I was lucky enough to be able to have a brief conversation with Aart Kraay, the acting Head Economist at the World Bank. He is Director of Research in the Developmental Research Group, and an expert on international capital movements, growth and inequality, governance, and the Chinese economy.
Our discussion was largely centered around a paper, published by the World Bank in January 2020, that described the recent academic advancements made in developmental economics.
The paper can be found here: https://blogs.worldbank.org/developmenttalk/thirteen-insights-successful-development-policies
Could you briefly outline to our readers how the field of development economics has changed over the last few decades? Do the solutions of the past still work today?
I think those are two different questions, and I think I should take them one at a time. I think the field of development has changed enormously- the field of development economics in the past, if you permit me the liberty to extend the question past the last decade, say within the last 20 years, was in academia a very niche field, in which very few people worked. That was an exciting reason for me to join the World Bank when I did in the 1990s, because of the fact that then it was there that you could find most of the people in the world who were working on the subject. Since then it has exploded as a field, having gone from niche to mainstream, being represented in all major and not-so-major economics departments, and the amount of work and research that people are doing now has really exploded. This has been enormously helpful in terms of informing policy, at the point when we have so many more smart people thinking hard and asking hard questions when it comes to development.
In terms of how development economics has expanded, there have largely been two parallel changes going on in the field, which to a great extent has been mirroring what has happened in economics more broadly. One of them is massively greater availability of data. Even twenty years ago the sort of household survey data was much more scarce and infrequent than it is now- there have been huge efforts to expand the coverage of households in data sets particularly for developing countries, more recently there’s been huge efforts to harness non-traditional forms of data, and by that I don’t just mean exotic stuff such as satellite data, but also we’ve been able to tap into administrative data which has become available thanks to close interactions with governments, with a clear example of that being the ability of many researchers to mine tax data. Tax authorities have been able to collect vast amounts of data about taxpayers- this sheds a huge amount of light not only on how tax systems work, (for example, there’s a whole literature that has exploited the fact that when there are discontinuities in tax schedules, such as when your marginal tax rate jumps abruptly when an income boundary is crossed, it’s not at all surprising that when you look at people’s reported income, a large number of them ‘bunch up’ below these thresholds), but we’ve also been able to gain insights in the US on issues such as income mobility over long periods of time by linking together tax records. Data is one big change.
Another big area where there’s been change, largely not unique to development economics, economists as a profession have worked much harder to discover causal inference, something that is terribly important- by the time you take a high-school statistics course you realize that correlation is not the same as causation, and that sorting the two out empirically is crucial to being able to make informed policy decisions. In development economics in particular, but not exclusively, one big innovation in the last 20 years has been the widespread use of randomized control trials, very similar to ones that have been used in medicine for long periods of time, but which have only been implemented recently in economics. This makes it far easier to differentiate causation and correlation- the beauty of randomized control trials is that we get a far better picture of how a particular policy might work in a particular setting, by randomly choosing individuals to whom that policy would apply. I should also say that, because we are economists, there’s tons of healthy debate within the profession and there’s a lot of healthy debate about the costs and benefits of relying on these randomized control trials. The benefits are clear- we get far better evidence of the best policies necessary for certain settings, but the problem is that there are many questions which could be answered with a certain form of randomization in one country, but not in another, given all the particular adjustments you have to make. Ultimately, it’s less clear how transferable the results are from one area to another. So the two big changes in the field have been better data, and better analysis.
How do you think this has affected developmental economics’ conclusions and policy prescriptions?
I think that’s exactly the right question for someone particularly interested in working at a place like the World Bank, where obviously we care not just about sort of the intellectual or the academic findings but also how those findings are being taken advantage of, about how those findings are being used to influence policy. I think there are a couple of broad lessons, and the first is that as the profession has been taken more seriously, although I think with a lag but slowly and steadily over time, they’ve also persuaded policymakers to insist on more rigorous evidence. And they might otherwise, you know, be tempted to rely on weaker standards to inform policy decisions. How has that evidence changed policy? I mean there, I think it’s important not to sort of over generalize. What I’m trying to do here is I’m trying to strike a balance between giving users very specific illustrations which may be too narrow, and general lessons which maybe are too broad- it really depends on the country you are looking at.
Clearly, the World Bank is very successful at institutionalizing policies and bring in short term fixes. Do you feel like it is having difficulties at genuine political capacity building, which has proved a bottleneck in several countries?
That’s a fantastic question, and a super relevant one. There was a day when development economic economists thought about development in purely sort of technical terms. How do you build a power plant? What are the right engineering specifications? What’s the right fuel to use? What is the sort of cost benefit analysis that you get for a given capital cost to build the power plant, and how are you going to pay for it? You did sort of very technical, technocratic analysis. And I think over time the World Bank and other policymakers have come to realize that that’s only part of the question. The other really important question is who decides what the tariff or tax is going to be? What happens if political pressures mean that the tariff is set so low that the utility can’t cover its costs? What happens if initially, in order to encourage take up of electricity the electricity rates are subsidized, but over time those subsidies are reduced? What would be the political reaction and what is politically feasible? I think development economists and the World Bank recognizes that the political context in which you give policy advice is as important as the policy advice itself.
Moving on, in 2000 you noted that relatively smaller states tend to be more productive, have more open economies and usually higher GDP per capita. Do you still think that at the moment smaller states have advantages over larger ones when it comes to development?
That’s a paper that I worked on a long time ago. It’s one of the first projects that I undertook at the World Bank. The point of that paper was to try to simply and systematically document how different country characteristics varied with country size across countries, and to us one of the surprising findings was that a lot of sort of good outcomes, like income per capita, were not systematically lower in small countries. We worked on that paper because it was relevant to some policy discussions going on at the time- I now have to confess that since it was 20 years ago, I’m not going to be able to give you more than just the broad outlines. The question was: should the World Bank when distributing assistance deliver more resources to countries that are smaller, because of the fact that they’re smaller? There was an active discussion going on about the potential reasons why small countries might have disadvantages compared to large ones. This paper was one piece of evidence that we were contributing to the discussion.
There are pros and cons to being small. The big potential downside small countries have to overcome is the fact that it’s more difficult for them to get economies of scale both in production and in policy making. Even when you think about microstates, for example, there are always fixed costs of maintaining different ministries, different government agencies and different diplomatic missions, and so on. On the other hand, there are potential benefits to being small as well, including the greater ease of forging political consensus. And so this sort of theoretical discussion goes both ways. This particular paper was one bit of evidence, not the final word on anything. It was a bit of evidence at the time that suggested that the balance of those forces wasn’t particularly negative for small countries.
How would you respond to the critics who claim that World Bank funding supports and legitimizes corrupt and inefficient regimes in developing countries?
This isn’t a new question. This is a question that the World Bank and other development policy actors have been struggling with for decades. The fundamental challenge for aid donors is that they want to help poor people (the fundamental mission of the World Bank) and reduce absolute poverty- but many poor people live in countries that also are not particularly well governed. That’s not a surprise- it’s precisely because of governance challenges that you see some countries with very low levels of development that persist for long periods of time.
So an organization like the World Bank always has to strike the appropriate balance when it’s choosing how to allocate resources across countries between how poor on average people are in the countries that you would like to assist, in other words it’s need, but also the capacity of the country to use the resources that the World Bank provides through its lending for good, or its absorptive capacity. And unfortunately there are no easy answers to this question.