“Archaeology can’t give social scientists population or GDP, but here are some things we can measure that might be useful for social science.”
Statistical Modeling, Causal Inference, and Social Science 2026-07-12
Apropos of our recent discussion on the estimation of historical population sizes, Sean Manning writes:
Some archaeologists have measured house sizes for Gini-coefficient-style studies aside from studying human remains to measure nutrition and rates of illness. I think that was what Michael E. Smith meant when he talked about hypothetical data: “archaeology can’t give social scientists population or GDP, but here are some things we can measure that might be useful for social science.”
I asked Manning where the quote came from, and he replied:
I think I got the idea from this response by Smith to a published paper:
This model of inequality in the Aztec Empire is not based on empirical data. While there is nothing wrong with hypothetical models per se, the paper is phrased as if it presents empirical findings. … There are simply not enough data available to do the kind of analysis presented in this paper. The tweaking of data and methods do not produce results that satisfy me as being reasonable estimates of the level of inequality in the Aztec Empire. Perhaps this is just an epistemological difference between our approaches to science and knowledge. Economists might look at this paper as a fine analysis, whereas archaeologists and historians will probably look at it as a study based on hypothetical data, and therefore divorced from the Aztec reality that we study.
Smith has a book that talks about the archaeology of inequality in Aztec Mexico: Timothy A. Kohler and Michael E. Smith, editors, Ten Thousand Years of Inequality: The Archaeology of Wealth Differences (University of Arizona Press, 2019).
Often in social science there is tension between what we can measure and what we would like to know.