There is no randomising a technological revolution

Development economics needs to step up.

Development economics is set up to fail on the most important questions currently defining development.

If you are a policymaker in a low- and middle-income country, you might be wondering how best to grow your economy, manage the fallout of a war that’s spiking energy and fertiliser prices, or plan for the AI age.

If you are a typical development economist, you likely haven’t got answers. More worryingly, you probably aren’t spending your time thinking about, or working, on these questions.

To be fair, they are hard questions. But development economics as a discipline is not set up to even attempt to answer them. Individual researchers aren’t to blame, but something needs to change.

In this blog, I’ve focused on AI as a ‘moment’ which development economics is failing to meet. I’ve been thinking about this a lot, and I recently finished a series of nine episodes with Deena Mousa for VoxDev’s new podcast – Ideas in Development – on topics relevant to the AI and development debate.

Below, I delve into why development economics needs to rapidly step up to meet this AI moment, why I’m not confident it will, and outline some of the questions we need answers to.

Uncharted, muddied waters

There is a dearth of independent advice available to governments navigating these uncharted AI waters. This seems particularly pronounced for developing countries who are thinking about their AI policies. Who should they turn to? The World Bank (who might be their lender), AI labs (who want investment and are thinking about future revenues), other governments (who are trying to navigate the China vs US geopolitics), or consultancies/private firms (who are in the habit of selling unnecessary and expensive enterprise solutions to governments).

If the correct policy advice is – actually minister, you don’t need to borrow to build a bunch of data centres, and you can wait a few years for capable and compressed open weights models that are suitable for most government applications, just make sure to sort out you data and energy infrastructure with some smart investments – who is motivated to give that advice?

This could be flawed. For the record, I feel it’s still not clear what the optimal approach to AI policy would be. But those currently offering advice have no incentive to recommend this type of approach.

So, enter development economists? This could be a chance for them to step in and be the independent, ‘evidence-based’ voice of reason. But they don’t have the evidence, and I’m not sure whether they will in time for it to matter.

And in their absence, we’ve ceded the public debate on economic impacts and AI policy to technologists. This group dominates the current headlines and is mostly made up of computer scientists on the West Coast, who have a poor grasp of economics, and of the world outside Silicon Valley.

The AI 2027 forecast last year made a lot of noise, encouraging everyone and their nan to weigh in with their take on timelines. What stood out to me were, timelines aside, the insane predictions on global poverty. There were two scenarios in this forecast. According to the positive AI 2027 forecast, by 2029:“Even in developing countries, poverty becomes a thing of the past, thanks to UBI and foreign aid”. And even in the negative scenario, where humanity is extinct by the mid-2030s, at least by 2029 there is “an end to poverty”.

Just because development economists can’t suddenly produce clean causal estimates about the effectiveness of different AI-related policies, or how AI will impact different economic outcomes, they can still do a lot better than the current standard of debate.

There is no randomising a technological revolution

While development economists have spent the past few decades of the credibility revolution and randomista movement honing their causal inference playbook, for the big questions on AI, I fear that’s not what we need.

No doubt, they’ll do a great job running RCT’s on the various applications of AI in developing countries.

I’m sure there are hundreds of RCT’s underway testing how, now out-of-date, generative AI tools, perhaps used by some type of public servant (teacher, nurse, bureaucrat), improved a health or education outcome. Run the first draft through refine.ink and I’m sure we’ll learn a useful, if very specific, lesson from the working paper – even if it’s completely irrelevant by the time the paper ends up being published five years later.

We should be running these types of experiments, but there is a huge gap on the more important, more ‘macro’ economic questions related to AI and development. And that is not a surprise. Development economics is not set up to grapple with these big picture questions.

Partly, this is an academic incentives story. But it’s also a story of the methods and questions development economists have gotten used to, which are completely unsuited to the important questions on AI, the type that rely on scenario planning, and macro modelling.

There is a growing body of researchers and thinkers in the US and Europe starting to grapple with the big economic questions of the AI age, but not yet in development economics. And this is despite some of these countries potentially being more exposed to the initial economic disruption AI is set to cause.

For sure, there are some bright spots in the discipline, with tools emerging that are suited to this class of questions. The Structural Transformation and Economic Growth (STEG) programme, run by Doug Gollin and Joe Kaboski, stands out as one. STEG coordinates and funds research on macro questions in developing countries, including structural change, productivity and growth, often drawing on micro data.

But even if the methods evolved, and everyone started doing STEG-style research, there’s still a bridge to be crossed. Given the rapid pace of technological change we’re seeing, we can’t over-index on current impacts, even if we were to measure them accurately. We desperately need speculative research, planning and thinking.

How can development economics step up

We need a range of development economists involved big-picture thinking, informed predictions and careful thinking through of future scenarios, even if these methods lack the rigour needed for a top five publication.

This does not mean abandoning evidence or economic theory. But there will be a small group of people shaping economic narratives and stories in the near future that will influence the direction, and diffusion, of AI and AI-related policies. I’d rather that economists working and living in different contexts are able to carefully think through what AI might mean for their country, rather than completely ceding this space to computer scientists in the US.

This might ring some alarm bells as, for good reasons, many development economists have retreated from the big-picture grand theorising of previous generations. But if developing countries want to keep up, RCT’s are not going to cut it.

The conversations I’ve had on this topic typically end with, ok, but what about academic incentives? This is a tricky one, as effectively communicating the potential impacts of AI poses a number of problems.

For example, this more speculative work might mean putting your name to an article, paper, or idea, that is shown to be completely wrong. Clearly this wasn’t an issue for the development heavyweights of the past, cc Jeffrey Sachs. But ironically, in this moment, I would argue that we need economists willing to put their neck on the line and put ideas in the public sphere, even if they only cross the 70-80% confidence threshold, rather than the 90-95% they need to feel about their work before submitting a paper. Shying away from the big questions might stop individual researchers from being embarrassingly wrong, but it cedes the debate to people less likely to be right.

I’m not sure this will actually happen. All economists, and in my experience especially development economists, have been conditioned into extreme caution. So, we need to think about incentivising speculation, and coaxing experts to carve out time to give their two cents.

What would this look like? In the short term, we need Alex Imas and Anton Korineks for Africa, Asia and Latin America. Alex and Anton are quite a unique blend of senior enough to have tenure and therefore step away from the publishing rat-race to dedicate time to thinking about AI, but in the loop enough to have been using these tools and be relatively up to date with the AI frontier. This narrows the field of suitable candidates. We don’t really want dinosaurs who’ve outsourced their code to RA’s for decades suddenly opining on AI. And while it would be great for more PhD students and Assistant Professors to weigh in, it’s not really a reasonable expectation, given the incentives they face, to sacrifice papers for a weekly Substack.

Money is one solution, but I’m not sure where this would come from – Anthropic’s economics team only has so much funding. Ventures like the African Tech Futures Lab (founded by the excellent Rose Mutiso) also aim to start enticing Africans to contribute to this debate.

From personal experience, at VoxDev, very few people say no to writing for us, even though we don’t pay authors. Clearly, they have an intrinsic motivation to contribute to a public good while also valuing the platform we can offer their research – even if a VoxDev article is not going to add much to their academic CV. I think academics appreciate being prompted to write and being helped with the writing process. So, it’s not an impossible challenge to solve.

Telescopes to microscopes: From epistemic authority to context authority

One side note, which perhaps reflects wishful thinking on my behalf, is that this feels like an opportunity to finally wrest control of development economics away from universities in the US and Europe.

Development economics is massively desensitised to its domination by Americans and Europeans. With any objective lens, even after the limited progress of recent years, it’s obscene. Left to its own devices, the discipline has remained an enterprise for elite universities thousands of miles from the countries being studied. We literally still host development economics conferences in places which are impossible, or prohibitively expensive, for academics from the regions being studied to actually attend in person.

AI will likely be a force against localising research in the short-term, as the AI adoption gap, and AI capability gap, between economists at MIT and Makerere widens. But longer-term, as the models necessary for economic analysis diffuse, get cheaper, and easier to run, my hope is that this period of change will result in those based at institutions in Africa, South Asia and other LMIC’s on top of the discipline. Imagine that.

What will we need in five years’ time, if the barriers to learning and adopting the cutting-edge econometric methods have lessened? Local data, local context and local questions.

Maybe this is wishful thinking, but I hope that we’re moving into an era of economists putting their own economies under a microscope. We’ll learn more than using a telescope from afar.

This needs to be coordinated somehow

While academics get excited about doubling their productivity and churning out papers, we need to think about an architecture that pieces together the puzzle.

This includes platforms that screen for quality and relevance and then disseminate open access versions of reliable papers alongside accessible summaries that tease out the main takeaways. A VoxAI, if you will. (Let me know if you’re interested, or if you’d fund this – I’ve not had success in pitching it so far, which is either a reflection on the idea, or my pitching skills, it remains to be seen).

We also need living literature reviews, that look to piece together insights from the slew of new papers to draw overall reliable takeaways as they emerge and compare and update different predictive exercises.

With the state of recent job markets for economists, I imagine there will be more than a few highly talented PhDs in need of a job. With a bit of money, they could get to work on this type of project, if those who were lucky enough to get hired can no longer justify it career wise. I see a huge opportunity here to create a high impact public good that curates good quality research on the economics of AI in an accessible way for policymakers and the public, while also coordinating synthesis work.

Papers I’d like to read

I realise I’ve pointed a lot of fingers, and talked at length about the big questions, the important questions, that development economics needs to answer.

So, what exactly are these questions? Well, I thought I’d list a few of my main ones here.

Speaking of misaligned incentives, there is a lot of room in the short term for rich descriptive research on AI in LMIC’s, even if that doesn’t get an economists blood pumping like a regression. We need more tracking of the adoption of AI in developing countries, the costs of AI in different settings, and the performance of AI in different languages.

We need to immediately start thinking about jobs in developing countries. We spoke in Ideas in Development about our fears that patient zero for AI induced job loss will be those reliant on Fiverr-style, or call centre-type, basic, repetitive service jobs exported from countries like Pakistan and the Philippines to wealthier economies. This is a worry based on best guesses, so we need economists tracking this, for example by matching adoption of AI in high-income countries to their outsourcing of different tasks and business processes to countries with cheaper labour.

This links to a broader question, what is the bottom rung of the development ladder in 2026, 2030, 2035? Matt Yglesias had an interesting piece in the Argument Magazine, which I broadly agreed with, exploring the future of garment factories – as automation gets cheaper and better, and we need less human labour in those types of jobs, where is the ladder?

AI may be a way off impacting manufacturing, but, as discussed above, basic service exports seem significantly and imminently more vulnerable. Thinking forward five years, or ten years, or whenever we have cheap AI models replacing basic service jobs as well as cheap robots being deployed at scale in factories, what industries will still provide employment as well as help on the path to development, particularly given other worrying trends like rising protectionism and increasing conflict across the world.

And for those people who lose their jobs, what policies make sense. I liked Deena’s thread which highlights why this is an area where development economists are needed. Social protection in countries like the US might be strong enough to act as a buffer to AI-induced unemployment. They could even be beefed up a bit by redistributing some of the enormous profits that AI companies bring into the American economy. But for the countries that capture none of this value, and still lose jobs, how do they expand social protection, which already allows many to fall through the cracks.

What have economists done that can help us think about these future AGI scenarios? Does it require a new paradigm? What happens to the existing human population, when the total labour population rapidly expands beyond it in number and skill. Do we have any examples of a group that becomes outnumbered by a far smarter and economically valuable population?

Are power dynamics set to become even more unequal? How does a country approach a negotiation on critical minerals exports with a superpower which could always overpower them militarily, if at great cost and illegal under international law, that now has access to AI models/tools that could easily turn off their power grid, rig their next election, paralyse their institutions, and precisely assassinate key leaders, for very little cost in a world without international law. (From the perspective of the developing world, I don’t trust any superpower with AGI, although a least-bad option may present itself.)

As you can probably tell by the tone of my questions, I’m not exactly feeling optimistic. There is certainly huge promise across a range of applications, like health and education, but we are not thinking enough about the economic risks.

I get it, these are uncomfortable questions. I imagine that simply by asking them I’ve put some people off, as they seem ridiculous, ludicrous, sci-fi. But any serious attempts to answer them are valuable. Here’s hoping I’m just a crank.