Unlearning Economics has a nice piece on the limits of sophisticated empirical techniques and the virtues of eyeball econometrics, reminiscent of Dave Giles’ advice “Always, but always, plot your data.” One extension I’d make to this is the importance of Big Facts. Sometimes, it’s more important that a theory explain a single important fact – or at least be consistent with it – than a range of small facts.
Take, for example, the efficient markets hypothesis. Researchers have found countless small facts that seem to refute this – well over 100 anomalies at the last count. All these, however, run into the Big Fact – that fund managers do not beat the market. This is not inevitable: they could in theory out-perform at the expense of retail or overseas investors. So why don’t they?
It might well be because the anomalies are not, in fact, that strong. They might be just non-replicable patterns in noisy data (pdf). Or it might be that once traders learn of them, they get bid away – something that seems more true in the US than Europe. Or it might be that high dealing costs mean the anomalies can’t in fact be exploited in the real world. And perhaps those anomalies that are robust are in fact a reward for taking risk: the defensive (pdf) anomaly, for example, exposes fund managers to benchmark risk, the danger of losing their jobs because they under-perform in a bull market.
Here’s a second example. A Big Fact is that the unemployed are significantly less happy than those in work. This is inconsistent with ideas that unemployment is voluntary: people should be happy if they’re on holiday. It thus refutes labour market-clearing real business cycle theories.
A third Big Fact is that mainstream economic forecasters have consistently failed to predict recessions – something which pre-dates the 2008 crisis – and in fact are much worse recession predictors than the simple yield curve. This might tell us that recessions are inherently unpredictable. Or it might tell us there’s something wrong with orthodox macroeconomics (and not just DSGE models). Whatever your story, it must fit this Big Fact.
A fourth Big Fact is that a significant slowdown in productivity and GDP growth has followed an increase in inequality (in the sense of the share of income going to the 1%). This might tell us that increased inequality has caused slower growth – perhaps because it’s the wrong sort of inequality, Or maybe something independent of inequality caused growth to slow. Whatever it is, we have a Big Fact which defenders of inequality need to confront.
No doubt you can add other examples.
My point here should be a trivial one. Not all facts are equal. And given that no theory in the social sciences fits all facts, I’d rather they fitted the big ones than the little ones. One useful thing to do when reading any theory is to ask: what is the biggest fact that supports this claim, and what is the biggest that is inconsistent with it?