Historiography: Quantitative

Desktop computers, and laptop computers, are now cheaper and more widespread with greater power and storage capacity. The use of spreadsheets and databases brings a new potential to historians especially when combined with statistical techniques carried out on the computer. Rather like typing did to writing, computer use sharpens the questions. But the power available is only as good as the original data, which is often unreliable. (See Green, Troup, 1999, 148) Not just mass data can be tackled, but small samples too subjected to statistical analysis using computers.
There is soft and hard quantitative history. The soft, less controversial approach, is the comparison of datasets over time, sometimes called serial history and of interest to many kinds of historian including the sociological and Annales (see Green, Troup, 1999, 144). Changes in figures give clues for other changes in population, by generating related explanations or even hypotheses for further research (similar to sociology). This, through census and similar data (like parish records) leads on to demographic studies and much that can be derived and postulated. A great deal of detail, like in genealogy (Green, Troup, 1999, 146), can be generated. Genealogy, however, is often a beautiful fiction, hiding incest, affairs, mistakes and lies. Much matching of family data is time consuming for little result and interest, perhaps only for the family. All comparisons and explanations in any case need care because some may well be misleading, and show the limitations in constructing figures and assumptions in one period may not apply in an earlier period.
Another technique is to take statistics that are reasonably reliable in one period over a section of time and then project backwards. This cannot happen too far back, and a temptation then would be to realise varibles and to do modelling.
This leads on to the hard Quantifiable History, where the discipline of history overlaps with numeric economics. There has, of course, long been a discipline of economic history. However, this has tended to be descriptive, literate, and making those soft connections with data like statistics and graphs. Hard Quantifiable history includes going that stage further into the techniques of economics, including economic modelling, specifically econometrics. Of course this is as controversial as getting into the deeper theories of historical sociology (this way around) and the geographical determinism found in aspects of the Annales movement. This is because it departs from the inductive method and from following closely historical evidence.
The evidence for quantitative historians is datasets produced by government, public institutions and researchers including themselves. Now these are produced with computer power. Large datasets are not always required because of the use of strong statistical techniques. Their use should not be controversial; what is controversial is the use of economic modelling that posits a situation not in the real world, but supposes human behaviour to be rational (Green, Troup, 1999, 143). It allows for counterfactual deduction and contrast, which should be meaningless to inductive historians.
Here are key points for what has been called New Economic History:
Precise questions and know the operating variables
Model based only on the above
Evidence of actual events
Compare the model and the events and give the counterfactual example (Davis, 1966, in Green, Troup, 1999, 142)
The key history for this was the study of American railways and measuring their impact by the counterfactual of their non-existence (some 3.1% only) (Fogel, 1966, in Green, Troup, 1999, 142-43)
An issue is how new and relevant is the counterfactual analysis. For some, counterfactual statements have always been made, but loosely, including in Economic History (Green, Troup, 1999, 143). Structural linguistic analysis highlights when opposites are being implied by a particular statement. If something happened because, does it mean then that it would not have happened if because not? However, when the counterfactual statement is made with figures, it leaves itself open to a debate about the variables used. The variables removed, or made static, perhaps cannot be so isolated, in that changing one thing changes another. It does not take a practioner of chaos theory to know how 'knock on' are changes in even minute effects. So there is something of the fictional in quantitative history done to the econometric level. It looks impressive, as do all statistics (and it often inputs statistics called data) but things aren't quite as certain as the presentation.
Also, history is not about commonalities, like economics and sociology, but about the situation that took place. It is not about what did not happen, but what did happen.
So softer quantitative history is often preferred. There are other examples. Content analysis makes quantitative detail from a qualitative source. This can be quite revealing and suggest what is not otherwise seen. Over time it also shows changes, particularly in writing of identities and understandings. (Green, Troup, 1999, 147)
Nevertheless historical data is frequently interpretation, and history on to that is interpretive. The illusion of the quantitative presentation as factual should always be held in mind: in this new age the human world and its interactions remains analogue rather than digital.

Some Personalities:

Dates in brackets are some times of impact just in areas mentioned.

Adrian Worsfold



Davis, L. (1966), 'The New Economic History: II. Professor Fogel and the New Economic History', Economic History Review, 19, 1966, 657, in Green, Troup, 1999, 148.

Fogel, R. W. (1966), 'The New Economic History: I. Its Findings and Methods', Economic History Review, 19, 1966, 652-3, in Green, Troup, 1999, 148.

Green, A., Troup, K. (eds) (1999), The Houses of History: A Critical Reader in Twentieth-Century History and Theory, Manchester: Manchester University Press, 141-150.