For development economists like me, Deaton was a revolutionary and a visionary.
- By Christopher BlattmanChristopher Blattman is an associate professor of public affairs and political science at Columbia University. He blogs at chrisblattman.com and tweets at @cblatts.
Every year, there’s a Monday in October when the world wakes up to the latest Nobel Prize winner in economics and collectively wonders: “Who?”
Even economists like me often have to scramble to figure out who the winners are and what they did, knowing my mom or a neighbor will expect me to have an intelligent answer at some point this week. For me, at least, this year is different than most, since the winner, Angus Deaton of Princeton University, is such a towering figure in my field of international development. This is a prize for advances in our understanding of poverty and inequality. It couldn’t be better deserved.
I’d never met Deaton in person until a couple of weeks ago, when he presented a paper at a small development economics seminar at Columbia University, where I work. (In retrospect, that might well have been his last normal, intimate academic talk. Ever.) But like many development economists, I can point to more than one time in my career where Deaton’s thinking had great influence. Speaking from personal experience, three times stand out, and all help to illustrate the work that got him the Nobel.
The first was almost 15 years ago. I was a master’s student at Harvard, more interested in economic history than anything else, when my econometrics professor, Rob Jensen, hired me to spend the summer in India to run a household survey — a questionnaire on every family member’s work activities, earnings, health, education, and what food and items they consumed. I didn’t know the first thing about household surveys, and so I bought a couple of books to bring with me.
One was Deaton’s Analysis of Household Surveys, a technical manual on analyzing poverty data. The other was Deaton’s hefty 3-volume manual on Designing Household Surveys, written with Margaret Grosh, which weighed probably 20 pounds in reality (and 50 in my memory). I needed a separate suitcase to bring them all to India. It’s only when I arrived I realized that Volume 1 held a CD on the back page with the full text of the books. Even so, I couldn’t bring myself to give away these paper treasures, and carted the heavy bastards around the country for four months.
Deaton’s books contained the models for every measure of economic well-being the profession possessed. At the time, I took for granted they existed, and it never once occurred how hard they were to invent and get people to use.
As we walked to the office today, my colleague Suresh Naidu put it nicely. Before there were evaluations of anti-poverty programs, or analysis of inequality trends, or really most of empirical development economics, there had to be something more fundamental: measurement. We had to know how to assess poverty, and we needed to have large-scale data to do so, to challenge our assumptions, and provide new answers. And Deaton as much or more than anyone else made this happen.
Deaton helped bring about this data and measurement revolution in several ways. One was to help establish expenditures and consumption as the best of a bunch of bad measures of poverty. Annual income works fine in a country like the United States where most people earn a regular salary from one source, but it’s meaningless when a household has dozens of small activities, varying day to day, most of which produce things the family consumes themselves. The other contributions were to show just how much we could pull out of these simple data, and (as impressively) to actually measure it in huge household surveys — in countries that had never had that kind of data before.
We take for granted today that there are repeated health surveys of Uganda, or consumption data going back dozens of years in Cote d’Ivoire. But there was a time not that long ago when all of this had to be conceived and invented. Dozens of important people and organizations pushed this ahead. Deaton was one of the leading lights.
Most of us also know Deaton for some other big contribution. In my case, this was his work on commodity prices and how their fluctuations tormented developing country economies in the 1970s and 1980s. This was my second intellectual encounter with Deaton, when as a first-year Ph.D. student I started asking whether sudden shocks to oil or coffee or other commodity prices could send a country into political chaos as well. Of course, Deaton had beaten me to it.
Sudden falls in coffee and oil prices destroyed poor countries in the 1970s and 1980s, especially in Africa. Deaton had done some of the best work, both on the behavior of commodity prices, their effects on growth, and also on the reasons they caused macroeconomic chaos. He also peeked into what the effect was on coups and political instability. In later years, hundreds upon hundreds of scholars would start using data from many countries to analyze why wars and coups happen. Deaton was one of the very first.
Deaton didn’t win the Nobel for this commodity or political instability research. But it’s a nice example of why the Nobel committee chose him out of all the data and consumption revolutionaries: his interests were so wide and his contributions so influential, in so many big questions of development. His idle afterthoughts helped start huge literatures, like the cross-country study of coups and wars, often years before others caught on. Twenty years after his first paper on commodity prices and political stability, I wrote what I think is a much more comprehensive and definitive paper. But it’s much easier to try to finish a debate than ignite and inspire one.
The third influence Deaton has had on me is much more recent: his critiques of randomized trials in development. In developed countries, scientists and governments often test the effectiveness of new pills or new employment programs by comparing a treatment group to a control group, where the people who get the treatment are drawn randomly by lottery.
Until recently, these randomized trials were almost unknown in poor countries. Billions of aid dollars were going into untested programs with untested assumptions. Proponents of randomized program evaluation — the so-called randomistas — argued more field experiments were needed to learn what worked. Deaton pushed back hard. If you’re a below the age of 30 in the world of international development and have heard of Deaton, it’s probably for this controversy.
Today, about half my work has used some kind of field experiment — such as what happens to poverty when the poorest people get cold, hard cash instead of food or skills training — so obviously I paid attention when an intellectual hero took aim at the method.
I think the early arguments between Deaton and the randomistas involved more bluster than substance, on all sides. Randomistas called their trials the “gold standard” of evidence. Deaton called this misleading. But what seemed like an attack on randomized trials was more subtle — it was Deaton’s plea for modesty in what randomized trials can tell us.
Two weeks ago, he came to Columbia and presented a new paper on the use and misuse of randomized trials. It’s a more balanced and evolved piece after, I presume, many years of debate. It was my favorite presentation of the year at Columbia. I hope with the Nobel he will still have time to get it public soon (though I fear not).
The basic idea is this: as development agencies and scholars have rushed to do more randomized trials, they’ve overstated what those trials can tell us. The smartest randomistas of the movement know the limitations of experiments, but are happy to let the policy world’s enthusiasm get feverish, since good evidence is long overdue. But Deaton fears the average policymaker might take the results too seriously.
Sitting in my office a couple of weeks ago, he told me a story about the day he and his fellow economists started running regressions for the first time. It seemed like a magic tool to give all answers. And indeed it vastly increased our knowledge.
But many decades later, we recognize that most regressions are misleading, and know what to look for in good analysis. We teach regressions like a disease, one where we must find the cures for all its ailments. He wants us to think of randomized trials the same way: better than what we had before, but still profoundly diseased and in need of a lot of care and attention before we believe any of the analysis it produces. I will not preempt his paper with his unpublished arguments and prescriptions, but (as usual) I think he’s right.
These are just the top three moments for me. I could go on. I use my personal blog like a memory bank, to drop in my idle thoughts and reflections about development so I can find them later. I did a search for “Deaton” this morning, and no less than 16 posts over seven years came up, on a huge range of topics, including a response to his other controversial claim of recent years: that aid is a roadblock to development.
His book, The Great Escape, is a nice introduction to Deaton’s thinking on big trends in development, and where the rich world gets it wrong. I loved the diagnosis, and didn’t agree with the cure, but you will find few better books on development to work through. If you want more links and discussion on Deaton’s Nobel, the best I’ve seen so far are Alex Tabarrok’s round up of Deaton’s work and another from Tyler Cowen.
All in all, this is a great decision from the committee from the perspective of caring about poverty, inequality, and getting the evidence right.
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