When Different Prices Don’t Mean Discrimination: Rethinking Algorithmic Pricing
Recent headlines have reignited public concern about “algorithmic price discrimination,” following a study that found different users were offered different prices for the same grocery items on Instacart. To many observers, this appeared to confirm a troubling narrative: that digital platforms are using personal data to extract the maximum possible willingness to pay from each consumer.
A closer look at the evidence, however, tells a very different story.
What the Study Actually Found
The widely cited study—subsequently amplified by The New York Times—documented short-term price differences across users who selected the same products, from the same store, using the same fulfillment method. Crucially, it found no evidence that these price differences were linked to individual characteristics such as income, ZIP code, or shopping history.
Despite this, the findings were framed as an example of algorithmic price discrimination and used to support calls for regulatory intervention, including action by the Federal Trade Commission. This leap from observed price variation to claims of personalized exploitation is where the analysis breaks down.
Four Very Different Reasons Prices Can Vary
Price variation is not a single phenomenon, and conflating its causes leads to flawed conclusions. There are at least four distinct mechanisms that can generate different prices for identical goods:
Randomized price experiments (A/B testing)
Firms routinely vary prices across users on a randomized basis to learn how demand responds to price changes. This is standard practice across digital markets and is not based on profiling individual consumers.Strategic price randomization
In competitive settings, firms may randomize prices to avoid predictable undercutting by rivals. Customers pay different prices, but purely by chance—not because of who they are.Dynamic pricing
Prices can change in response to market conditions such as time, demand intensity, or supply constraints. Surge pricing in ride-hailing or airline ticket pricing are familiar examples.Actual price discrimination
This occurs when firms deliberately charge different prices based on consumers’ willingness to pay—using observable traits or behaviors. Student discounts, coupons, and loyalty programs are explicit and longstanding examples.
The Instacart study aligns with the first category. Yet public commentary largely assumed the fourth.
The Evidence–Narrative Gap
Even after acknowledging that the study found no personalized pricing, much of the public discussion pivoted to what platforms could do in the future rather than what they did do in practice. This transforms an empirical finding about randomized experiments into a speculative warning about algorithmic surveillance.
Policy debates should be grounded in observed behavior, not hypothetical capabilities.
Would Banning Price Experiments Help Consumers?
Intuition often suggests that stable, uniform prices are inherently fairer. Economic theory suggests otherwise.
When prices fluctuate around a given average, consumers tend to buy less when prices are high and more when prices are low. The gains from low-price periods typically outweigh the losses from high-price periods. From a standard consumer-surplus perspective, banning randomized price variation does not make consumers better off.
Arguments for strict price stability therefore rest on assumptions—such as strong consumer risk aversion—that go beyond conventional welfare analysis and should be made explicit if they are to justify regulation.
Why Price Discrimination Is Not Always Harmful
Price discrimination has existed for decades and is ubiquitous across industries. Streaming services offer discounted student plans. Airlines charge different fares for the same seat. Retailers use coupons and loyalty programs to segment customers.
Importantly, economic theory shows that price discrimination can increase total output by enabling firms to serve customers who would otherwise be priced out of the market. In competitive settings, it can even increase consumer surplus, as firms compete aggressively for price-sensitive buyers.
Whether price discrimination benefits or harms consumers is an empirical question, and the literature offers mixed results. What it does not support is blanket condemnation.
The Real Policy Questions
Legitimate concerns remain about algorithmic pricing, including transparency, competition, and the long-run effects of data-driven decision-making. These are important issues that deserve careful analysis.
But the Instacart episode does not meaningfully advance that discussion. As the original analysis by Brian Albrecht makes clear, the study documented routine price experimentation—not personalized price extraction.
Before calling for regulatory intervention, we should ask a more precise question: Compared to what realistic alternative are consumers worse off? Economics exists precisely to discipline intuition and clarify such trade-offs. We should use those tools before drawing sweeping conclusions.
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