Thursday, April 14, 2011

Prediction is hard

President George W. Bush in 2001:

Many of you have talked about the need to pay down our national debt. I listened, and I agree. We owe it to our children and our grandchildren to act now, and I hope you will join me to pay down $2 trillion in debt during the next 10 years. At the end of those 10 years, we will have paid down all the debt that is available to retire. That is more debt repaid more quickly than has ever been repaid by any nation at any time in history.

I think that the core issue here, presuming good faith on all sides, is that second order effects are really hard to model. So tax cuts (both business and individual cuts) are seen to stimulate the economy. But accurately predicting that is very hard in a large and non-linear system like the United States economy. It's even possible that tax cuts could have perverse effects of lowering growth (I am not saying that they do -- it's just that complex, non-linear systems which are sensitive to initial values are very hard to predict).

So perhaps the real lesson here is to focus on first order effects. Link tax cuts directly to program cuts. And vice versa, new programs should have taxes that are linked to them. In my world, that would include wars (notice how World Wars I and II led to large tax increases to finance) which would make the debate about military intervention a lot more involved. I don't know if this would be a complete solution to deficit woes, but I worry that the current approach relies way too heavily on statistical models to predict the consequences of tax and budget policy (and, as we know from chaos theory, these types of models are notoriously difficult to use for prediction).

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