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Allan Saldanha

 that allows you to have your donation to AMF quadrupled this Christmas. This is a great opportunity to increase the impact of your donation! Read on to learn more about Allan's motivation for matching donations.

I have also always been keen on match fundraising. Not only does it increase the amount of money raised, it also involves collaborating with other people and psychologically makes people feel better about the money they donate. I always look for donation matching appeals at christmas time (when they tend to occur) to increase the impact of my donation.

There are critics of donation matching who argue that it can distract donors and lead to a misallocation of donations, and that donors should focus on donating to the most effective charities. Research by Givewell and other charity evaluators clearly shows that some charities are many times more effective than others, and clearly this can dwarf the impact of matching.

However I think it's possible to have the best of both worlds, and since I joined Giving What We Can, I'm keen to maximise the impact of my donations by looking for matching opportunities for the most effective charities.

 has kindly offered to match all donations to the 

 up to 31st December 2014, so I've set up the below fundraising page offering to match all donations up to a total of £5,000. After Charity Science's matching, this then means that donations to the page will be quadrupled (to raise a total of up to £20,000).

Many thanks for your generosity- please donate before the 31st December 2014 deadline.

Quadruple your donation to AMF- in praise of donation matching

Alison Woodman

It’s that time of the year again where we’re frantically buying presents for loved ones, decorating our homes and planning Christmas dinner. Despite the hectic nature of it all this is a great time to think more about giving, and to encourage others to do so too.

 took place on 2nd December and promoted just this; it asked people to donate to charities and to make their donations public, motivating more people to join in. The idea was launched in the US in 2012, and this is the first year it has been promoted in the UK as well. It was a cleverly timed antidote to the shopping frenzies of Black Friday and Cyber Monday, and the event’s promoters, 

, reported a 43% increase in donations compared with any other Tuesday this year.

It’s fantastic that campaigns like this have an immediate high impact by increasing interest in charitable giving. However, such seasonal awareness-raising also bring new opportunities to encourage one another to think more deeply about charity. In fact, in the past few weeks students in Cambridge have been doing just this. A campaign, initiated by medical student Ravi Patel, is inviting people this Christmas and New Year to take Giving What We Can’s 

 to give 10% of their income to effective charities. So far it’s raised incredible support and this December Giving What We Can has already received more new pledgers than in any previous month. Besides 

, here are a few ideas for anyone who would like to really make a difference this giving season:

Keeping a lookout for offers of donation matching is a brilliant way to make your donations multiply in impact. This usually involves an individual or organisation offering to 'match' the amount donated to their cause. For example, for every £1 donated, they will add £1 of their own money. Excitingly, this December a member of Giving What We Can has offered to match donations up to a total of £5000 when made through his 

 for the Against Malaria Foundation, one of our top charities. In addition, Charity Science will double the amount raised, resulting in the original donations being quadrupled! For anyone considering where to give this December, this is an option well worth checking out.

Years of dedicated research have led up to Giving What We Can’s recommendations of its 

. These are all organisations which we believe will use your donation in the most effective way. If you find the evidence compelling, you could make a donation to one of these as a gift to a loved one, or ask your friends and family to donate on your behalf. One of the charities, the 

 for buying mosquito nets which allows the gift recipient to track where their nets go.

If you are already interested in, or perhaps already involved with, effective altruism, why not communicate this to others around you? We already list our pledgers’ names on our site to encourage openness and enthusiasm about giving. In addition, Christmas offers many of us valuable time to catch up with our friends and family, and can be a good chance to discuss the things which matter most to us. It can sometimes be difficult to persuade people to give more, and to more effective organisations. One of our 

 offers useful advice on ways to discuss ideas which encourage others to be more receptive to them.

 is an excellent option for people who want to increase their charitable impact but don’t feel ready to commit to the Giving What We Can pledge. You can choose what percentage of income you aim to give and how long you wish to donate for. After your initial donation period ends, you can then decide what to do next. If it worked for you, you can extend the period, increase the percentage you give, or take the pledge. This is a brilliant way to ensure that your charitable giving continues into the New Year.

These are just a few thoughts about ways we can all make the most of seasonal giving and maximise our impact on the lives of others as we move into 2015. Wishing you a very Merry Christmas and a Happy New Year from everyone at Giving What We Can!

Making the most of seasonal giving

Owain Johnstone

 I discussed how social problems aren't just lying around in the world waiting for us to discover them – we construct them ourselves by deciding what counts as problematic and in what ways.

In the next few posts I'm going to focus on different aspects of how problems are constructed, with each post looking at one of those aspects.

Evidence is something we all intuitively think of as central to defining any problem. We rely on evidence to tell us about the nature of the problem, its causes and consequences, and what solutions might be effective in addressing it.

Often, when we think we don't know enough about a particular problem, we will decide that we need more evidence before we can really tackle it effectively. And when we want to persuade other people that a problem is important we turn to evidence in order to make our case.

Those ideas about evidence tend to assume that we define and address problems based on the evidence that we find. In fact, the process can often work the other way round. 

The problems we decide need addressing and the ways in which we define them can dictate what evidence we collect and how we interpret any evidence that already exists.

A good example of a problem where this occurs is the heated topic of immigration in the UK. And a good example of evidence-gathering in this area is a recent piece of research by two academics at UCL that analysed the fiscal contribution made to the UK economy by migrants over the past 15 to 20 years.

Their report made for some attention-grabbing headlines, which split broadly into two camps.

The Guardian and the Financial Times enthusiastically proclaimed that EU migrants arriving in the UK between 2001 and 2011 had contributed a net £20 billion to the UK economy. [1][2] The Telegraph and the Daily Mail trumpeted the shocking finding that non-EU migrants had cost the UK a net £120 billion in the period 1995-2011. [3][4]

Both of those figures were from the same report. The choice of which figure to run with in the headline (and what kind of emphasis to apply within the article) is broadly down to the political leanings of each newspaper. That is, they chose from the evidence available the figure that would fit best with their existing view of the problem. (Although that's obviously a simplification – the Independent referenced both figures in its headline). [5]

This is an obvious point, but an important one. Clearly 

we do not rely entirely on evidence for our problem constructions if we decide which bits to believe or emphasise based on our existing ideas

Equally, the evidence itself is not innocent – it can, as in this report, be 

legitimately open to very different interpretations

. Evidence in the social and political world is usually quite messy, consisting of partial or ambiguous findings that may not always fit neatly with the problem definitions we want to employ.

This means that we argue a lot about the significance, relevance and reliability of different pieces of evidence.

There are several ways in which we can argue about evidence; I've picked out a few here from the media coverage of the report by way of illustration.

, i.e. whether the facts and figures involved are really correct or not. The Telegraph and the Daily Mail both cited comments by the Director of MigrationWatch (a generally anti-immigration think tank) disputing some of the figures in the report.

challenge the authority of the source of the evidence

 – in this case the two UCL academics. The Telegraph noted that both MigrationWatch and Civitas had previously criticised research by the two authors. The Express noted an immigration-related prediction made in 2003 by one of the authors that subsequently turned out to be incorrect. [6]

challenge how others are using or interpreting the evidence

. The Telegraph said the the authors themselves 'emphasised their findings on the contribution of European migrants and gave less prominence to the findings on the costs of non-EEA immigration.' The Daily Mail said that the report 'sought to put an overwhelmingly positive gloss on the economic impact of mass immigration.'

 – especially numerical evidence – by recasting it in a different light, suggesting that it does not in fact show what others might think it does. Andrew Green of MigrationWatch (cited by the Telegraph) did this quite skilfully by suggesting that the total amount contributed by EU migrants to the UK economy (the £20 billion figure) actually worked out at less than £1 per person per week over the period examined in the report.

 found in the evidence. The Daily Mail argued that 'Critics will say the report is backward looking – focusing on the taxes paid by the influx of Eastern Europeans when they are young, single and healthy – but not the future burden their families may place on schools, hospitals and the welfare state.' Channel 4 noted that the report hadn't taken into account illegal immigration. [7]

I've concentrated mostly on the right-wing press here because in this instance the report itself did emphasise the £20 billion figure – so critique of the report came largely from the right whereas the left-wing outlets were mostly just endorsing it. The techniques of evidence interpretation and framing are easier to spot and illustrate in criticism than they are in approval.

That's not to say that the right-leaning press were somehow distorting the information that their left-leaning counterparts were faithfully presenting. The latter had their frames and interpretations too. The Guardian, for example, argued that the report 'reveals that Britain is uniquely successful, even more than Germany, in attracting the most highly skilled and highly educated migrants in Europe.' That, again, is a very partial interpretation of the evidence.

Unfortunately, much of this sort of debate is dismissed as 'spin'. We can tend to dismiss such comments as exactly what we'd expect of media outlets with particular political preferences. Actually, 

all of the techniques I've outlined above can be legitimate if used properly

 – although in practice they may often not be (for example, personal criticism of one of the report authors which clearly has no bearing on the quality or otherwise of the report's findings).

There are a couple of important points to take from all this.

evidence is, to an extent, what we make of it

The rider 'to an extent' is key, of course. I do not mean to suggest that all evidence is relative and all that matters is our interpretation. What I am arguing is that, 

when we come across evidence relating to a particular social problem, we tend to want to fit it into our existing preconceptions about that problem

. In order to do so we might find ourselves employing one or more of the methods outlined above.

we should be keenly aware of the ways in which evidence is debated and interpreted

Such interpretation can be dangerous if it leads to a basic misrepresentation of the evidence. It can also be dangerous if, more subtly, it leads us to cherry pick just those bits of evidence that happen to conform to our existing views. All that produces is unchanging opinions which struggle to respond to a changing world.

But looking at how others interpret evidence can also be very informative. A closer look at the interpretations they offer reveals a great deal about the preconceptions they have regarding the problem under discussion.

Finally, it is worth emphasising that, despite these issues, defining and addressing problems based on evidence is clearly better than just making things up as we go along. But evidence isn't the be all and end all, either. A better awareness of its uses and abuses, and of our own biases, can help us become more aware (and perhaps critical) of implicit problem constructions as well as put forward our own.

This has important implications for choosing which charities to support. As I discussed in my last post, choosing which charity to support implies choosing which problem to address. Evidence plays an important part in how we choose which problems to address and what to do in order to address them.

To the extent that such evidence is ambiguous and uncertain we need to be keenly aware of its role in informing our decisions.

More profoundly, it is worth asking ourselves whether our existing preconceptions about particular problems are influencing the evidence we collect and our approach to interpreting it. 

It might just be that we need to rethink the problem before we rethink the evidence

The Guardian: UK gains £20bn from European migrants, UCL economists reveal

Financial Times: EU migrants pay £20bn more in taxes than they receive

The Telegraph: Immigration from outside Europe 'cost £120 billion'

The Daily Mail: Migrants from outside the EU have taken £120billion more from the state than they paid in taxes over 17 years

The Independent: European immigrants contribute £5bn to UK economy but non-EU migrants 'cost £118bn'

Express: Mass immigration as been a total disaster for the UK

Channel 4: Are newspapers misleading you about immigration?

The use and interpretation of evidence

Eliz Kilich

A promising study on the long-term effects of deworming

Currently Giving What We Can supports two charities working on deworming – the well-established 

, which primarily targets Schistosomiasis. and the promising but less established 

, which aims to enable countries to implement deworming programs relating to Soil Transmitted Helminths.

In October 2014 GiveWell suggested that a new unpublished 

, by Croke (2014, 'The long run effects of early childhood deworming on literacy and numeracy: Evidence from Uganda'), provided promising data on the long-term effects of deworming, indicating that it represented a major update in this field of research. The cluster randomised trial compared the literary and numeracy test scores of children 7-8 years after mass deworming (22%-66% of the children in the parishes) for soil-transmitted helminths of pre-school children in Eastern Uganda parishes to the scores of a control population which had not undergone mass deworming during this time period. The author found improved test scores in those treated with deworming, and concluded that this strengthens the evidence for cognitive benefits for mass deworming in areas of high worm prevalence. Whilst appreciating the strengths of the study, as highlighted by Jake Marcus in 

, I analysed some of the limitations of the study.

Cluster vs. Individual Randomised control trials

One of the main criticisms of Croke’s study lies in the study design adopted - a cluster randomised trial. A cluster randomised trial is used when one cannot randomise individual participants, so instead randomises groups. For instance, a cluster randomised trial might look at the effects of different teaching styles (A & B) on school classes. This is appropriate because school teachers are not individual tutors but can only provide for the collective when giving a lesson. Since the single intervention affects a large group and it cannot be specifically directed at individuals, the important outcome is the effect on the whole class.

The outcome being measured in the Croke study is collective improvement in cognitive ability, which may seem reasonable. However the use of cluster randomisation is less intuitive here because children are individually being dewormed. Therefore randomising individuals would be a better study design.

On the other hand, cluster randomisation could account for any spillover effect - deworming having additional benefit to children in close proximity to those dewormed. Individually randomised trials cannot take into account spillover effect, so they could underestimate the effect of deworming. Further, randomising individuals would have been substantially more expensive.

Nonetheless, by being unable to follow up participants directly as individuals and instead relying on population-based data, the study is subject to a greater propensity for confounding factors to influence the results.The propensity for confounding in cluster randomised trials is based on baseline differences and on the hidden effects of unmeasured changes to the groups during the study. For instance, the two groups may differ substantially in baseline characteristics which could affect outcome. In the example above, if children in class A are more clever than those in class B, finding that class A improved after a different teaching style is confounded by the children being more amenable to learning.

Croke tries to control for potential confounders by showing that there is no difference in socioeconomic status amongst groups. But there is a flaw in this method. The socioeconomic data (which is collected independently from the study and only includes 22 of the 48 parishes in the original study) does not apply to the original sample (intended for deworming or control) at the beginning of the study. So it only shows that socioeconomic differences were not present at the end of the study (2010) when the cognitive ability studies were performed. Whilst it is plausible that there were not socioeconomic differences at the beginning of the study, this has not been directly shown in his paper.

When considering the data Croke acknowledges the limits of a cluster randomisation and so applies analysis to show that his data is “robust” to obvious potential confounding factors. Croke attempts to show “robustness” by analysing the data where certain factors were controlled, such as restricted water access, along with other measures. This involves checking if the results still stand despite standardising a potential confounding variable between the two groups.

For instance, if one was looking at death rates between two places in the country they may find that in place A this is much greater than place B. However this could be because of the confounding factor that the majority of people living in A are much older than B. Therefore one can standardise the data to compare all those in the same age range categories to test if that result still stands despite the differences in average age being controlled in post data analysis.

In Croke’s study, one way in which robustness of data is illustrated is by showing that the youngest and oldest children in the test classes would have smaller differences in test scores than the intermediate age groups, as they would not have been in the population eligible for deworming 7 years earlier. Whilst intuitive that those at the extremes of age would have had reduced number of deworming protocols, and so may have less differences in cognitive ability, this has actually not been evidenced. The graph provided shows no confidence intervals and did not indicate any statistics used to compare the subgroups, so cannot be assessed appropriately.Even if those statistics were used, the study was not powered to detect differences in this subgroup population. In addition, standardising the data cannot account for confounders that are not tested for and may be less obvious.

Further to these limitations, the study assumes that the populations surveyed during deworming and 7-8 years later are comparable,the latter containing the population that was originally dewormed. However, 

 revealed that a total of 3.1 million out of 23 million persons (13 percent) were enumerated outside their district of birth and hence classified as internal migrants.3 This data suggests that the population sampled in the parishes randomised for deworming or control may not correspond to the populations from which the cognitive function data were collected.

Another issue is that the endpoints for the study – differences in standard error of Maths and English tests - are limited measures of cognitive ability. Further, there is no reasonable suggestion as to why deworming would greater effect Maths tests than English tests, as the results indicate. It is also unclear what these differences actually correspond to in absolute terms, on a practical level for the populations analysed. In addition, other studies have shown increased school attendance, which this study fails to show. The reason for this inconsistency is unclear.

Finally, crucial to the analysis is an absence of evidence for combination deworming - there is no data on whether Schistosomiasis deworming occurred in the interim between Soil Transmitted Helminth deworming and cognitive testing of the populations, or on what other public health interventions occurred.

In conclusion, the study has significant limitations. But this should be seen in the context of the wider difficulties of researching the effects of mass deworming. Cognitive benefits are difficult to measure, especially over significant periods of time. This is made even more difficult when looking at substantial benefits to groups (such as spillover effects), as studying group data introduces various confounding factors. Functional outcomes and the ability of improved cognitive abilities to dramatically affect the prospects of a child in this context are difficult to demonstrate, as abundant difficult to measure factors contribute. These difficulties explain why controversy in the field of deworming is rife. This study has significant limitations with data subject to confounders and therefore it may not be as “promising” as we hoped. Further blog posts regarding how we might re-evaluate the evidence will be posted soon.

Some questions on the cognitive benefits of deworming

Andreas Mogensen

If we find that a deworming programme in one region improves school performance, how much have we learned? Should we be confident that a programme carried out by a different organization in another country will have similar effects? Will the programme be equally effective if it’s handed over to the government and scaled up?

 is a Post-Doctoral Research Fellow at NYU’s Development Research Institute whose work addresses questions like these. She founded 

 to synthesize data from different impact evaluations and improve the assessment of development interventions. Her paper 

“How much can we generalize from impact evaluations?”

described as a must-read for development economists

. I recently got in touch with Eva to learn more about her research.

Could you tell us about your work as a development economist? How does your academic research relate to your work with AidGrade?

Right now I'm focusing on leveraging AidGrade's data for research because there is a lot more that can be said with it. But I am interested in other topics in development as well and have several other studies in progress. I'm lucky because my academic work only allows me to set up the structure for and manage AidGrade on the side, and the staff are so great that they can take it from there.

Your paper assesses to what extent we can generalize from evaluations of programs implemented by one organization in a particular location to the efficacy of similar programs implemented by other organizations elsewhere. Why is this issue important? Does it matter for individual donors?

How much one can extrapolate across settings is critical to understanding what we can take away from any one study. Without this kind of work, we could make fantastically wrong predictions. For example, a similar issue came up in medicine, where many studies were traditionally done on men but there has been increasing acknowledgement that effects of some drugs might be different in women or children. The issue is likely to be even more important in development economics, because there are so many more things that could interact with the intervention and determine its effects. So one reason this work is important is because it teases apart some of the factors associated with the effects of a program and helps to improve our ability to predict.

Apart from the prediction issue, knowing the distribution of effects is also important. If we were equally unsure of the effects of all development programs, perhaps we could get away with only looking at the average effect for a particular intervention, but if we know some have spanned a broader range than others that could make a difference as to which we want to support. A former professor and inspiration, Adrian Wood, liked to say that first we should just do no harm; one can imagine perhaps we should pay more attention to the lower end of the range of possible effect sizes because if you do end up doing an intervention with poor results you might sow distrust, and research in psychology shows people dislike losses more than they like gains. Alternatively, if there are poverty traps, perhaps we should weight the large, positive effect sizes more highly. Either way, it is important to consider the full range of possible outcomes.

Finally, and less relevant to individual donors but more relevant for researchers, this kind of work can help tell us where we should allocate more attention -- where are the big unknowns and unexplained variation.

How do you assess the generalizability of impact evaluations?

AidGrade gathered data from about 600 impact evaluations of 20 different kinds of development programs. These were academic studies, and you can think of our data as essentially comprising the results tables along with characteristics of each paper (e.g. whether it was a randomized controlled trial, whether it was randomized by cluster, characteristics of the sample being studied, etc.). We also coded what kind of outcome the results captured (e.g. attendance rates in percentage points, height in cm, etc.).

For the main results, I run leave-one-out meta-analyses of all the papers within each intervention-outcome combination. For example, there might be six papers estimating the effect of school meals (intervention) on attendance rates (outcome). I take one of those estimates out, generate the meta-analysis result from the other five, and see how well that meta-analysis result predicts the one left out, cycling through all possible permutations. It's a bit more complicated than that, but that's the quick summary.

You find that a positive, significant result in one study is a poor predictor of finding a similar result if the same intervention is studied again: there’s only a 32% chance that the next results with be positive and significant. How worried should we be in light of this finding?

It does suggest some caution. One should also consider the sample size of the studies. Studies are very frequently underpowered, meaning that there is just too small a sample to be likely to observe a significant effect even if the program really had a positive effect. In general, especially with regards to how we commonly talk about programs or see them discussed in the press, I think we don't know quite as much as we think we know. Academics are better at being careful about this.

You find that government-implemented programs generally fare worse than programs implemented by academics and NGOs. Why do you think we find this pattern of results? What lessons can we take away?

There are a few possible explanations. The first thing that comes to mind for most people is that government-implemented and NGO/academic-implemented programs are qualitatively different – the government-implemented programs, in particular, tend to be much larger. There could be "equilibrium effects" at a large scale that reduce the observed impact. For example, perhaps microfinance programs help some business owners but at the expense of others -- if one looked at a small-scale program, it would appear to help, but it might not when scaled up. Alternatively, it could simply be harder to implement a program well at scale. Another possibility is that NGO/academic-implemented programs are simply better-managed or devote more effort or other resources to each beneficiary.

In my paper, I show that while it is true that larger programs tend to fare worse, government-implemented programs still perform worse even controlling for sample size, so size is not the full story and it appears to be a combination of factors. More work is needed to distinguish between the remaining possibilities.

Based on your findings, are there any changes you think we should make in how we go about trying to identify the best charities to support with our donations? For example, should we significantly increase our confidence in an organization like GiveDirectly, where we have an evaluation that looks directly at their program?

I think that is a correct inference to draw. There is definitely still reason to sometimes support unstudied charities, but the full range of possible results and our uncertainty about them should enter into that calculation. We should also be cautious as to whether the evaluation of a charity was cherry-picked in any way.

What steps can be taken to improve the generalizability of impact evaluations? Are development economists following these steps or is generalizability a neglected concern?

Great question. First, there are things that people could do to help measure the local generalizability of their results -- for example, include subgroup analyses in their pre-analysis plans. This means that they commit to examining differences in results for different subsets of their own sample. While this would only give us within-sample variation, I found that variance in results within a paper is associated with variance in results across the papers that look at the same intervention-outcome, and it might be relevant locally (such as if the program were to be expanded), so it would seem a step in the right direction. There should also be better coordination across people working on different papers so that they use more of the same outcome measures. Paul Gertler and co-authors, among others, have been setting up impact evaluations that ask the same questions in many different settings. This is very valuable work.

Blog

Quadruple your donation to AMF- in praise of donation matching

Making the most of seasonal giving

The use and interpretation of evidence

Some questions on the cognitive benefits of deworming

“Without this kind of work, we could make fantastically wrong predictions”: An Interview with Eva Vivalt

Our recommended charities for the year

80,000 Hours is seeking a Head of Research

Christianity and Giving

The data revolution: what's behind the barricades?

A Brief Update on Ebola - Are There Any Effective Interventions in Sight?