My Monday link round-up included this Big Think piece asking eight young economists about the future of their field. But, I wanted to highlight the response from Justin Wolfers:
Economics is in the midst of a massive and radical change. It used to be that we had little data, and no computing power, so the role of economic theory was to “fill in” for where facts were missing. Today, every interaction we have in our lives leaves behind a trail of data. Whatever question you are interested in answering, the data to analyze it exists on someone’s hard drive, somewhere. This background informs how I think about the future of economics.
Specifically, the tools of economics will continue to evolve and become more empirical. Economic theory will become a tool we use to structure our investigation of the data. Equally, economics is not the only social science engaged in this race: our friends in political science and sociology use similar tools; computer scientists are grappling with “big data” and machine learning; and statisticians are developing new tools. Whichever field adapts best will win. I think it will be economics. And so economists will continue to broaden the substantive areas we study. Since Gary Becker, we have been comfortable looking beyond the purely pecuniary domain, and I expect this trend towards cross-disciplinary work to continue.
I think it's broadly true that economics will become more empirical, and that this is a good thing, but I'm not convinced economics will "win" the race. This tracks somewhat with the thoughts from Marc Bellemare that I've linked to before: his post on "Methodological convergence in the social sciences" is about the rise of mathematical formalism in social sciences other than economics. This complements the rise of empirical methods, in the sense that while they are different developments, both are only possible because of the increasing mathematical, statistical, and coding competency of researchers in many fields. And I think the language of convergence is more likely to represent what will happen (and what is already happening), rather than the language of a "race."
We've already seen an increase in RCTs (developed in medicine and epidemiology) in economics and political science, and the decades ahead will (hopefully) see more routine serious analysis of observational data in epidemiology and other fields (in the sense that the analysis is more careful about causal inference), and advanced statistical techniques and machine learning methodologies will become commonplace across all fields as researchers deal with massive, complex longitudinal datasets gleaned not just from surveys but increasingly from everyday collection.
Economists have a head start in that their starting pool of talent is generally more mathematically competent than other social sciences' incoming PhD classes. But, switching back to the "race" terminology, economics will only "win" if -- as Wolfers speculates will happen -- it can leverage theory as a tool for structuring investigation. My rough impression is that economic theory does play this role, sometimes, but it has also held empirical investigation in economics back at times, perhaps through publication bias (see on minimum wage) against empirical results that don't fit the theory, and possibly more broadly through a general closure of routes of investigation that would not occur to someone already trained in economic theory.
Regardless, I get the impression that if you want to be a cutting-edge researcher in any social science you should be beefing up not only your mathematical and statistical training, but also your coding practice.