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Callum Calvert

How Can We Model Vaccine Development? 

Diseases in the developing world cause an enormous amount of suffering. Controlling them requires vast sums of money. Could funding the development of a vaccine, ending the problems caused by the specific disease once and for all, be an immensely cost-effective way to donate? The case for this type of charitable giving is obvious: giving money to people doing this research and development (R&D) will lead to an earlier vaccine-day (the time at which a working vaccine is ready for service). Bringing forward this point by even a day could prevent a serious number of DALYs from being inflicted. We've been looking at a few ways we could model the cost-effectiveness of funding a general vaccine development charity. This post will explore the first of these models.

The Sabin Vaccine Institute is a good example of this type of charity. Sabin is conducting non-profit R&D for a number of different neglected disease vaccines, but GWWC are particularly interested in their work on schistosomiasis (also knows as bilharzia, bilharziosis or snail fever), a vacc ine for which is scheduled to enter phase 1 clinical trials next year. The aspiration of this series of blog posts will be to get a reliable cost-effectiveness estimate of donations to this schistosomiasis vaccine development program.

To begin with we’ll assume that a vaccine will definitely be produced eventually, either by the program we donate to or by a different one. Then with any model there's a natural way to split the calculation up into 2 parts. Firstly we want to know just how much our donation will bring forward V-day. Secondly we want to find out how bringing forward this momentous occasion by 1 unit of time translates to positive effects in the developing world (measured in DALYs). Then we’ll combine these to get a figure for positive effect per donation.

So how much will our donation affect V-day? Say the charity is at a certain stage of its research, and plans to begin its next stage at some particular point in the future 

 they fill up a funding pot, of size G. If not, they will postpone to another date T years after the first date. We’re only concerned with whether my donation (of size D) makes the difference between the research going ahead and being postponed; so how effective we think it is to donate will depend a lot on how much we think the charity will fundraise elsewhere. If we think that they'll fill their pot and then some, or that everyone else will stay away, then it’s unlikely my donation will make much difference to the outcome. The problem of course is that we don't know how much everyone else is going to donate! To get around this we can try to estimate a distribution of how much we think everyone else will give in total. Plausibly, the total amount donated (excluding our donation) is distributed normally [The reason for this is that F is the sum of lots of other people’s donations. Assuming (for simplicity) that the individuals' donations choices are independent, the central limit theorem says that their sum will be approximately normal]. In that case, the probability that our donation will make a difference is P[G - D < F < G]; this is the probability that the total amount donated (excluding our donation) will be within D of filling the funding pot, so that our donation fills the pot (this probability is represented by the grey area in the graph below). So our expected time impact on V-day will be P[G - D < F < G]

p where p is the probability that the program successfully produces a vaccine (If the program fails to produce a vaccine then our funding has not brought forward the usage of any vaccine, and unfortunately, 

Now for part 2! There are 2 important positive effects which might come from having a vaccine available earlier.

Firstly people who are at risk from the disease will benefit from the vaccine being ready to use earlier. For the moment we'll assume that eradication is achieved exactly T years earlier if the vaccine is successfully developed without the delay in research due to lack of funding. (There are possible reasons why this might not be true, for example that the vaccine might not be the limiting factor in the eradication effort). Assuming that eradication is achieved eventually in all cases and happens in the same way whenever the eradication effort begins then by looking at the graph below we can see that our impact depends on the global burden of disease (DALYs caused by the disease annually) between T1, the time at which the vaccine will be ready if funding is reached, and T2, the time at which the vaccine will be ready if the next stage of development is delayed due to lack of funding. So if the GBD of the disease is B DALYs/year then the expected benefit of our donation is the expected time impact of our donation multiplied by B. This is B* P[G - D < F < G]

The second benefit is because the annual cost of controlling a disease could be massive. If we assume that the money being spent on control would otherwise be diverted to other health interventions in the developing world then potentially this would avert vast numbers of DALYs. Let the cost of controlling the disease be $C/year. If the average cost-effectiveness of health interventions in the developing world is X DALYs/$ then for each dollar diverted the benefit is C

X DALYs/year. Therefore multiplying as before gives expected benefit of our donation due to diverted dollars as C

Thus our first basic benefit estimate for a donation of size D to a vaccine development charity is the sum of the 2 benefits of our donation, (B+C*X)*P[G - D < F < G]

p DALYs. Assuming some fairly conservative numbers for the inputs and the parameters of the distribution of funding suggests that vaccine charities have the potential to be quite cost effective.

There is a other factors still to be taken into consideration. For instance, some vaccines are only partially effective and some diseases might be controlled by other means before a vaccine is developed. Allocating scarce biomedical researchers to vaccine research will take them away from other R&D programs. In future blog posts, as we explore some other models, we hope to find some ways to deal with these things.

Toby Ord

Malaria in the News

Several recent articles have reported on interesting developments in the fight against malaria. See below for a brief summary of these (with links if you are keen to do some further reading).

Efforts to eradicate malaria have significantly increased over the past decade. In 2011 alone, the three main donors, the WHO’s Roll Back Malaria Partnership, the Global Fund and the President’s Malaria Initiative, spent 

 on treatment and prevention. The increase in funding appears to have had a positive effect on the fight against the disease, according to WHO estimates there has been a 26% reduction in death rates since 2000. The UN has called for an even greater increase in funding. A $6.7 billion investment between 2012 and 2015 could prevent 640m malaria cases and 3m deaths. However, the prospects of malaria eradication, even given extra funding, might be less rosy than this picture suggests.

First of all, a recent study published in the 

 has found that the WHO’s estimates of deaths from malaria might be too conservative. Based on statistics gathered from 105 countries since 1980, the study found that malaria accounted for 1.24m deaths in 2010, nearly double as many as the 655 000 reported by the WHO. Even if the WHO did significantly underestimate the number of deaths caused by malaria, this of course doesn’t mean that efforts to fight the disease haven’t been successful, but it might lead us to question the amount of money donors would need to invest in order to eradicate it.

 in the Scientific American highlights a second potential concern. One of the principle ways of preventing malarial infections, bed nets, might not be as successful as it seems. Nets are treated with synthetic insecticides which repel mosquitoes and kill the ones that land. Bed nets also provide a mechanical barrier between the sleeper and insects. One of the reasons why bed nets work so well is that the periods of the day when they are in use coincide with periods when mosquitoes, including malaria carrying ones, are the most active as mosquitoes tend to be nocturnal. It has been known for some time that mosquitoes can evolve to resist the insecticide that the bed nets are coated with, however a recent study has found that some mosquitoes have started adapting their feeding times too. The study found that over a period of three years mosquitoes changed their feeding times from around 2 to 3 am to 5 am. The study was fairly comprehensive, it was conducted at two different locations in Benin and samples were collected before, during and after bed nets were distributed. What are the potential consequences of this for humans? Worryingly, people are more likely to be out and about during the new feeding times, and therefore are more likely to be bitten and potentially infected. On the other hand, mosquitoes that feed on humans during daytime are also more likely to be spotted by the victim. Furthermore, mosquitoes feeding at dawn might also be faced with new predators, such as frogs and lizards. Given the uncertainties surrounding the consequences of the findings of this study, bed nets still remain to be one of the most effective ways of fighting malaria.

Even if the mechanical effectiveness of bed nets is reduced by behavioural changes in mosquitoes, luckily bed nets are not the only card up the sleeve of those battling malaria. The BBC 

 last week that a recent study in the Lancet has come up with encouraging results when researching the effectiveness of a new anti-malaria drug scheme. The Affordable Medicine Facility for malaria set up by the Global Fund, subsidises drugs in 7 African countries. The study has found, that although the scheme had a low effect in Niger and Madagascar, it reduced the price of drugs in the other countries it was trialled in. 

, the scheme has been criticised, most notably by Oxfam, for limitng access and for diverting attention from more established methods of fighting the disease.

Michael Peyton Jones

Estimating the Effectiveness of DCP2 - Post 2

Warning: We're now going to start assuming some serious statistics understanding! Not everyone will be able to follow the guts of the modelling, but the bottom line should still make sense. As before, we're using R to do most of the work.

, I explained a simple statistical model for assessing the effectiveness of donating to DCP. In this post I would like to describe some improvements to the model. The most important of these is allowing for the possibility of error in DCP's assessment process. Some of the variation in the DCP's results are due not to real differences in the cost effectiveness of treatments, but rather simply measurement error on their part. We will run simulations where we vary how large this error is, and see how much it reduces the value of DCP's work. The conclusion is that given our guesses about the size of the errors and the money their research moves, DCP still appears much better to fund than the best treatment they identify. Some objections to this model are then noted.

Simon Jenkins

GWWC: Birmingham Launch

This blog post is going to be a kind of summary of the recent and very exciting launch event for Giving What We Can: Birmingham. Summaries are rarely exciting, and I would have said “review” but I can’t really review an event that I had a hand in organising without being accused of bias. So, rather than trying to be guarded against bias by carefully selecting a spread of positive and negative things to say about the event, I’m just going to wholeheartedly embrace my bias and go on and on about how great the launch was. As well as being an opportunity to blow my own trumpet, this blog post might provide some useful tips for anyone else looking to launch a Giving What We Can chapter or similar event (though saying that might just be blowing my own trumpet too).

The launch was quite a long time in the making. The first step and the thing that really got the ball rolling was contacting potential speakers to give lectures at the event. I had prepared a sizeable list of potential speakers, and my plan was to start from the top and work my way down. As it turned out I didn’t have to move down the list very much at all and I was pleasantly surprised by how willing and able everybody was to help. The first thing that speakers usually want to know is “when is this going to be?” and so contacting the speakers provided the impetus needed to fix a date for the launch. This, then, was a point to work towards. A 

. And it got things working a lot faster.

After this I spent many evenings staying late (later than usual anyway) in the office making all kinds of arrangements for the event. Professor Larry Temkin, one of our speakers, and I sent each other thousands of emails discussing travel arrangements, hotel ideas, timings and all the rest of it. I think I’m probably now qualified to work as some kind of travel rep, but as I’m committed to not being an evil person, that isn’t the life for me (1). Prof Temkin even asked me about must-see sights in Birmingham. I think my response was something to do with curry.

After months of organising (obviously I was kind of doing other things too) the event finally loomed upon me, and on the day of the launch I was still desperately scrabbling around trying to get Skype installed in the room that we’d be using. Peter Singer was due to give his talk over Skype, and so obviously without Skype, this would have been deeply difficult to achieve. Fortunately some of the excellent people at Global Ethics and Political Science (departments here at the university who also helped greatly by funding the Birmingham leg of Larry Temkin’s European trip) came to the rescue and we got Skype set up, literally just a few minutes before Prof Singer was due to be live on the air, beamed into the room onto the screen of a laptop with a microphone pointed at the tinny laptop speakers. It was clumsy and lo-fi, and those with questions at the end had to come up to the front of the room and talk into the laptop like some bizarre audition, but it all worked pretty well.

But that is a digression into a technical headache. Let me put some meat on the bones by telling you about the talks themselves. The content was of course of a very high quality, as to be expected with three eminent profs in the room (three if you count Peter Singer’s virtual presence anyway). Larry Temkin (Rutgers University) gave a talk that was enviable in the elegance of both its construction and delivery, talking directly to the “but I’m a poor student and can’t do anything” response to charitable giving by illuminating the multitudinous ways in which people waste their money, but with a warmth and honesty (admitting that he himself has two fondue pots) that stopped it from being preachy. Peter Singer (Princeton University) used his time-honoured thought experiment of the child drowning in a shallow pond, and the idea that some in the room might have been hearing this example for the first time was as inspiring as watching a child being born. Finally, Alan Fenwick (director of Schistosomiasis Control Initiative, one of Giving What We Can’s top recommended charities) gave a talk that was both moving and visceral, not shying away from graphic detail (and video clips) of the horrors of the neglected tropical diseases that his organisation fights.

So there we have it. Giving What We Can: Birmingham has launched, and you can even watch a video of the launch event 

. Thanks to everyone for their help (there isn’t room but you know who you are) and I’ll see you at the philanthropy games next week, which I may as well shamelessly plug here: it’s a debate about where to allocate a real sum of money. Room G41, 90 Vincent Drive, University of Birmingham (Edgbaston Campus), Wednesday November 7th, 5.30-7.30pm. No prior skills/knowledge required, and for every person that comes, an extra £10 will be donated!

(1) DISCLAIMER: I DO NOT THINK ALL TRAVEL REPS ARE EVIL

Estimating the Effectiveness of DCP2

When searching for high-effectiveness charities, there are a few resources that are incredibly useful. One of those is the output of the 

 (DCP), which provides an analysis of the cost-effectiveness of a great number of health interventions. Given the value of this data for us as donors, we might wonder just how valuable this information is, and whether it might be worth donating to DCP to produce more of it.

In this post I will present a statistical model (designed by myself and Nic Dunkley) for estimating the effectiveness of donating to DCP. Although this will inevitably over-simplify the situation, it will be a first step that we can build from. Although the model may be useful for looking at other similar organizations, the estimates that I will derive rely on data about DCP itself.

Here's the idea. Firstly, we assume that DCP has some number of followers who make their donation decisions based on DCP's recommendations; that is, they put their donations towards whatever intervention DCP recommends most highly. If DCP says the best thing is supporting homeless shelters in the UK, then that's where the followers will send their money. So the current situation would have all that money going towards the intervention that the DCP2 report claims is most effective. We'll refer to the money donated as DCP's "money moved". Furthermore, we'll assume that this money is all translated into DALYs at the rate that DCP claims.

Now, suppose that there's a big bag of interventions that DCP assesses. Then we assume that for some amount of money, DCP can pick a random intervention from the bag and accurately assess its effectiveness. If it comes out better than their current best recommendation, then when they report on this fact, their followers will shift their donations accordingly.

This may not be the best way to model the DCP’s impact. Governments which make use of the DCP may not use it to identify the one ‘best’ treatment in order to fund that, but rather just to work out which treatments pass a minimum cost effectiveness threshold and which don’t. The higher above this threshold a project is, the more likely it is to get done. This would require a more detailed model, which we may produce at a later time.

However, If the original description were a good model of how DCP works, then we now have enough information to work out how much good we can do by donating to DCP! I'm going to be using 

, a statistics package, to do the heavy lifting.

Firstly, we need to know something about the distribution of the interventions in the bag. A quick reminder about probability distributions: the probability distribution of some random variable usually looks like a humped graph. Along the bottom are all the options we could have. In this case, that's the possible cost-effectiveness of a random health intervention, i.e. pretty much any real number. Along the side it shows how likely that possibility is. One very common type of distribution is a “normal” distribution. This just looks like a big hump in the middle that spreads out to the sides (often called a “bell-curve”). Intuitively, that means that most of the time you get one of the possibilities in the middle, and the other possibilities get less likely as they get further away from the middle. Normal distributions are quite nice to work with, and they're also pretty common. Importantly, they can be totally specified by just two parameters: the mean of the distribution (the average outcome you get), and the standard deviation (which tells you how far away from the mean things tend to be). Between the two of these you know where the middle of the hump is (the mean), and how wide it is (the standard deviation), and that's all you need to know.

Back to our health interventions. Fortunately, we have a lot of data from DCP about the cost-effectiveness of interventions: if we plot the effectiveness of the interventions they assessed in the DCP2 report, we get a distribution that looks a bit like this: That's definitely not a normal distribution, but it is a “log-normal” distribution. A log-normal distribution is fairly straightforward: if you take the logarithms of all the possibilities along the bottom, and then plot the distribution, you get a normal distribution. And if we do that for the DCP data, then we do indeed get a very plausible-looking bell-curve. Now, this makes it looks pretty plausible that the DCP data really does follow a (log-)normal distribution, and it'll be helpful if we can work out the two parameters for it that I mentioned above. For the moment, let's assume that taking the mean and standard deviation of the logarithms of the data gives us the right parameters – we can be more sophisticated (in particular we're going to want to think about the possibility of error), but I'll leave that for a later post.

Having the parameters for the distribution means that we can get R to make up entirely new data points as if they came from the original data set. And if the actual data really is distributed in the way we think it is, then this is a lot like DCP finding and assessing a new random intervention. We can then do the rest of our model process – check whether it's a better charity than we had before, and if so work out what the difference is – and get an answer for how much difference that one attempt would make. We can then use R to simulate doing this a large number of times, and then simply average the results! Thus: we generate a large number of samples, to represent a large number of investigations by DCP. We can then work out the difference in effectiveness between each “new” intervention and the current best option. The current best option is better than average, so we'll expect the difference in general to be negative. We can regard all those results as 0, however, as in that case there would (in our model) be no change in where the money goes. Finally, we can then average these results. Call that average A – that is, the average improvement in the effectiveness of the most recommended charity due to DCP doing one investigation.

At this point, we need to plug in two final, very important parameters. These are figures for the money moved (M), and for the cost of DCP doing an investigation (C). These are particularly important as the our final estimate is going to be the average good done by an investigation (A*M), divided by the cost of doing it (C). Our final estimate will therefore be directly proportional to M, and inversely proportional to C. We'll discuss these a little more in a future post. For now, I'm just going to give you some figures that are hopefully about right: namely $530 million for M, and about $100,000 for C.

Putting all this together, we get an estimate for the effectiveness of DCP of around 89 DALYs/$. For comparison, the best intervention that DCP claims to have found so far clocks in at 0.33 DALYs/$! As mentioned earlier, of course, this estimate is only as good as our model, but it certainly looks promising. In the next post I'll talk about how we can improve the model in a couple of ways, most importantly including the possibility of error. This will lead to a significant reduction in our estimate of DCP's effectiveness, but it will still appear to be extremely competitive.

This kind of research will be a high-variance strategy: most of the time nothing useful will happen, but occasionally there will be really, really good outcomes.

The hidden parameter in this model is the effectiveness of DCP's current strongest recommendation. In particular, if new interventions are discovered that have much higher effectiveness, then the need to do further research drops.

The model assumes that the distribution of interventions DCP would evaluate with more funding looks the same as the distribution of interventions they have already identified. In practice they may have looked at the most promising options first.

These results are not immediately applicable to charity-evaluators such as GiveWell. GiveWell looks at charities, not interventions, and we don't have a data-set for that in the way that we have for DCP. It may be that if we make some assumptions we might be able to adapt the data, but this is an open question.

Peter Hurford

In Giving, It Pays to Precommit

, Odysseus’s crew sailed past the island of the Sirens. In Greek lore, the Sirens were notorious for singing a song so enchanting that those who listened would sail towards the Sirens and end up crashing into the rocks, sealing their own fate.

The common solution of the era was to avoid listening to the song altogether – plugging one’s ears with beeswax. But Odysseus wanted to listen to the song… though, of course, he didn’t want to end up crashing his ship and killing his crew.

Thus, not trusting his future willpower, Odysseus had his crew tie him to the mast of the ship and precommitted to not be let go. Predictably, when he heard the song he begged to be released, but his crew followed his earlier orders and didn’t let him go. Odysseus survived on the basis of precommitment.(1)

The motivation for precommitment stems from a problem in human nature: there are some goals we find desirable (to know how to dance competently, to have good grades, to be fit and healthy, etc.) but still struggle to do certain activities that would accomplish these goals (take dance lessons, study hard for the upcoming test, exercise and eat right, etc.).

People are frequently averse to taking on short-term costs even if they would achieve long-term gains. (2) People value their current time much more than the future (called 

),(3,4) struggle with low expectancy of success, struggle with the drudgery of actually performing the task, and are occasionally impulsive and forgetful with their tasks.(5,6)

Such failures to do what we want and act against our considered judgments are commonly called akrasia, or “weakness of will”, a term first coined by Aristotle more than two thousand years ago. Odysseus recognized that he wouldn’t have enough willpower to resist the Siren’s song, so he took steps to ensure he would end up acting the way he wanted. He used precommitment to increase his willpower.

In our own lives, we face problems where we really want to do something, but are undermined by our lack of willpower and procrastination. In looking for a solution, we can be like Odysseus… except without our lives having to be on the line. Precommitment is helpful at reducing the impulsiveness which drives procrastination and other forms of akrasia.(7) While we don’t actually have to get tied to a mast, the idea is the same – one takes an earlier and easier action now (paying for a gym membership, promising a friend, or getting tied to a pole) that makes it much easier to perform the harder yet still desirable action later on.

Many of us think giving to charity is personally desirable. People frequently pay more for products when they’re linked to charity. The act of giving also enriches people’s lives and makes them lastingly happier. (9,10,11) However, impulsiveness means 

, as people forget to donate despite wanting to, and pursue giving in a haphazard and unstructured way. Just like it’s hard to eat right and exercise right, it seems hard to donate right as well.

 to donate at least 10% of their income to whichever organisation they sincerely believe will do the most to help alleviate extreme poverty in the developing world. Such a pledge represents a precommitment to donate future earnings. But does it actually help people donate more than they otherwise would?

In one study, smokers who wanted to quit were offered a savings account in which they could deposit a considerable amount of funds for six months. After the sixth month period, the smokers were tested for nicotine via a urine test. If they were clean, they got back all the money in their savings account. If not, all the money wasvforfeited to charity. 11% of the smokers randomly offered the program took it on – about the same rate as those that accept other smoking treatments. Of those who accepted the program, 33% were able to quit smoking, which is 3% better than other top quitting programs.

More importantly, those who were smoke-free at the end of the program were also smoke-free at a surprise follow up test six months after the first study, a year after the participants had signed-up. It seems precommitment had been able to produce lasting change in a way that other methods like financial incentives and nicotine replacements were not able to do. (12)

Along the same lines, despite some examples of people buying gym membership but never using it, a deeper examination does suggest that those people who precommit to the gym membership by paying for it in advance are more likely to go to the gym than those who express a desire but don’t get a membership. This is not just because those who would be willing to pay upfront have a stronger desire to go to the gym, those who renewed their precommitment monthly were much more likely to go to the gym than those who committed for a year. (13)

Precommitment isn’t just for those who want to quit smoking or want to exercise more. Indeed, precommitment can work for those who recognize they can donate a portion of their income and benefit the world significantly, yet have trouble getting around to actually donating. In another experiment, monthly donors were randomly asked to either increase their donation immediately or precommit to increasing their donation in two months. They had not been told of the experiment. Those who were asked to precommit ended up increasing their donations on average 32% more than those who were asked to donate immediately and a year later cancellation rates were statistically identical in both groups.(14) These results were duplicated when people were asked to precommit both imaginary and real raffle winnings in separate experiments.(15)

Precommitment works well when we deliberately set things up so we cannot back out easily (backing out would be very expensive or downright impossible), but it also works even when the punishment for backing out is light. For example, backing out of a Giving What We Can pledge risks just the potential disappointment of a few people you don’t know on a personal level, or looking weird for having gone back on a public statement. So how is precommitment so powerful?

Neuroscience studies reveal that people give to charity for two primary reasons – pure altruism, or a desire to genuinely help other people, and the “warm glow” of self-satisfaction.(9,14) For many people, donating creates a tension because the pure altruism payoff is not realized for a while, but they face the prospect of parting with money immediately. By introducing a delay via precommitment, the self-satisfaction is realized immediately at the time of the precommitment, but the monetary cost (the actual donation) can be viewed with psychological distance, making us think more rationally and less impulsively about what the money can do.(3,4,5,14) Thus precommitment helps by providing the benefits upfront and costs later, which requires a type of choice, involving delayed costs, that comes more naturally to us.(2,3,4,5,14)

Additionally, precommitment creates adds a cost to backing out which, when compared with the minimal cost of complying with the precommimtent, seems not worth paying. Thus, precommiment can almost be considered “inverse procrastionation”, we procrastinate about reneging on our precommitment, even if we have changed our minds.(14)

Precommitment is a good way to reliably motivate ourselves to solve problems of impulsiveness and “weakness of will”, helping us do something we would not have otherwise been able to do. It is a weird quirk of our psychology that we have to deal with akrasia and have to “trick” ourselves into doing something we really, in the end, would have liked to have done all along. It mainly comes from our nature, for better or for worse, as impulsive hyperbolic discounters – we value the immediate present much more than the future.

Precommitment is rooted in inversing the impulsiveness and getting it to work for us, by setting up an immediate cost for not performing the long-term action. Furthermore, precommitment allows us to gain the benefits of self-satisfaction without having to immediately face its costs. Lastly, precommitment helps create the psychological distance needed to get over more automatic and immediate feelings.

Donating is one of those actions that is hard to do, especially in a structured way. But by making a public commitment to it that creates some social cost for backing out, it becomes a lot easier to keep up the habit. As most GivingWhatWeCan members can attest, skimming off the 10% of income to a top charity is not only second-nature, but fun. And all it took was a little precommitment.

1: I owe this analogy, and some of the precommitment research references, to Luke Meulhauser’s "

2: Lowenstein, George and Richard H. Thaler. 1989. "Anomalies: Intertemporal Choice" 

3: Streich, Phillip and Jack S. Levy. 2007. "Time Horizons, Discounting, and Intertemporal Choice" 

4: Trope, Yaacov and Nira Liberman. 2003. "

 10 (3): 403-421. American Psychological Association.

The Nature of Procrastination: A Meta-Analytic and Theoretical Review of Quintessential Self-Regulatory Failure

" Psychological Bulletin 133 (1): 65-94. American Psychological Assocation.

6: I owe some of the procrostination research references to Luke Muelhauser’s "How to Beat Procrastination".

7: Elfenbein, Daniel W., and Brian McManus. 2010. "

A Greater Price for a Greater Good? Evidence That Consumers Pay More for Charity-Linked Products

American Economic Journal: Economic Policy

Procrastination, Deadlines, and Performance: Self-Control by Precommitment

9: Harbaugh, William T., Ulrich Mayr, and Daniel R. Burghart. 2007. "Neural Responses to Taxation and Voluntary Giving Reveal Motives for Charitable Donations" "Voluntary Giving Reveal Motives for Charitable Donations" 

Prosocial Spending and Well-Being: Cross-Cultural Evidence for a Psychological Universal

" National Bureau of Economic Research. Working Paper #16415.

11: Dunn, Elizabeth W., Lara B. Aknin, and Michael I. Norton. 2008. "Spending Money on Others Promotes Happiness" 

12: Giné, Xavier, Dean Karlan, and Jonathan Zinman. 2010 "

Put Your Money Where Your Butt Is: A Commitment Contract for Smoking Cessation

American Economic Journal: Applied Economics

13: DellaVigna, Stefano and Ulrike Malmendier. 2006. "Paying Not to Go to the Gym" 

" Stockholm School of Economics Working Paper.

15: Meyvis, Bennett, & Oppenheimer. 2010. “Precommitment to Charity” in Oppenheimer & Olivola (eds.), 

Chuggers

This blog post is going to serve as something of a pep talk for charitably-minded people. But first, a story (of sorts).

I used to work as a charity fundraiser. When I say that, people usually imagine those guys you get in the street - "charity muggers", or "chuggers" as I've heard them called. Before I tell you what kind of fundraiser I actually was, I'd like to pick apart this delightful little moniker.

I appreciate the similarities. You get collared on the street by someone you weren't expecting to see, who ends up making you part with money that you ordinarily wouldn't part with. In a way, they're doing it for their own benefit - they get paid to do it, and sometimes they get commission based on how many people they sign up, too. So in that sense, too, they share a similarity with a mugger.

One difference that you might point to is the apparent weakness of will that must give rise to the suggestion that a person who ends up writing out his sort code and account number is somehow "mugged" into doing so. A mugger, typically, has some kind of threat on his side, to the tune of (and I was genuinely asked this once) 'do you want to be shanked*?' (my reply was 'no' - what else can you say?). Without this threat, your surrendering your bank details isn't a mugging - it's just you not being able to say "no".

I don't quite accept this line of argument, though. A charity fundraiser 

 have a kind of threat on his side. Fundraisers are trained not to use language quite as direct as this, but there is always an underlying hint that "if you don't give us some money, something bad will happen" (it's usually framed as "if you do give us some money, we can prevent something bad from happening"). That sounds quite threatening. There are, however, two important differences between the fundraiser's threat and that of the actual mugger. The first is that the fundraiser's threat isn't about you. The threat is that something bad will happen 

. Sure, if you don't act in a certain way, you might feel bad, go home and feel guilty, and so on. You might even kick the cat. In this way, the threat is 

 shared by you - it becomes "something bad will happen 

". But still, the reason you feel guilty is because something bad is happening to someone else. This, I think, is quite distinct from the rather direct threat of "give me money or I will shank you" offered by the mugger.

The second distinction is that the fundraiser, though they may be preaching about the threat, is not the one who is actually going to perpetrate the bad thing. They do not stand on street corners with kittens, threatening to shoot them unless someone gives them money. They are seeking funding for projects to avert badness, rather than considering the possibility of increasing the badness in the world but with a willingness to refrain if paid off.

The reason for this diatribe about the difference between muggers and "chuggers" is to make a wider point about the status of those who try to do good. While my last blog post bemoaned the fact that global suffering has become a cliché, the purpose of this one is to carp about the fact that those individuals who try to alleviate that suffering are criticised for doing so. The state of society needs some serious revision if it comes to be that the phrase "do-gooder" is a pejorative one. "Ergh, get a load of that guy - he wants to 

My overall message, then, to Giving What We Can members and charitable, altruistic people of any stripe, is not to feel downtrodden or browbeaten by those who would ridicule your efforts. Doing good is good, and I don't think this requires much argument. By the same token, doing the most good is the best thing to do. (I am in fact surprised (and a little disappointed) that my spellchecker did not attempt to correct "most good" to "best".) So, the overall moral of this story is not to feel bad about doing good - the incongruity of feeling that way should be obvious.

Oh, and I was a telephone fundraiser, but it doesn't really matter for this story.

**"Street" in this context is an adjective meaning "streetwise", for those of you who aren't as- oh, never mind.

Julia Wise

Rooting For Your Home Team 

There's a gap between charity evaluators and charity workers.* Look at any article on effective charity, and you're likely to see responses from NGO staff voicing their frustration with the evaluation methods:

"You don't understand how hard it is to persuade donors to fund evaluation."

"We're reaching a population that no one else is reaching."

"The type of work we do just isn't measurable by randomized controlled trials."

"It's hard for really small organizations to get noticed."

The thing that worries me about these responses is that it's hard to be objective about your own project. I've been there. When I worked for a non-profit doing global development and relief work, I was rooting for them. I knew their values, I knew their staff, and I loved both. You see this in devoted donors, too - once you've helped with a project, you want to see it continue. It's only natural, once you feel like part of the group.

But which charity you support is not the point. The point is to help people who need help. Right? That's why we're doing this, remember?

The flaws in any evaluation (and there will be flaws) are bad not because they short-change charities, but because they short-change the people who should be getting better help. The question we should be asking is not, "Is the evaluation fair to these organizations?" but "How can donors help the most?"

My home team isn't in an office in Boston. My home team is in a village somewhere. I'm rooting for them.

Read more about what giving makes possible 

Conrad Cotton-Barratt

Neglected Tropical Diseases - Are they cost-effective to treat?

This blog post is going to be a quick summary of the major things we have discovered about the NTDs and the interventions targetting them, and what we now believe the cost per disability adjusted life year (DALY) achieved by treating NTDs to be.

When we launched in 2009, we estimated the cost-effectiveness of deworming treatments to be around $5 per DALY, which made it the most cost-effective charitable intervention that we could find. However, some of the sources used to arrive at this figure were subsequently shown to contain significant errors. As a result we are now much less confident about this figure. GiveWell has produced an independent cost-effectiveness estimate for deworming of around $170 per DALY, which we believe to be reliable as far as it goes. On the other hand, we think that this fails to fully take into account some factors - including the work the charities do to hand the programs over to governments - which will significantly raise the cost-effectiveness of deworming projects. Taking this into account, we expect deworming charities to be as cost-effective as preventing malaria by providing insecticide-treated nets, and possibly more cost-effective. Nonetheless we are quite uncertain about this, and recognise that more work needs to be done to more accurately estimate the full impact.

The term 'neglected tropical diseases' covers about 15 or so conditions which are particularly prevalent in low income regions in Africa, Asia, and the Americas. A lot of them are parasitic infections, and they are mostly quite cheap to treat. However these treatments are not well funded. We are going to focus on two types of NTDs: Soil-Transmitted Helminths (STHs), and Schistosomiasis (also knows as bilharzia, bilharziosis or snail fever), as these are the ones we believe are particularly cost-effective to treat, and are the target of two of our recommended charities.

STHs - more commonly known as intestinal worms - can cause diarrhoea, abdominal pain, weakness, and anaemia, and may impair learning ability and physical growth. Schistosomiasis is another parasitic worm, whose larvae live in and infect humans from, pools of freshwater. Schistosomiasis can cause progressive damage to the intestinal and urinary systems. Both STHs and schistosomiasis are easy to treat with cheap orally administered drugs that cost around $0.50 per person. Moreover, these treatments have no serious side-effects, so it is possible to give the treatment to whole populations without going through the expensive screening process first.

I'm going to talk about three main sources of information that have come to light since we first made the webpages. The first of these will be the errors we found in the Disease Control Priorities Project (DCP2) estimates, the second is going to be research GiveWell has done to come up with a better cost-effectiveness estimate for deworming treatment, and the third will be the new, updated, Cochrane review of STH-deworming treatments. These aren't the only new sources of information to come out over the last few years, but they are the three most significant.

One of the main sources we first used when recommending deworming as a cost-effective charity was the second round of the Disease Control Priorities Project (DCP2), which aimed to collate cost-effectiveness estimates, in $ per DALY, for a wide range of different interventions. DCP2 gave the cost of treating STHs as $3.41 per DALY saved (i.e. approximately one DALY averted for every seven $0.50 treatment given), and of between $3.36 and $6.92 per DALY for schistosomiasis. These numbers were in the ballpark of several other pieces of data we had, included a prominent paper by Miguel and Kremer (2004), and so these figures were then used to calculate the rough cost-effectiveness of deworming interventions.

However, in 2011 it emerged that in reporting estimate for schistosomiasis, DCP2 had mistakenly inserted a decimal point, and the figure should actually have been $336 - $692 per DALY. Moreover, it transpired that the spreadsheets used to calculate these figures for STHs had other serious mistakes, which led to this estimate also being wrong by a factor of about 100, making the revised figure for treating STHs to be $326. (For more details, see GiveWell's 

) On further investigation into the DCP2 figure, it emerged that the methods missed some important factors, and used error-prone estimates, making it likely that the corrected estimates were significantly more pessimistic than they ought to be.

Now, in light of this, GiveWell has been attempting to produce a transparent, reliable cost-per-DALY estimate of deworming treatments. They have produced an account of combination deworming, and a spreadsheet to estimate the cost-effectiveness. Without delving into the details, this model seems to be a corrected version of DCP2's methodology, and gives an estimate for combination deworming of around $5000 per life saved. Obviously, this is a slightly different measure to the DALY measure we were using before, and conversion between the two is non-trivial. As a rough, and probably conservative, estimate of this in DALYs, we can consider saving a life to be equivalent to approximately averting 30 DALYs, which makes the figure for combination deworming around $170 per DALY.

In July 2012 there was an updated Cochrane review, which aggregated 42 papers on the effects of STH-deworming treatment. The review came to a couple of main conclusions: that in small, screened trials - where people are first screened for STHs and then treated - treatment of infected individuals was associated with an increase in weight and haemoglobin, which implies a reduction in anaemia. However, it also concluded that there was no significant evidence that unscreened deworming - i.e. in which a whole population is treated without first checking for infections - had a significant effect on weight, nutrition, haemoglobin, or school performance (all of which have been previously thought to be an effect of the treatments).

Now, there being a significant effect in screened trials but not in unscreened trials doesn't make much sense, as it is the same drug in both cases, and the screening process change the bottom line. The most plausible explanations are that either the smaller sample sizes in screened trials make the smaller studies' results spurious, or that this is a result of how the significance of an effect is measured: those not infected 'dilute' the measurable effect the treatment has on the infected, causing the effect to drop out of statistical significance in unscreened populations. In any case, the review seems to indicate that STH-deworming is less effective than we at first thought, and it is clear that more research on the true impact is needed.

Reasons to Think Cost-Effectiveness is Still High

Those remain the main new and concrete sources of information on NTDs. However, there are still a couple of things we need to consider and several reasons why we think the cost-effectiveness might still be higher than the GiveWell estimate and the Cochrane review might lead us to believe.

One of the conclusions of the Cochrane report is that there is no good evidence that deworming has a positive effect on school attendance. However, Miguel and Kremer (authors of the influential 2004 paper) together with other experts in the field, have written a response arguing that this finding is misleading, draw erroneous conclusions about school attendance from the data, and maintaining that the educational benefits alone are sufficient to warrant deworming treatments. These arguments seem persuasive, and it appears likely that there are significant educational benefits. We would welcome further research on this subject.