Learning from cliometrics
As part of my graduate coursework, I learned about cliometrics: a rigorously numerical and quantitative approach to answering historical questions, common in the 1970s but now largely fallen out of favor.
Everyone I talk to tells me a different version of why cliometrics became unfashionable. Some people say that the technology of the time, which required rooms of punchcards to answer relatively basic questions, required too much labor. (Every single person reading this probably has more computing power than all the 1970s cliometricians put together.)
Most historians I’ve talked to point to Fogel and Engerman’s Time on the Cross (1974) as a kind of shark-jumping moment for cliometrics. Attempting to analyze slavery by whether it was economically rational seemed ethically tone-deaf to many, and the book was roundly criticized for flaws in method. Heavily quantitative approaches have persisted in the subfield of economic history, but relatively few historians in recent years have used (or trained in) these methods.
Lately, I’ve been looking at some recent digital–methods approaches in history and wondering: what makes this work different from cliometrics? Is this a style of work or an analytical approach that runs the same risks? (I’m thinking in particular of heavily GIS-based work, which requires specialist training and large data sets, and of some text–mining strategies.) In order to answer that question usefully, I need to understand more about what caused the shift away from cliometrics, and it seems that most of that story isn’t in the journal literature.
Was cliometrics, being closely allied with economic history (largely the province of white men), never that fashionable among Americanists to begin with? Did it not gain traction because it was antithetical to the ways most other historians were being trained to read sources? Did its requirements for large–scale computing resources limit the numbers of historians who could practice its methods successfully? I sense that the answer to all three of these questions is yes, but if you have any further ideas, I’d love to hear them (especially if they include citations.)
There’s another twist here. The fields I learned my methods from– women’s history, African–American history, queer history– are fundamentally built on questioning quantification, categorization, and other positivist impulses. As interested as I am in the possibilities for, say, mapping rural women’s lives in history, I need to prepare good answers for why what I’m doing isn’t simply cliometrics 2.0. I know that part of the answer is networks and collaboration–fewer data silos– but I’m still trying to figure out the rest.