As part of our interview series, we ask renowned experts in the field about the future of research in development economics, and for their advice for young researchers. For our next interview, we got the opportunity to talk to Lauren Falcao Bergquist.
Lauren Falcao Bergquist is an Assistant Professor in the Economics Department and the Jackson School at Yale University. She is an economist studying international development, with a focus on agricultural markets and firms in sub-Saharan Africa. Recent research explores constraints to agricultural growth, intermediation in domestic supply chains, and incentives for quality provision. Much of her work employs randomized control trials, with an eye towards how such trials can inform policymaking at scale.
This post refers to a presentation by Lauren Falcao Bergquist: “Search Costs, Intermediation, and Trade: Experimental Evidence from Ugandan Agricultural Markets”.
ETRM: How would you define development economics on the frontier right now?
LB: There are lots of different frontiers, but I personally am very interested in what happens when you scale up programs. A few decades ago, when development economists started doing RCTs, there was often this critique that RCTs are helpful, but they don’t really tell us what programs look like at scale. And now folks who do rigorous empirical work are kind of pushing back against that and saying, no, actually, we can inform policymaking at scale, using well-identified approaches, with a combination of theory and experiments. So I’m really excited about that area. Its goal is to directly inform questions that are relevant to policymakers, but doing it with the rigor of well-identified empirical work.
ETRM: How do you usually come up with research ideas?
LB: I personally love traveling and doing fieldwork is my data-generating process. For example, my job market paper was about the market structure among agricultural intermediaries in Kenya. I got that idea after spending a summer talking to traders in Kenya. As a longer backstory, in another project of mine, we were looking at why farmers aren’t storing maize, given the backdrop of large seasonal price fluctuations. Why are farmers selling right at harvest when prices are low, why aren’t they waiting until later in the season to sell when prices are high? And so, we implemented an RCT, and it looks like credit constraints are a big part of the reason. Smallholder farmers have a need for immediate cash to pay for school fees and other bills at harvest, so they don’t store and wait to sell later at a higher price. But project this got us thinking, why aren’t the larger scale intermediaries — who are less credit constrained — engaging in storage? And so we got a small pot of funds to go look at that. And I basically spent a summer just talking to traders and asking them, why are you not doing this? And one common answer was: “yes, we can make money storing maize, but we can make so much more money doing what we’re currently doing,” which is spatial arbitrage. Then they would write out the exact math of their business to calculate their weekly profits, which were large. And it got me thinking: why are they making such large profits? That’s when I got interested in learning about the market structure among traders. By spending lots of time in these markets, I also got to understand things like the physical layout of traders’ stores in the market and that they were positioned right next to each other, that they knew each other well, etc. — features matter for their ability to collude. Anyway, you can see how in some ways these two projects are focused on two very different questions: smallholder maize storage and intermediary market power. But one question leads to the next puzzle. More generally, it was fun to apply and adapt the economic models I had in my head after my second year of grad school to what I saw in the field. I’d think, this is the model we apply to storage behavior or credit constraints or profit maximization. But then, to actually talk to traders and figure out where those economic models fit (and they don’t!) was really interesting. You learn a lot of humility doing field work: people almost always know better about their markets or constraints or lives than you do, so it’s fun to hear their answers and realize: okay, we need to tweak our models in this and that way.
ETRM: I hope that’s what happens! I’m also going this summer to do scoping work in Kampala.
LB: Just to talk to a ton of people. It’s also a great opportunity to seek out partnerships. Depending on your style of work, you can learn about some data source that exists, or meet someone who happens to be working in an organization doing some intervention that maybe you could study. Meet as many people as possible!
ETRM: When do you think someone is ready to go into the field? If you’ve just finished undergrad and you go to the field, you might have different lenses on when you go after taking PhD level classes.
LB: I think anytime is the right time to go in the field. It’s just the best thing you can do. But I do think that the summer after your second year, in particular, is a really great time because you’ve already read some papers, you have some sense of where the frontier of literature is in the space that you’re interested in; you have the existing frameworks in mind. You’re also open to a lot of ideas; you’re fresh to it all. And so, that to me, that was a special time. That said, I was in the field starting the summer after my first year, and I was there on and off for the remaining years of my PhD. So I think just spend as much time as you can. But I should also say here that I’m also giving you a very American-based perspective of like “go to the field” and have this new experience. This is why we need more diversity in the field of economics. It’s important to have economists who know their own context, who can reflect on their lived experience, and say, how do I apply economic models to that?
ETRM: For students with raw research ideas for field experiments, how do you suggest dealing with stakeholders like the government or private agents?
LB: The personal connections are key. For example, for our maize storage experiment, my co-author was friends as an undergrad with the country director of the NGO that we partnered with. But you don’t have to know someone ahead of time; you can forge new relationships by putting in time to cultivate them. The big thing is understanding someone else’s incentives. You as a grad student have an incentive of getting a job market paper or publication, but that doesn’t matter to your partner. You also have this deeper incentive of wanting to produce knowledge. But even producing knowledge – at least generalized knowledge — may not be what your partner cares about. They care about knowing whether their program works (and often about documenting that to constituents, stakeholders, funders, etc.). Understanding their incentives is key because you’re asking them to do something kind of inconvenient. That’s certainly true if you’re asking them to run an RCT, but that’s true even if it’s just sharing data – there could be also confidentiality concerns, etc. And so getting out of your own head and thinking about what their incentives are is really important. And you can’t do that without getting to know your partners well.
