Integralds

Gene, I'm an economist. I do not understand your argument. Could you clarify on the following points?

1. About half of those 15 million hard-working Americans on the minimum wage are teenagers. Why is it a national priority to raise the wages of teenagers?
2. If raising incomes of poor households is an important policy goal, then why don't we use the EITC - which we know is a more highly-targeted tool towards low-income households? Dollar for dollar, the EITC does more good, so why aren't you going with that?

Thank you for your time.

Hi Scott,

I have a question about communication strategy under an NGDP futures target.

Currently the Fed targets inflation and unemployment using an interest rate instrument. The communication strategy during recessions is pretty Old Keynesian: the Fed "cuts nominal rates, which reduces real rates, which spurs real activity." it's a simple message, but works well enough when the nominal rate is well away from the zlb.

Suppose the Fed implemented NGDP futures targeting. What would the communication strategy look like? How would the Fed explain monetary policy to the public? And what advantages would it have over current communication strategy - especially at the zero lower bound?

Thanks,

Integral

C'mon, Gene, this would take like five minutes to implement and would please economists everywhere. Dropping the penny would be an actual Pareto improvement.

The great thing about STEM is that, normally, you can run experiments and induce variation. In economics, it's harder to run pure experiments, so we have to get fancy with our methods. The variation we see in the data is not due to pure chance, but is due to a host of causal factors. We try to isolate specific causal factors using sophisticated statistical methods.

The classic example in economics is the effect of education on wages.

If we wanted to assess how an additional year of schooling affected wages, and we were allowed to do whatever we wanted, what would we do? We'd track a bunch of kids until they were 17, then randomly assign some of them to drop out of school and let the others take their senior year. We'd then track outcomes (wages, industries, whatever you like) over their lifetimes. If we randomized properly, we'd be able to get an estimate of the effect of one additional year of education at age 17 on wages by looking at the difference in life paths of the ones who dropped out and the ones who didn't.

But we can't do that in the real world! We aren't allowed to tell some people to drop out and tell others to finish school. Instead, we simply observe that some people drop out at age 17 and others finish high school. We also observe that people who drop out earn less over their lifetimes. But people drop out for all sorts of reasons. Some of the decline we observe in their income is due to less education, certainly, but it's also due to the fact that the very people who drop out also tend to face a host of other economic and social difficulties that would also reduce wages. Just looking at the raw, observational data tells us nothing about how large the effect of dropping out is.

Economists have an entire subfield, called econometrics, dedicated to figuring out ways to adjust for these sorts of problems.

---

Let's look at another example of "bad controls."

Let's say you're trying to assess the effect of education on wages. You notice that industry controls help your goodness of fit: if you add industry to your regression, you can better predict wages.

But that's not the point! You're not trying to predict wages, you're trying to assess the effect of education on wages. These are two totally different things. In this setting, it is almost always a bad idea to regress wages on education and industry.

The whole point of an education is to get a better job. You don't go to college so that you can move from assistant burger-flipper to chief burger-flipper, you go to college to move from burger-flipper to manager. The part of your wage gain that comes from switching job titles should properly "count" as part of the causal effect of education on wages. If you "control for industry," you miss the fact that people get an education, in part, to switch industries. (When you "control for X," the estimates you obtain will largely be driven by variation within cells of X. If you don't control for X, your estimates will [mostly] be driven by variation across cells.)

In economics, high R² (high goodness-of-fit) is usually bad, because it usually means you put too much stuff on the right-hand side.

(with credit to John Cochrane, p10, for the example)

I'll also point out that you can sue for a lot of stuff -- but that doesn't mean you'll win.