Test the Predictions - Or Check the Assumptions
By Bryan Caplan
Where do economists draw the line between science and dogma? In most cases, they say something like this: “A model is scientific if and only if it makes true testable predictions.” Perhaps this is why Arnold was dissatisfied by my brief reply to last week’s challenge. Arnold:
My question is how to reconcile low employment with low unit labor
costs. Presumably, low unit labor costs would cause labor demand to be higher rather than lower.
My translation of what they have to say is this:
Bryan: Notwithstanding remarkably low unit labor costs, if unit labor costs were even lower, we could have full employment.
Scott: Notwithstanding remarkably low unit labor costs, if we had higher nominal GDP, we could have full employment.
Each of their positions amounts to a non-falsifiable hypothetical.
Not that we should be shocked by non-falsifiable statements. This is
macro, of course.
I agree that testable predictions are one sign of science. But there’s another, equally good, sign: Whether the assumptions empirically check out. In practice, economists frequently examine the realism of their models’ assumptions. Furtiveness aside, I see nothing wrong with this approach.
Case in point: The main reason I believe that nominal wage rigidity is an important cause of unemployment isn’t the powerful predictions the approach implies. The main reason I believe that nominal wage rigidity is an important cause of unemployment is that the assumptions seem so undeniably true:
Assumption #1: Nominal wage cuts hurt workers’ morale – and therefore their productivity.
Assumption #2: Demand for labor slopes downward.
Convince me that either assumption is false, and I’ll change my mind.