Has development economics lost its way?

What should development economists be working on – and how does their work actually reach the people making decisions?

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What questions should researchers be working on? Has development economics lost its way by focusing too much funding and effort on the micro? When development economists have results, how should they package those in a way that encourages take up? And what types of institutions and organisations can help with that process of ensuring the best evidence informs policy decisions?

Some easy questions then!

Rachel Glennerster has spent her career working in both the places where evidence is made, and the places where it is used. She joined me to discuss all this and more.

Radical simplification, a year on

Last year, Rachel joined myself and Stefan Dercon for a conversation on the future of foreign aid, in which she outlined her proposal for a radical simplification of aid. This started from her observation of how donors work: agencies spread themselves far too thinly, across too many countries and too many projects, reinventing the wheel with every new programme design. Rachel’s alternative is for a bilateral donor to pick perhaps six or seven areas of genuine comparative advantage – not broad sectors like ‘health’ or ‘education’, but specific, evidence-backed programmes such as structured pedagogy or the ultra-poor graduation approach – and to run them deeply, in fewer countries.

A year after she first made the case, she is more convinced, not less. The reason is political.

“Congress is much more supportive of areas where they really understand what USAID or the State Department was doing.”

PEPFAR retains support on Capitol Hill because it is legible: the public can see what the money bought and how many lives it saved.

“When you basically have an aid programme that says it’s all very complicated, leave it to the experts... it’s kind of unsurprising that you lose support for aid.”

Simplification, in this view, is not just an argument about efficiency. It is a survival strategy for aid budgets in rich countries.

What counts as growth?

Where does economic growth – the thing that actually lifts people out of poverty at scale – fit into a simplified portfolio of proven programmes?

Rachel pushes back on the premise of the question, arguing that the mental model in which there are ‘micro things’ on one side and ‘growth things’ on the other is a misreading of the evidence. Human capital, agricultural productivity and energy are not alternatives to growth policy; they are its ingredients.

Rachel points to recent work tracing the chain from the Green Revolution’s improved seeds through better health and education, demographic change, and on to structural transformation. Energy is another clear case, where evidence shows that supporting firms’ growth requires providing access to good, reliable, cheap energy.

She then adds a subtler point that she thinks is missed in almost every discussion of growth strategy. Not everyone can become Singapore, because the world economy only has so much room.

“There’s sort of a slot for doing cheap manufacturing goods at any one time... It’s not like every African country can move into cheap manufacturing at the same time, particularly when a huge country like China is occupying that space.”

Researchers can study the characteristics of whoever takes the slot next, but it does not follow that every country adopting those characteristics would get one. There is a hunger among policymakers for someone to name the sector that will deliver Singapore in a decade, and you can make a lot of money by going around saying that you can, but the honest answer is that no such single lever exists.

The micro-macro divide – overstated?

For those cursed with a Twitter account, you would have noticed the recent social media row over whether development economics has lost its way by privileging micro over macro. I wrote some of my own thoughts here. Rachel, who also weighed in at the time, explained why she is not persuaded that there is a real dispute.

“In most of the academic literature, there isn’t a debate about should you do micro or macro. People just want good work.”

Rachel also points to feasibility. The discipline moved away from cross-country growth regressions because they were fragile – tweaking the specification would change the answer. What replaced them is not an absence of macro but a different kind of it – structural models disciplined by micro identification, cross-country trade papers – the type of work funded by STEG, for example.

And Rachel feels that some of the classic macro questions simply got solved. Hyperinflation in Latin America dominated Rachel’s undergraduate exams; today the profession broadly knows how to end hyperinflation and how to run monetary policy in developing countries, so the papers stopped.

“To some extent you have to go where you can answer the question. And we have answered a lot of macro questions, which is great.”

Ideas come from the ground

Asked what the macro-development agenda should learn from twenty years of micro ecosystem-building, Rachel’s first lesson is about where ideas come from. The great behavioural shift of the RCT era was that researchers stopped running regressions from their desks and started spending serious time in the field.

“That’s the story of a lot of people – their best ideas came when they were in the field and talking to people.”

She credits Doug Gollin, whose STEG programme (with Joe Kaboski) at CEPR funds exactly the bridging work she wants to see, by pairing big structural models with a deep understanding of what is happening on the ground.

The second lesson is about communities. The dense networks of researchers, shared data knowledge and skilled local staff that grew up around places like western Kenya meant a PhD student could arrive and start productive work immediately – it became a learning lab where everyone stood on the shoulders of everyone before.

The consensus machine

We then turn to how evidence, once accumulated, gets packaged for governments – and specifically to the evidence panel behind the Smart Buys in education. The model convenes economists, psychologists, education specialists and practitioners from across the world, runs a rigorous systematic review, and then hashes out consensus recommendations that put cost-effectiveness and scalability at the centre.

“We don’t want to recommend things based on small pilots. We want to recommend things that governments can do at scale.”

Two features make these recommendations powerful. First, they are directional rather than prescriptive – reduce the ‘time to school’ can mean building schools, community schools or bicycles for girls, depending on context – and each comes with a short account of the contexts where it applies. School meals, for instance, look very different where nutrition is the barrier to attendance than where most children are already enrolled. Second, because the World Bank, UNICEF and FCDO sit in the convening group, a government hears the same advice from every advisor in the room. The consensus is hard to build, but once built it is solid, and the lists are re-evaluated every few years as evidence accumulates.

Rachel situates the panels within a three-box framework she uses to think through evidence-based policymaking:

1. Evidence on the need, which is local.

2. Generalisable knowledge about how people should respond, which is global and is the slot the panels fill.

3. Implementation, which is context-specific.

Could the panel model work for the economic impacts of AI in low- and middle-income countries, where policymaker demand is intense but the evidence base barely exists? Rachel splits the question. More panels, yes – a skills-for-jobs panel is underway with the World Bank, and she would love one on nutrition. But panels work where evidence is plentiful and not yet synthesised. AI is not there yet, and it moves too fast for the standard research machinery.

CGD’s growing AI team has instead built an evaluation framework for funders and governments – lighter-touch questions that can be answered quickly. Some are strikingly simple. Many AI learning tools fail because teachers never use them, and you do not need a three-year trial to measure usage.

Expert-based policymaking and the economics career ladder

In the last part of our conversation, we discuss who gets to advise and who gets to produce the research. Rachel acknowledges that the standard career arc in development economics looks something like – earn tenure first, do policy later. She recalls how (her now co-author) Kate Casey had an extraordinary ability to access data and work with government in Sierra Leone – but Rachel could not even mention that in Kate’s PhD recommendation letter, as it was seen as a negative!

Rachel resists the idea that senior academics are the only node of evidence-based policymaking, pointing to panellists like Ben Piper and Rukmini Banerji who came up through consulting and NGOs rather than the standard academic ladder. But the circle clearly remains too tight.

On the concentration of research production in the global north, she believes that we should separate the objectives before designing fixes. Research quality demands deep contextual knowledge:

“It’s not about where you’re based, it’s how much time you’ve spent on the ground and how well you know the institutions.”

Building institutions so that students in low-income countries can gain expertise is a different problem, with different solutions. Things like Leonard Wantchekon’s African School of Economics tackles the maths-training pipeline, while the Weiss Fund supplements the extremely low salaries that stop trained researchers returning to universities at home. The test for Rachel is not whether a representation box is ticked, but whether poor people in these countries end up with better policy, and better lives.