Guo of recently discussed the benefits of open data for economic research. He writes, “There used to be a time when data was costly: There was not much data around. Comparable GDP data, for example, has only been collected starting in the early mid 20th Century. Computing power was expensive and costly: Data and commands were stored on punch cards, and researchers only had limited hours to run their statistical analyses at the few computers available at hand.”

He goes on, “Today, however, statistics and econometric analysis has arrived in every office: Open Data initiatives at the World Bank and governments have made it possible to download cross-country GDP and related data using a few mouse-clicks. The availability of open source statistical packages such as R allows virtually everyone to run quantitative analyses on their own laptops and computers. Consequently, the number of empirical papers have increased substantially. The [above] figure (taken from Espinosa et al. 2012) plots the number of econometric (statistical) outputs per article in a given year: Quantitative research has really taken off since the 1960s. Where researchers used datasets with a few dozens of observations, modern applied econometricians now often draw upon datasets boasting millions of detailed micro-level observations.”

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Image: Courtesy OpenEconomics