Translating Economics

The last post was about things we know that we don’t know. This one is about things we don’t know that we know.

Macroeconomics is a difficult subject. Not only the aggregate economy is extremely complex, but the data is lacking. You may ask, “What about 148,000 time series from FRED?” 148,000 series help a little. Well, physicists have 10^80 atoms in the universe and still struggle with some unified field theory. You need right data.

Unfortunately, macro needs bad events to collect evidences that help prevent bad events in the future. Macroeconomists are not so evil to knock down the world financial system for research purposes. They have to wait. After a crisis had come, they get their part of criticism for bad economics and then collect the new facts about the economy.

But economists from other fields have more alternatives. They conduct experiments, use natural experiments to isolate certain factors, and reach facts no one previously cared about. Results attract much less attention than macro does. Unlike macro, which concerns everyone in a pretty straightforward way, broader economics studies events that have an indirect impact on people. Public demand for these studies is lower, studies rarer get into news, and politicians worry only about a small fraction of respective topics.

The public is mostly unfamiliar with academic research outside macro. Actually, economists are unfamiliar with it either once they get outside their home field. But it’s more important to establish a intergroup connection from researchers to users, rather than among distant researchers themselves.

Mostly political discussions about economic aggregates in public show that intergroup connections are possible when both sides have personal interest in understanding the subject. The most popular economic blogs either discuss politics, which cause fury and is always in demand, or tell about practical matters.

The most promising way of delivering knowledge is its framing into either emotional or practical matters. It’s easier now because research itself became more specific. Take Al Roth’s school matching or Esther Duflo’s works on education in India. Fifty years ago it was Gale–Shapely matching algorithm and Becker’s or Schultz’s returns on education. Too abstract to be accepted outside academia. Once the matching algorithm got its specific application in schools and hospitals, it was accepted. As for education, the World Bank now has to pay more attention to the efficiency of its programs.

But any economics still requires translation into the language of public interests. Communication problems leave too much knowledge unnoticed. And if you look around, you notice thousands of things that would benefit from this missing knowledge.

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