Tuesday, October 4, 2011

Effect Sizes

I think that this is very insightful:

They mistake small truths for large ones, and use the small truth to obfuscate the big one. So, the truth - that a few of the unemployed don’t want to work - is exaggerated and used to hide the bigger truth, that the vast majority of unemployment has other causes.

Mark and I have often discussed how effect size can be easily overlooked in modern debates. In epidemiology, for example, it can be the case that a drug has a serious side effect that is so rare that it basically cannot change the risk-benefit calculus. So, for example, statins can cause rhabdomyolysis (as an adverse drug side effect) despite have massive benefits on all-cause mortality (in secondary prevention of cardiovascular disease). But the rare side effect is often newsworthy and may discourage patients from seeking a beneficial therapy. Fortunately, we have randomized trials to sort out what the net impact of the benefits and risks of the drugs is like across a whole population.

I think lacking these experiments makes it easy to get focused on the details in macroeconomics. Policies that may increase utility across the whole population (e.g. immigration) may have costs to individual workers. Failing to properly specify the relative effect size of different interventions may lead to a focus on "second or third order effects". Or, even worse, to misjudging the net impact of a policy.

I think that might well be correct in the example above, as well. It is certain that there are people who would hire more if the minimum wage was to drop. But it is unclear that adjusting the minimum wage would have a major impact on the >9% unemployment rate we have in the United States. We may have to look elsewhere for solutions.

Now, implementing this advice is rough. Which is why I am pleased we have experiments over here in epidemiology.

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