Editing: You Figure It Out

If you want to change how a field works, you have a few options. You can do what you want to see more of, but you are only one person, and perhaps not the one best equipped to make things better. Or you can encourage others to work differently- but why would they listen to you?

Academics often serve as peer reviewers for the work of others. If a reviewer recommends that a paper be rejected, it usually is; if you recommend specific minor changes they usually get made. But you can’t really tell people that they should work on a totally different topic. Journal editors for the most part simply have a scaled-up version of the powers of peer reviewers to steer the field. But unlike reviewers, their positions are public and fairly long-lasting. This means they can credibly say “this is the sort of work I’d like to see more of- if you do this kind of work, there’s a good chance I’ll publish it”.

This is part of why I’ve been hoping to be a journal editor some day, and why I’m excited to be guest-editing for the first time: a special issue on Health Economics and Insurance for the Journal of Risk and Financial Management. The description notes:

Of particular interest are empirical articles evaluating the effect of changes in health policy and regulations, articles on the determinants of healthcare spending or quality, articles developing value-added measures of healthcare quality, and articles studying innovations in managing the financial risks of unexpected health shocks

The language of economics has rapidly shifted toward causal inference, and the limits of our language are becoming the limits of our world. Increasingly, we only attempt to answer questions of the form “what is the effect of X on Y”, which our current econometric toolbox is good at answering, while ignoring all the questions our toolbox has a harder time with, including “what are the main factors that infuence Y” (e.g., why is US healthcare spending so high?). While modern causal inference is great, and its generally good to focus on the strengths of your tools, it is possible to go too far and like some others I think we have.

Some types of currently out-of-favor work, like the “determinants of Y” type papers I just mentioned, are easy to do but just currently hard to get published. Other types, though, are simply hard to do. This is why I’m encouraging others to “develop value-added measures of healthcare quality“. The existing rankings of healthcare systems are obviously wrong- they tend to rely on measures of health outcomes (which are influenced more by what happens outside of hospitals and clinics than inside them) or measures of inputs or processes (things that we think are associated with good care, but aren’t themselves the essense of good care). Which hospitals or state/country-level health care systems do the best job of adding to life expectency compared to if the same patients received average care? I have no idea, and I don’t even have a great idea for how to find out. But if you do, I’d love to see it. This is the editor’s privilege: to say “I have no idea, but I’d love to see you figure it out”.

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