a romantic view of causal inference

In Mary Shelley’s 1818 novel Frankenstein; or, The modern Prometheus, the title character, scientist Victor Frankenstein, is trying to solve a mystery when an idea occurs to him, and we come across this fabulous line.

“I could not doubt it. The mere presence of the idea was an irresistible proof of the fact.”

My papers would be a lot shorter (and a lot of them are already short) if I could rely on “proof by idea.” In an authorial twist, Leslie Klinger’s annotated edition claims that these sentences were added by Percy Shelley to Mary Shelley’s draft, lest you feel tempted to hold Ms. Shelley responsible for these unidentified claims.

Research and answers

Peter Dizikes has a nice profile of economist Amy Finkelstein and her work in health economics in the MIT Technology Review. If you’re not familiar with her work, Finkelstein won the John Bates Clark Medal in 2012 and a MacArthur “genius” fellowship in 2018. (You can find her research here.) Dizikes includes a quote from Finkelstein that really captures the motivation for research that I feel.

“If you made me king or queen of the world, it’s not obvious how we should be designing our health-care system,” she says. “Which makes me a very bad cocktail party conversationalist, because when people say ‘What do you think of Medicare for All?’ or ‘How should we design health insurance?’ my usual reaction is ‘Well, I don’t know the answer, and that’s why I work on it.’ There are a lot of things I know or think I know the answer to, but those are not the things I do research on.” (emphasis added)

I’ve worked in development economics for some years now, and I’ve carried out repeated research on a few topics: education (especially teachers); social safety nets (especially cash transfers); and health. This research has certainly given me views on topics, but there are so many things to know and there are so many different contexts with different variables that I usually go into new research projects with little idea of what I’ll find, even in areas where I’ve worked before.

Here’s to finding new answers through good research.

Introducing Development Economics as Choose-Your-Own Adventure

I sometimes get the opportunity to give a brief introduction to development economics to students of various types. In the past, I’ve written about introducing development economics to middle schoolers in 20 minutes, introducing high school students to development economics with chocolate, and a practical activity for teaching regression discontinuity design that I’ve used with government workers in various countries.

Last week, I spoke to a group of undergraduate economics majors in a development economics class about development economics in practice. Like those popular Choose Your Own Adventure books from my childhood, I turned control of the lecture over to the students. In case it’s useful, here’s a recap of what I did.


Since these students are already studying development economics, I didn’t explain what it is, but I did explain how I practice it, in two parts. First, I research solutions to alleviate poverty; and second, I work with governments and others to incorporate that evidence into policy. I provided a little bit of photographic evidence.


I talked briefly about the two places where I’ve spent most of my career – the World Bank and the Center for Global Development.

Then, I put up my own low-stakes Jeopardy board with 30 of my finished or ongoing research projects and randomly selected students to pick a project to hear a little bit about. Each square linked to a slide with a little bit of information about the study. For each one, I discussed the research question, the finding, and a little bit about the interaction with policymakers.


I only got through a handful of topics in each class (I taught two classes). Over the course of just 25 minutes, the students got a practical sense of the array of work a development economist gets to work on along with some of the ups and downs of trying to influence policy. You can scroll through the individual study slides below.

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It was fun to deliver and to seemed like it was fun for the students. They got to decide what they thought was interesting. What are your favorite tips for introducing development economics research to students?

Development Economics in 20 minutes to high schoolers: This time with chocolate!

Two years ago I posted about a presentation I made to middle schoolers (seventh-graders, to be specific) on economic development.

This week I was asked to give a 15-20 minute presentation to a youth group of 14 and 15-year olds. Everyone seemed engaged, and they asked good questions. Here’s what I did.

1. I showed them the world’s income distribution using relatively recent data from Pew.

1 who has the money

2. Since it was clear I was going to focus on income — we had a short discussion of why money was important and what kinds of important things money could buy. After they shared their ideas, I listed a few, but they had already proposed many more.

2 what can money buy

3. Based on the income distribution data, I put each person in the room somewhere in the income distribution. I opened up an Excel sheet where I could enter the number of people in the room, and I had programmed it to — as I listed the name of each person in the room — assign them to an income group so that when I finished everyone in the room, it would reflect the global income distribution.

3 where do you fall

Then, to make it a little more concrete (and fun!), I distributed chocolate based on youth’s assigned income groups. So the upper-middle income youth got a chocolate bar, the middle income people got mini chocolate bars, the low income people got Hershey’s Kisses, and the the poor person got a single Hershey’s Dot (about the size of an M&M). [I actually forgot the Kisses, but that was how it was supposed to work.] If you’re budget conscious, like I am, I was able to finance the whole thing for a few dollars at a discount store.

4. I showed examples — using pictures from Dollar Street — of what households in each income group might look like. We talked about the housing materials.

5. Extreme poverty has fallen dramatically, but there are still nearly 800 million people in the world who are extremely poor. So there’s a long way to go. (Thanks to Our World in Data for the figure!)

5 falling poverty

6. I talked through two sides to development economics, the macro (how can poor countries grow prosperous?) and the micro (how can poor individuals and families exit poverty and enjoy prosperity?).

6 devt economists

7. I asked them what they think makes a country grow? After they shared some ideas, I talked briefly about four types of capital.

7 what makes grow

8. I talked about three specific projects that I’ve worked on: (a) how Rwanda can get on a rapid growth path, (b) how Tanzania can implement an effective safety net, and (c) the economic impact of the Ebola epidemic of 2014.

9. I talked a little bit about where my work has taken me. (Blue indicates conferences and seminar. Green indicates a research project or policy discussions.)

9 where to go

10. Finally, I talked about both my path to become a development economist, and a few of the other jobs that allow people to work in international development. Of course, there are many more! This was just to give a taste.

10 path

That’s it! It was fun. What have you done to explain these concepts to young people?

What else I’ve been writing

In addition to my reviews of (mostly African) writing here, I’ve been doing some other writing elsewhere. Check it out!

How I used behavioral science to run a marathon

I recently took a few days off between jobs, and I thought, “Hey, it would be fun to run a marathon while I have some time on my hands, just to see if I can!” I haven’t been training for a marathon, but I have been running, and I’ve run long distances in the past.

On the first day of vacation, I jogged from my house to a nearby lake that is about five miles around, figuring I’d do laps until I got to my 26.2 miles. But just after I passed 13 miles, I was out of energy and walked home with only a half-marathon to show for it.

On the last day of vacation, I decided to give it one more try. Now, one of the principles that I’ve learned from behavioral science is the value of commitment mechanisms, whether it’s a savings account that restricts access once you make a deposit (which increased in savings in the Philippines), committing in advance to a financial loss if you don’t quit smoking (which decreased smoking in the Philippines), or letting farmers pre-commit to purchasing fertilizer (which boosted fertilizer use in Kenya).

So I found an 18-mile trail near my house, parked my car at one end, and ran 13.5 miles in one direction. At that point, I had few alternatives to running the 13.5 miles back to my car. It’s true, I could have run the last 5.5 miles to the other end of the trail, but it would have been a pain to get back to my car. I also could have walked, but that just would have meant hours of walking in the cold with a dying phone and few supplies. (This is what Bryan, Karlan, and Nelson call a “soft commitment,” where the consequences are principally psychological rather than economic.) So I jogged back. Slowly, but jogging all the way. I made it back to my car just as my phone told me I’d clocked 27 miles.

A simpler way to communicate learning results

I have a new paper with Fei Yuan on how to communicate learning results more accessibly than in standard deviations. Here’s the paper. Here’s a summary blog post.

Here are the abstract and title of the paper:

Equivalent Years of Schooling: A Metric to Communicate Learning Gains in Concrete Terms

Abstract: In the past decade, hundreds of impact evaluation studies have measured the learning outcomes of education interventions in developing countries. The impact magnitudes are often reported in terms of “standard deviations,” making them difficult to communicate to policy makers beyond education specialists. This paper proposes two approaches to demonstrate the effectiveness of learning interventions, one in “equivalent years of schooling” and another in the net present value of potential increased lifetime earnings. The results show that in a sample of low- and middle-income countries, one standard deviation gain in literacy skill is associated with between 4.7 and 6.8 additional years of schooling, depending on the estimation method. In other words, over the course of a business-as-usual school year, students learn between 0.15 and 0.21 standard deviation of literacy ability. Using that metric to translate the impact of interventions, a median structured pedagogy intervention increases learning by the equivalent of between 0.6 and 0.9 year of business-as-usual schooling. The results further show that even modest gains in standard deviations of learning — if sustained over time — may have sizeable impacts on individual earnings and poverty reduction, and that conversion into a non-education metric should help policy makers and non-specialists better understand the potential benefits of increased learning.

Ambiguity in Scientific Language

Precision of language is a virtue so lauded as to seldom be questioned. And yet, a new article by Peter McMahan and James Evans in the American Journal of Sociology — “Ambiguity and Engagement” — shows the potential upside of ambiguous language. This, from Evans’s Facebook post:

Everyone from scientists writing a research paper to criminals under interrogation use ambiguity to widen their appeal or claim more or less than they know. In “Ambiguity and Engagement”, we measure ambiguity in language and explore its consequence for social life. We build a measure of ambiguity in language and demonstrate that when calculated on New York Times articles captures most of the ambiguity perceived by surveyed readers. Next, we assessed ambiguity across millions of article abstracts from science and scholarship, revealing that the humanities and social sciences use language most ambiguously, while chemistry, biology and biomedicine use it most precisely. Finally, we show that more ambiguity systematically—in all time periods and subject areas—is associated with greater association and engagement, as readers reference one another in prolonged conversations. While ambiguous language could lead to fragmentation and disconnection, as audiences understand it in conflicting ways, these findings demonstrate that instead it draws competing interpretations together into conversation with one another as they build on it. [emphasis added]

Association and engagement seem to be measured through fragmentation of citations: Greater fragmentation means that articles are cited by other articles in sub-literatures that don’t cite each other: the academic citation version of cliques.

Here’s how different disciplines line up on ambiguity:

ambiguity 2

Here’s a word from the article’s discussion:

Articles that use more ambiguous language tend to result in more integrated streams of citations tracing intellectual engagement. This pattern underscores the interpretation of ambiguity not only as a limitation but also as a potentially fruitful characteristic of language. Ambiguity leads to individual and collective uncertainty about communicated meanings in academic discourse. Uncertainty drives social interaction and friction, which yields coordination.

Disclosure: James Evans is my brother.