Part of the challenge of tracking down significant trends and developments in old datasets is to work out how the original compilers would have used the results. Fortunately, the Board of Trade left some clues in this snapshot of long term food price trends. The index weighting listed at the foot of the page suggests a way of reverse-engineering price differentials in a fairly robust way.
Having copied the products and the weighting factor into a two column listing, applying a SUM function to the weighting factor column, we find that there are a total of 360 tweaks applied to the base sample. This is an interesting figure, possibly because it is easier to handle in pre-decimal currency calculations.
Table 6 shows a series of index numbers (1900=100) for retail prices of food in London for the years 1892 to 1914. Note that 1914 records values for January to August 1; September 1 to December and whole year. The year will be evaluated separately, since there is more movement in the economy.
Taking the figures up to 1913,
Taking the index year (1900=100) and applying the weighting, we get the following profiles.
Group I: Bread 50+Flour20+Rice3+Tapioca1+Oatmeal5+Potatoes18: total = 87.
Group II: Beef 48+Mutton 24+Pork15+Bacon19: total = 106
Group III: Milk 25+Butter 41+Eggs 19+Cheese 10: total = 115
Group IV:Tea 22;Coffee +2;Cocoa+4 total = 28
Group V: Sugar+19;Jam+4; Treacle+2; Marmalade+4; Currants+3; Raisins+2 total = 34
In a spreadsheet, this looks like:
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