An Applied Forecaster’s Bad Dream

This is the sort of thing that freaks me out every time I’m getting ready to deliver or post a new set of forecasts:

In its 2015 States of Fragility report, the Organization for Economic Co-operation and Development (OECD) decided to complicate its usual one-dimensional list of fragile states by assessing five dimensions of fragility: Violence, Justice, Institutions, Economic Foundations and Resilience…

Unfortunately, something went wrong during the calculations. In my attempts to replicate the assessment, I found that the OECD misclassified a large number of states.

That’s from a Monkey Cage post by Thomas Leo Scherer, published today. Here, per Scherer, is why those errors matter:

Recent research by Judith Kelley and Beth Simmons shows that international indicators are an influential policy tool. Indicators focus international attention on low performers to positive and negative effect. They cause governments in poorly ranked countries to take action to raise their scores when they realize they are being monitored or as domestic actors mobilize and demand change after learning how they rate versus other countries. Given their potential reach, indicators should be handled with care.

For individuals or organizations involved in scientific or public endeavors, the best way to mitigate that risk is transparency. We can and should argue about concepts, measures, and model choices, but given a particular set of those elements, we should all get essentially the same results. When one or more of those elements is hidden, we can’t fully understand what the reported results represent, and researchers who want to improve the design by critiquing and perhaps extending it are forced to box shadows. Also, individuals and organizations can double– and triple-check their own work, but errors are almost inevitable. When getting the best possible answers matters more than the risk of being seen making mistakes, then transparency is the way to go. This is why the Early Warning Project shares the data and code used to produce its statistical risk assessments in a public repository, and why Reinhart and Rogoff probably (hopefully?) wish they’d done something similar.

Of course, even though transparency improves the probability of catching errors and improving on our designs, it doesn’t automatically produce those goods. What’s more, we can know that we’re doing the right thing and still dread the public discovery of an error. Add to that risk the near-certainty of other researchers scoffing at your terrible code, and it’s easy see why even the best practices won’t keep you from breaking out in a cold sweat each time you hit “Send” or “Publish” on a new piece of work.

 

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