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Cavallo (2016) "Scraped Data and Sticky Prices" - Review of Economics and Statistics Forthcoming
Please cite this paper if you use the data
This paper introduces Scraped Data as a new source of micro-price information to measure price stickiness. Scraped data, collected from online retailers, have no unit values, time averaging, or imputed prices that can bias pricing statistics in traditional sources of micro price data. Using daily prices of 80 thousand products collected in five countries with varying degrees of inflation, I show that relative to previous findings in the literature, scraped online prices tend to be stickier, with fewer price changes close to zero percent, and with hump-shaped hazard functions that initially increase over time. I show that the sampling characteristics of the data, which minimizes measurement errors, explains most of the differences with previous results in the literature.
Paper and Appendix