Ancient Economy Seminar

May 26-Jun 12 witnessed an ancient economy “workshop” here at Columbia, funded by William Harris and the Mellon Foundation, and officially under the auspices of the Center for the Ancient Mediterranean (CAM). Participating were young scholars from around the world–only a few Americans–all working on a variety of topics of economic import: slavery, piracy/pillaging, law, risk management, trade, fuel, and (in my case) the credit economy. It was a fantastic group of people, a real pleasure to work with.

The seminar consisted of two main parts: The first was daily lectures/discussions about various topics in modern economic theory: laws of supply and demand, comparative advantage, quantifying labor, Malthus, Keynes, etc. Most of it was very basic stuff that anybody doing economic history needs to know. The second was regular discussions about the personal work of the participants: each of us was required to prepare a written paper, present it, and undergo trial by fire for roughly an hour each. This second part was, in my opinion, the most informative. It presented the opportunity to learn about specific issues of research that I really knew little about.

One thing that became increasingly apparent to me as days turned to weeks is how paramount it is for economists who foray into ancient history to fully understand the particularities and limitations of ancient data sets. It is too easy to, for instance, impose modern assumptions about price formation, market integration, or (in my case) credit and banking, and consequently arrive at unworkable conclusions about how ancient economies and commerce functioned. On the flip side, however, it also became increasingly apparent that historians of the ancient economy must become familiar with not only basic economic theory and its limitations, but the basic statistics as well. Given the paucity of our data, it is essential to know methods by which one can test hypotheses, perform error analysis, calculate confidence intervals based on sample size, etc. This is required before we can draw conclusions about data trends and correlations, for instance.

The people were by far the most enjoyable aspect of the whole experience. Everyone was wonderful and a real joy to work with.