For years, conversations about the trillions in household spending that flow through everyday purchases have included what is often called the “pink tax.” That’s been shorthand for the idea that similar products can cost more when marketed to women: think personal care items (like a pink vs. blue razor).
But historically, those differences have not always been easy for consumers to see or compare in real time. They were typically identified through comparisons or broader studies after the fact.
Now, as Mother’s Day spending is projected to reach $38 billion this year, a different pricing system is changing how those disparities can appear.
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As retailers increasingly use dynamic pricing, the question is shifting from whether prices vary to how they are determined and how visible that process is to shoppers.
Beyond the pink tax: How product pricing is changing
To start, it helps to know what the pink tax is.
The pink tax usually means higher prices on products marketed to women. The term is also used to refer to sales taxes on menstrual products like tampons and pads. Even though they’re essential, many states still tax them while exempting items like groceries and medicine.
And, because men typically don’t pay the same high prices for personal care items, the added cost of certain products merely because they are traditionally marketed to women has been dubbed “the pink tax.”
But it’s important to note that the pink tax and dynamic pricing aren’t the same. They do, however, raise a similar problem: price differences that are difficult for consumers to see or verify.
- Both involve pricing differences that are difficult for consumers to detect, compare, and evaluate in real time, even though the underlying mechanisms differ.
- The pink tax is rooted in product-level variation across categories, and dynamic pricing emerges from data-driven systems that adjust prices algorithmically.
How algorithmic and surveillance pricing work
Dynamic pricing can adjust prices based on factors like demand, timing, and inventory levels. Another term you might have heard, “Surveillance pricing,” incorporates behavioral data, e.g., browsing patterns, shopping frequency, or location.
- Retailers can use signals like whether a shopper is logged in, how often they purchase, which device they use, how long they browse, and whether they abandon a cart before checkout.
- Individually, these may seem minor. Together, they may help determine what price a consumer sees at a given moment.
These AI-driven models are now reportedly used in grocery delivery platforms, online retail, pharmacy services, and subscription products, categories that account for a growing share of household spending.
Research from the Brookings Institution suggests companies are increasingly using machine learning to adjust prices on everyday purchases. That can mean different shoppers see different prices for the same item.
Why this matters for household spending
This shift matters less because of who these systems are designed for, and more because of how household spending is often structured.
Data from the U.S. Bureau of Labor Statistics show that women continue to spend more time on unpaid household labor and caregiving than men. In practice, that often translates into greater responsibility for routine purchasing decisions.
Much of that spending is on everyday items — groceries, household basics, personal care, kids’ products, and subscriptions — where these pricing systems are common. As a result, the effects can vary depending on who does most of the shopping, even without direct targeting.
So are women disproportionately impacted?
Dynamic and surveillance pricing systems don’t seem to explicitly target specific consumer groups, and there isn’t yet clear evidence that these systems deliberately set prices differently for men versus women.
The concern is more indirect: because women are more likely to make routine household purchases, they may encounter these pricing systems more frequently and across a wider range of categories.
That has led some policymakers and consumer advocates to question whether data-driven pricing could, in practice, replicate patterns similar to the pink tax.
Lawmakers and regulators are starting to respond
The Federal Trade Commission (FTC) has increased scrutiny on how companies use consumer data in pricing, focusing on whether consumers are clearly informed when data influences the prices they see.
At the federal level, some lawmakers have introduced proposals to limit the use of personal data in algorithmic or individualized pricing, including the Stop Price Gouging in Grocery Stores Act and the Preventing Algorithmic Collusion Act of 2025.
These proposals reflect concerns about transparency and the role of automated pricing systems, though most remain in the early legislative stages.
At the state level, some lawmakers are beginning to explore limits on how consumer data can be used in pricing, particularly in essential goods like groceries.
Maryland, as Kiplinger has reported, recently enacted a first-of-its-kind surveillance pricing law limiting how retailers can use personal data to set individualized prices in grocery stores.
In a press release announcing the Protection from Predatory Pricing Act, Gov. Wes Moore stated, “At a time when Marylanders are already stretched by the rising cost of groceries, housing, and everyday necessities, we must ensure that new technologies are not used to drive up the bill for working families.”
Dynamic pricing: What you can do
- Compare prices across devices or sessions, since browsing history can influence what is shown.
- Track prices over time. Changes may reveal patterns not visible in a single visit.
- Pay attention to the total cost, including fees and add-ons.
- Consider retailers that are clear about how pricing works on their site.
And while the pink tax hasn’t disappeared, more than 25 states have adopted measures addressing gender-based price differences.
Now, as you shop, maybe for Mother’s Day, there’s even more to consider since pricing systems can adjust in real time based on consumer data.
Those small differences, which can add up over time, are harder to detect, explain, and verify. So stay tuned.