Is a factory job better than a cash grant and some training? Chris Blattman and Stefan Dercon have a recent study in Ethiopia where they test these two options with a randomized-controlled trial. Back in December, Chris Blattman discussed with study with Russ Roberts on the EconTalk podcast.
In one interesting bit, Blattman highlights how holding onto an idea and repeatedly seeking an opportunity to implement it can ultimately bear fruit. I transcribed it (abridging a little for readability).
Since you have 300 people lining up for these jobs, instead of taking the first 50 in line who are qualified for the job and hiring them, why not see if we can find a factory owner who will find 150 who are qualified and instead of taking the first 50, we’ll flip a coin and we’ll take 50 out of those 150 qualified applicants as random and we’ll follow them over time and we’ll look at what happens to their incomes and their health and their career trajectories.
I had this idea as a graduate student 10 or 12 years ago, and I always thought, “Every time I meet a factory owner I’m going to feel him out. And I did. Once in a while I’d be on a plane to Uganda to work on one of my projects, usually related to poverty or conflict, and maybe I’d sit by a factory owner, and I’d say here’s this idea that I have, and they’d usually look at me kind of funny. They wouldn’t leap at the possibility. I was just this person they met on a plane, and I was a graduate student. I probably didn’t approach it well, and so it never really materialized.
So I was at a conference in London and there was an Ethiopian businessman who was sort of a real estate mogul. He was giving a talk to a group of development economics at the International Growth Centre, and I approached him afterwards and said, “That was terrific,” and I really enjoyed talking to him and we kept chatting and I said, “I had this idea. I think that your firms not only help achieve growth, but I think they might actually be tools of poverty alleviation. Here’s an easy way to answer that question.” And he said, “That sounds great. Let’s do it.” And so literally five or six weeks later we were on the ground in Ethiopia doing the first randomization.
I recommend the whole conversation.
This is Antoinette Schoar of MIT.
Theory is a way of organizing your thinking. In the end it’s setting up a hypothesis so that you have something to test. That’s why I also think that theory is most useful when it gives enough structure that it can actually allow you to refute hypotheses. The most frustrating theories are the ones that are so flexible that they fit any finding. This doesn’t help me to make better sense of the world. But it’s important to start from a theoretical framework rather than just a story because it forces you to be more precise. I think of myself primarily as an empiricist, so theory to me is very helpful when it helps me to unearth new empirical insights.
On field experiments:
What I really like about running field experiments is that even if you’re working with one bank or one NGO because you have to engage with the organization to implement something on the ground, if you want to do a good job, you have to get involved in the details. Which means that you get constant feedback on what is feasible and what is implementable or what is practical and what is just a pipe dream. What sounds great in an ivory tower may be impossible in the real world.
Of course, there are many more things in economics than theory and field experiments, but they are two important things.
From Tim Ogden’s Experimental Conversations: Perspectives on Randomized Trials in Development Economics
This is Xavier Giné at the World Bank
Part of the take-up problem, especially in the case of savings, is that some of these products are pretty crappy. If we see no demand for these products, maybe that’s a good thing actually. If you put money into one of these accounts, check the balance a few times, make a few withdrawals, half or all the money has been eaten up by fees. So the characteristics of the product are very important.
That’s from Tim Ogden’s Experimental Conversations: Perspectives on Randomized Trials in Development Economics.
Beyond savings products, this is important because there is a temptation to lump interventions together (How effective are home-based child care visits? How effective is teacher training?) when in fact there is massive diversity in the particulars of the interventions. Sara Nadel and Lant Pritchett have referred to this as “high dimensional design space,” and Popova et al. document it in the case of teacher training interventions.
There is no reason to believe — ex ante — that all interventions in the same “category” will have the same effect. Indeed, in work Anna Popova and I did analyzing randomized controlled trials of education interventions, we found that in many cases, the variation in impact within categories exceeded the variation across categories, per the table below.
So, as Giné says, “the characteristics of the product are very important.”
I always try to reduce things to three questions—even in simple interventions like passing out nutritional supplements to infants and toddlers. These are theory-driven questions that you should be using whenever you’re justifying any intervention whatsoever.
And the questions are
- “What’s the market failure? Why isn’t the market and the invisible hand working?”
- “How does this intervention specifically solve the market failure?”
- “What’s the welfare impact of solving the market failure?”
Karlan makes the point that experiments should absolutely be driven by theory. But theory doesn’t have to mean three pages of math. The theory can be simple, and the questions above sum up what your theory should predict.
from Tim Ogden’s interview with Dean Karlan in Experimental Conversations: Perspectives on Randomized Trials in Development Economics
Two years ago, Anna Popova and I put out a working paper examining whether beneficiaries of cash transfer programs are more likely than others to spend money on alcohol and cigarettes (“temptation goods”). That paper has just been published, in the journal Economic Development and Cultural Change.
The findings of the published version do not vary from the working paper: Across continents, whether the programs have conditions or don’t, the result is the same. The poor don’t spend more on temptation goods. But for the published version, we complemented our vote count (where you sum up how many programs find a positive effect and how many find a negative effect) with a formal meta-analysis. You can see the forest plot below. (The results are not substantively different from the “vote count” review that we did in the working paper and maintain in the published version as a complement to the meta-analysis.)
As you can see, while there are only two big negative effects, both from Nicaragua, most of the effects are slightly negative, and none of them are strongly positive. We do various checks to make sure that we’re not just picking up people telling surveyors what they want to hear, and we’re confident that cannot explain the consistent lack of impact across venues.
Why might there be a negative effect? After all, if people like alcohol, we might expect them to spend more on it when they have more money. We can’t say definitively, but even unconditional transfer programs almost always come with strong messaging: Recipients hear, again and again, that this money is for their family, that this money is to make their lives better, and so on and so on. We know from others areas of economics that labeling money has an effect (called the flypaper effect).
So you can be for cash transfers or against cash transfers, but don’t be against them because you think the poor will use the money on temptation goods. They won’t. To quote the last line of our paper, “We do have estimates from Peru that beneficiaries are more likely to purchase a roasted chicken at a restaurant or some chocolates soon after receiving their transfer (Dasso and Fernandez 2013), but hopefully even the most puritanical policy maker would not begrudge the poor a piece of chocolate.”
Esther Duflo and Abhijit Banerjee of MIT and JPAL weigh in during an interview in Tim Ogden’s forthcoming book, Experimental Conversations, which I am enjoying thoroughly:
Esther: “I think it’s been completely won in that I think it’s just happening. A lot of people are doing it without us. It’s being used. I think it is now understood to be one of the tools. The argument within the economics profession [over the value of RCTs] had two main consequences, both good. First, it raised the profile. If something was debated, people began to believe it must be significant. Second, it did force us to answer the challenges. There were a lot of valid points that were raised and it forced us to react. We’ve become more intelligent as a result.”
Abhijit: “I am less certain that it has been won. The acid test of whether an idea has come to stay is that it becomes something that no one needs to justify using. … RCTs aren’t there yet: it is true almost everyone is doing them, but many of them are taking the trouble to explain that what they do is better than a ‘mere RCT.’ We need to get to the point where people take RCTs to be the obvious tool to use when possible to answer a particular class of empirical questions.”
Earlier this month, Esther Duflo of MIT gave a talk at the IMF, and the slides are available here. I found the framing insightful.
She goes on to give three examples from impact evaluations in India that seek to improve “the rules of the game,” creating systems for better governance: (1) “fixing the pipes” — eplatform for workfare payments, (2) “changing the faucet” — biometric identification for welfare payments, and (3) “replacing the meter” — inspections on polluting compliance.
The modern movement for RCTs in development economics…is about innovation, as well as evaluation. It’s a dynamic process of learning about a context through painstaking on-the-ground work, trying out different approaches, collecting good data with good causal identification, finding out that results do not fit pre-conceived theoretical ideas, working on a better theoretical understanding that fits the facts on the ground, and developing new ideas and approaches based on theory and then testing the new approaches.
This is from an insightful interview with Michael Kremer, Harvard economics professor “generally given credit for launching the RCT movement in development economics with two experiments he led in Kenya in the early 1990s,” and my graduate school advisor.
The interview is in Tim Ogden’s book Experimental Conversations: Perspectives on Randomized Trials in Development Economics.
On this morning’s commute, I caught up on a new NBER Working Paper: “Education Quality and Teaching Practices,” by Marina Bassi, Costas Meghir, and Ana Reynoso.
Here is the abstract: “This paper uses a RCT to estimate the effectiveness of guided instruction methods as implemented in under-performing schools in Chile. The intervention improved performance substantially for the first cohort of students, but not the second. The effect is mainly accounted for by children from relatively higher income backgrounds. Based on the CLASS instrument we document that quality of teacher-student interactions is positively correlated with the performance of low income students; however, the intervention did not affect these interactions. Guided instruction can improve outcomes, but it is a challenge to sustain the impacts and to reach the most deprived children.”
Why no effect for lower-income students? To expand a bit on the abstract: “The most striking result from the table is the association between better student teacher interactions (reflected in a higher CLASS score) and the performance of low income students. In effect, one additional standard deviation in the principal component of CLASS scores is associated with a higher SIMCE test score for low income students of between 15% and 20% of sd units. These results are robust to adjustments in p-values to control for the FWE rate. For higher income students, effects are smaller and in some cases insignificant.”
So if the quality of interactions is particularly important for lower-income students, and the intervention isn’t affecting those interactions, then that could explain the differential effects. It’s an interesting hypothesis, and it points to the ongoing need to better understand what’s happening in the classroom.
Here’s a little more on the intervention: “The main intervention of the program was to support teachers through a modifed method of instruction by adopting a more prescriptive model. Teachers in treated schools received detailed classroom guides and scripted material to follow in their lectures.”