Scraped Data and Sticky Prices

Abstract

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.