What’s at stake: Rather than being unified by the application of the common behavioral model of the rational agent, economists increasingly recognize themselves in the careful application of a common empirical toolkit used to tease out causal relationships, creating a premium for papers that mix a clever identification strategy with access to new data.
Economics imperialism in methods
Noah Smith writes that the ground has fundamentally shifted in economics – so much that the whole notion of what “economics” means is undergoing a dramatic change. In the mid-20th century, economics changed from a literary to a mathematical discipline. Now it might be changing from a deductive, philosophical field to an inductive, scientific field. The intricacies of how we imagine the world must work are taking a backseat to the evidence about what is actually happening in the world. Matthew Panhans and John Singleton write that while historians of economics have noted the transition in the character of economic research since the 1970s toward applications, less understood is the shift toward quasi-experimental work.
Matthew Panhans and John Singleton write that the missionary’s Bible is less Mas-Colell and more Mostly Harmless Econometrics. In 1984, George Stigler pondered the “imperialism” of economics. The key evangelists named by Stigler in each mission field, from Ronald Coase and Richard Posner (law) to Robert Fogel (history), Becker (sociology), and James Buchanan (politics), bore University of Chicago connections. Despite the diverse subject matters, what unified the work for Stigler was the application of a common behavioral model. In other words, what made the analyses “economic” was the postulate of rational pursuit of goals. But rather than the application of a behavioral model of purposive goal-seeking, “economic” analysis is increasingly the empirical investigation of causal effects for which the quasi-experimental toolkit is essential.
Nicola Fuchs-Schuendeln and Tarek Alexander Hassan writes that, even in macroeconomics, a growing literature relies on natural experiments to establish causal effects. The “natural” in natural experiments indicates that a researcher did not consciously design the episode to be analyzed, but researchers can nevertheless use it to learn about causal relationships. Whereas the main task of a researcher carrying out a laboratory or field experiment lies in designing it in a way that allows causal inference, the main task of a researcher analyzing a natural experiment lies in arguing that in fact the historical episode under consideration resembles an experiment. To show that the episode under consideration resembles an experiment, identifying valid treatment and control groups, that is, arguing that the treatment is in fact randomly assigned, is crucial.
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