Working Paper: NBER ID: w24649
Authors: Austan D. Goolsbee; Peter J. Klenow
Abstract: We use Adobe Analytics data on online transactions for millions of products in many different categories from 2014 to 2017 to shed light on how online inflation compares to overall inflation, and to gauge the magnitude of new product bias online. The Adobe data contain transaction prices and quantities purchased. We estimate that online inflation was about 1 percentage point lower than in the CPI for the same categories from 2014--2017. In addition, the rising variety of products sold online, implies roughly 2 percentage points lower inflation than in a matched model/CPI-style index.
Keywords: inflation; ecommerce; Adobe Analytics; CPI; new product bias
JEL Codes: E31; O47
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| online inflation rates (E31) | CPI inflation rates (E31) |
| increasing variety of products available online (L81) | CPI inflation rates (E31) |
| net entry of new goods (E20) | measured inflation (E31) |
| online pricing dynamics (D49) | overall inflation metrics (E31) |