I am an early-retired Harvard Ph.D Physicist and Clean Energy Policy Analyst and charity entrepreneur. I have been organizing and experimenting with clean energy projects in Africa for 30 years. Currently, I am lead organizer of a social venture that consists of a small US non-profit and a local Malawian for-profit partner.
Robert Van Buskirk
Karma: 139
Solar4Africa Project #4 - General Intervention Refinement and Cost-Effectiveness (CE) Analysis of Solar Electric Cooking Systems: Final Analysis and Justification of Intervention
Solar4Africa Project 2: Small solar cars for non-emergency health services access — Preliminary cost effectiveness analysis
I am amazed that the EA community has such a negative reaction to someone pointing out the possibilities of institutional/AI-leadership sexism.
Within minutes my comment got −14 karma points. Interesting!
It is also the two women board members are also now off the board. So I would also like to hear what happened from a woman’s perspective. Was it another case of powerful men not wanting to cede authority to women who are occupying positions of authority?
There could be many layers to what happened.
Thanks Emily:
I see some resonance between the behavioral science “Mistakes” that you think EAs might be making and differences that I find in my approach to EA work compared to what seems to be documented in the EA literature.
Specifically, I was recently reading more thoroughly the works of Peter Singer, (specifically The Life You Can Save and The Most Good That You Can Do), and while I appreciated the arguments that were being made, I did not feel as though they reflected or properly respect the real beliefs and motivations of the friends and family that donate to support my EA activities.
In this sense I also see a set of behavior science mistakes that the EA movement seems to be making from my particular individual perspective.
So in my 30 years of doing Africa-focused, quantitatively-oriented development projects, I have developed a different “Theory of Change” for how my personal EA activities can have impact.
This personal EA-like Theory of Change has three key elements: (1) Attracting non-EA donors for non-EA reasons to support EA Global Health and Welfare (GHW) causes, (2) Focusing on innovation to increase EA GHW impact leverage to 100:1, and (3) Cooperating with other EAs with the assumption that each member of the EA community has a different set of beliefs and an individual agenda. Cooperation serves aligned and community interests.
I would appreciate it if you might comment on whether some of my divergences from general EA practice might address some of the “behavioral science” issues that you have identified.
The first element of my Theory of Change is that for my EA causes to be successful, my projects have to be able to attract mostly non-EA donors. I recognize that my EA-type views are the views of a relatively tiny minority in our larger society. Therefore, I do not personally try to change people’s moral philosophy which seems to be Peter Singer’s approach. When I do make arguments for people to modify their moral philosophy, I find that people usually find this to be either threatening or offensive.
Element #1: While the vast majority donors do not donate based on “maximum quantitative cost-effectiveness,” they do respond to respectful arguments that a particular cause or charity that you are working on is more important and impactful than other causes and charities. When “maximum quantitative cost-effectiveness” is a reason for someone that they know and respect to dedicate their life effort and money to a cause, many people will be willing to join and support that person’s commitment. So while only a few people may be motivated by EA philosophical arguments, many more people can support the movement if people that they like, know and respect show a strong commitment to the movement.
This convinces people to support EA causes because they see that EAs are honest, dedicated, and committed people that they can trust. You do not have to convince people of EA philosophy to have people donate to EA cause/efforts. Most people who donate to EA causes could potentially have strong philosophical disagreements with the EA movement.
The second element of my Theory of Change is that EA projects need to have very large amounts of impact leverage. So it is important to constantly improve the impact leverage of EA projects. Statistics on charitable donations indicates, that most people donate only a few percent of their income to charity, and may donate less than 1% of income to international charitable causes.
Element #2: If people are going to donate less than 1% of income to international charitable causes, then in order to try to address the consequences of international economic inequality, EA Global Health and Welfare charities should strive to have 100:1 leverage or impact. That is, $1 of charitable donation should produce $100 of benefit for people in need. In that way, it may be possible to create an egalitarian world over the long term in spite of the fact that people may be willing to give only 1% of their income on average to international charitable causes.
In my little efforts, I think I have gotten to 20:1 impact leverage. I hope I can demonstrate something closer to 50:1 impact leverage in a year or two.
The third element of my EA theory of change is that I assume that every EA has a different personal agenda that is set by their personal history and circumstances. It is my role to modify that agenda, only if someone is open to change.
Element #3: Everyone in the EA movement has a different personal agenda and different needs and goals. Therefore my goal in interacting with other EAs is to help them realize their full potential as an EA community participant on their terms. Now since, I have my own personal views and agenda, generally I will help the agenda of others when it is low cost to my work or when it also make a contribution to my personal EA agenda (i.e. encouraging EA GHW projects to have 100:1 impact leverage). But if I can keep my EA agenda general enough, then there should be lots of alignment between my EA agenda and the agenda/interests of other EAs and I can be part of a substantial circle of associates that are mutually supportive.
Now this Theory of Change or Theory of Impact is to some extent assuming fairly minimal behavior change. It assumes that most people support EA causes for their own reasons. And it also assumes that people will not change their charitable donation behavior very much. It puts most of the onus of change on a fairly small EA community that achieves the technical accomplishment of attaining 100:1 impact leverage.
Does this approach avoid the mistakes that you mention, while at the same time making a minimal impact on changing behavior???
Just curious. I hope this response to your presentation of EA behavior science “Mistakes” is useful to you.
We have made a post for the project: Solar pumps in Malawi: Creating ~$20 of income per $1 of donation for ~$1/capita/year beneficiaries where:
“Every $100 of marginal donation will add one brushless DC pump and 100m of irrigation hose to a container shipment of solar equipment that we will purchase in February to be delivered to low-income rural women’s groups and farmers in rural Malawi starting in June 2024. ”
Beneficiaries pay for the solar panels to power the pumps, and we estimate that each pumping system will generate roughly between $2000 and $5000 of new income for $1/capita/day beneficiaries in total over the next 3 to 10 years.
Marginal Funding Week, Solar pumps in Malawi: Creating ~$20 of income per $1 of donation for ~$1/capita/year beneficiaries
Thanks Jamie: our method is most useful when one has a relatively small sample of field data. In that case it is easy to calculate the averages of the bottom third, middle third, and top third of values and this is good enough because the data sample is not sufficient to specify the distribution with any greater precision.
Our method can also be calculated in any spreadsheet extremely easily and quickly without using any plug-ins or tools.
But agreed, if someone has the time, data and capacity, your method is better.
I don’t know if this is useful to you, but we have a somewhat easier way of solving the same problem using what we call a simplified Monte Carlo estimation technique. This is described in the following post:
It is not quite as accurate as your method because it approximates the probability distributions as a three-value distribution, but it addresses the issue of CEA inputs being uncorrelated and can be done in any spreadsheet without a need of using any extra tools or web services. You just calculate for all combinations of inputs, and then use standard spreadsheet tools like cumulative probability plots or histogram calculators to get the probability distribution of results:
“In our CE estimation with uncertain inputs, we implement a highly simplified Monte Carlo method that we call a simplified Monte Carlo or “poor man’s” Monte Carlo calculation.
In our simplified Monte Carlo calculation, we initially estimate ranges for all or most of the input parameters, and represent these ranges by low, median, and high values. Given a probability distribution of what values a parameter may take, the low value represents the average value of the lowest 1⁄3 of possibilities, the median value represents the average of the middle 1⁄3 of probable values and the high value represents the average of the largest 1⁄3 of probably values. This approximates a probability distribution of possible input parameter values by three discrete values of equal probability.
Once all of the input parameters are represented by three values of equal probability, then the CE result is calculated for all combinations of input parameters. If each of the input parameters are independent and uncorrelated, then the set of CE values that result from all combinations of inputs all have equal probability. A histogram of the full set of CE results is then constructed to illustrate the full range of possible CE values and their respective approximate probabilities. ”
I completely agree that GiveDirectly could explain this a hell of a lot better. I suspect that their team has a diversity of points of view and this prevents them from committing to a more concrete explanation like what I presented. I explained the capital accumulation in terms of what I see when I visit actual households: someone starts with mud walls and a thatch roof, then they take their surplus and build a bigger house with concrete floors, brick walls and metal roof, move into that and then they can buy nicer furniture because dirt isn’t falling down from the thatch roof all of the time. They have obviously invested in higher productivity housing services which increases their consumption income. But this evidence is anecdotal.
I think GiveDirectly tried to anecdotally explain how cash transfers get invested with their shop example in the video. But again, I totally agree that their explanation has holes that make it hard for people who don’t already agree with them to have the explanation that they need to understand why cash transfer requirements are likely to decrease substantially over time.
You claim that investments that improve the quality of services consumed (such as cement floors, brick walls and metal roofs) are not “productive investments” and do not produce a long-term improvement in income.
This is our key point of disagreement. I disagree with your claim. We can agree to disagree.
I think we are disagreeing on how income/poverty is properly measured.
In my work in low-income developing country conditions the most relevant measurement of income is “consumption income” the value of the goods and services that people consume.
Where I work, it is common for 1⁄3 to 1⁄2 of household consumption to be self-produced: i.e. generated with no cash income, and no cash expenditure.
In these conditions, an investment in improved housing does produce a long-term increase in consumption income because it increases the value of the housing services that the family is consuming over many years with no further cash expenditure. It is a one-time cash expenditure and a long-term consumption income increase.
Increasing consumption income is what households in rural Africa see as real, tangible poverty reduction.
I agree that GiveDirectly did not support its claims in ways that certain people can find convincing. Because many people in richer countries live in places where most consumption has an associated financial transaction, and many folks do not have familiarity with living in a subsistence economy that can enable an intuitive understanding of un-monetized production and consumption economics.
But for those who do spend time working in the practice of improving rural African household economic conditions, GiveDirectly’s claims seem well-supported because in rural Africa it is very common for people to make cash expenditures to increase consumption income over the long term. People like me consider such improvements in housing and infrastructure as productive investments even if they produce benefits that are not monetized.
GiveDirectly has shown that the impact of cash transfers are multi-year, and if the benefit lasts more than one year, then the cash transfer in an subsequent year can be less than the cash transfer in the previous year to maintain a household above the extreme poverty threshold. Thus, IMHO, GiveDirectly has shown what it needs to show to make the claim that cash transfers likely can decrease year-over-year to maintain incomes above the extreme poverty threshold in a larger scale program.
I have worked in the field in rural Africa on projects for almost 30 years, and from my experience you seem to be missing a pretty huge factor in your argument that there is insufficient evidence that the annual cost of extreme poverty reduction through cash transfers would decline over time.
I don’t quite understand how your argument can ignore household capital accumulation?
A portion of the $100 billion per year that the poor recipients would receive would be spent on durable goods and productive assets that raise the standard of living to some degree for several years for the beneficiary households. Say each year 25% of money is spent on durable goods and assets that have a lasting effect of three years. Then the first year, you need $100 billion, but the next year, you need only $75 billion, and then year after that you need only 0.75 x 75 = $56 billion … One could of course build a much better conceptual model and estimation of this, but any durable goods and productive asset investments by beneficiaries should decrease the need for future cash transfers.
And there are many investments that people make in rural Africa that have very long, durable effects: sending kids to school, cement floors, durable metal roofs, buying a motorcycle to replace your bicycle, a mechanical irrigation pump so you can grow crops in the dry season and increase agricultural income.
In order NOT to have a fairly rapid decrease in the amount of annual cash transfers (i.e. the hypothesis of your post), you would have to have only a very small amount of the cash transfers spent on productive or durable assets that last two to several years.
I think the studies have satisfied a very high burden of proof with respect to showing that a substantial portion of the cash transfers are spent on durable and productive assets and that there exists a multi-year positive impact.
I think that now the burden of proof is on you to show or demonstrate how spending on productive and durable assets would not lead to capital accumulation that would decrease the cost of maintaining a certain minimum standard of living in subsequent years. I don’t think you have shown that yet in your post.
If a portion of income is spent on something that has a positive return, then that positive return needs to be subtracted from the income subsidy for the following year that is needed to maintain the minimum income level. But maybe I am I missing something in this logic?
In the course of my work over the past 30 years, I have talked to hundreds of rural African households about their finances and economics. When they get more money, they spend some on consumption, but they also invest. Very low-income households in rural Africa also have many very small investment opportunities that have payback times of months, not years. The investments they make can give them a return which increases income over the next year of which they take a portion of the surplus and invest again. This becomes household equity that is used to sustain a long-term increase in household income. When you visit villages in Africa over many years, you see the dynamic of increased durable infrastructure and household financial equity over time: Dirt floors change to cement floors, mud walls become brick walls, thatch roofs become metal roofs, kids playing in the dirt become kids going to school, people who used to have to walk everywhere are able to ride motorcycle or minivan.
Perhaps this comment is useful to you. Or maybe I am missing something and you can enlighten me as to why household asset accumulation would not necessarily decrease the cost of a global cash transfer program that is meant to eliminate extreme poverty at large scale.
I think a hypothesis or framework that you might want to try for examining the question is “What is the characteristics of the market for EA-based philanthropy?” or in less elitist language: “Follow the money!”
A large fraction of the money for the EA movement comes from very wealthy people. Founders pledge consists almost exclusively of wealthy entrepreneurs. Open Philanthropy is funded mostly by very wealthy people. Very wealthy people tend to take the approach of paying a higher price for a premium product or service. It is only logical if you have a lot more money than other people in the market.
According to the Federal Reserve, the top 0.1% of households own $18.6 trillion, the next 0.9% of households own $27.2 trillion, and the next 9% own $54.8 trillion. The remaining 90% own $44.3 trillion in assets. So the top 10% of households own $100.6 trillion which is more than twice as much as everyone else. So naturally the EA movement preferentially serves elite donors, and as a result it has many of the characteristics of an elitist movement: focus on elite universities, lack of diversity, etc.
What is the alternative? Does the EA movement want to bear the cost of resisting the incentives it has to preferentially serve elitist donors and to perhaps unconciously take on elitist characteristics?
The easiest path for most of the EA movement is to simply say that the key focus is on maximizing impact of each particular organizations. In this case, each organization will maximize funding over the short-term by focusing on serving morality-aligned donors who have the most to give and who provide the greatest short-term donation potential. This will keep the EA movement dependent on richer donors.
But the resulting elitism will probably have an adverse PR impact over the long-term. This is because giving less attention to 90% of donors because they have less money than the elite donors will probably alienate a majority of the philanthropic public and prevent the movement from reaching its full growth potential over time.
Keeping this in mind, it might be useful for some of EAs current elite donors to invest in less elitist grassroots EA outreach in order to minimize long-term EA movement PR damage. While this is probably not revenue-maximizing over the short-term, it may make greater inroads into the donations from the $44.3 trillion in assets (and some portion of income) of the 90% majority of people in richer countries who might eventually support EA and increase the movement’s impact in ways that complement the donations that they give.
As a follow-up: Jewish Voice for Peace seems to be having a big influence in pushing for a cease-fire, and their annual budget is about $3 million per year:
https://projects.propublica.org/nonprofits/organizations/900018359
So if their influence can decrease the bombardment of Gaza by more than just two days! They might actually be more cost-effective than GiveWell at saving lives!
With the current bombardment, people in Gaza are being killed at an average rate of 2,500 lives lost per week and about 6,000 injured per week. Lets assume that this amounts to about 4,000 life-equivalents per week assuming that each injured person is losing 0.25 of a life on average. These estimates may be low, as there are many people missing in the rubble who are not included in these figures.
The government of Israel appears to be committed to continuing the bombardment for a long time in spite of the civilian casualty rate. So it appears that if there is a charity that can have an influence on decreasing the length of time that the government of Israel bombards Gaza civilians, this would be cost-effective for EAs if this influence cost less than 4000 x $5000/life = $20 million for every week of Gaza civilian bombardment that is avoided.
It is more or less a political question of determining how the length of the bombardment time can be lessened. But if there is an organization that you think might actually have an impact of decreasing bombardment time by a week, and if they have a budget of less than $20 million, then such an organization might be cost-effective from an EA perspective.
I hope this suggested approach is useful to you.
Hi Stan: Thanks for the great comments and questions.
You are completely correct on noting that I need to subtract the cost that the customer pays from the present value net benefits. I have edited the post to incorporate that correction.
BUT the benefits of the solar lights is still very high, and therefore it continues to invite scrutiny.
Trying to address your other points:
THE key reason, the solar lights from our intervention produce benefits that are so much higher for other solar lights, is because, we use a battery technology that is currently not used at all in solar lighting systems because it is quite a bit more expensive than other battery technologies. The technology is lithium titanate battery chemistry. And the cycle-life of that battery tech is about 10 TIMES longer than lithium ion batteries and lead-acid batteries which dominate the market.
If you look at the “Rechargeable characteristics” at:
https://en.wikipedia.org/wiki/Comparison_of_commercial_battery_types
You will see that regular lithium ion which is listed as lithium cobalt-oxide has a cycle life of 500 to 1000 cycles, while lithium titanate has a cycle life of 6,000 to 10,000 cycles.
BTW, we are currently installing subsidized small solar lighting systems to about 1000 to 2000 household per year.
The much much longer cycle life allows for a much more long-lasting solar light, which is what produces the huge benefit. One expects the market to normally equilibrate where the benefits approximately equal the cost. So perhaps it is not surprising if you introduce a technology into the market which lasts 10 times longer than the current market equilibrium, and then subsidize the cost so that the price is about the same as the low-quality technology, then the net benefit is 8 times the cost of the old technology, which is what our analysis indicates.
As for people getting access to electricity and making the lights obsolete. In Malawi, where we work, this is pretty unlikely. Many people do have power lines nearby, but people use so little electricity, and the cost of connecting is so high, that very few people connect to the grid. And the electric company does not like connecting folks and may take over a year to satisfy a request for a connection even when someone pushes hard to have one. That is because the cost of connecting and serving low-use customers to the grid is subsidized, and the national electric company is often having budget problems.
Looking at World Bank statistics, perhaps this dynamic is working at a larger scale in Africa. For example, if we look at the World Bank data for electricity access in rural SubSaharan Africa (SSA):
https://data.worldbank.org/indicator/EG.ELC.ACCS.RU.ZS?locations=ZG
We see that access has increased from 12% to 30% from the year 2000 to 2021 in rural SSA. I think it is safe to say that more than half of rural SSA will still be largely without electricity access by 2030.
This is evidence that it is fairly likely that hundreds of millions of rural Africans will be able to benefit from more beneficial off-grid solar lighting for several decades to come.
Addressing some of the other points.
Re: the amount people spend on batteries and cell phone charging. We have done surveys. See:
I have discussed with people in village in Malawi how crazy it is that they spend so much on batteries. This argument makes it pretty easy to market the subsidized solar systems. But people live very hand-to-mouth in rural Malawi. So they will spend to buy something every week rather than invest to reduce a cost if the investment takes more than six months or a year to pay back.
I agree that the $200 income should be modeled with the Monte Carlo. But we can also target the subsidies to areas where incomes are lower. So the analysis indicates in my mind that the subsidies should be targeted to households that have incomes of $200 or less.
We do need to do more work assuring that the lighting systems are guaranteed to last 5 to 15 years, but from an engineering perspective, there is no reason the system cannot last that long given the battery we are using. The remaining engineering issue is going to be how to make sure all of the other parts are very durable, or very cheap and easy to replace.
Thanks again. I hope that answers most of your questions and concerns.
A simplified cost-effectiveness estimation methodology for use in Solar4Africa’s Fall-23 student projects
If the problems of poverty and health inequality start being solved, then this should be reflected in the cost per life-saved going up over time. Because as the problem gets solved at a global scale, there are diminishing returns for new investments in solving the problem.
So the strategy would be to save while keeping an eye on the cost trends in saving a life. If it looks like the cost per life saved is increasing faster than the value of savings is increasing, then you shift the savings to donations. But as long as the rate of increase of the cost of saving a life is substantially less than your return on investment in your savings account, you will save more lives by keeping the money in the savings account and paying for the lives-saved later.
This also demonstrates that if you have low-yield in your retirement savings, then it is better to donate now to save a life rather than wait later because if the yield is lower than inflation, you don’t wind up saving any more lives. And given a choice between saving one life now and one life ten years from now, it is at least a little better to save it now.
I have many years of experience with the Energy Efficiency policy analysis community where there is a very strong emphasis on impact evaluation. I also have a little experience seeing impact evaluations implemented in the international development community.
I see four reasons why impact evaluation is not as prevalent in EA as other professional communities:
(1) Impact evaluation can get very bureaucratized and lead to high administrative overhead expenses and organizational constraints which can make programs more costly to implement and less efficient in their operations;
(2) Impact evaluation if it is to be quantitative needs clear metrics to measure performance, and this requires consensus on goals and theories of change. While EAs have an open mind, they also have a fairly large diversity of opinions on the details of which specific near-term goals are highest priority. This diversity of opinion, makes it difficult to make a singular theory of change and makes it difficult to select clear performance metrics for many projects (because of the lack of theory of change).
(3) Much of EA is focused on providing a low-overhead service to charitable donors so that their donations can have the most cost-effective impact. Since donors also have a diversity of opinions with respect to specific objectives and goals, the focus is on cost-effectiveness calculations and transparency using a variety of metrics. Thus impact is viewed from the perspective of a diversity of donors who may have different impact goals. Thus in practice impact is often evaluated in terms of “funds directed” which is the response that donors have in terms of the EA movement meeting their image of good cost-effectiveness.
(4) Maximizing marginal cost-effectiveness may be better and more efficient than doing impact evaluations: If there is a supply of “world improving opportunities” and a demand of “wanting to improve the world”, then maximizing the marginal cost-effectiveness of “wanting to improve the world” purchases (i.e. EA donations) should maximize the net surplus value produced from “world improving activities” in general given constrained resourced available to those wanting to pay for a better world. This just the law of supply and demand applied to people donating to make a better world. Thus the EA approach of focusing on marginal cost-effectiveness may be better and more economically efficient than performing lots of impact evaluations of specific EA activities.
In short, the “impact evaluation” of the EA movement is in terms of maximizing marginal cost-effectiveness, maximizing the number of people involved and maximizing the funds directed to the causes that meet maximum marginal cost-effectiveness criteria. This may not require a special impact evaluation effort with some specific, consensus “theory of change,” but it may simply require a bit of movement statistics collection and compilation to track the cost effectiveness, the amount of money directed, and the number of people participating in the EA movement.
Thank you for clarifying the voting system for me. So my comment most likely irritated some folks with lots of karma.
I certainly don’t want to say things that irritate folks in the EA community . I was giving voice to what I might hear from some of my women friends, something like: “Yes Helen Toner was an EA, but she was a woman who was questioning what Altman was doing too.” According to this article, Altman tried to push out Helen Toner “because he thought a research paper she had co-written was critical of the company.” But she was on a board whose responsibility to make sure that AI serves humanity. So here job was in some sense to be critical of the company when it might be diverging from the mission of serving humanity. So when she tries to do her job, some founder-guy tries to her because the public discussion about the issue might be critical of something he implemented?
I think this information indicates that there is not only an EA/non-EA dimension to that precursor event, but I think most women would recognize that there is also a gender/power/authority dimension to that precursor event.
In spite of such considerations, I also agree with the idea that we should not focus on differences, conflict and divisions. And now I will more fully understand the karma cost of irritating someone who has much more karma than me on the forum.
Thank you for the feedback on my comment. It has been informative.