I am an economist, and I have spent many hours of my life trying to fight misleading stereotypes about what I do. People, for example, often assume that when I say “I’m an economist”, I mean “I worship the creed of self-righting markets”.
In response to such assumptions or accusations, some of the world’s leading economists are attempting to explain #WhatEconomistsReallyDo. This is an important effort. But I think this discussion is also a great opportunity for economists to reflect on their own prejudices, in particular those regarding other social sciences.
Duncan Green, senior strategic adviser at Oxfam, recently wrote on Twitter that, while working at the UK’s Department for International Development (DFID), he “got into trouble” at an “economics for non-economists” course for asking when the “non-economics for economists” course would be running.
There is something to this: it’s unhelpful when people criticise economics without understanding what economists actually do, but it is also unhelpful when economists criticise or even entirely disregard research from other disciplines simply because they use different methodologies.
Let’s start with “economics for non-economists”.
The different views on what economists actually do can be nicely captured in metaphors. I find the cartography metaphor spot on: economists try to create and use maps to navigate the world of human choices.
If economists are cartographers, then economic models are their maps. Models, just like maps, make assumptions to abstract from unnecessary detail and show you the way.
Different maps are helpful in different situations. That’s what makes this metaphor helpful. If you are hiking in the Alps and you want to find your way, you will want a map that gives you a simplified perspective of the terrain – steep changes in altitude should stand out. A map with elevation contour lines will be very helpful.
On the other hand, if you are an engineer trying to calibrate the compass in an airplane, then a hiking map is not going to be of much use to you. Instead, you’ll want a map that gives you a simplified perspective of how magnetic fields change – a map that highlights magnetic variation by showing you isogonic lines.
Deciding whether it is a good idea to rely on a specific map in a concrete situation is difficult. You need to understand the map’s limitations, as well as the limitations and strengths of available alternatives. For your hike in the Alps, a map of magnetic forces won’t be very helpful. There is nothing wrong with mapping isogonic lines; we should just know what they are and when to use them.
This example illustrates a key point. When people criticise economics they are often criticising how a specific map is unhelpful to answer a specific question, rather than criticising the foundational methods of the discipline.
Still, criticising how economists practice the discipline is important. Economic advice often feeds into policy decisions that affect a large number of people. So if economists make mistakes, or if they are unclear or ineffective communicators of the assumptions and limitations of their models, it can have large implications.
Now let’s move to “non-economics for economists”. If economists make maps, then what do, say, sociologists do? As it turns out, they also make maps: just different kinds of maps.
Economists often think that they have a monopoly over this “social science cartography”. This is both wrong and counterproductive. The fact that other social scientists don’t (usually) write their theories in mathematical notation, or that they rely on qualitative rather than quantitative research methods, doesn’t mean that they can’t make helpful maps of the world, or that their maps are in some way inferior. I personally like using maths to formalise my ideas. But maths is just a language. Unfortunately, some economists sometimes forget this; they mistake the language for the message.
I think a big part of the bias that many economists have against qualitative research is grounded in ignorance about what rigorous qualitative research actually looks like, and how it might be successfully used in practice.
Suppose that we are tasked with hunting for archaeological treasures buried in London. A qualitative researcher may try to make a map by relying on non-numerical data – for example, by going around the city, and interviewing people in a systematic and rigorous way.
If people keep on saying that there is an old folk song that tells the story of a hidden treasure in the South Bank, then that’s a sort of map. How helpful is such map? Well, who knows. It depends on the specific situation and the alternatives available. It also depends on what you ask, how you ask, who you ask. But the point is that it would be silly to dismiss the information just because it is qualitative.
In the hypothetical treasure hunt situation above, a simple map of the streets of London might be useless on its own. But in combination with the extra information from the folk songs, it might actually be useful – it might help us get to the South Bank treasure trove. Indeed, alternative research methods can be (and often are) complements rather than substitutes. There are many concrete real-world situations where new insights emerge from combining quantitative and qualitative research methods.
For example, randomised policy evaluations can be complemented with qualitative methods to help uncover the underlying mechanisms that produce quantifiable outcomes. Here’s a concrete instance: in a multi-country impact evaluation of interventions aimed at improving women’s empowerment, qualitative studies are being used to unpack the role that social norms have in perpetuating barriers to women’s empowerment.
There are many other fascinating examples that illustrate the power of combining methods in the social sciences. Researchers have been able to cast light on the economics of street prostitution in Chicago by combining official arrest records with data on “tricks” (transactions) collected in cooperation with sex workers; and we have been able to learn about the workings of deliberative democracy in South India from research that combines data from linguistic divisions, household surveys, and transcripts from discussions in village parliaments.
There is plenty of evidence showing the potential of multidisciplinary research. Social scientists, economists included, would all benefit from explaining what they do better, while trying harder to identify opportunities for collaboration.
Esteban Ortiz-Ospina receives funding from the project Our World in Data – all funders of this project are listed here: https://ourworldindata.org/about