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
A Scheme for using WorldCoin to provide electricity & smart phone access in rural Malawi, Africa
I would like to offer a suggestion for how to manage the potential funding gaps from the perspective of a grassroots volunteer organizer that might apply to the funds.
Good project organizers with uncertain funding often have several contingent versions of their project that they can implement at various funding levels.
The application process does this already to some extent, but the fund could more aggressively respond to a project with a “We like the project, we have funding gaps, can you send us a version of the project at 1⁄3 of the initially proposed funding level.” From my perspective as an applicant, this would be a perfectly fine, positive response as an organizer.
Also as a small-scale EA-minded organizer, I am constantly trying to increase the “impact- productivity” of my projects (i.e. impact per donated dollar). This can be done by leveraging and cultivating non-cash resources (e.g. volunteer labor) or selectively cutting the least productive component costs of a project. If your interaction with applicants can help preserve the most productive pieces of the applicant projects during times of low funding, then by keeping more good projects in your funding pool (albeit at lower per-project funding), then the projects can increase their “impact productivity” over time.
The impact of the EA movement grows over time both through funding growth, and increasing the productivity and efficiency of the funds spent. IMHO, as much effort should be spent increasing the productivity of donated fund spending, as is spent marketing for increased donations. EA donors are a pretty limited pool. Many donations that might be received could counterfactually have been spent on other high priority EA causes.
Maybe I missed it, but I did not see in the post any mention of how funding productivity might be increased in response to some of the forecasted funding limitations.
Thank you for your response. I find this piece of the response interesting:
“we want to be mindful of not underfunding projects or leaving grantees with deals they would rather turn down but feel uncomfortable doing so due to grantmaker-grantee power imbalances.”
As someone on the grantee side of the equation (though I don’t apply to this particular fund), I would much prefer an under-funded response of 30% of initial budget request rather than a rejection which is effectively an offer of 0% of the request. But I have pretty thick skin.
I think what I am asking for is for as much communication and negotiation between the grantmaker and grantee as possible in adjusting the project offer to better match the resources request (demand) from the grantee to the resources supply available to the grantor. A better match and greater information exchange increases systemic supply/demand efficiency.
This also allows the grantee to design a better project ask the next time so that grant-giving productivity increases over time. 100% rejection can lead to disengagement. I think it is better for the EA movement if innovating project organizers who are proposing fundable projects can stay engaged.
Though I understand that grant evaluation time is a constraint, so there may not be resources for the extra grantor/grantee communication.
I also understand your point that fundraising is underprioritized. But given that donors may already be giving to other high-impact areas, this might be somewhat OK.
I guess I am SORT OF one of those people you want to know.
I think AI non-allignment with humanity’s interests is often very closely related to the non-allignment of capital investment and large corporate interests with humanity’s interests.
That is because I think it is most likely that large capital investments will be creating the most powerful AGI in the future, and that investment is controlled by what the old Marxists call the class interest of international capital.
Thus, I think that it is inevitable that profit-maximizing AGI will have interests that diverge from humanity’s interests to a greater and greater degree over time. This is similar to how the interests of the profit-maximizing wealthy humans diverge from the interests of the low-income global majority over time.
Thus the highly likely x-risk is that humans have little or no influence over society 100 to 1000 years from now. I think there is pretty close to zero chance that humans will become physically extinct (but this could be debated).
Thus I consider any neartermist work that strives to better align the interests of profit-maximizing wealthy humans with the interests of the current global low-income majority to be contributing strongly to the longtermist effort to align the interests of a future profit-maximizing AGI (which is likely to be the most powerful future AGI) with the interests of the majority of a future humanity.
Extrapolatinf wealth inequality trends, the vast majority of a future humanity is likely to be hundreds or thousands of times less wealthy than the most powerful AGIs.
This, my personal feeling is that neartermist “wealth inequality alignment” work is more likely to be effective than much longtermist work, because I think it is very likely that the vast majority of current longtermists have little or no verifiable knowledge about how to they can actually influence the nature or character of the future most powerful AGI’s that are likely to dominate our future solar system. My personal “guess” is that it is those future AGIs that are likely to create the greatest risks for humanity.
Who these AGIs are or what they will be like, I don’t think anyone really knows at the present time. This isn’t because the problem is untractable in the classic sense, but it is because the chaotic system that will create the AGIs very likely has a very high internal dynamic instability that creates large sensitivity to initial conditions (which makes the result predictably unpredictable via the “butterfly effect”).
I think that by working on neartermist problems, it is possible for us to change the societal conditions that are the initial conditions for the creation of future AGIs. I would argue that if we make our current human society such that humans are nicer to humans in the near term, then this will create a set of initial conditions where it is also more likely that future human-initiated AGI is nicer to humans in the future.
Thus, I think there is lots of neartermist work on wealth inequality that can potentially be very effective at preventing the future AGI x-risk of a completely marginalized humanity. I think that work is both more tractable, has far more certain results AND addresses some of our most probable longtermist x-risks.
Much of my past technical research was in the field of “Chaotic Dynamics” which I don’t think has been applied to EA longtermist philosophy yet. My experience with the dynamics of complex systems makes me very skeptical about any forecasts of the particulars regarding the future of AGI and the agency that individuals are likely to have in creating that future.
That background is the technical context for my views.
Is that what you wanted to get to know?
You write:
“Now you have an ROI of 2 (LTV / CAC), which is quite good for most organizations.”
Note, in the case where the client may have donated to some other organization, the impact of this marketing is:
LTV x IE1 - LTV x IE2 - CAC
Where IE1 is the impact efficiency of what you are fundraising for IE2 is the counterfactual impact efficiency of what the customer would have donated to, and CAC is client acquisition cost.
I think what you are missing is that EA’s are fundraising to make a better world, not to grow their organization at the expense of other organizations, necessarily. Thus they want to grow the EA movement, while not having their cause area compete with other highly impactful cause areas. You do not seem to be considering this perpective in your marketing tutorial.
I hope you find this comment useful in customizing your advice to the EA context.
Solar4Africa launches six Fall-23 student projects in partnership with EA at UC Berkeley
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.
A simplified cost-effectiveness estimation methodology for use in Solar4Africa’s Fall-23 student projects
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.
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.
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 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.
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. ”
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.
Marginal Funding Week, Solar pumps in Malawi: Creating ~$20 of income per $1 of donation for ~$1/capita/year beneficiaries
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.
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.
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!
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.
There are of course both upside risks and downside risks on the price. But even with a price drop of 5X to 10X, in rural Malawi it could still be a very interesting resource.
I recommend reading the Worldcoin tokenomics white paper: https://whitepaper.worldcoin.org/tokenomics
They have a very aggressive set of adoption goals, and the vast majority of the world has incomes that are 10X to 100X higher than rural Malawi and many other parts of rural Africa, so to keep the adoption rates high, they will have to keep the adoption incentives significant relative to rural Africa’s very low incomes. This bodes well for the future prospect of the incentive retaining a significant value for rural Africans.
Worldcoin also has produced 1500 Orbs, and obviously will be expanding to many more countries. Of course, part of the whole project for Malawi would include setting up Orb operators in Malawi.