Scraped Data and Sticky Prices
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 time averaging or imputed prices that can affect 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, including the US, I find 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 minimize measurement biases, explain most of the differences with the literature. Using the cross-section of countries, I also show that only the relative frequency of price increases over decreases correlates with inflation.
Published Versions
Alberto Cavallo, 2018. "Scraped Data and Sticky Prices," The Review of Economics and Statistics, vol 100(1), pages 105-119. citation courtesy of 
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