Mathematicians are quiet people. Instead of arguing about their case, they are more often busy thinking about the boundary conditions of their assertions. In the rare moments of speech, they come across as inflexible and pedantic to many.
Although married to mathematics, economics seems to behave differently. Economists discuss a lot and even all non-economists seem to have an opinion about the economy – allowing economists to be popular and approachable. The down side is that those who argue about conclusions of rational expectations theory do not always recollect the expectations formula from their undergraduate math. An unfortunate side-effect is also that many students of economics in their early years assume that the economic variables – capital, disposable income and utility are real, measurable entities – and exist beyond the models.
But these variables are not as real as let’s density, mass or viscosity. The reality bites those of us who choose to work with data and spend a good amount of time mitigating data measurement errors and finding justifiable proxies. The early researches who talked of utility – Houthakker, Prais among others – had been unequivocal in pointing out that the average consumer doesn’t actually exist except in the discussion of the models. That our economic conclusions often rely on certain simplifications of reality should never be forgotten in any discussion of economics.
This would not only bring humbleness to arguments in economists – but help create more attention to data analysis among economics. A specific problem that I have been concerned with is the lack of prices survey in the consumption microdata. We tend to know details of what consumer bought- but often the estimate on prices isn’t available. One way to get around the problem would be to ignore that the prices exist and assume that consumers within the same geographical area (or what class or whatever parameter that clusters price) all face the same prices for the same purchase out of the same consumer basket that is available to them. In fact, the researchers often use the unit-values as prices in the model – a method that invalidates the use of AIDS methodology solely to the measurement errors it causes (the measurement error that this results in is mentioned with great detail in a brilliant paper by John Gibson and Bonggeun Kim – “Quality, quantity, and spatial variation of price: Back to the bog”).
Considering such problems, is it worth exercising caution in the classroom discussions of micreconomic theory and telling the interested parties beforehand that the prices themselves can be approximations in the real-world and the accuracy of model prediction is likely to suffer because of it. Asking what utility would mean – if the items (apples, oranges etc.) are not uniformly available to all the consumers and if their prices are not recorded in a survey – would allow the students to structure their arguments based on data – rather than the terminology from the theories that they have assumed to be true. Asking students to calibrate utility curves before learning about utility is akin to putting the cart before the horse, but admitting that such problems shape our view of utility could help us bring a humbleness that working with real-world data often requires.
Catching Drifts 
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