Working Paper: NBER ID: w28639
Authors: Diego Aparicio; Zachary Metzman; Roberto Rigobon
Abstract: Matched product data is collected from the leading online grocers in the U.S. The same exact products are identified in scanner data. The paper documents pricing strategies within and across online (and offline) retailers. First, online retailers exhibit substantially less uniform pricing than offline retailers. Second, online price differentiation across competing chains in narrow geographies is higher than offline retailers. Third, variation in offline elasticities, shipping distance, pricing frequency, and local demo- graphics are utilized to explain price differentiation. Surprisingly, pricing technology (across time) magnifies price differentiation (across locations). This evidence motivates a high-frequency study to unpack the patterns of algorithmic pricing. The data shows that algorithms: personalize prices at the delivery zipcode level, update prices very frequently and in tiny magnitudes, reduce price synchronization, exhibit lower menu costs, constantly explore the price grid, and often match competitors’ prices.
Keywords: algorithmic pricing; online grocery; price differentiation
JEL Codes: D9; L1; L2; M31; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Algorithmic pricing (D40) | price differentiation (D49) |
| Algorithmic pricing (D40) | price dispersion (L11) |
| Online retailers exhibit less uniform pricing (D49) | Algorithmic pricing (D40) |
| Pricing technology (D49) | price differentiation (D49) |
| Shipping costs (L87) | price variation (D46) |
| Local demographics (R23) | price differentiation (D49) |