Common problem. E.g., how to "aggregate" data in astronomy? For a simple first cut answer, work hard, get a lot more data, work hard, have some good ideas, ge

Common problem. E.g., how to "aggregate" data in astronomy? For a simple first cut answer, work hard, get a lot more data, work hard, have some good ideas, get a lot more data, work hard, etc. So, for astronomy, use spectroscopy to identify the elements in the star. Use the brightness of the star and the red shift. Get empirical distributions of various characteristics of the stars and begin to guess that stars go through stages and where the distributions are low the stages are short. So, start by observing, getting a lot of data, and being just descriptive; economics didn't and still doesn't do nearly enough here. Then look a little deeper and start to make some sense of it; economics has done next to nothing here, necessarily so since they didn't get a good grade in the first grade with the descriptive work. For something with a little promise, e.g., Leontief's work , they don't like that and regard it as not acceptable in Economics Club. So, with such work, in astronomy get Hertzsprung–Russell diagrams. Nice progress. Now, continue: Take what is known about weights in the periodic table and observe that if press together two deuterium atoms to make one helium 4 atom, then lose some mass and, thus, get some energy. Keep this up across the periodic table and see that iron has the least such energy. Observe that can keep pressing light atoms together to get heavier ones, e.g., carbon, oxygen, ..., iron and get off energy. Do a lot of model building of the reactions in the centers of distant stars. Check with the data, e.g., the Hertzsprung–Russell diagrams. Eventually come up with a well tested, apparently quite solid, model of stars. A lot work, bright ideas, spectroscopy, expansion rate of the universe and red shift, nuclear physics, etc. Didn't say it was easy. Much the same in other fields of science that have been successful. So, if economics wants to copy that methodology, then they need to get their hands on more data. Then, say, they need to look at the flows that, intuitively, appear to be generating the data. I know; I know; too soon want a lot of curves on propensities, and will have a tough time there. Not guaranteed to be easy. But neither were the other sciences that were successful. My view of academic economics is that they sit in small, dark rooms with the doors closed, hope for a single stroke success comparable with E = mc^2 in physics, dream up models, essentially of imaginary economies, hope, but not get very far. So, they don't want to do the first, observational, low level grunt work of data collection and basic description that astronomy, chemistry, biology, etc. did. There have been suggestions that the Princeton econ department has two halves with one half, then with Bernanke, the more empirical. If so, good for the Fed and the US since we will be less likely to be stuck with crack pot ideas from "defunct economists". A danger here: So, with such work with imaginary economies, some to be defunct economist makes a lot of absurd assumptions and finds some optimal solution -- maybe he studied some linear programming (Kantorovich, Solow), Kuhn-Tucker theory (Arrow), Lagrange multiplier theory (Debreu), or dynamic programming (Samuelson). Then, seeing a real economy and an opportunity for fame, status, prestige, tenure, lots of undergraduate co-eds for secretaries, he takes the solution from his imaginary economy and says that that is what the real economy should do. Bummer. Good way to kill tens of thousands of people from unemployment, busted homes, street crime, infant mortality, drug abuse, suicide, etc. Incompetence is a bad thing; incompetent economists are really ugly things. Your point about unemployment is an example: Early on I looked at it and asked, where the heck is the definition, that is, the empirical or operational definition? Next, one level deeper, what the heck are we really counting? E.g., might we be partitioning the data in some way that would be better? So here what do we want for better? Sure, we want something that can help us with reductionism, that is, clear causality. But the actual unemployment data is so poorly defined and so "aggregated" that our hope for anything causal is just smoking funny stuff, like the astronomers calling stars just points of light and not looking deeper with spectroscopy, the periodic table, nuclear physics, etc. Finally I understood what the empirical economists intended to do with data such as unemployment: They just said, it's a measurement; hopefully it is done in a consistent way month by month; we agree we don't have a good definition and that the data is too aggregated and has basic problems in measurement, but we just take the data as it is; with the data, we will do just empirical statistical modeling. Then, recognized or not, they have essentially given up making unemployment data an input to anything reductionist, causal, or scientific. Suckage. Meanwhile academic economists are driving late model cars, and millions of people are suffering from unemployment likely for no very good reasons. Suckage.