When looking at programs that mostly target infant mortality, I’ve mostly thought of them as “population-increasing” programs. I’ve sympathized with donors who say that bigger populations might be the last thing poor villages need, and I’ve also assumed that “strict utilitarians” are likely to value such programs more than I do. It’s interesting to see the Report of the Commission on Macroeconomics and Health try to turn this issue on its head:

…high mortality rates of children tend to provoke high fertility rates among poor couples. In general, the high fertility more than compensates for the high mortality … In one numerical illustration, households whose children have a 75-percent survival rate choose to have six children, of whom an average of 4.5 survive. The households whose children have a 95-percent survival rate have two children, of whom an average of 1.9 survive … This pattern helps us to understand the surprising fact that countries with high infant mortality rates have the fastest growing populations in the world…

The report includes charts (see Pgs 35-38) showing a pretty strong association between low infant mortality and low population growth across the world’s nations.

This statistical association doesn’t necessarily mean the above logic is correct; it could be that something else, such as economic prosperity or education, is associated with both low infant mortality and low population growth, and that simply lowering infant mortality wouldn’t reduce fertility.

Not being aware of any studies on this specific relationship, I looked a little further using Gapminder. Rather than looking across the world’s countries (as the Report does), I looked specifically at countries within sub-Saharan Africa over time, which seems more relevant to the hypothesis that lowering infant mortality in high-mortality, high-fertility countries (such as those in sub-Saharan Africa) is associated with lowering fertility.

Since 1950, these countries have seen noticeable declines in both infant mortality and fertility (children per woman). However, the trends don’t sync up. In particular, since 1990, infant mortality has largely remained flat while fertility has continued to decline. (Note that literacy has improved over this time period as well.)

I’m hesitant to go as far as the report in calling infant mortality a primary, or the primary, driver of lower fertility. Still, it is clear at least that reducing infant mortality need not result in population growth. I don’t know of any more thorough studies on the link, and would be interested in any references.

The cause of “water” is one of the more (initially) emotionally appealing, and probably marketable, causes in developing-world aid. Here are some thoughts on the cause, fresh off of reading the Copenhagen Consensus report on it:

From what I’ve seen – both in terms of water-related literature and in terms of general morbidity data – outright lack of water (i.e., dehydration in otherwise healthy people) is not a widespread problem. If you know of data showing otherwise, even for particular parts of the world, please share. However, I think most water and sanitation projects are instead concerned with:

1. Access to convenient water sources. Some people lose hours to maintaining their filters and/or boiling their water for cleanliness; people who live far from water sources can lose far more time (see Pg 11 of the Copenhagen Consensus report for a stark example). Improving water infrastructure may therefore free up time and make them economically better off. However, when this is the goal, it seems important to consider not only how much time potential beneficiaries would save, but how much this time is worth (i.e., what else they could do with it). Depending on market and/or weather conditions, extra time may not translate into extra money, or into much extra quality of life.

2. Access to clean water. Contaminated water can contribute to a variety of diseases that generally cause severe diarrhea (see Pg 34). However, it’s important to note that:

• Water is not the only source of contamination, and clean water alone – when unaccompanied by other sanitation interventions – can only dent the burden of these diseases (again see Pg 34).
• There are a variety of ways to purify water at the “point of use,” some of which – like boiling – are extremely simple and relatively inexpensive (see Pgs 90-91).
• Communities that suffer from contaminated water may also suffer from a host of other health problems (such as malaria and malnutrition) that can be at least as damaging as waterborne infections, while having solutions (such as supplementation, bednets, etc.) than are far cheaper and simpler than the provision of clean water.

In our first year, we saw no cases of well-documented water-focused projects that address key questions such as whether water quality and use were verified, whether an effect on quality of life was documented, etc. Literature on past programs’ effects also seems relatively thin.

At this point, I think of water projects as being pretty far from the sort of “proven, effective, scalable” programs we are looking for. If I change my mind, it will likely be for a program in the first category – providing water to people who otherwise would be spending inordinate amounts of time retrieving it – rather than for a program focusing exclusively on clean water.

If you could accomplish any of the following for the same cost, which would you choose?

(1) Prevent 100 deaths-in-infancy, knowing that in all likelihood these 100 people will grow up to have consistently low income and poor health for their ~40-year-long lives.

(2) Provide consistent, full nutrition and health care to 100 people, such that instead of growing up malnourished (leading to lower height, lower weight, lower intelligence, and other symptoms) they spend their lives relatively healthy. (For simplicity, though not accuracy, assume this doesn’t affect their actual lifespan – they still live about 40 years.)

(3) Prevent one case of relatively mild non-fatal malaria (say, a fever that lasts a few days) for each of 10,000 people, without having a significant impact on the rest of their lives.

For me, the answer is definitely #2. I am very excited by the idea of changing someone’s life in a lasting and significant way (2); I’m much less excited by the idea of a temporary, less significant life change (3), and I don’t think that the quality of a life equals the sum of the quality of the days in it. (1) excites me the least – I just don’t put that much value in “potential lives” (I think the death of a 20-year-old is more tragic than the death of an infant), and I especially don’t put much value in saving “potential lives” riddled with health problems.

I’m not interested in having a long philosophical argument about the validity of my views. I believe that different donors likely have fundamentally different values that you can’t change by throwing any number of thought experiments or philosophical abstractions at them. Our research will aim to serve as many different sorts of donors as possible, rather than holding up one philosophical value set as the “rational” one. But I am interested in what others think, and whether my attitude is common or rare.

To give a quick sense of the practical relevance of this question: programs targeted directly at under-5 mortality (including some vaccination programs and some micronutrient programs) are much more likely to get you (1)-type results; programs that distribute bednets or other health materials en masse are more likely to get you (3)-type results; an economic empowerment program (particularly focused on improved farming techniques) may aspire to (2)-type results, but I believe that these types of results are the most difficult and expensive to bring about.

Over the last few days I have been wondering just how severe and how fixable developing-world malnutrition may be. For a striking illustration, see “Grandmothers and Granddaughters: Old Age Pension and Intra-household Allocation in South Africa” by Esther Duflo.

This paper analyzes a survey of 9000 families in the early 1990s in South Africa, when a public pension program was broadened significantly, and tries to look at how the program (significant cash transfers to low-income, elderly people) affected child nutrition. Its key argument is that cash transfers to women significantly improved the nutrition of female children (but that cash transfers to men had no discernible effect on child nutrition).

I’m not convinced that the data support this argument (my chief concern is Figure 1, which makes it look like the children of eligible women got healthier before but not more than other children, and that the observed effect is an artifact of the time period studied). However, the argument reflects three key themes that, from the paper’s references and a few other things I’ve seen, seem to have significant research supporting them:

1. Malnutrition is widespread and severe among the very poor. Table 3 shows that prior to the program’s expansion, these low-income South African children were far shorter (for their age) than American children: their height-for-age averaged around -1.5 standard deviations (under standard assumptions, this would put the average South African child around the 7th percentile of American children). Height-for-age is strongly linked to nutrition in early childhood, as Duflo explains (with references) starting on Page 13.

2. Curbing malnutrition is not necessarily a thorny problem. Regardless of whether Duflo’s hypothesis about female vs. male recipients is correct, there is no question that the height gap described above fell sharply (down to 0.5 standard deviations, which corresponds to the 30th rather than 7th percentile of the general population) after the introduction of the pension program. Bear in mind that the program was no elaborate health intervention, but merely a cash transfer to families.

3. In the developing world, not all household income is equivalent. Duflo argues that income that comes to women is more likely to improve children’s health. This is the most questionable claim in the paper for me (for reasons outlined above), but the general idea is also argued in this study of Cote d’Ivoire, and possibly in other literature as well; this would give some explanation of why so many microfinance programs explicitly focus on women.

We completed our first year of research a few weeks ago, and are now starting up our second. (Our annual review and plan discuss what we’ve learned from our first year, and the many ways in which we’re changing our approach for year 2.)

We found some strong organizations the first time around, but our “bottom-up” approach (counting on our applicants to tell us about their activities) left us with a very partial picture of things. At this point we’re basically starting over, and trying to answer the following questions:

1. What are all the obstacles faced by people in different parts of the developing world?

I want to know as much as possible about the groups of people we’re helping (not just the diseases we’re fighting), because this helps us to (a) form better guesses about the most important problems to focus on; (b) focus on areas where a little aid goes a long way; (c) paint a picture for donors of what sort of difference a good program can make, beyond the usual idealized anecdotes or “cost per life/DALY saved” figures. (For example, when you save a life from malaria, what sort of a life is it? When you help improve someone’s income, what does that mean for what they’re able to buy?)

Ideally, we would know the following about as many different parts of the developing world as possible:

• Full details of the prevalence and severity of different health problems, including diseases (HIV/AIDS, tuberculosis, malaria, diarrhea, pneumonia, and NTDs), malnutrition (vitamin A deficiency, anemia, low weight-for-height and height-for-age), vision problems (including cataracts), and deformities (including cleft palate and obstetric fistula)
• Availability, and cost, of basic quality-of-life goods and services including health care, water, sanitation, electricity, financial services (savings, loans, insurance), and basic entertainment (televisions, radios, festivals).
• Availability, quality, and content of schooling.
• Common occupations, along with necessary skills/qualifications for each. I’m particularly interested in what it would take for someone to improve their occupation and income (are there plenty of opportunities if only they had basic help, along the lines of nutrition assistance or financial services? Or is the set of possible jobs highly limited?)
• What people most want to change about their lives, and what they most want help with.

I’ve been reading academic papers that answer some of these questions for certain areas using survey data; I’ve also talked to a couple of people who’ve spent significant time in the developing world, just to get a basic picture. I’ll share what I’ve learned so far in a future post.

2. What impact do different health problems generally have?

There are some diseases, such as AIDS, that we have a fairly good picture of in terms of their impact on quality/length of life. There are others – particularly the NTDs – that we know very little about. We need to examine enough medical literature to have a good sense of what possible symptoms are associated with different diseases, as well as a basic idea of how different health problems interrelate (for example, the extent to which malaria increases susceptibility to HIV/AIDS).

3. What are the most promising interventions and charities?

Answering #1 and #2, at least for some parts of the world, should have a major effect on how we think about #3, so we are focusing on #1 and #2 for now. The goal is to start with people and places, rather than programs. However, we want to make sure we’re checking out any particularly highly recommended, or otherwise promising, programs we come across (for example, recently we took a look at the Fred Hollows Foundation on several people’s recommendation).

We don’t expect to get anywhere close to “answering” all of the above questions, but that’s our framework for learning as much as we can to frame our investigations of charities. We have a lot of work to do. We’ll be sharing our findings as we go.