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The usage of big data is likely to transform economic measurement in ways that we are only beginning to grasp (Cavallo and Rigobon 2016). Big data encompasses four fundamental shifts from standard datasets: volume, veracity, velocity, and variety. It is commonplace to focus on the first three of these. Clearly, scanner data offers us more observations, fewer opportunities for human input errors to creep in, and the capacity to measure prices and sales almost instantaneously instead of waiting for monthly surveys. These benefits are creating opportunities for improving the timeliness and quality of existing measurement techniques. However, in a new paper, we argue that the most exciting part of the big data revolution is likely to come from the new varieties of data that have become available (Redding and Weinstein 2016).
One of the principal challenges in producing numbers like real GDP or real wages is that while nominal variables are easy to measure, the measurement of real variables requires a theory of economic behaviour. Just as accountants like to joke that “sales are a fact; profits are an idea”, in economics, we face a similar conundrum -“consumer expenditures are a fact; real income is an idea”. While few people would disagree about what the nominal sales of any firm are or how much a consumer spends on a product, translating nominal numbers into real output or welfare is challenging (and was a key component of the path-breaking work of the recent Nobel Laureate, Angus Deaton, as in Deaton and Muellbauer 1980).
Unfortunately, the problem is not that we don’t know how to convert nominal expenditures into welfare; it is that we know too many ways of doing it. Broadly speaking, the profession has settled on three disjoint approaches. First, macroeconomists typically assume there are no demand shifts when measuring real income movements. A foundational assumption in these models is the idea that taste parameters never shift, so the utility function is constant. Economists make this assumption in order to derive a ‘money-metric’ utility function, which guarantees that welfare can be measured if one only knows income and prices. Applied microeconomists take a very different approach by assuming that there are time-varying demand and supply curves. Although it is not typically acknowledged, the existence of these time-varying demand curves is inconsistent with the macroeconomist’s idea that taste parameters are fixed. Demand shifts reflect the fact that a consumer likes one product more than another, which in general will mean that utility is not money-metric. Finally, actual price and real output data is constructed by statistical agencies using formulas that differ from either approach.
The inconsistencies are so deep that the same assumptions that form the foundation of demand-system estimation can be used to prove that standard price indexes are incorrect, and the assumptions underlying standard price indexes invalidate demand-system estimation because if no demand parameter ever shifts, one can recover the demand elasticity without recourse to estimation. In other words, extant micro and macro welfare estimates are inconsistent with each other as well as the data.
One can ignore these problems in conventional datasets, because one typically does not observe all the key features of each transaction. Thus, one can assume errors in demand systems are due to unobserved quality changes, measurement error, or something other than a shift in demand. However, it is much harder to ignore these contradictions in barcode data because we observe prices and quantities sold of precisely defined products. While conventional price measurement is based on identifying ‘the price’ for heterogeneous bundles of goods (milk, carbonated beverages, computers) that contain substantial quality variation within product categories, barcode data eliminates the ambiguity in the definition of a product. Put differently, while one might be able to assume that shifts in demand for carbonated beverages are the result of unobserved quality upgrading, it strains credulity to make the same assumption for a 330mL can of Coke Zero.
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