall, we have more confidence in our understanding of Deworm the World and its parent organization Evidence Action’s spending, revenues, and financial position than we did in previous years. While trying to better understand this information this year, we found several errors. We are not fully confident that all errors have been corrected, though we are encouraged by the fact that we are now getting enough information to be able to spot inconsistencies. Evidence Action has been working to overhaul its financial system this year.
Our review of Deworm the World has focused on two countries, Kenya and India, where it has worked the longest. In 2016, we saw the first results of a program in another country (Vietnam), as well as continued high-quality monitoring from Kenya and India. The Vietnam results indicate that Deworm the World is using similar monitoring processes in new countries as it has in Kenya and India and that results in Vietnam have been reasonably strong.
Evidence Action hired Jeff Brown (formerly Interim CEO of the Global Innovation Fund) as CEO in 2015. Recently Evidence Action announced that he has resigned and has not yet been replaced. Our guess is this is unlikely to be disruptive to Deworm the World’s work; Grace Hollister remains Director of the Deworm the World Initiative.
We believe that there is a 50% chance that Deworm the World will be slightly constrained by funding in the next year and that additional funds would increase the chances that it is able to take advantage of any high-value opportunities it encounters. We estimate that if it received an additional $4.5 million its chances of being constrained by funding would be reduced to 20% and at $13.4 million in additional funding, this would be reduced to 5%.
In the next year, Deworm the World expects to expand its work in India and Nigeria and may have opportunities to begin treatments in Pakistan and Indonesia. It is also interested in using unrestricted funding to continue its work in Kenya, and puts a high priority on this program. Its work in Kenya has to date been funded primarily by the Children’s Investment Fund Foundation (CIFF) and this support is set to expire in mid 2017. It is unclear to us whether CIFF will continue providing funding for the program and, if so, for how long. Due to the possibility that Deworm the World unrestricted funding may displace funding from CIFF, and, to a lesser extent, the END Fund and other donors, we consider the opportunity to fund the Kenya program to be less cost-effective in expectation than it would be if we were confident in the size of the gap.
More details in our full review.
Schistosomiasis Control Initiative (SCI)
Our full review of SCI is here.
SCI (imperial.ac.uk/schisto) works with governments in sub-Saharan Africa to create or scale up deworming programs. SCI’s role has primarily been to identify recipient countries, provide funding to governments for government-implemented programs, provide advisory support, and conduct research on the process and outcomes of the programs.
SCI has conducted studies in about two-thirds of the countries it works in to determine whether its programs have reached a large proportion of children targeted. These studies have generally found moderately positive results, but leave us with some remaining questions about the program’s impact.
As noted above, we believe that SCI is slightly less cost-effective than Deworm the World, more cost-effective than AMF and the other deworming charities, and about 8 times as cost-effective as cash transfers.
Important changes in the last 12 months
In past years, we’ve written that we had significant concerns about SCI’s financial reporting and financial management, and the clarity of our communication with SCI. In June, we wrote that we had learned of two substantial errors in SCI’s financial managment and reporting that began in 2015. We also noted that we thought that SCI’s financial management and financial reporting, as well as the clarity of its communication with us overall, had improved significantly. In the second half of the year, SCI communicated clearly with us about its plans for deworming programs next year and its room for more funding.
SCI reports that it has continued to scale up its deworming programs over the past year and that it plans to start up new deworming programs in two states in Nigeria before the end of its current budget year.
This year, SCI has shared a few more coverage surveys from deworming programs in Ethiopia, Madagascar, and Mozambique that found reasonably high coverage.
Professor Alan Fenwick, Founder and Director of SCI for over a decade, retired from his position this year, though will continue his involvement in fundraising and advocacy. The former Deputy Director, Wendy Harrison, is the new Director.
We estimate that SCI could productively use or commit a maximum of between $9.0 million (50% confidence) and $21.4 million (5% confidence) in additional unrestricted funding in its next budget year.
Its funding sources have been fairly steady in recent years with about half of its revenue in the form of restricted grants, particularly from the UK government’s Department for International Development (this grant runs through 2018), and half from unrestricted donations, a majority of which were driven by GiveWell’s recommendation. We estimate that SCI will have around $5.4 million in unrestricted funding available to allocate to its 2017-18 budget year (in addition to $6.5 million in restricted funding).
SCI has a strong track record of starting and scaling up programs in a large number of countries. SCI believes it could expand significantly with additional funding, reaching more people in the countries it works in and expanding to Nigeria and possibly Chad.
More details in our full review.
Our full review of GiveDirectly is here.
GiveDirectly (givedirectly.org) transfers cash to households in developing countries via mobile phone-linked payment services. It targets extremely low-income households. The proportion of total expenses that GiveDirectly has delivered directly to recipients is approximately 82% overall. We believe that this approach faces an unusually low burden of proof, and that the available evidence supports the idea that unconditional cash transfers significantly help people.
We believe GiveDirectly to be an exceptionally strong and effective organization, even more so than our other top charities. It has invested heavily in self-evaluation from the start, scaled up quickly, and communicated with us clearly. It appears that GiveDirectly has been effective at delivering cash to low-income households. GiveDirectly has one major randomized controlled trial (RCT) of its impact and took the unusual step of making the details of this study public before data was collected (more). It continues to experiment heavily, with the aim of improving how its own and government cash transfer programs are run. It has recently started work on evaluations that benchmark programs against cash with the aim of influencing the broader international aid sector to use its funding more cost-effectively.
We believe cash transfers are less cost-effective than the programs our other top charities work on, but have the most direct and robust case for impact. We use cash transfers as a “baseline” in our cost-effectiveness analyses and only recommend other programs that are robustly more cost effective than cash.
Important changes in the last 12 months
GiveDirectly has continued to scale up significantly, reaching a pace of delivering $21 million on an annual basis in the first part of 2016 and expecting to reach a pace of $50 million on an annual basis at the end of 2016. It has continued to share informative and detailed monitoring information with us. Given its strong and consistent monitoring in the past, we have taken a lighter-touch approach to evaluating its processes and results this year.
The big news for GiveDirectly this year was around partnerships and experimentation. It expanded into Rwanda (its third country) and launched a program to compare, with a randomized controlled trial, another aid program to cash transfers (details expected to be public next year). The program is being funded by a large institutional funder and Google.org. It expects to do additional “benchmarking” studies with the institutional funder, using funds from Good Ventures’ 2015 $25 million grant, over the next few years.
It also began fundraising for and started a pilot of a universal basic income (UBI) guarantee—a program providing long-term, ongoing cash transfers sufficient for basic needs, which will be evaluated with a randomized controlled trial comparing the program to GiveDirectly’s standard lump sum transfers. The initial UBI program and study is expected to cost $30 million. We estimate that it is less cost-effective than GiveDirectly’s standard model, but it could have impact on policy makers that isn’t captured in our analysis.
We noted previously that Segovia, a for-profit technology company that develops software for cash transfer program implementers and which was started and is partially owned by GiveDirectly’s co-founders, would provide its software for free to GiveDirectly to avoid conflicts of interest. However, in 2016, after realizing that providing free services to GiveDirectly was too costly for Segovia (customizing the product for GiveDirectly required much more Segovia staff time than initially expected), the two organizations negotiated a new contract under which GiveDirectly will compensate Segovia for its services. GiveDirectly wrote about this decision here. GiveDirectly told us that it recused all people with ties to both organizations from this decision and evaluated alternatives to Segovia. Although we believe that there are possibilities for bias in this decision and in future decisions concerning Segovia, and we have not deeply vetted GiveDirectly’s connection with Segovia, overall we think GiveDirectly’s choices were reasonable. However, we believe that reasonable people might disagree with this opinion, which is in part based on our personal experience working closely with GiveDirectly’s staff for several years.
We believe that GiveDirectly is very likely to be constrained by funding next year. GiveDirectly has been rapidly building its capacity to enroll recipients and deliver funds, while some of its revenue has been redirected to its universal basic income guarantee program (either because of greater donor interest in that program or by GiveDirectly focusing its fundraising efforts on it).
We expect GiveDirectly to have about $20 million for standard cash transfers in its 2017 budget year. This includes raising about $15.8 million from non-GiveWell-influenced sources between now and halfway through its 2017 budget year (August 2017) and $4 million from donors who give because GiveDirectly is on GiveWell’s top charity list. $4 million is much less than GiveWell-influenced donors gave in the last year. This is because several large donors are supporting GiveDirectly’s universal basic income guarantee program this year and because one large donor gave a multi-year grant that we don’t expect to repeat this year.
GiveDirectly is currently on pace (with no additional hiring) to have four full teams operating its standard cash transfer model in 2017. To fully utilize four teams, it would need $28 million more than we expect it to raise. We accordingly expect that GiveDirectly will downsize somewhat in 2017, because we do not project it raising sufficient funds to fully utilize the increased capacity it has built to transfer money. Given recent growth, we believe that GiveDirectly could easily scale beyond four teams and we estimate that at $46 million more than we expect it to raise ($66 million total for standard transfers), it would have a 50% chance of being constrained by funding.Other charities worthy of special recognition
Last year, we recommended four organizations as “standouts.” This year we are calling this list “other charities worthy of special recognition.” We’ve added two organizations to the list: Food Fortification Initiative and Project Healthy Children. Although our recommendation to donors is to give to our top charities over these charities, they stand out from the vast majority of organizations we have considered in terms of the evidence base for their work and their transparency, and they offer additional giving options for donors who feel highly aligned with their work.
We don’t follow these organizations as closely as we do our top charities. We generally have one or two calls per year with each group, publish notes on our conversations, and follow up on any major developments.
We provide brief updates on these charities below:
- Organizations that have conducted randomized controlled trials of their programs:
- Development Media International (DMI). DMI produces radio and television programming in developing countries that encourages people to adopt improved health practices. It conducted a randomized controlled trial (RCT) of its program and has been highly transparent, including sharing preliminary results with us. The results of its RCT were mixed, with a household survey not finding an effect on mortality (it was powered to detect a reduction of 15% or more) and data from health facilities finding an increase in facility visits. (The results, because the trial was only completed in the last year, are not yet published.) We believe there is a possibility that DMI’s work is highly cost-effective, but we see no solid evidence that this is the case. We noted last year that DMI was planning to conduct another survey for the RCT in late 2016; it has decided not to move forward with this, but is interested in conducting new research studies in other countries, if it is able to raise the money to do so. It is our understanding that DMI will be constrained by funding in the next year. Our full review of DMI, with conversation notes and documents from 2016, is here.
- Living Goods. Living Goods recruits, trains, and manages a network of community health promoters who sell health and household goods door-to-door in Uganda and Kenya and provide basic health counseling. They sell products such as treatments for malaria and diarrhea, fortified foods, water filters, bednets, clean cookstoves, and solar lights. Living Goods completed a randomized controlled trial of its program and measured a 27% reduction in child mortality. Our best guess is that Living Goods’ program is less cost-effective than our top charities, with the possible exception of cash. Living Goods is scaling up its program and may need additional funding in the future, but has not yet been limited by funding. We published an update on Living Goods in mid-2016. Our 2014 review of Living Goods is here.
- Organizations working on micronutrient fortification: We believe that food fortification with certain micronutrients can be a highly effective intervention. For each of these organizations, we believe they may be making a significant difference in the reach and/or quality of micronutrient fortification programs but we have not yet been able to establish clear evidence of their impact. The limited analysis we have done suggests that these programs are likely not significantly more cost-effective than our top charities—if they were, we might put more time into this research or recommend a charity based on less evidence.
- Food Fortification Initiative (FFI). FFI works to reduce micronutrient deficiencies (especially folic acid and iron deficiencies) by doing advocacy and providing assistance to countries as they design and implement flour and rice fortification programs. We have not yet completed a full evidence review of iron and folic acid fortification, but our initial research suggests it may be competitively cost effective with our other priority programs. Because FFI typically provides support alongside a number of other actors and its activities vary widely among countries, it is difficult to assess the impact of its work. Our full review is here.
- Global Alliance for Improved Nutrition (GAIN) – Universal Salt Iodization (USI) program. GAIN’s USI program supports national salt iodization programs. We have spent the most time attempting to understand GAIN’s impact in Ethiopia. Overall, we would guess that GAIN’s activities played a role in the increase in access to iodized salt in Ethiopia, but we do not yet have confidence about the extent of GAIN’s impact. It is our understanding that GAIN’s USI work will be constrained by funding in the next year. Our review of GAIN, published in 2016 based on research done in 2015, is here.
- IGN. Like GAIN-USI, IGN supports (via advocacy and technical assistance rather than implementation) salt iodization. IGN is small, and GiveWell-influenced funding has made up a large part of its funding in the past year. This year, we published an update on our investigation into IGN’s work in select countries in 2015 and notes from our conversation with IGN to learn about its progress in 2016 and plans for 2017. It is our understanding that IGN will be constrained by funding in the next year. Our review of IGN, from 2014, is here.
- Project Healthy Children (PHC). PHC aims to reduce micronutrient deficiencies by providing assistance to small countries as they design and implement food fortification programs. Our review is preliminary and in particular we do not have a recent update on how PHC would use additional funding. Our review of PHC, published in 2016 but based on information collected in 2015, is here.
We plan to detail the work we completed this year in a future post as part of our annual review process. Much of this work, particularly our experimental work and work on prioritizing interventions for further investigation, is aimed at improving our recommendations in future years. Here we highlight the key research that led to our current recommendations. See our process page for our overall process.
- As in previous years, we did intensive follow up with each of our top charities, including publishing updated reviews mid-year. We had several conversations by phone with each organization, met in person with Deworm the World, SCI, and AMF (over the course of a 4-day site visit to Ghana), and reviewed documents they shared with us.
- In 2015 and 2016, we sought to expand top charity room for more funding and consider alternatives to our top charities by inviting other groups that work on deworming, bednet distributions, and micronutrient fortification to apply. This led to adding Sightsavers, the END Fund, Project Healthy Children, and Food Fortification Initiative to our lists this year. Episcopal Relief & Development’s NetsforLife® Program, Micronutrient Initiative, and Nothing but Nets declined to fully participate in our review process.
- We completed intervention reports on voluntary medical male circumcision (VMMC) and cataract surgery. We asked VMMC groups PSI (declined to fully participate) and the Centre for HIV and AIDS Prevention Studies (pending) to apply. We had conversations with several charities working on cataract surgery and have not yet asked any to apply.
- We did very preliminary investigations into a large number of interventions and prioritized a few for further work. This led to interim intervention reports on seasonal malaria chemoprevention (SMC), integrated community case management (iCCM) and ready-to-use therapeutic foods for treating severe acute malnutrition and recommending Malaria Consortium for its work on SMC.
- We stayed up to date on the research for bednets, cash transfers, and deworming. We published a report on insecticide resistance and its implications for bednet programs. A blog post on our work on deworming is forthcoming. We did not find major new research on cash transfers that affected our recommendation of GiveDirectly.
GiveWell and the Open Philanthropy Project are planning to split into two organizations in the first half of 2017. The split means that it is likely that GiveWell will retain much of the assets of the previously larger organization while reducing its expenses. We think it’s fairly likely that our excess assets policy will be triggered and that we will grant out some unrestricted funds. Given that expectation, our recommendation to donors is:
- If you have supported GiveWell’s operations in the past, we ask that you consider maintaining your support. It is fairly likely that these funds will be used this year for grants to top charities, but giving unrestricted signals your support for our operations and allows us to better project future revenue and make plans based on that. Having a strong base of consistent support allows us to make valuable hires when opportunities arise and minimize staff time spent on fundraising.
- If you have not supported GiveWell’s operations in the past, we ask that you consider checking the box on our donate form to add 10% to help fund GiveWell’s operations. In the long term, we seek to have a model where donors who find our research useful contribute to the costs of creating it, while holding us accountable to providing high-quality, easy-to-use recommendations.
* For example, if $30 million were available to fund gaps of $10 million, $5 million, and $100 million, we would recommend allocating the funds so that the $10 million and $5 million gaps were fully filled and the $100 million gap received $15 million.
We’re planning to release updated top-charity recommendations in mid-November, and one of the questions our staff has been debating recently is whether to recommend New Incentives as a top charity.
We’ve decided that New Incentives doesn’t currently meet our criteria for a top charity because its program doesn’t have sufficient evidence supporting it. However, we have been extremely impressed with and think very highly of New Incentives’ staff and are considering how best to support them in the future and incentivize others to found an organization like they did.
In this post, we summarize the answers to the key questions we asked to determine whether New Incentives meets our criteria for a top charity recommendation and the options we’re considering for future support.Background
New Incentives operates a conditional cash transfer (CCT) program in Nigeria to incentivize pregnant women to deliver in a health facility. New Incentives originally intended its CCT program to focus primarily on prevention of mother-to-child transmission (PMTCT) of HIV. However, under this model the program did not reach enough HIV-positive pregnant women to justify its operating costs, and in 2015, New Incentives expanded its program to target both HIV-positive women and HIV-negative women.
New Incentives was the first organization we supported as part of our experimental work to support the development of future top charities. It has been about two and a half years since New Incentives received its initial grant, and it now has a long enough track record implementing its program to be considered for a top charity designation.Is New Incentives’ intervention evidence-backed?
New Incentives’ impact is made up of three components: (a) delivering cash to very poor people, (b) incentivizing HIV-positive pregnant women to deliver in clinics and get the medicines that prevent mother-to-child transmission of HIV, and (c) incentivizing pregnant women to deliver their babies in a health facility.
Because a relatively small portion of New Incentives’ beneficiaries are HIV-positive, because it costs New Incentives more than GiveDirectly to deliver each dollar, and because it is likely reaching individuals with higher incomes than GiveDirectly does, the impact that has the dominant effect on our view about whether or not New Incentives meets the standard we have for a top charity’s cost-effectiveness is the impact of facility delivery on neonatal mortality.
The evidence we have for the impact of facility delivery comes from (1) relevant randomized controlled trials (RCTs), (2) monitoring that New Incentives carries out, and (3) non-RCT evidence on the impact of facility delivery.
Overall, the evidence from the RCTs increases our confidence that an intervention that offers improved neonatal care could have a significant impact on neonatal mortality, but the evidence we have seen and New Incentives’ current monitoring of its program is insufficient to convince us that increasing the number of women who deliver at facilities has a similar impact.
Randomized controlled trial evidence
Two RCTs of low-intensity training programs for traditional birth attendants found significant (30-45%) reductions in neonatal mortality. These interventions are different than New Incentives’ intervention but may have a similar effect since they aim to increase the knowledge of traditional birth attendants so that they offer similar care to that which is offered in health facilities. We did not find any RCTs on facility delivery itself; these two RCTs are the most similar ones to New Incentives’ program that we identified. The interventions varied:
- In Gill et al. 2011, the intervention group received training and supplies related to common practices to reduce neonatal mortality immediately following birth. The study observed significant differences between the treatment and control group on practices such as drying the baby with a cloth and then wrapping it in a separate blanket (as opposed to using the same blanket), clearing the baby’s mouth and nose with a suction bulb (instead of a cloth), and using a pocket resuscitator (instead of mouth to mouth) (see Table 5, Pg. 8).
We have not closely vetted this study but note some significant-seeming differences between the treatment and control birth attendants–in particular, the treatment group had significantly more education than the control group (see Table 1, Pg. 4).
- In Jokhio et al. 2005, the intervention group received supplies and 3 days of training focused on antepartum, intrapartum, and postpartum care, including activities such as: “how to conduct a clean delivery; use of the disposable delivery kit; when to refer women for emergency obstetrical care; and care of the newborn.” The intervention group was “asked to visit each woman at least three times during the pregnancy (at three, six, and nine months) to check for dangerous signs such as bleeding or eclampsia, and to encourage women with such signs to seek emergency obstetrical care.”
New Incentives’ monitoring
New Incentives’ staff interviews a nurse and conducts additional inspection at each health facility it considers working with. New Incentives reports the results of these interviews. Two questions are most relevant to our assessment of the similarity between the interventions studied in the RCTs discussed above and the care offered in facilities New Incentives works with.
New Incentives asks nurses at each health facility: 1) “What multiple steps do you take immediately after delivery?” and 2) “What are the essential steps immediately after birth in ensuring that the baby can breathe and is warm?”
For the first question, New Incentives counts how many of the following steps nurses say they take (without being prompted by the New Incentives staff member asking the question): a) Dry baby with cloth, b) Slightly rub baby, c) Clear airways, d) Use air mask if necessary, e) Regulate temperature (put on mother’s belly), f) Don’t know/refused to answer. For the second question, New Incentives captures a free form answer.
We have limited information about the differences in practices between the intervention and control groups in Jokhio et al. 2005, but we do have this information for Gill et al. 2011. (See Gill et al. 2011, Table 5, Pg. 8.) It does not appear that the way New Incentives evaluates answers to its first question can tell us whether nurses in the facilities with which it works follow the improved practices from Gill et al. 2011.
We aggregated the answers to the second question, and 17 of 54 answers explicitly mentioned using a bulb syringe or mucus extractor, which we would guess is equivalent to clearing the baby’s mouth and nose with a suction bulb in Gill et al. 2011 (another 11 mentioned ‘clear airways’ or ‘suck’ which might refer to the procedure used in Gill et al. 2011). We were not able to get additional relevant information from nurses’ answers to the second question.
New Incentives does not appear to ask questions that fully address the other major difference between the intervention and control groups in Gill: use of a resuscitation intervention.
The intervention offered by Jokhio et al. 2005 includes antenatal care in addition to intrapartum and postpartum care, and we don’t know what impacts each part of the intervention had.
Note that New Incentives does not systematically collect data on the type of care women who enroll in its program would have received had they not delivered in a facility, though it has done some limited surveys of traditional birth attendants in the areas it works in.
Non-randomized evaluations of the impact of facility delivery
We have not carefully reviewed these studies, and the studies we identified found mixed effects (including some studies finding higher neonatal mortality in facilities) but we have major questions about these studies’ ability to assess facilities’ causal impact. In particular, women may be more likely to go to a facility for childbirth when they are experiencing complications, which could bias the results.What is our best guess about New Incentives’ cost-effectiveness?
The most important questions in assessing New Incentives’ cost-effectiveness are (a) the impact its cash transfers have on rates of facility delivery and (b) the impact that increased facility delivery has on neonatal mortality.
New Incentives is conducting an RCT of its impact on (a) and preliminary results indicate that it had a significant impact on facility deliveries: 48% of women in the treatment group (i.e., all those who were offered the opportunity to enroll in the program even if they chose not to do so) delivered in a facility versus 27% in the control group. However, there are differences between the program studied by New Incentives’ RCT and its current program; the RCT only targeted HIV-positive women, so some portion of the impact may be attributable to educating women about the importance of PMTCT. The program studied in the RCT also provided larger cash transfers than New Incentives will provide in its ongoing program: the program originally gave 6,000 naira (approximately 19 US dollars) for enrollment, 20,000 naira for delivery, and 6,000 naira for an HIV test; the program currently gives 1,000 naira for enrollment and 10,000 naira for delivery.
As noted above, we have very limited information to rely on when forming an estimate of the impact of facility delivery on neonatal mortality, and we do not see the evidence from the RCTs described above as particularly relevant or informative.
However, in trying to arrive at our best guess of the impact of the program, we also considered the facts that:
- The interventions described in Gill et al. 2011 and Jokhio et al. 2005 are relatively low cost and of limited intensity, and they find significant decreases in neonatal mortality. This increases the plausibility that merely referring women to facilities for childbirth could have a similar, significant impact.
- Our intuition (supported by what appears to be conventional wisdom in the global health community) strongly implies that delivering in a facility (in general, without respect to the specific facilities New Incentives works with in Nigeria) is likely to lead to lower mortality than alternatives.
Philosophical value judgments
Based on the results from the RCTs, we would expect New Incentives’ program to primarily prevent deaths of very young children (largely those within the first days or week of life). In internal, staff discussions about New Incentives, we have asked ourselves how we value the lives of newborn children vs. the lives of those saved by malaria nets (the other life-saving intervention we currently recommend). We have not completed a thorough assessment of the ages at which people die from malaria, but our impression is that the median age of death is approximately 1.
We believe there is no “right” answer to this question, but depending on one’s values, the answer could have a significant impact on the relative cost-effectiveness of New Incentives vs. the Against Malaria Foundation, and by extension our other top charities.
Key considerations include:
- One could simply sum the number of remaining years of life lost due to a death of a newborn vs a 1-year-old.
- One could focus solely on lives saved and treat all lives as equivalent.
- One might say that families and society have invested more in 1-year-olds and that 1-year-olds have more self-awareness and “personhood” than newborns, leading to valuing the 1-year-old more than the newborn.
Primarily for the last reason, the GiveWell staff who participated in these discussions tend to value 1-year-old lives over newborns, though our relative weights vary considerably.
Best guess cost-effectiveness estimate
Ultimately, we don’t have enough information to arrive at a reliable estimate of the impact of facility delivery on neonatal mortality. Our best guess is extremely rough, based primarily on intuitions formed based on limited data, and one that could easily shift significantly. We asked all staff who primarily work on GiveWell research to (a) guess the likely effect of New Incentives’ program on neonatal mortality and (b) enter the philosophical values discussed above. This yielded a median staff estimate that New Incentives was approximately as cost-effective as cash (in GiveDirectly’s program). Our cost-effectiveness model is here (.xlsx).Is New Incentives transparent?
Yes – extremely. New Incentives has shared all of the information we have requested (and more) in a timely fashion. We feel that it is as good as any other organization we have ever engaged with on this criterion.Options we’re considering for future support of New Incentives and/or its staff
We have discussed each of the following options with New Incentives and plan to let New Incentives’ preference drive our decision about which one to choose. In considering these options, we took into account (a) the likely direct impact funding would have and (b) the incentives that funding would create for others considering starting a new organization like New Incentives.
- Recommend that Good Ventures (a foundation with which we work closely that has provided past funding for our experimental work) provide an “exit grant” of approximately $1.2 million to New Incentives. New Incentives relied heavily on funding we recommended in its scale up, and abruptly stopping funding could cause it significant harm. Our impression is that funders often give grantees exit grants to offer them time to comfortably adjust their plans for fundraising and spending; this has been GiveWell’s experience with support from institutional funders. We would plan to benchmark our recommendation to the level of support New Incentives could have expected from us over the next two years (January 2017 – December 2018) as of the last time Good Ventures made a grant (March 2016). $1.2 million represents half what we would have projected New Incentives spending to be in 2017 and 2018 as of March 2016. (It grew faster than we expected since March 2016, so this is less than 50% of its projected operating expenses.)
- Recommend that Good Ventures agree to support some portion of New Incentives’ ongoing operations and a randomized controlled trial of New Incentives’ program’s impact on neonatal mortality. New Incentives’ program doesn’t seem cost-effective enough that we’d be willing to recommend that Good Ventures fully fund an RCT and New Incentives’ ongoing operations, but we’d consider recommending some, significant support (very roughly, we’d cap a recommendation at 50% of the total cost) if New Incentives could raise the rest of the funding elsewhere. This option would provide New Incentives with the opportunity to demonstrate that its program is more effective/cost-effective than we currently expect it to be as long as it is able to convince other funders to provide some support as well.
- Provide support to New Incentives/the New Incentives team to do something new. If New Incentives or its staff were interested in starting a new charity aiming to be a GiveWell top charity or significantly changing its program to focus on something more cost-effective, we would recommend that Good Ventures provide support.
We hope to decide soon about which option to pursue.
 We identified two relevant meta-analyses. Chinkhumba et al. 2014, a meta-analysis of six prospective cohort studied of perinatal mortality in sub-Saharan Africa found 21% higher perinatal mortality in home deliveries compared to facility deliveries (OR 1.21 [1.02-1.46]) using a fixed-effects model, but this difference was not significant using a random effects model (OR 1.21 [0.79-1.84]).
We are also concerned that studies limited to the perinatal period may not capture longer-term neonatal effects. Tura et al. 2013, a meta-analysis of 19 studies (of various methodology) of the effect of facility delivery on neonatal mortality, found mixed results. Pooled results from low- and middle-income countries showed 29% reduction in risk of neonatal death associated with facility delivery. However, results of the studies were highly heterogeneous. Of the 8 studies in sub-Saharan Africa, 4 found effect near the pooled mean, and the other 4 did not find a statistically significant effect. (Of the four that did not find a significant effect, two studies found a nonsignificant effect close to the pooled mean of all studies, and two found no effect.)
A retrospective study based on the demographic and health surveys in Nigeria found that facility delivery is associated with increased neonatal mortality (adjusted odds ratio 1.28 [1.11-1.47], Fink et al. 2015, Figure 1, Pg. 5).
In our mid-year update, we continued to recommend that donors give to the Against Malaria Foundation (AMF), and we wrote that we believe AMF has the most valuable current funding gap among our top charities. We also briefly wrote about some new concerns we have about AMF based on our research from the first half of 2016.
This post describes our new concerns about AMF’s transparency and monitoring in more depth. We continue to believe that AMF stands out among bed net organizations, and among charities generally, for its transparency and the quality of its program monitoring. AMF makes substantial amounts of useful information on the impact of its programs—far more than the norm—publicly available on its website, and has generally appeared to value transparency as much as any organization we’ve encountered. But our research on AMF in 2016 has led us to moderately weaken our view that AMF stands out for these qualities. In short, this is because:
- The first two post-distribution check-up surveys following AMF’s bed net distribution in Kasaï-Occidental, Democratic Republic of the Congo (DRC) were poorly implemented. AMF told us that it agrees that the surveys were poorly implemented and is working to improve data quality in future surveys.
- We learned that the methodology for selecting communities and households for post-distribution check-up surveys in Malawi is less rigorous than we had previously thought.
- AMF was slower to share information with us in 2016 than we would have liked. Unfortunately, we aren’t fully confident about what caused this to happen. We believe that AMF misunderstood the type of information we would value seeing, and this may have caused some (but not all) of this issue.
These updates somewhat lower our confidence in AMF’s track record of distributing bed nets that are used effectively (i.e., present, hanging, and in good condition) over the long term and in its commitment to transparency; however, this is only a moderate update, and we don’t think what we’ve learned is significant enough to outweigh AMF’s strengths. We continue to recommend AMF and believe that it has the highest-value current funding gap of any of our top charities.
Going forward, we plan to continue to learn more about AMF’s transparency and monitoring through reviewing the results of additional post-distribution surveys and continued communication with AMF.Background on AMF’s strengths and evidence of impact
We recommend AMF because there is strong evidence that mass distribution of long-lasting insecticide-treated bed nets reduces child mortality and is cost-effective, and because of AMF’s strengths as an organization: a standout commitment to transparency and self-evaluation, substantial room for more funding to deliver additional bed nets, and relatively strong evidence overall on whether bed nets reach intended destinations and are used over the long term.
In particular, AMF requires that its distribution partners implement post-distribution check-up surveys every six months among a sample (around 5%) of recipient households for 2.5 years following a bed net distribution, and has publicly shared the results of these surveys from several of its bed net distributions in Malawi. It’s our understanding that AMF is quite unusual in this regard—other organizations that fund bed nets do not typically require post-distribution check-up surveys to monitor bed net usage over time, and do not publicly share monitoring data as AMF does.Evidence of the impact of AMF’s bed net distribution in Kasaï-Occidental, DRC
AMF has sent us reports and data from two post-distribution check-up surveys (from eight months and twelve months after the distribution) from Kasaï-Occidental, DRC.
Donors may not realize that AMF has a short track record of major distributions. It has historically primarily worked with Concern Universal in Malawi, so these are the first surveys we’ve seen from large-scale AMF bed net distributions outside of Malawi. AMF’s post-distribution check-up surveys are intended to provide evidence on how many AMF nets are used effectively over the long-term, but we (and AMF) believe that these surveys in Kasaï-Occidental, DRC were poorly implemented (details in footnote).
Due to the extent of the implementation issues, we don’t think the post-distribution check-up surveys provide a reliable estimate of the proportion of bed nets distributed in Kasaï-Occidental, DRC used effectively over the long term. (Note that AMF earlier provided a distribution report, registration data, and photos and videos as evidence that bed nets originally reached intended destinations.) It seems plausible to us that the reported rates of nets in-use (hung over a sleeping space) from the AMF distribution in the 8-month post-distribution check-up survey (~80%) and the 12-month post-distribution check-up survey (64-69%) are either substantial overestimates or substantial underestimates.Non-random sampling in post-distribution surveys in Malawi
This year, we learned that Concern Universal, AMF’s distribution partner in Malawi, does not use a completely random process to select participants for post-distribution surveys. We have received some conflicting information from AMF and Concern Universal on the specifics of how the selection process deviates from pure randomization, so we aren’t confident that we fully understand how the selection process works in practice (details in footnote).
Our earlier understanding was that Concern Universal randomly selected villages and households for post-distribution surveys without any adjustments.
We are now concerned that the results from post-distribution surveys from Malawi could be somewhat biased estimates of the long-term impact of AMF’s distributions (though we wouldn’t guess that the effect of the bias on the results would be very large, since AMF and Concern Universal described selection processes that seem likely to produce reasonably representative samples).
AMF told us that it may reconsider its requirements for random selection of participants in future post-distribution surveys and invited us to make suggestions for improvement.AMF’s transparency and communication
Although we still believe that AMF stands out among bed net organizations for its commitment to transparency, AMF has recently been less transparent with us than we’ve come to expect.
In early 2016, we requested several documents from AMF (including the 8-month and 12-month post distribution surveys from Kasaï-Occidental, DRC, malaria case rate data from clinics in Malawi, and audits of household registration data from Malawi), which AMF told us it had available and would share once it had the capacity to review and edit them. Although we eventually received reports and data from the two DRC post-distribution surveys in June, we still haven’t seen the other documents we requested. AMF responded to these concerns here.
We are concerned that AMF did not tell us about the poor implementation of the first two Kasaï-Occidental, DRC surveys earlier, and that we only recently learned about the details of Concern Universal’s adjustments to random sampling for post-distribution surveys in Malawi. AMF told us it agrees that it should have communicated more clearly with us about these two issues and believes that it did not because it misunderstood the type of information we would value seeing. We are not confident that this fully explains AMF’s lack of transparency.What we hope to learn going forward
AMF’s track record of providing evidence of impact on its bed net distributions outside of Malawi is currently very limited. Our impression is that DRC is a difficult country for charities to work in; we’re uncertain whether the methodological issues with the first two surveys from Kasaï-Occidental were due to the difficulty of working in DRC specifically, to more general issues with AMF starting programs in new countries and working with new implementing partners, or to the relatively poor performance of an implementing partner.
AMF has told us that it expects the implementation of future post-distribution surveys in DRC to improve, and that it has made several changes to its practices in response to the issues discussed above, including:
- Hiring a Program Director, Shaun Walsh, whose primary job is to work in-country with distribution partners on planning, executing, and monitoring bed net distributions.
- Requiring more detailed budgets and plans from distribution partners for upcoming post-distribution surveys in Ghana, Uganda, and Togo.
- Focusing on improving timeliness of reporting on distributions and post-distribution surveys.
We plan to communicate closely with AMF on its upcoming post-distribution surveys, and update our views on AMF’s track record outside of Malawi when more survey results are available.
AMF’s reports on the surveys indicate that:
- It seems likely that different data collectors interpreted ambiguously-worded questions differently for both the 8-month and 12-month surveys. “Number of nets available” (translated from French) was variously interpreted as the number of nets hung, the number of nets hung plus the number of nets present but not hung, or the number of nets present but not hung. This led to internally inconsistent data (e.g. different numbers of nets reported for a single household for different survey questions) for a large proportion of households (42% in the 8-month post-distribution survey and around half in the 12-month post-distribution survey). AMF excluded households with internally inconsistent data from its analysis of the proportion of nets from the distribution still in use.
- AMF addressed this issue by re-writing survey questions after the 8-month survey, but the corrected survey questions were not put onto the data collectors’ smartphones before the 12-month survey.
- Household members sometimes reported inaccurate information to data collectors when survey questions were asked outside of a home. Data collectors later confirmed that the information was inaccurate (e.g. the household owned more bed nets than reported) by direct observation inside the home, but were not able to correct the data already entered into their smartphones.
- Data collectors did not distinguish between nets from the late 2014 AMF distribution and bed nets from other sources. AMF notes that the average level of previously-owned nets was around 2.5% so this would not have materially influenced the results of the post-distribution survey.
- AMF told us:
Concern Universal selects villages for post-distribution surveys in each health center catchment area where AMF nets were distributed. Concern Universal divides each health center catchment area into three “bands:” a short distance, medium distance, and far distance away from the health center. In each band, Concern Universal randomly selects between 25% and 50% of the villages. In each of those villages, Concern Universal randomly selects around 20% of the households.
- In April 2016, we spoke with a representative of Concern Universal, who told us that, in addition to the stratification of villages by geographic location described by AMF, that villages selected in one post-distribution survey are excluded from being selected for the following post-distribution survey.
Our goal with hosting quarterly open threads is to give blog readers an opportunity to publicly raise comments or questions about GiveWell or related topics (in the comments section below). As always, you’re also welcome to email us at firstname.lastname@example.org or to request a call with GiveWell staff if you have feedback or questions you’d prefer to discuss privately. We’ll try to respond promptly to questions or comments.
You can view our June 2016 open thread here.
An important question to ask when deciding where to give is “what would happen if this charity didn’t receive my donation?”
To investigate this, we focus on charities’ “room for more funding,” i.e., what will additional funding for this organization allow it to do that it would not be able to do without additional support from the donors GiveWell influences?
This question is relevant to the Against Malaria Foundation (AMF), currently our #1 rated charity, which provides funding to support malaria net distributions in Sub-Saharan Africa. In the past, we focused intensely on the question of whether AMF would be able to absorb and commit additional funds.
Recently, we asked another question: how likely is it that the bednet distributions that AMF supports would have been funded by others if AMF hadn’t provided funding? That is, would another funder have stepped in to provide funding in AMF’s absence?
If this were the case, our assessment of AMF’s impact would be diminished because it would seem likely that, in the absence of giving to AMF, the distributions it might have supported would occur anyway.
We can’t know what other funders might do in the future, so to learn more about this we looked back at cases from 2012 and 2013 where AMF had initially considered a distribution but then didn’t end up providing funding. We asked whether, and when, those distributions were eventually funded by others.
We looked at five cases where AMF considered funding a distribution but did not end up moving forward. In short:
- In two cases, major delays (18 months and ~36 months) occurred before people in the area received bednets from other sources.
- In two cases, other funders filled the gap six to nine months later than AMF would have.
- In one case, funding was committed soon after AMF’s talks fell through.
(For context, we use an “8%-20%-50%” model to estimate the longevity of bednets, which assumes that 92% of nets are still in use through the first year, 80% through the second, and 50% through the third (and none after the end of the third year). On average, then, we estimate that nets last about 27 months.)
More details are available in our full report on this investigation.
Of course, these cases aren’t necessarily predictive:
- It’s possible that the distributions were atypical, and that the reasons that led AMF to not carry out these distributions were the same reasons that led other funders to not fund them. This would mean that a typical AMF distribution might, in fact, be more likely to be funded by someone else, if AMF doesn’t fund it, than these results predict.
- It’s possible the global funding situation has changed since the cases we investigated in 2012 and 2013 – if more funding is now available overall, it would make it more likely that if AMF didn’t carry out a given distribution, another funder would step in.
That said, even if other funders would always step in if AMF didn’t carry out a distribution, it’s still possible that AMF is increasing the total number of bednets distributed, if there’s an overall funding gap for bednets globally. We’ve written more about the global bednet gap here. For this to be the case, it would likely require there exists some additional pool of funding that can be directed to bednets when necessary.
Overall, we think that the cases we looked at offer support to our conclusion that there is a real need for additional funding for bednets, and that AMF is not primarily displacing other funding for bednets.
The post Would other organizations have funded AMF’s bednet distributions if AMF hadn’t? appeared first on The GiveWell Blog.
We try to communicate that there are risks involved with all of our top charity recommendations, and that none of our recommendations are a “sure thing.”
Our recommendation of deworming programs (the Schistosomiasis Control Initiative and the Deworm the World Initiative), though, carries particularly significant risk (in the sense of possibly not doing much/any good, rather than in the sense of potentially doing harm). In our 2015 top charities announcement, we wrote:
Most GiveWell staff members would agree that deworming programs are more likely than not to have very little or no impact, but there is some possibility that they have a very large impact. (Our cost-effectiveness model implies that most staff members believe there is at most a 1-2% chance that deworming programs conducted today have similar impacts to those directly implied by the randomized controlled trials on which we rely most heavily, which differed from modern-day deworming programs in a number of important ways.)
The goal of this post is to explain this view and why we still recommend deworming.
Some basics for this post
What is deworming?
Deworming is a program that involves treating people at risk of intestinal parasitic worm infections with parasite-killing drugs. Mass treatment is very inexpensive (in the range of $0.50-$1 per person treated), and because treatment is cheaper than diagnosis and side effects of the drugs are believed to be minor, typically all children in an area where worms are common are treated without being individually tested for infections.
Does it work?
There is strong evidence that administration of the drugs reduces worm loads, but many of the infections appear to be asymptomatic and evidence for short-term health impacts is thin (though a recent meta-analysis that we have not yet fully reviewed reports that deworming led to short-term weight gains). The main evidence we rely on to make the case for deworming comes from a handful of longer term trials that found positive impacts on income or test scores later in life.
For more background on deworming programs see our full report on combination deworming.
Why do we believe it’s more likely than not that deworming programs have little or no impact?
The “1-2% chance” doesn’t mean that we think that there’s a 98-99% chance that deworming programs have no effect at all, but that we think it’s appropriate to use a 1-2% multiplier compared to the impact found in the original trials – this could be thought of as assigning some chance that deworming programs have no impact, and some chance that the impact exists but will be smaller than was measured in those trials. For instance, as we describe below, worm infection rates are much lower in present contexts than they were in the trials.
Where does this view come from?
Our overall recommendation of deworming relies heavily on a randomized controlled trial (RCT) (the type of study we consider to be the “gold standard” in terms of causal attribution) first written about in Miguel and Kremer 2004 and followed by 10-year follow up data reported in Baird et al. 2011, which found very large long-term effects on recipients’ income. We reviewed this study very carefully (see here and here) and we felt that its analysis largely held up to scrutiny.
There’s also some other evidence, including a study that found higher test scores in Ugandan parishes that were dewormed in an earlier RCT, and a high-quality study that is not an RCT but found especially large increases in income in areas in the American South that received deworming campaigns in the early 20th century. However, we consider Baird et al. 2011 to be the most significant result because of its size and the fact that the follow-up found increases in individual income.
While our recommendation relies on the long-term effects, the evidence for short-term effects of deworming on health is thin, so we have little evidence of a mechanism through which deworming programs might bring about long-term impact (though a recent meta-analysis that we have not yet fully reviewed reports that deworming led to short-term weight gains). This raises concerns about whether the long-term impact exists at all, and may suggest that the program is more likely than not to have no significant impact.
Even if there is some long-term impact, we downgrade our expectation of how much impact to expect, due to factors that differ between real-world implementations and the Miguel and Kremer trial. In particular, worm loads were particularly high during the Miguel and Kremer trial in Western Kenya in 1998, in part due to flooding from El Niño, and in part because baseline infection rates are lower in places where SCI and Deworm the World work than in the relevant studies.
Our cost-effectiveness model estimates that the baseline worm infections in the trial we mainly rely on were roughly 4 to 5 times as high as in places where SCI and Deworm the World operate today, and that El Niño further inflated those worm loads during the trial. (These estimates combine data on the prevalence of infections and intensity of infections, and so are especially rough because there is limited data on whether prevalence or intensity of worms is a bigger driver of impact). Further, we don’t know of any evidence that would allow us to disconfirm the possibility that the relationship between worm infection rates and the effectiveness of deworming is nonlinear, and thus that many children in the Miguel and Kremer trial were above a clinically relevant “threshold” of infection that few children treated by our recommended charities are above.
We also downgrade our estimate of the expected value of the impact based on: concerns that the limited number of replications and lack of obvious causal mechanism might mean there is no impact at all, expectation that deworming throughout childhood could have diminishing returns compared to the ~2.4 marginal years of deworming provided in the Miguel and Kremer trial, and the fact that the trial only found a significant income effect on those participants who ended up working in a wage-earning job. See our cost-effectiveness model for more information.
Why do we recommend deworming despite the reasonably high probability that there’s no impact?
Because mass deworming is so cheap, there is a good case for donating to support deworming even when in substantial doubt about the evidence. We estimate the expected value of deworming programs to be as cost-effective as any program we’ve found, even after the substantial adjustments discussed above: our best guess considering those discounts is that it’s still roughly 5-10 times as cost-effective as cash transfers, in expectation. But that expected value arises from combining the possibility of potentially enormous cost-effectiveness with the alternative possibility of little or none.
GiveWell isn’t seeking certainty – we’re seeking outstanding opportunities backed by relatively strong evidence, and deworming meets that standard. For donors interested in trying to do as much good as possible with their donations, we think that deworming is a worthwhile bet.
What could change this recommendation – will more evidence be collected?
To our knowledge, there are currently no large, randomized controlled trials being conducted that are likely to be suitable for long-term follow up to measure impacts on income when the recipients are adults, so we don’t expect to see a high-quality replication of the Miguel and Kremer study in the foreseeable future.
That said, there are some possible sources of additional information:
- The follow-up data that found increased incomes among recipients in the original Miguel and Kremer study was collected roughly 10 years after the trial was conducted. Our understanding is that 15 year follow-up data has been collected and we expect to receive an initial analysis of it from the researchers this summer.
- A recent study from Uganda didn’t involve data collection for the purpose of evaluating a randomized controlled trial; rather, the paper identified an old, short-term trial of deworming and an unrelated data set of parish-level test scores collected by a different organization in the same area. Because some of the parishes overlap, it’s possible to compare the test scores from those that were dewormed to those that weren’t. It’s possible that more overlapping data sets will be discovered and so we may see more similar studies in the future.
- We’ve considered whether to recommend funding for an additional study to replicate Baird et al. 2011: run a new deworming trial that could be followed for a decade to track long term income effects. However, it would take 10+ years to get relevant results, and by that time deworming may be fully funded by the largest global health funders. It would also need to include a very large number of participants to be adequately powered to find plausible effects (since the original trial in Baird et al. 2011 benefited from particularly high infection rates, which likely made it easier to detect an effect), so it would likely be extremely expensive.
For the time being, based on our best guess about the expected cost-effectiveness of the program when all the factors are considered, we continue to recommend deworming programs.
The post Deworming might have huge impact, but might have close to zero impact appeared first on The GiveWell Blog.
In addition to evaluations of other charities, GiveWell publishes substantial evaluation of ourselves, from progress against our goals to our impact on donations. We generally publish quarterly updates regarding two key metrics: (a) donations to top charities and (b) web traffic (though going forward, we may provide less frequent updates).
The tables and chart below present basic information about our growth in money moved and web traffic in the first quarter of 2016 compared to the previous two years (note 1).
Money moved and donors: first quarter
Money moved by donors who have never given more than $5,000 in a year increased about 50% to $1.1 million. The total number of donors in the first quarter increased about 30% to about 4,500 (note 2).
Most of our money moved is donated near the end of the year (we tracked 70% or more of our total money moved in the fourth quarter each of the last three years) and is driven by a relatively small number of large donors. Because of this, we do not think we can reliably predict our growth and think that our year-to-date total money moved provides relatively limited information about what our year-end money moved is likely to be (note 3). We therefore look at the data above as an indication of growth in our audience.
Web traffic through April 2016
Growth in web traffic excluding Google AdWords increased 10% in the first quarter. GiveWell’s website receives elevated web traffic during “giving season” around December of each year. To adjust for this and emphasize the trend, the chart below shows the rolling sum of unique visitors over the previous twelve months, starting in December 2009 (the first period for which we have 12 months of reliable data due to an issue tracking visits in 2008).
We use web analytics data from two sources: Clicky and Google Analytics (except for those months for which we only have reliable data from one source). The raw data we used to generate the chart and table above (as well as notes on the issues we’ve had and adjustments we’ve made) is in this spreadsheet. (Note on how we count unique visitors.)
Note 1: Since our 2012 annual metrics report we have shifted to a reporting year that starts on February 1, rather than January 1, in order to better capture year-on-year growth in the peak giving months of December and January. Therefore, metrics for the “first quarter” reported here are for February through April.
Note 2: Our measure of the total number of donors may overestimate the true number. We identify individual donors based on the reported name and email. Donors may donate directly to our recommended charities and not opt to share their contact information with us, or donors may use different information for subsequent donations (for example, a different email), in which case, we may mistakenly count a donation from a past donor as if it was made by a new donor. We are unsure but would guess that the impact of this issue is relatively small and that the data shown are generally reflective of our growth from year to year.
Note 3: In total, GiveWell donors directed $2.6 million to our top charities in the first quarter of 2016, compared to $2.0 million that we had tracked in the first quarter of 2015. For the reason described above, we don’t find this number to be particularly meaningful at this time of year.
Note 4: We count unique visitors over a period as the sum of monthly unique visitors. In other words, if the same person visits the site multiple times in a calendar month, they are counted once. If they visit in multiple months, they are counted once per month.
The post Update on GiveWell’s web traffic / money moved: Q1 2016 appeared first on The GiveWell Blog.
A major question we’ve asked ourselves internally over the last few years is how we should weigh organizational quality versus the value of the intervention that the organization is carrying out.
In particular, is it better to recommend an organization we’re very impressed by and confident in that’s carrying out a good program, or better to recommend an organization we’re much less confident in that’s carrying out an exceptional program? This question has been most salient when deciding how to rank giving to GiveDirectly vs giving to the Schistosomiasis Control Initiative.
GiveDirectly vs SCI
GiveDirectly is an organization that we’re very impressed by and confident in, more so than any other charity we’ve come across in our history. Reasons for this:
- GiveDirectly has successfully grown their operation from distributing $340,000 in fiscal year 2011-12 to a recent pace that corresponds to distributing $18 million per year. Our impression has been that they have maintained the same quality of their operation despite this growth.
- GiveDirectly has always communicated extremely clearly and directly with us, and appears to value transparency as much as any organization we’ve encountered. They’ve consistently anticipated questions we’ll have before we ask them, so they typically have good answers.
- GiveDirectly has an impressive commitment to monitoring and self-evaluation, including participating in a randomized controlled trial of its own program. Nearly all of its existing cash transfers are currently integrated into some form of study to gain more information.
- GiveDirectly was transparent about the case of staff fraud it discovered, writing about it here.
- While expanding its operations, GiveDirectly has also continually expanded its ambitions in other ways and shown an interest in trying new things – from a for-profit aiming to improve the efficiency of cash transfer distributions in the developing world (not formally related to GiveDirectly, but with significant staff/Board overlap) to its recent project to launch a basic income pilot.
But, we estimate that marginal dollars to the program it implements — direct cash transfers — are significantly less cost-effective than bednets and deworming programs. Excluding organizational factors, our best guess is that deworming programs — which SCI supports — are roughly 5 times as cost-effective as cash transfers. As discussed further below, our cost effectiveness estimates are generally based on extremely limited information and are therefore extremely rough, so we are cautious in assigning too much weight to them.
Despite the better cost-effectiveness of deworming, we’ve had significant issues with SCI as an organization. The two most important:
- We originally relied on a set of studies showing dramatic drops in worm infection coinciding with SCI-run deworming programs to evaluate SCI’s track record; we later discovered flaws in the study methodology that led us to conclude that they did not demonstrate that SCI had a strong track record. We wrote about these flaws in 2013 and 2014.
- We’ve seen limited and at times erroneous financial information from SCI over the years. We have seen some improvements in SCI’s financial reporting in 2016, but we still have some concerns, as detailed in our most recent report.
More broadly, both of these cases are examples of general problems we’ve had communicating with SCI over the years. And we don’t believe SCI’s trajectory has generated evidence of overall impressiveness comparable to GiveDirectly’s, discussed above.
Which should we recommend?
One argument is that GiveWell should only recommend exceptional organizations, and so the issues we’ve seen with SCI should disqualify them.
But, we think that the ~5x difference in cost-effectiveness is meaningful. There’s a large degree of uncertainty in our cost-effectiveness analyses, which is something we’ve written a lot about in the past, but this multiplier appears somewhat stable (it has persisted in this range over time, and currently is consistent with the individual estimates of many staff members), and a ~5x difference gives a fair amount of room for SCI to do more good even accounting both for possible errors in our analysis and for differences in organizational efficiency.
A separate argument that we’ve made in the past is that great organizations have upside that goes beyond the value of conducting the specific program they’re implementing. For example, early funding to a great organization may have allow it to grow faster and increase the amount of money going to their program globally, either through proving the model or through their own fundraising. And GiveDirectly has shown some propensity for potentially innovative projects, as discussed above.
We think that earlier funding to GiveDirectly had this benefit, but it’s less of a consideration now that GiveDirectly is a more mature organization. We believe this upside exists for what we’ve called “capacity-relevant” funding, which is the type of funding need that we consider to be most valuable when ranking the importance of marginal dollars to each of our top charities, and refers to funding gaps that we expect will allow organizations to grow in an outsized way in the future, for instance by going into a new country.
Our most recent recommendations ranked SCI’s funding gap higher than GiveDirectly’s due to SCI’s cost-effectiveness. We think that SCI is a strong organization overall, despite the issues we’ve noted, and we think that the “upside” for GiveDirectly is limited on the margin, so ultimately our estimated 5x multiplier looks meaningful enough to be determinative.
We remain conflicted about this tradeoff and regularly debate it internally, and we think reasonable donors may disagree about which organization to support.
The post Weighing organizational strength vs. estimated cost-effectiveness appeared first on The GiveWell Blog.
GiveWell does not recommend any charities focused on vaccine funding and distribution. But we remain excited about vaccinations as a health intervention. The vaccination programs we’ve researched have been backed by strong, independent evidence of effectiveness and appear likely to be competitive with our top charities in their cost-effectiveness. We’d be excited to support a charity to implement these programs. This post will describe why we don’t right now.
In brief, we’ve been looking for vaccination giving opportunities over the last few years, but have continued to fail to find them. This is due to (a) lack of room for more funding and (b) UNICEF’s decision not to participate in our review process.
In particular, over the past 1-2 years, we’ve been looking for funding opportunities for measles, meningitis A, and maternal and neonatal tetanus vaccination. Each of these is discussed in greater detail below.
Measles and Rubella Initiative
We have nearly completed an assessment of the evidence and cost-effectiveness for supplementary measles and rubella campaigns. (We’ve summarized our current take below; more detail will be available in our full intervention report, which we hope to publish on our website this year.) These campaigns supplement routine, childhood immunization and aim to vaccinate all children against measles and rubella. In particular, children between the ages of 9 months to 14 or 15 years are targeted. The evidence that such campaigns — when targeting children under age 5, for whom the disease is most likely to be fatal — are effective appears to be strong and the cost-effectiveness (per measles death averted) is competitive with that of our top charities.
However, we don’t believe that the Measles and Rubella Initiative (the primary entity that supports these campaigns) has room for more funding to vaccinate children under age 5. We spoke with M&RI in January 2016 and representatives there told us (p. 4 at that link) that M&RI has a funding gap of approximately $36 million in 2016. Of the $36 million, $31 million would fund a campaign targeting 5-14 year olds in Ethiopia. Vaccinating children age 5-14 would, by reducing the number of people who could contract and transmit the disease, reduce infections in children under 5 and could potentially save lives. We have not pursued this opportunity because our guess is that this will be less cost-effective than our top charities. Gavi, a large alliance which funds vaccinations, has fully funded M&RI’s gap for vaccinations of <5-year-olds in Ethiopia but has not fully filled its 5-14 year-old gap. Though we don’t have information about why Gavi made this funding decision, it is consistent with our impression that filling the 5-14 year-old gap may be less cost-effective than the <5-year-old gap.
We believe the remaining $5 million gap for 2016 is very small compared to the size of M&RI’s total budget. As of November 2015, M&RI estimated (p. 15 at that link) $662.6 million in resource requirements for 2016. Note that this does not include the large campaign in Ethiopia, which has been carried over from 2015 to 2016. (Details here (p. 35-36) and here (p. 3).)
Thus, our impression is that the $5 million funding gap beyond the Ethiopia campaign represents an extremely small fraction of the total M&RI budget for 2016. The $5 million gap is so small relative to the total that we would guess that M&RI would be able to raise funding for it if it represented a pressing need, either from Gavi or another source. (We would guess that it doesn’t, since Gavi funds so much of M&RI’s work that it seems very unlikely that it would leave such a small gap.)
It also appears to us that there is no room for more funding in meningitis A vaccinations. We didn’t complete a full assessment of the evidence of effectiveness and cost-effectiveness for meningitis A vaccines because we learned early on in our investigation that there was unlikely to be room for more funding. However, our guess is that this intervention would be competitive with our top charities’ cost-effectiveness. Our meningitis A write-up concludes:
…[I]t seems unlikely that there will be room for more funding to support additional mass campaigns (or related immunization activities) in the meningitis belt in the near future. Gavi, a large funding vehicle for vaccinations, appears to have enough funding to fulfill its commitment to support all such activities in all 26 countries in the meningitis belt.
We’re not aware of any organizations other than Gavi funding meningitis A vaccine programs.
Maternal and neonatal tetanus campaigns
Vaccination campaigns to prevent maternal and neonatal tetanus appear potentially as cost-effective as our top charities. (More details are in our full report on this intervention.)
We have been following UNICEF’s work in this area, the Maternal and Neonatal Tetanus Elimination Initiative, since 2012. UNICEF recently informed us that it was declining to participate in our review process. We plan to write more about our understanding of UNICEF’s decision in the future. Our impression is that UNICEF is the primary funding vehicle for maternal and neonatal tetanus campaigns.
This is another example where we tried but failed to find a way to fund vaccinations. We are not aware of organizations conducting similar work, but would be interested in considering a similar opportunity if we identified one.
The post Why we don’t currently recommend charities focused on vaccine distribution appeared first on The GiveWell Blog.