Regulatory Comments

ICLE Comments to Competition Bureau Canada on Examining the Food Supply Chain

I. Introduction and Overview

The International Center for Law & Economics (ICLE) is a nonprofit, nonpartisan research centre that applies economic analysis to legal and regulatory questions. We appreciate the opportunity to respond to the Competition Bureau’s examination, Behind the Price Tag: Examining the Food Supply Chain.[1] These comments address Question of Interest No. 3, which covers retail pricing and six related sub-questions.

ICLE uses law & economics analysis to determine whether challenged conduct harms consumers by raising prices, reducing output, lowering quality, or diminishing innovation. Our analysis builds on submissions to the Bureau concerning algorithmic pricing,[2] anti-competitive conduct and agreements,[3] merger enforcement guidelines,[4] and the future of Canadian competition policy.[5] We have also studied grocery competition in work on the proposed Kroger/Albertsons merger,[6] state grocery-pricing laws,[7] and online food-delivery fees.[8]

Canadian grocery prices rose sharply after 2021,[9] and Canadians reasonably want to understand why. That concern has focused attention on loyalty programs, pricing algorithms, and less visible changes such as “shrinkflation,” which reduces package size without changing the nominal price, and “skimpflation,” which lowers quality or reformulates a product to reduce costs.

The Bureau should evaluate each practice by its demonstrated effects on competition and consumers, not by whether it is new, automated, or unpopular. That inquiry must account for error costs—the harms caused by mistaken intervention or nonintervention. Wrongly condemning beneficial pricing practices may suppress them across the market, while failing to address harmful conduct may allow it to persist where entry barriers are high or switching is costly. Ambiguous cases therefore require market-specific evidence rather than presumptions based on the form of the conduct.

That caution applies in particular to Section 78(1)(k) of the Competition Act, which identifies “excessive and unfair selling prices” as a potential anti-competitive act without defining either term. High prices alone do not establish exclusion or harm to competition. Treating them as sufficient could turn abuse-of-dominance enforcement into price regulation. The Bureau has recognized that the Competition Act does not authorize it to regulate grocery prices and that price controls are “blunt tools” because authorities cannot reliably identify or continuously adjust the right price.[10]

These comments instead examine the conditions under which consumers can find, compare, and act on prices, and those under which firms can coordinate or exclude rivals. Across Item 3’s six sub-questions, we focus on consumer information, switching costs, entry barriers, deception, coordination, and exclusion. Grocery-price levels reflect broader macroeconomic forces and supply-chain conditions that competition enforcement cannot directly manage. The relevant questions are what conduct occurred, how it changed the choices available to firms and consumers, and what the evidence shows about its competitive effects.

A. Error Costs Counsel Evidence-Based Restraint

Competition enforcement operates under uncertainty. The error-cost framework, developed by Frank Easterbrook and extended in the modern literature, assesses legal rules by the consequences of mistaken decisions. A false positive, or Type I error, wrongly condemns conduct that benefits consumers or does not harm competition. A false negative, or Type II error, fails to stop conduct that does.[11]

These errors are not symmetric in a low-margin, high-volume industry such as grocery retail. False positives can prove both costly and durable. If enforcement condemns lawful discounting, loyalty pricing, or algorithm-driven responses to market conditions, firms may abandon those practices across their operations to avoid liability. Market forces cannot readily restore the lost benefits. False negatives are more likely to invite a market response because elevated margins attract entry, expansion, and substitution. As Easterbrook observed, a monopolist’s higher prices attract rivalry.

This asymmetry supports restraint in ambiguous cases, not inaction when the evidence clearly establishes harm. False negatives may persist where artificial barriers impede entry or consumers face high switching costs. Enforcers should require market-specific evidence of those conditions rather than presume that they exist.

B. Section 78(1)(k) Requires Cautious Enforcement

Bill C-56 (2023) amended the Competition Act’s abuse-of-dominance provisions to identify the imposition of “excessive and unfair selling prices” as an anti-competitive act.[12] Yet the Act defines neither “excessive” nor “unfair,” creating significant interpretive uncertainty. High prices are not inherently exclusionary. They allocate scarce goods and signal opportunities for entry. Deciding that a price is excessive requires judgments about common-cost allocation, the value of intangible assets such as brands and intellectual property, and the appropriate return for risk. Competition authorities cannot make those judgments reliably, particularly for retailers that sell many products and platforms that serve several interdependent groups of users.

The European Union’s experience enforcing Article 102 of the Treaty on the Functioning of the European Union illustrates the problem.[13] In United Brands v. Commission, the European Court of Justice established a two-part test that asks whether the margin between price and cost is excessive and, if so, whether the price is unfair in itself or compared with competing products.[14] Despite that framework, the European Commission rarely brings excessive-pricing cases because measurement remains difficult and the risk of error remains high.[15]

Comparative studies likewise counsel intervention only in exceptional cases. Surveys of EU and national case law, along with Organisation for Economic Co-operation and Development (OECD) roundtables, show that these cases typically involve regulated or quasi-regulated sectors, legal privileges, or earlier exclusionary conduct. Authorities rarely challenge prices as exploitative without some related restraint on competition.[16]

David S. Evans and A. Jorge Padilla,[17] Massimo Motta and Alexandre de Streel,[18] and Lars-Hendrik Röller[19] similarly argue that excessive-pricing claims are most defensible when linked to exclusionary conduct or durable structural barriers that competition law can identify and address. Policing high prices in markets where entry remains feasible creates a serious risk of measurement error and turns competition authorities into price regulators—a role for which they lack both the tools and the institutional mandate.

The Bureau should therefore presume that pricing conduct benefits competition unless exclusionary conduct prevents rivals from entering or expanding. High prices alone should not create liability without evidence that they result from, or reinforce, conduct that substantially lessens competition. The 2025 expansion of private access to the Competition Tribunal heightens the risk by allowing more private parties to bring cases.[20] Canadian law also lacks a screen comparable to the U.S. antitrust-injury doctrine, which requires a private plaintiff to show that its injury flows from harm to competition rather than from competition itself.[21]

The practices covered by the Bureau’s six sub-questions do not have a common competitive effect and require individual assessment. Our response to Sub-Question 1 illustrates the point. Pricing algorithms may automate lawful market monitoring, or they may facilitate coordination by pooling confidential data. Loyalty discounts may expand output, or they may exclude rivals when combined with market power and substantial foreclosure. Sub-Questions 4 through 6 present similar trade-offs. Disclosure may reduce consumers’ search costs, but it may also help competitors detect departures from a coordinated price.[22]

The Bureau should ask what conduct occurred, how it changed the choices available to firms and consumers, and what the evidence shows about prices, output, quality, innovation, entry, and switching. The analysis should turn on the conduct’s mechanism and demonstrated effects.

II. Sub-Question 1: Which Pricing Practices Help, and Which Hurt, Competition

The Bureau asks which pricing practices, including loyalty programs and pricing algorithms, help or harm competition. Most strengthen competition by improving price discovery, helping supply respond to demand, reducing waste and shortages, and intensifying rivalry for consumers. Algorithms often automate lawful pricing decisions, dynamic pricing helps markets adjust to changing conditions, and personalized prices and loyalty discounts can expand output and deliver genuine savings.

Competitive harm arises through narrower, fact-specific mechanisms. Firms may coordinate through shared confidential data or a common pricing agent, while dominant firms may use exclusive loyalty terms to foreclose rivals. Existing competition law already reaches those cases. The Bureau should target evidence of agreement or exclusion, not price variation, software use, or loyalty programs themselves. Questions about notice, transparency, and data use may warrant consumer-protection scrutiny, but they do not by themselves establish harm to competition.

A. Algorithms Can Compete or Coordinate

Pricing algorithms usually automate tasks that human managers have long performed, including monitoring competitors’ public prices, tracking inventory, and estimating how demand responds to price. Airlines and hotels have used software to adjust fares and room rates for decades. Scale and speed have changed, but the underlying activity has not.

Maureen K. Ohlhausen, then acting chair of the U.S. Federal Trade Commission, captured this point with the “guy named Bob” test. If a pricing manager named Bob may lawfully review competitors’ public prices and adjust his firm’s prices, an algorithm may perform the same task.[23] Automating lawful conduct does not make it unlawful. Appendix A provides a fuller discussion of algorithmic pricing and competition.

The empirical evidence confirms that pricing algorithms can produce different effects in different markets. Zach Y. Brown and Alexander MacKay studied high-frequency price data for over-the-counter allergy medication sold online.[24] They found that retailers using algorithms to update prices within hours systematically charged lower prices than competitors that updated prices daily or weekly.

Sophie Calder-Wang and Gi Heung Kim studied rental housing and found that landlords using pricing algorithms reduced rents more quickly during demand downturns, including the Great Recession.[25] Although rents increased more quickly during expansions, the software improved occupancy and helped the market clear when demand weakened.

Stephanie Assad and her co-authors reached a different result in their prominent study of German retail-gasoline stations.[26] They found that algorithmic pricing increased average prices and margins by roughly 15%, but the effects varied sharply with market structure. Adoption had little effect in monopoly markets. In duopoly markets, where two stations competed and both adopted pricing software, margins rose by 38% relative to markets where neither station adopted.[27] The authors called the result “tacit collusion,” reasoning that the algorithms learned not to undercut because competitors would quickly match price cuts.[28]

The evidence, though, indicates conscious parallelism—firms independently making similar decisions while recognizing their mutual interdependence—rather than an agreement. The German stations did not communicate, signal conditional strategies, coordinate through software vendors, agree to use common software or pricing rules, or exchange competitively sensitive information. Each station independently adopted pricing software, and each algorithm independently learned that aggressive price cuts were unprofitable.[29] Similar tools responding to similar market conditions produced parallel outcomes without a meeting of the minds. As the Bureau’s proposed enforcement guidelines correctly recognize, conscious parallelism alone does not violate Canadian competition law.[30]

The 9th U.S. Circuit Court of Appeals applied this distinction in Gibson v. Cendyn. Competing hotels had independently entered vertical software-licensing agreements at different times. The plaintiffs alleged that the agreements collectively produced anti-competitive effects, even though “neither the terms nor the operation of the licensing agreements [were] alleged to have harmed competition by affecting the competitive incentives in the relevant market, nor did the licensing agreements restrain any party’s ability to compete.”[31] The court held that conclusory allegations could not survive even the pleading stage. Plaintiffs still had to allege “a restraint of trade that causes an actual adverse effect on competition.”[32]

The 3rd U.S. Circuit Court of Appeals’ 2026 decision in Cornish-Adebiyi v. Caesars marks the other side of the line.[33] The court reversed a dismissal because casino-hotel guests had plausibly alleged a horizontal price-fixing agreement under Section 1 of the Sherman Act. Competing hotels allegedly delegated room pricing to a shared algorithm, supplied it with real-time nonpublic pricing and occupancy data, and followed its recommendations roughly 90% of the time. The court held that staggered adoption over 14 years affected the weight of the evidence, not whether the allegations supported an inference of conspiracy.

The legal question is whether firms agreed to coordinate. Parallel price movements alone do not establish that consensus. The decisive allegation in Cornish-Adebiyi was that competitors pooled confidential, nonpublic data through a shared pricing agent—a mechanism capable of converting an ordinary competitive input into a coordination device.

B. Dynamic Pricing Balances Supply and Demand

A single fixed price can be too high during slack periods, leaving willing buyers unserved, and too low during peak periods, producing shortages and long waits. Dynamic pricing—prices that change as supply and demand change—can lower off-peak prices to encourage consumption and raise peak prices to allocate scarce supply to those who value it most.

The practice long predates modern pricing algorithms. Airlines have managed seat availability against forecast demand since Ken Littlewood developed his booking rule in 1972. By 1992, American Airlines estimated that its yield-management program, which adjusted fares and seat availability based on expected demand, had generated $1.4 billion in quantifiable benefits over three years.[34]

Evidence from airline markets shows that dynamic pricing can expand output and increase total welfare. Kevin R. Williams found that it increased output by 2.7% and total welfare by 1% relative to charging one price per flight. Dynamic fares could fall for early, price-sensitive travellers and adjust when demand surged. They also reduced stockouts. Under uniform pricing, 29.4% of flights sold out before departure, compared with 18.7% under dynamic pricing, and uniform-price flights sold out earlier.[35]

An efficiency gain does not mean every buyer pays less or receives the benefit. In Williams’ study, airline revenue increased by 7.6%, while total consumer surplus fell by 6.3% relative to uniform fares. Airlines captured much of the gain. Early-booking leisure travellers generally benefited, while late-booking business travellers paid more.

The evidence from ride-hailing is more favourable to consumers. Juan Camilo Castillo found that surge pricing in Houston’s Uber market increased total welfare by 2.15% of gross revenue, or roughly $0.25 per trip, relative to uniform pricing. Riders at every income level benefited through lower average prices, shorter pickup times, and more reliable trips, with low-income riders gaining the most.[36] The gains were not universal. Driver surplus fell by 0.98% of gross revenue, leading Castillo to conclude that drivers “might be right to complain.”

Andrew Sweeting documented the same basic logic in another market for a perishable product: Major League Baseball tickets, which lose all value once a game begins. Sellers reduced prices by 40% or more as game time approached, and the ability to reprice increased the average seller’s expected payoff by roughly 16%.[37]

Grocery shoppers encounter the same practice when stores mark down bakery items, meat, and produce approaching their sell-by dates. Grocery pricing remains much closer to uniform pricing than to surge pricing. Its principal dynamic elements—perishable-goods markdowns and discounts that respond to demand—help sell inventory that might otherwise spoil or remain unsold.

The relevant comparison is therefore not always a fixed price. It may be the nonprice rationing that replaces flexible pricing, including stockouts, queues, spoilage, and unused capacity. Time-varying prices are familiar even for regulated essential services. In the PowerCentsDC experiment, established through a settlement approved by the District of Columbia’s utility regulator, households facing dynamic electricity rates produced “stable, predictable, and sizable demand reductions” during critical peak periods.[38]

Price increases during periods of scarcity can still raise legitimate questions about transparency and notice. Those are consumer-protection questions about how firms disclose prices, and consumer-protection tools can address them. They do not establish that adjusting prices to market conditions harms competition.

Pricing practices that may harm competition operate through different mechanisms, including prices tied to a shopper’s identity and algorithmic coordination among rivals. We address the first in the next subsection and the second in Appendix A (ICLE comments to the Bureau on algorithmic pricing and competition).

C. Personalized Pricing Is Not Inherently Harmful

Personalized pricing means offering different prices to different customers based on data-driven estimates of their willingness to pay. A targeted discount to a price-sensitive shopper who would otherwise buy nothing expands output and can benefit both the seller and the shopper. Brian Albrecht shows that, under competition, firms minimize consumer prices when they can identify customers who view their products as close substitutes because that information intensifies competition for those customers.[39] An empirical study of platform personalization similarly found that 62.5% of consumers would benefit. Losses were concentrated among the least price-sensitive customers, and most of the aggregate consumer harm arose from the platform’s market power rather than personalization itself.[40]

Price differentials are also common in competitive markets, so their presence in grocery retail does not show that competition has failed. James Dana demonstrates that these discount patterns can arise among firms that earn no economic profit and lack power to set the market price. As he observes, price discrimination “is common in many industries that appear to be extremely competitive.”[41]

Empirical studies reach the same conclusion. Joanna Stavins found that airline discounts tied to ticket restrictions were largest on the least concentrated routes: $253 at the 25th percentile of route concentration, compared with $165 at the 75th percentile. She cautioned that her sample covered only 12 routes.[42] Using a much larger sample, Severin Borenstein and Nancy Rose described competition’s significant positive effect on price dispersion—the variation in prices paid by different customers—as their strongest result.[43]

The Bureau also has evidence from Canada. Using Air Canada fare data, Ambarish Chandra and Mara Lederman of the University of Toronto found that competition reshapes price differentials rather than eliminating them. Each additional rival reduced fares in the middle of the distribution by 7% to 8% while barely changing the cheapest and most expensive fares. Some price gaps therefore narrowed while others widened, making aggregate measures of price dispersion potentially uninformative about how competition affects prices.[44]

The price differentials that persisted under rivalry reflected brand loyalty, which raises questions about consumer switching rather than market power. Canadian grocery shoppers do switch. The average Canadian regularly shops at 2.3 grocery stores, and multihoming—shopping across multiple stores and retail formats—is the industry norm.[45]

Businesses also appear to use personalized pricing rarely and on a limited scale. The European Commission conducted a mystery-shopping study across 160 e-commerce websites in eight member states. It found no price difference in 94% of 34,403 matched product pairs. Among the remaining 6%, the median difference was less than 1.6%.[46] Reputational concerns help explain firms’ caution. The OECD observes that businesses may avoid personalized pricing to protect their reputations and risk stigma when they adopt it.[47] We address the broader “surveillance pricing” narrative, and the gap between technical capability and observed conduct, in Sub-Question 3.

The Instacart episode illustrates how observers can mistake randomized price testing for personalized pricing—and how that mistake can produce legislation. A December 2025 study by the Groundwork Collaborative and Consumer Reports found that shoppers sometimes saw different prices for the same item at the same store and time. For example, a dozen Lucerne eggs at a Washington, D.C., Safeway appeared at five prices ranging from $3.99 to $4.79.[48]

The study’s own regressions undermine the claim that Instacart profiled individual shoppers. Across 193 cleaned submissions, price differentials associated with shoppers’ Instacart histories and demographic characteristics—including age, income, gender, and race—were “small and not statistically meaningful.” The study found no evidence that Instacart based prices on income, ZIP code, shopping history, or other individual characteristics.

The observed pattern instead resembles A/B testing, in which a company randomly assigns customers to groups to compare their responses to different prices. A random process, not a customer profile, determines the price each shopper sees. Instacart’s explanation that it tested randomized cohorts based on product category and location fits the study’s data.[49] The vendor’s marketing materials establish that its technology can target individual shoppers, but capability does not establish use. As Albrecht concludes, the episode “is a story about something that might happen, not something that did happen.”

Labelling the practice “surveillance pricing” assumes what the evidence failed to establish. Even scholars offering cautionary accounts of surveillance pricing recognize “complex trade-offs between efficiency gains and consumer welfare,” rather than uniform harm.[50]

The legislative response shows the cost of that mistaken inference. Maryland enacted its Protection from Predatory Pricing Act in April 2026 after the Instacart narrative dominated legislative debate. The law prohibits personalized prices based on a consumer’s personal data but exempts loyalty programs, memberships, subscriptions, and prices offered to consumers who consent to provide personal data. It therefore does not reach the A/B testing that prompted the debate.[51] When Colorado’s governor vetoed a similar bill, he identified the problem directly: It “would punish differentially lower prices, not just higher prices.”[52]

D. Loyalty Programs Deliver Genuine, Measured Savings

The United Kingdom’s Competition and Markets Authority (CMA) conducted a rigorous recent review of grocery loyalty pricing. It analysed a year of daily prices covering roughly 30,000 of the 86,000 loyalty promotions offered during that period, encompassing about 50,000 products at five supermarkets. These dual-price promotions offered one price to loyalty members and another to nonmembers.[53]

The CMA found that 92% of the products offered members genuine savings against the usual price. It applied a concrete test: The nonmember price during the promotion had to be the same as, or lower than, the price available to all shoppers during the preceding 28 days. The CMA found “very limited evidence of price changes which could indicate that supermarkets may have inflated prices to make their loyalty prices appear misleadingly attractive.” Across the five retailers, average member savings ranged from 17% to 25% of the nonmember price.

The pattern most likely to raise concern—a nonmember price higher than the price charged both before and after the promotion—appeared in only 0.6% of the roughly 50,000 products analysed. Other promotions in the surrounding days explained many of those cases. The CMA concluded that the potentially concerning examples “do not represent a systemic problem or suggest retailers are trying to mislead shoppers.”

The review also tested the suspicion that supermarkets fund loyalty discounts by inflating their regular prices. The data did not support it. Loyalty membership is nearly universal in the United Kingdom: 97% of shoppers belong to at least one scheme, and members hold three memberships on average. Yet 55% of surveyed shoppers believed that nonmember prices during loyalty promotions exceeded the usual price. That figure rose to 71% among shoppers aged 18 to 29. The CMA found that pattern in only 0.6% of the products analysed—roughly six in every 1,000.

Cross-retailer comparisons produced similar results. Of the 429 branded products the CMA compared, the loyalty price was the cheapest or tied for the cheapest available price for 368, or 86%. When compared with another retailer’s nonpromotional price, 52% of loyalty prices were cheaper, usually by 11% to 26%. Shoppers should still compare prices across stores. For 61 products, or 14%, the cheapest available price was not a loyalty price. That caveat supports better cross-retailer comparison tools, which we address in Sub-Question 6. It does not make the case against loyalty pricing.

Loyalty pricing is a longstanding feature of competitive markets, not a novel algorithmic harm. Frequent-flyer programs were already distinguishing loyal customers from occasional travellers in the deregulated U.S. airline industry during the 1980s.[54]

The CMA identified two practical caveats. First, some supermarkets could do more to ensure that shoppers without smartphones and those younger than 18 can access loyalty prices or understand how to obtain them. That calls for better program design, not abandoning loyalty pricing. Second, the CMA found no consumer-law concerns arising from supermarkets’ collection and use of personal data. Only 7% of nonmembers cited data concerns as their reason for not joining. The regulator that examined loyalty data most closely found no actionable evidence.

Genuine competitive harm from loyalty programs takes a different form: A dominant firm may impose exclusive terms that foreclose rivals or raise switching costs without providing offsetting value. We address those circumstances in “The Narrow Harm Cases” below, including our amicus brief in the EpiPen litigation. That brief explains why a legal test that cannot distinguish procompetitive discounting from exclusion will chill the discounting itself.

E. Liability Should Turn on Agreement or Exclusion

Three fact patterns can produce genuine competitive harm. Each depends on evidence of agreement or exclusion, not the presence of software or a loyalty card.

First, competitors may coordinate through a common vendor whose algorithm prices each firm’s products using a pool of rivals’ competitively sensitive data. That conduct differs from each firm independently using its own information and publicly available data. It raises a conspiracy question under section 45 of the Competition Act, whose criminal prohibition targets “naked restraints”—agreements such as price fixing that do not support a legitimate collaboration—and, on the civil side, under section 90.1.[55]

The Bureau has already drawn the relevant line. Its Competitor Collaboration Guidelines state that, “[i]n general, the Bureau does not consider publicly available information to be competitively sensitive.” Its proposed 2025 enforcement guidelines identify the common-vendor concern more specifically. If a third-party developer’s “algorithm uses commercially sensitive information provided to the developer by other firms,” the arrangement “could lead to coordinated outcomes without any direct information sharing between industry participants.”[56]

Second, a hub-and-spoke arrangement may include a “rim”—a horizontal agreement among competing firms to adopt and follow a common vendor’s recommendations. The rim distinguishes a conspiracy from a series of independent vertical contracts between the vendor and each firm. This, too, raises a section 45 question. The Competitor Collaboration Guidelines ask whether the parties reached a “meeting of the minds.” Conscious parallelism alone does not establish an agreement, although “parallel conduct coupled with facilitating practices, such as sharing competitively sensitive information,” may prove one.[57]

Third, a dominant grocer may impose exclusive loyalty terms that foreclose rivals or raise switching costs without providing offsetting value. That conduct may constitute abuse of dominance under sections 78 and 79. The Act’s enumerated anti-competitive acts include inducing a supplier to refuse to sell to a rival to impede its entry or expansion, and selling below acquisition cost to discipline or eliminate a competitor. Section 79 also requires that the effect on competition “is not a result of superior competitive performance.”[58]

The Bureau’s proposed guidelines take the same balanced approach. They identify volume and loyalty discounts as possible means of inducing exclusivity but recognize that “[e]xclusive dealing is not necessarily anti-competitive.” The concern arises when exclusivity forecloses rivals by denying them the scale, inputs, or sales channels needed to compete.[59] In each case, the agreement or exclusion creates the potential harm. The software or loyalty card does not.

The Bureau’s guidance already distinguishes these cases from lawful conduct, and U.S. algorithmic-pricing decisions show that courts can administer the distinction. Those decisions do not bind Canadian tribunals, but they help identify the allegations that can make a claim actionable.

In Gibson v. Cendyn, in which ICLE participated as amicus curiae, the 9th U.S. Circuit Court of Appeals explained that competing hotels could violate Section 1 of the Sherman Act by agreeing among themselves to follow a third party’s pricing recommendations. The pleaded claim failed because the software did not share one hotel’s confidential information with competing licensees. The court noted that allegations of such sharing could change the analysis.[60]

The district court reached a similar initial result in Cornish-Adebiyi v. Caesars, but the 3rd U.S. Circuit Court of Appeals reversed in July 2026. The appellate court read the complaint as alleging that each casino hotel supplied current, nonpublic room-pricing and occupancy data to a common platform, which processed those data alongside competitors’ information. Taken with the complaint’s other allegations, that pooling sufficed to plead a conspiracy.

In re RealPage involved the same critical allegation: The vendor pooled clients’ commercially sensitive pricing and supply data before returning pricing recommendations.[61] The relevant questions here concerned the flow of confidential data and any agreement to follow the common recommendation. A firm does not enter into a conspiracy merely by independently adopting software that relies on its own information and public data.

The Act also protects low prices because competition law seeks to promote them. An effects-based order under section 79 requires proof that the effect on competition “is not a result of superior competitive performance.” A lower price achieved through scale or efficiency reflects superior competitive performance, not abuse.[62] The Bureau’s proposed guidelines state the presumption directly: “Low prices are usually a sign of vigorous competition, which the Act is designed to encourage.”[63]

As ICLE and allied scholars argued as amici in the EpiPen litigation, loyalty discounts and the lower prices they produce routinely benefit consumers. A legal test that cannot distinguish procompetitive discounting from exclusion will chill the discounting itself.[64] A workable test should instead require objective evidence of foreclosure, including the share of the market covered and the duration of the challenged terms. Every sales contract “forecloses” the sales it wins, while short-term commitments that customers can easily terminate give rivals recurring opportunities to compete for their business.[65] A test based on the size of the savings rather than the extent of foreclosure condemns the discount itself—the costliest false positive in a low-margin industry.

III. Sub-Question 2: Impact on Small and Medium-Sized Businesses

The Bureau asks how these pricing practices affect small and medium-sized enterprises (SMEs). Modern pricing technology generally strengthens their ability to compete. Widely available third-party tools give smaller firms pricing capabilities once limited to large incumbents, reduce barriers to entry, and support new business models.

The greater risk comes from overbroad regulation. Same-terms mandates would erase legitimate cost efficiencies and raise consumer prices, while disclosure and documentation requirements impose fixed costs that fall most heavily on independent grocers. The Bureau should preserve smaller firms’ access to competitive tools and avoid rules that favour businesses large enough to absorb the cost of compliance.

A. Algorithms Lower Barriers to Entry

Sophisticated pricing strategies once required extensive data collection, specialized analytics, and continuous monitoring. Large incumbents could afford those resources, giving them a pricing advantage over smaller rivals. Widely available third-party pricing services now give firms of any size access to tools once available only through an internal analytics department. New entrants and smaller firms can adopt advanced pricing techniques without developing them in-house.

These services narrow the pricing-capability gap. A startup can use the same revenue-management software as an industry leader, reducing the risk that it will misprice its products or leave revenue unrealized. Algorithms thus reduce large firms’ scale advantage in pricing by allowing smaller firms to purchase sophisticated analysis through a vendor. The OECD has recognized this benefit, noting that algorithms can “reduce barriers to entry by allowing smaller entrants to gain market insights or develop new disruptive products at lower cost.”[66]

Algorithms can also support entry through new business models. Uber and other ride-hailing platforms built dynamic pricing into their services. By raising prices when demand surged, the platforms attracted more drivers and helped match supply with demand in real time. This addressed the peak-period shortages common in traditional taxi markets and helped establish a new market for on-demand rides. Consumers can now find rides more reliably at almost any time.

Had regulators prohibited surge pricing from the outset as “price gouging,” the platform model might not have attracted enough drivers or proved viable. Algorithms also enable products such as real-time price-comparison services, personalized shopping assistants, and dynamic-discount apps. These tools increase transparency, help consumers find better offers, and create new ways for firms to compete.

B. Same-Terms Rules Would Raise Prices

Rules requiring suppliers to offer every buyer the same wholesale terms would eliminate cost advantages associated with large-format retail. Emek Basker’s review of Walmart’s role in grocery markets found that its grocery prices averaged about 10% less than competitors’ prices. A Walmart Supercenter’s entry typically prompted competing grocery stores to reduce prices by roughly 1% to 2% in the short run. Over four years, the expansion of superstores, mass merchandisers, and warehouse clubs led traditional supermarkets to reduce prices by an estimated 3%.[67] Walmart’s presence therefore lowers prices for its customers and for consumers who continue shopping at competing stores.[68]

Using household panel data, Jerry Hausman and Ephraim Leibtag similarly estimated that the expansion of large-format supercentres produced substantial consumer-welfare gains. Those gains disproportionately benefited low-income households, which shop at price-sensitive formats more often than higher-income households.[69] A same-terms rule that erodes large retailers’ cost advantages would therefore raise prices most for shoppers with the fewest alternatives.[70]

Grocery prices already display substantial uniformity across stores. A same-terms rule would entrench that condition rather than correct it. Stefano DellaVigna and Matthew Gentzkow found that most large U.S. food, drugstore, and mass-merchandise chains charged nearly uniform prices across locations despite wide differences in local demographics and competition. The median chain sacrificed an estimated $16 million in annual profit by doing so.[71]

Uniform pricing is not the consumer-friendly benchmark it may appear to be. DellaVigna and Gentzkow’s model indicated that uniform pricing raised prices in the poorest decile of ZIP codes by 10% relative to the richest decile. Alberto Cavallo found similar uniformity across sales channels. Among 56 large multichannel retailers in 10 countries, online and offline prices were identical about 72% of the time overall and 52% of the time in food retail. Across Canadian sectors, they matched more than 90% of the time.[72] Requiring still greater uniformity would eliminate the price variation that most benefits low-income shoppers.

C. Compliance Costs Disadvantage Small Grocers

Disclosure and documentation rules designed for national platforms would burden small grocers disproportionately because compliance costs do not fall with transaction volume. Requirements for transaction-specific cost records or custom disclosure formats impose fixed costs that a large chain can spread across billions of dollars in sales but a small independent grocer cannot. Our comments to the U.S. Federal Trade Commission on online food-delivery fees examine this asymmetry in detail. They explain that the burden falls most heavily on small first-party operators—businesses that run their own delivery websites.[73]

IV. Sub-Question 3: Access to Data and Technology

The Bureau asks whether access to data and technology affects firms’ ability to adopt modern pricing practices and compete. In grocery retail, both inputs are widely available at modest cost. Third-party services give smaller firms sophisticated pricing capabilities, while grocery data depreciate quickly, lack strong network effects, and remain available from multiple commercial sources. Common software also does not imply pooled data. The current record does not show that data or pricing technology constitute scarce assets that foreclose rivals or entrench incumbents.

Regulatory intervention may create the greater competitive risk. Mandated data sharing and disclosure of confidential terms can facilitate coordination, weaken incentives to collect and analyse data, and impose disproportionate compliance costs on smaller firms. Claims that retailers use personal data to charge each shopper the most they will pay likewise require evidence, not inferences drawn from randomized price testing. The Bureau should distinguish firms’ use of their own or public data from the pooling of competitors’ confidential information, and leave genuine privacy concerns to privacy and data-protection law.

A. Common Algorithms Do Not Necessarily Pool Data

In its parallel consultation on algorithmic pricing, the Bureau observed that “[u]sing common pricing algorithms or pooling data among competitors may raise issues under the Competition Act.”[74] That concern is valid in principle but narrow in practice. The risk arises when third-party software ingests one firm’s confidential data and transmits it to a competitor, either directly or through recommendations based on pooled inputs. The common algorithm then becomes a conduit for competitively sensitive information.

Ordinary pricing software does not operate this way. Firms may independently use similar or even identical programs without pooling any data. A pricing algorithm, whether developed in-house or licensed from a vendor, generally relies on two types of information. The first consists of the firm’s commercially sensitive data, including sales volumes, inventory, and costs. Vendors can process that information while keeping it segregated from rivals. The second consists of lawfully collected public information, such as advertised prices and general market trends.

A hotel using revenue-management software, for example, may receive analysis of its own booking data, demand trends, and competitors’ advertised rates from public travel websites. It does not ordinarily receive a direct feed of a rival’s proprietary booking data. Reputable vendors design their systems to prevent the interfirm transfer of sensitive information because sharing it would expose their clients to liability.[75]

Monitoring competitors’ public prices is legitimate competitive conduct, whether performed manually or by software. Automation does not convert lawful information gathering into unlawful coordination. The Bureau’s Competitor Collaboration Guidelines generally treat public information as unproblematic and reserve concern for exchanges of competitively sensitive, nonpublic data.[76] They also distinguish direct exchanges between competitors, which present greater risk, from aggregated data supplied through an independent third party.

The controlling question is whether firms have moved beyond information gathering and agreed to coordinate their competitive decisions. Common analytical tools, similar market intelligence, and parallel outcomes do not establish such an agreement. An enforcer must still prove one.

Access to pricing data therefore does not ordinarily mean access to a rival’s secrets. It means access to a firm’s own records and publicly available market information. A policy that assumes algorithmic pricing necessarily pools competitors’ data would mischaracterize the technology and discourage lawful monitoring that helps discipline prices.

B. Pricing Technology Broadens Access

If access to technology affects firms’ ability to compete, pricing technology has broadened that access. This is the access dimension of the argument developed in Sub-Question 2, and it bears directly on the Bureau’s inquiry.

Sophisticated pricing once required scale. Firms had to collect data, acquire analytics expertise, and monitor markets continuously. Only large incumbents could justify those fixed investments, giving them a durable advantage over smaller rivals. Third-party pricing services have sharply reduced that advantage. They give a new entrant or small independent access to the revenue-management capabilities of an industry leader without requiring an in-house analytics department.

In economic terms, these services reduce the pricing advantages associated with firm size. A small firm can obtain sophisticated market analysis through a vendor’s platform. The OECD has recognized this benefit, observing that algorithms can lower barriers to entry by helping smaller firms gain market insights and develop new products at lower cost.[77]

Adoption data indicate broad diffusion rather than concentration. A 2020 survey by the Netherlands Authority for Consumers and Markets found that 80% of firms using pricing algorithms had developed them internally, while 20% relied on third-party providers.[78] In 2017, the European Commission found that 49% of European retailers monitored competitors’ prices. Of those retailers, 66.6% used price-monitoring software, meaning roughly one-third of all retailers used some form of algorithmic monitoring.[79] Among firms using monitoring software, 78% changed prices in response to competitors’ moves, and 35% used specialized algorithms to adjust prices automatically.[80] Surveys elsewhere in Europe show similar adoption patterns.[81]

Accessible pricing technology has also enabled new competitors and business models, including dynamic-pricing platforms, real-time price-comparison services, and discount apps. These products increase transparency and help consumers find lower prices. The competitive pressure they place on incumbents—often over incumbents’ objections—shows that the market remains open to entry.

The principal access risk comes from regulation itself. Compliance does not scale down. Disclosure, documentation, and audit requirements impose fixed costs that a national chain can spread across billions of dollars in sales but a small grocer cannot. Rules designed for the largest platforms could rebuild the entry barriers that accessible technology has lowered. We address this asymmetry in Sub-Question 2 and in our comments to the U.S. Federal Trade Commission on online food-delivery fees.[82] The Bureau should preserve pricing technology’s equalizing effect by avoiding compliance mandates that fall hardest on the smallest firms.

C. Grocery Data Does Not Create an Entry Barrier

A recurring theory treats large grocers’ accumulated transaction and loyalty data as a barrier to entry. On this account, incumbents use a self-reinforcing data advantage to price and target customers in ways that rivals cannot match. That theory fits grocery retail poorly for three reasons. The Bureau should require evidence of the mechanism rather than presume it.

First, grocery transaction data depreciate quickly and produce diminishing returns. Prices, promotions, product assortments, and consumer preferences change continuously. Each additional historical observation therefore adds less value, while stale data offer little predictive insight. A firm does not need every household’s complete purchase history to price competitively. It needs a representative and reasonably current picture of the market, which costs far less to obtain than the incumbent-scale archive assumed by the barrier theory.

Second, grocery retail lacks the demand-side network effects that can entrench data-driven platforms. Such effects arise when each additional user makes a service more valuable to other users. One shopper’s choice of a grocery chain does not make that chain more valuable to the next shopper. Grocery data therefore do not create a self-reinforcing loop that locks consumers into one retailer and denies rivals the scale needed to compete. Data accumulate, but they do not produce the winner-take-most dynamic found in markets where an entry-barrier theory is more plausible.

Third, the relevant data are neither exclusive nor unavailable to entrants. Consumers shop across supermarkets, supercentres, warehouse clubs, discount grocers, dollar stores, and online retailers. No single retail banner can see a household’s entire grocery basket.[83] Competition therefore spans formats well beyond the local supermarket.

Entrants can also purchase comparable market intelligence. Commercial sources offer scanner data, consumer-panel data, third-party analytics, and audience information. An entrant can buy much of the information that an incumbent collects internally. Data that are nonexclusive, rapidly depreciating, and available from multiple suppliers do not constitute an essential facility—an indispensable input that rivals cannot reasonably obtain elsewhere. Treating them as one would risk the durable false positive that the error-cost framework counsels against.

This point bears only on the Bureau’s data-access question. It is not a merger analysis. Our Kroger/Albertsons work addresses merger-specific concerns on their own terms.[84] The narrower conclusion here is that the current record does not show that access to grocery data creates a barrier requiring intervention.

D. Competitor Transparency Can Facilitate Coordination

The sub-question focuses on whether some firms have too little access to data. A more serious risk could arise from rules that compel firms to share data or disclose confidential terms in the name of equal access or “transparency.” The Bureau should distinguish consumer-facing transparency from competitor-facing transparency.

Consumer-facing transparency—including clear final prices, unit prices, and legible loyalty terms—reduces search costs and helps shoppers switch. Competitor-facing transparency can have the opposite effect. Mandating disclosure of supplier contracts, rebates, promotional allowances, or forward-looking pricing information can help rivals detect and punish departures from a coordinated price.

Tacit coordination rarely breaks down because firms cannot identify a common price. It breaks down because secret discounting makes deviations difficult to detect and punish. Anonymising disclosed terms does not eliminate that risk in a market with only a few large buyers. Volumes, shipping patterns, and timing may reveal the parties’ identities, and modern analytical tools make reidentification easier. Recent research shows that large language models (LLMs) can reidentify individuals or entities in ostensibly anonymised text by extracting identifying features and matching them with external data.[85]

The risk increases when disclosure rules cover only a small number of retailers. Even if supplier contracts omit retailers’ names, market participants may infer which chain received which terms. Disclosure could then make prices more uniform and higher. We develop this mechanism, including its interaction with statutory most-favoured-nation and same-terms rules, in Sub-Question 6.[86]

The same caution applies to data-sharing and data-portability mandates intended to “level” competitors’ access. Beyond facilitating coordination, compelled sharing weakens incentives to collect and analyse data. It may therefore degrade the very capability the mandate seeks to spread. Any such rule would also rest on the unsupported theory that grocery data create a barrier to entry, discussed in the preceding subsection. It would impose immediate, durable costs to address a harm that the evidence has not established—a strong case for error-cost restraint.

E. Personalized-Pricing Fears Outrun the Evidence

Much of the concern about data access rests on an unsupported narrative: Retailers use consumers’ personal data to charge each shopper the most they will pay. The episode that drove this narrative shows how misreading a study can produce poorly targeted law.

In December 2025, the Groundwork Collaborative and Consumer Reports published a study showing that Instacart shoppers sometimes saw different prices for the same item at the same store and time.[87] The study expressly found no evidence that Instacart based prices on individual characteristics such as income, ZIP code, or purchase history. The observed pattern instead matched routine A/B testing, in which random assignment determines the price a shopper sees.[88]

Unlike personalized pricing, A/B testing does not estimate what a particular shopper will pay. It assigns prices across randomized groups to learn about demand without relying on the shopper’s identity. Yet observers recast the study’s unexplained price variation as evidence of personal-data profiling.

The legislative response extended far beyond that finding. Lawmakers have introduced more than 40 bills addressing algorithmic or “surveillance” pricing across at least two dozen U.S. states, and Maryland enacted the first statute of its kind.[89] That law illustrates the risks of mismatched drafting.[90]

The statute’s title invokes predatory pricing—a settled term for below-cost pricing intended to exclude rivals—but its operative provisions address an unrelated practice by prohibiting prices personalized with consumers’ data. The law then exempts loyalty programs, memberships, subscriptions, and prices offered with consumer consent. It does not reach the A/B testing that prompted the debate. The triggering study did not find the practice the law targets, the statute’s title describes a different practice, and its exemptions sharply limit its reach. That is legislation shaped by alarm rather than measured effects.

Two facts should constrain any similar response in Canada. First, personalized pricing remains uncommon and modest in scale. The European Commission’s mystery-shopping exercise found price differences in only about 6% of matched tests, with a median difference below 1.6%. Those differences concentrated in industries such as airlines and hotels, where price discrimination is well established.[91]

Second, concerns about how businesses handle personal information fall primarily within privacy and data-protection law. Treating privacy concerns as competition problems produces rules poorly suited to either task. The Bureau should require evidence that a data practice harms competition or consumers before intervening and leave genuine privacy questions to the legal framework designed to address them.

V. Sub-Question 4: Price Comparison and Consumer Switching

The Bureau asks how the pricing practices at issue affect consumers’ ability to compare prices and value across retailers and their willingness to switch. Canadian shoppers actively compare offers, shop across multiple formats, and respond to price. Digital tools have reduced the time and effort required to compare retailers, while the pronounced shift toward discount stores provides direct evidence of price-driven switching. Loyalty pricing, dynamic pricing, and personalized pricing can make some comparisons more complex, but the evidence does not show that these practices suppress switching. Loyalty programs also lower effective prices and can intensify competition for shoppers.

The proportionate response is consumer-facing disclosure, including harmonized unit pricing, clear final prices, and legible loyalty terms, as discussed in Sub-Question 6. Canada’s concentrated grocery market and limited independent hard-discount competition raise separate concerns because major grocers own many discount banners. Those are supply-side questions about entry and expansion, which the Bureau’s 2023 market study addressed through structural recommendations. They do not establish that pricing practices prevent consumers from comparing prices or switching retailers.

A. Canadian Shoppers Compare Prices and Switch

Most Canadians buy groceries from five companies—Loblaws, Sobeys parent Empire, Metro, Costco, and Walmart—which together account for roughly two-thirds or more of national grocery sales.[92] The Bureau has found that concentration has increased in recent years[93] and that Canada has little independent hard-discount competition.[94] he degree of concentration remains contested. The Retail Council of Canada argues that Canada is not concentrated relative to peer countries, citing the rising shares of Costco and Walmart and the large majority of Canadians who regularly shop at discount retailers.[95]

When Canadians trade down to a discount grocer, they often remain within the same corporate family. Industry observers describe discount banners as a way to keep deal-seeking customers “within [the] estate” rather than lose them to a rival.[96] Still, roughly 6,900 independent grocers operate across Canada, and larger regional chains compete directly with national firms in some markets. The Bureau has found, however, that independents face significant barriers to becoming national competitors.[97]

Digital tools have reduced the cost of comparing prices. Weekly flyers, retailer apps, online storefronts, and third-party comparison services allow shoppers to survey competing offers before leaving home. That visibility disciplines attempts to price above competitive levels because consumers who see better alternatives can act on them.

Canadians regularly use that capacity. The Bureau’s survey found that 79% visit multiple grocery stores to find the best price at least sometimes. That figure includes 41% who do so sometimes, 21% often, and 17% very often.[98]

The clearest evidence of price-driven switching is the shift toward discount stores. The discount channel’s market share rose from roughly 35% in 2008 to near parity with conventional stores. Discount sales continue to grow faster than conventional sales by both dollars and volume.[99]

Online shopping provides another low-friction channel for comparison. About 30% of Canadians used at least one online option to buy groceries during a recent three-month period. That included 17% who used third-party delivery, 12% who used curbside pickup, and 9% who used home delivery.[100] Switching costs in these closely related channels can approach zero, as shoppers can open a competing app and compare offers side by side within seconds.

Individualized or conditional prices and tiered loyalty offers can complicate the comparisons made easy by flyers and shelf tags. The proportionate response is to improve comparison through clear final-price disclosure and, above all, unit pricing, which allows shoppers to compare prices per standard unit. This approach follows Recommendation 3 of the Bureau’s 2023 study, prompted by its finding that consumers have difficulty comparing even a few items across grocery stores.[101]

B. Loyalty Programs Can Strengthen Price Competition

Loyalty programs play a central role in Canadian grocery competition. About 75% of Canadians hold a loyalty or points card, and 61% say they are more likely to shop where they can earn rewards.[102] These programs affect switching in both directions. Points, targeted offers, and rewards earned across banners within the same corporate group make staying with one program more convenient, creating some switching friction. At the same time, they lower members’ effective prices. The Bureau has recognized that loyalty programs “can bring about lower prices for consumers” and “may also drive grocery stores to compete harder” for customers.[103]

The CMA’s 2024 loyalty-pricing review provides the most rigorous recent evidence. Of roughly 50,000 products offered through dual-price loyalty promotions, 92% delivered genuine savings. Average member savings ranged from 17% to 25%, and the CMA found very limited evidence that retailers inflated ordinary prices to make loyalty offers appear more attractive.[104]

Canadian consumers continue to compare prices and switch retailers. The pricing practices at issue have not prevented either behaviour. Switching friction alone does not establish competitive harm without evidence of deception, lock-in, exclusion, or market power. Restricting loyalty, dynamic, or personalized pricing based on an unsupported switching-harm theory would weaken the price competition that Canadians are driving through their shift toward discount retailers.

The Bureau should instead support consumer-facing tools that directly reduce comparison costs, including harmonized unit pricing, clear final prices, and legible loyalty terms. A wider range of competitors will strengthen comparison and switching more durably. Those competitors should remain free to use pricing tools that help them compete.

VI. Sub-Question 5: Shrinkflation and Skimpflation

The Bureau asks how shrinkflation and skimpflation affect consumers’ ability to compare price and value, and whether they influence purchasing and switching. Shrinkflation reduces a product’s quantity while holding its shelf price steady. Skimpflation reduces quality or changes a product’s formulation to cut costs. Both are rational nonprice responses to rising input costs, and both occur in competitive and concentrated markets. Neither practice alone reveals the level of competition.

The principal competition concern is comparability. Shrinkflation can succeed because consumers notice price changes more readily than changes in package size. Standardized unit pricing addresses that problem more directly than controls on product design.

Aljoscha Janssen and Johannes Kasinger provide the most comprehensive evidence to date. They analysed a decade of U.S. store-level scanner data and found that downsizing was more than five times as common as upsizing on a sales-weighted basis. Size reductions “usually occur with little or no accompanying price changes,” while the price per unit of volume averaged roughly 12% more during the year after a reduction.[105] A shopper can accurately read every posted price and still miss that a product has become more expensive per gram.

That evidence does not prove market power or “gouging.” Resizing is a predictable response to rising marginal costs, while broad increases in the price level chiefly reflect macroeconomic forces rather than the conduct of one firm. The evidence also does not establish the motive for any particular reduction. Janssen and Kasinger describe downsizing as a way for firms “to increase margins or respond to cost pressures.”[106] The comparability problem, and its appropriate remedy, remain the same in either case.

Existing Canadian cartel law addresses agreements among competitors to reduce package size or quality.[107] The best-documented shrinkflation case involved the three largest U.S. canned-tuna producers, which agreed to reduce the standard can size from six ounces to five ounces beginning in 2008. The U.S. Department of Justice brought criminal charges. Two of the three producers had pleaded guilty by 2018, and a chief executive received a prison sentence.[108]

Skimpflation presents the same comparison problem but is even less visible. Consumers may struggle to detect a quality change at the shelf, and unit pricing cannot capture it. The systematic evidence remains thin. The term arose in popular reporting about declining service quality, and grocery products have no evidence base comparable to the shrinkflation literature.

Existing safeguards address the clearest harms. Deceptive-marketing law reaches false or misleading claims about quality and net quantity, while repeat purchasing disciplines quality in products consumers buy weekly. Treating every reformulation as suspect would instead subject routine cost-saving product changes and genuine quality improvements to regulatory review.

The remedy should match the conduct: standardized unit pricing for hard-to-see quantity changes, deceptive-marketing enforcement for false claims, and cartel law for agreements among competitors. Product-design controls would address none of those problems as precisely.

VII. Sub-Question 6: Measures to Improve Transparency and Competition

The Bureau asks which measures could improve transparency and competition in retail pricing. The answer depends on the audience. Consumer-facing transparency helps shoppers compare offers and switch retailers. Competitor-facing transparency may expose confidential terms, discourage selective discounts, and facilitate coordination.

We recommend harmonized unit pricing, clear final prices, and legible loyalty terms. The Bureau should reject same-terms mandates, compelled disclosure of confidential contracts, and price or margin controls. Enforcement should target demonstrated agreements, deception, and exclusion while preserving lawful discounting and independent pricing. The most durable reforms operate on the supply side by reducing barriers to entry and expansion, strengthening independent wholesale infrastructure, and widening the range of grocers available to Canadian consumers.

A. Adopt Harmonized Unit Pricing

Our lead recommendation is accessible, harmonized unit pricing across every province and territory. Unit pricing displays the price per standard unit of measure, allowing shoppers to compare a 500-gram package with a 750-gram package at a glance. It addresses loyalty tiers, shrinkflation, package-size differences, and in-store and online comparisons without regulating prices.

This approach also follows the Bureau’s 2023 recommendation. The Bureau noted that only Quebec legally requires unit pricing and urged provincial and territorial governments to develop accessible, harmonized requirements.[109]

The rules should establish standard units for each product category and apply in stores, online, and in advertising. Retailers should display unit prices accurately, legibly, and close to the relevant item. Very small retailers could receive phased implementation or simplified compliance requirements. Unit pricing requires no disclosure of wholesale terms, margins, or supplier contracts. It provides transparency to shoppers without revealing competitively sensitive information to rivals.

Two additional consumer-facing measures follow the same principle. First, retailers should clearly disclose final prices and truthfully present discounts and reference prices—the prices used to frame an advertised saving. This would target misleading “was/now” offers and false scarcity claims without restricting dynamic pricing.

Second, retailers should present loyalty-program terms in plain language. Disclosures should compare the member price with the shelf price and clearly explain points’ redemption value, expiry dates, and material exclusions. We discuss those requirements further in Sub-Question 4.

B. Reject Same-Terms and Price-Control Mandates

The Bureau should reject same-terms and wholesale price-parity mandates. New York Senate Bill 8563, the proposed “Consumer Grocery Pricing Fairness Act,” offers a recent example. It would have required large suppliers to offer identical terms to retailers purchasing the same volume, capped at one truckload, and to disclose anonymized terms upon request.[110]

Such a requirement operates as a statutory most-favoured-nation clause. A most-favoured-nation clause promises one buyer terms at least as favourable as those offered to another. A privately negotiated clause limits a supplier’s ability to offer selective discounts after making that promise. A statutory mandate goes much further by imposing the obligation across the market. If a supplier must extend every discount to all similarly situated buyers, it has less incentive to discount at all. Suppliers will instead tend toward a uniform, higher wholesale price.[111]

The U.S. experience supports this concern. The Robinson-Patman Act discouraged discounting for decades by exposing sellers to litigation over price differences. In 2007, the bipartisan Antitrust Modernization Commission concluded that the law’s chilling effect on discounts likely raised consumer prices above the levels that would otherwise have prevailed.[112]

Supporters of same-terms rules often invoke the “waterbed effect,” under which a discount to a large buyer supposedly forces the supplier to charge smaller buyers more. Roman Inderst and Tommaso Valletti developed the leading model and derived a sufficient condition under which this effect would harm consumers.[113] Recent research questions whether their model supports that conclusion.[114]

Brian Albrecht shows that the condition for consumer harm is never satisfied in any equilibrium generated by Inderst and Valletti’s own model. Their analysis therefore identifies no equilibrium in which the waterbed effect harms consumers. Consumer surplus instead rises at the margin even when the smaller rival pays and charges more.

Empirically, the U.K. Competition Commission found little evidence of a systemic waterbed effect in grocery mergers, and courts have treated the theory skeptically, as in DeHoog v. Anheuser-Busch InBev, where the court noted that antitrust law protects the squeezed supplier, not the large buyer’s competitors.[115]

Any statutory cost-justification defence would offer little practical protection. A supplier would have to determine what portion of each discount reflected cost savings and maintain a continuous, transaction-level record to prove it. Those compliance costs would ultimately pass through to retail prices.[116]

Compelled disclosure of contract terms creates an additional coordination risk. George Stigler showed that the central obstacle to tacit coordination is detecting deviations from a common price. Publishing even anonymized terms lowers that obstacle in a market with only a dozen large buyers. Volumes, shipping patterns, and other details may reveal the parties’ identities, and modern LLMs make reidentification easier.[117] Once every buyer can demand a supplier’s lowest disclosed price, that price becomes a floor rather than a selective discount.

For the same reasons, the Bureau should avoid fee caps, margin caps, and cost-based pricing mandates. Its 2023 study recognizes that the Bureau cannot regulate grocery prices and that price controls are blunt tools because authorities struggle to identify the right price and adjust it as conditions change.[118] The food-delivery market illustrates the risk. A de facto cap can suppress the price signals needed to sustain service in high-cost areas and prompt providers to withdraw from them.[119]

C. Target Demonstrated Competitive Harm

The Bureau should enforce vigorously against conduct shown to cause harm. That includes price-fixing agreements, hub-and-spoke information sharing supported by a horizontal agreement among competitors, exchanges of confidential or forward-looking pricing data, exclusionary conduct that prevents entry or expansion, deceptive discounts or loyalty terms, hidden mandatory fees, and false claims about quantity or quality.

The Bureau should not presume liability from high prices, parallel price movements, ordinary monitoring of public prices, above-cost discounts, nonexclusionary loyalty programs, or algorithmic pricing without evidence of agreement or likely competitive harm. Its existing Competitor Collaboration Guidelines already provide a workable distinction between publicly available information and competitively sensitive information. That distinction should govern here.[120]

D. Promote Entry and Expansion

The durable way to discipline grocery prices is through greater entry and expansion. The most effective tools operate on the supply side: zoning and land-use reform, limits on restrictive property covenants, stronger wholesale and distribution infrastructure for independents, fewer interprovincial trade barriers, and support for independent, international, discount, and online grocers. The Bureau’s 2023 market study recommends several of these measures, including support for international and discount entrants and limits on property controls that impede competition.[121]

Wholesale-price regulation poorly addresses the food-access problem it often seeks to solve. U.S. evidence illustrates the mismatch. Hunt Allcott and his co-authors estimate that equalizing access and prices between low- and high-income households would close only about 10% of the gap in nutritional quality. Differences in demand account for the remaining 90%.[122] Zoning reform, targeted incentives, and transit investments address barriers to grocery access more directly than rules governing supplier prices.

VIII. Conclusion

The retail-pricing positions above follow the framework ICLE has applied across its submissions to the Bureau. Our algorithmic-pricing comments urged effects-based analysis rather than categorical rules. Our comments on the draft conduct guidelines emphasized asymmetric error costs, the uncertainty created by the excessive-pricing provision, and the need for workable market-power screens. Our merger comments treated structural presumptions as screening tools rather than verdicts. The common principles are demonstrated consumer harm, administrable standards, and recognition that false positives can prove especially costly in a low-margin industry.

The evidence reviewed here points in the same direction. Pricing algorithms usually automate lawful conduct. Dynamic pricing can expand output and reduce shortages, while personalized and loyalty pricing can lower effective prices and intensify competition. Competitive harm arises through narrower mechanisms, including agreements to pool confidential data, coordinated pricing, deception, and exclusion. High prices, parallel price movements, common software, or price differences alone do not establish that harm.

The Bureau can promote transparency without becoming a price regulator. Consumer-facing measures such as harmonized unit pricing, clear final prices, and legible loyalty terms help shoppers compare offers and switch retailers. They also address the comparability problems created by shrinkflation and complex pricing. Competitor-facing disclosure can instead expose sensitive terms, facilitate coordination, weaken investment incentives, and burden smaller firms disproportionately. Same-terms mandates, categorical presumptions against algorithmic pricing, and fee or margin caps carry similar risks.

Canadian consumers already compare prices, shop across formats, and switch in response to better offers. The more durable challenge lies on the supply side. Reforms that ease entry and expansion, limit restrictive property controls, improve wholesale access for independents, reduce interprovincial trade barriers, and support new retail formats will strengthen competition more effectively than controls on pricing practices.

The Bureau can best protect Canadian shoppers by making comparison easier, entry more feasible, and demonstrated coordination or exclusion riskier. We thank the Bureau for the opportunity to comment and welcome the chance to provide further analysis on any issue raised here.

[1] Competition Bureau Can., Notice: Behind the Price Tag: Examining the Food Supply Chain (June 16, 2026), https://competition-bureau.canada.ca/en/how-we-foster-competition/notice-behind-price-tag-examining-food-supply-chain [hereinafter Notice].

[2] Geoffrey A. Manne et al., Comments on Canadian Competition Bureau Discussion Paper on Algorithmic Pricing and Competition (Int’l Ctr. for Law & Econ., Aug. 4, 2025), https://laweconcenter.org/wp-content/uploads/2025/08/Canada-Algorithmic-pricing-and-competition-1.pdf (included as Appendix A).

[3] Ian Adams et al., Comments of the International Center for Law & Economics to the Competition Bureau Canada: Anti-Competitive Conduct and Agreements (Jan. 27, 2026), https://laweconcenter.org/wp-content/uploads/2026/01/Competition-Bureau-Canada-Comments-2026.pdf.

[4] Ian Adams et al., Comments of the International Center for Law & Economics to the Competition Bureau Canada: Proposed Merger Enforcement Guidelines (Feb. 10, 2026), https://laweconcenter.org/wp-content/uploads/2026/02/Competition-Bureau-Canada-Merger-Comments-2026.pdf.

[5] Geoffrey A. Manne et al., Comments from the International Center for Law and Economics on the Future of Competition Policy in Canada (Mar. 31, 2023), https://laweconcenter.org/wp-content/uploads/2023/03/ICLE-Canada-Comments.pdf.

[6] Brian C. Albrecht et al., Food-Retail Competition, Antitrust Law, and the Kroger/Albertsons Merger (Int’l Ctr. for Law & Econ., White Paper No. 2023-10-17, 2023), https://laweconcenter.org/wp-content/uploads/2023/10/Kroger-Albertsons-Merger-Full-Paper.pdf.

[7] Eric Fruits, The Waterbed Effect and the Price of “Pricing Fairness” (Int’l Ctr. for Law & Econ., Issue Brief No. 2026-03-17, 2026), https://laweconcenter.org/wp-content/uploads/2026/03/Grocery-Pricing-Legislation-2026.pdf.

[8] Brian Albrecht et al., Comments of the International Center for Law & Economics: Rule on Unfair or Deceptive Fees in Online Food Delivery Services, ANPRM, Federal Trade Commission Project No. P267101 (May 18, 2026), https://laweconcenter.org/wp-content/uploads/2026/05/FTC-Delivery-Fees-2026.pdf.

[9] Stat. Can., Consumer Price Index, February 2026 (Mar. 16, 2026), https://www150.statcan.gc.ca/n1/daily-quotidien/260316/dq260316a-eng.htm (reporting that grocery prices rose 30.1% from February 2021 to February 2026).

[10] Competition Bureau Can., Canada Needs More Grocery Competition: Retail Grocery Market Study Report 44 (2023), https://competition-bureau.canada.ca/sites/default/files/attachments/2023/CB-Retail-Grocery-Market-Study-Report-EN-2023-06-23.pdf.

[11] See Frank H. Easterbrook, The Limits of Antitrust, 63 Tex. L. Rev. 1 (1984).

[12] Competition Act, R.S.C. 1985, c C-34, § 78(1)(k) (Can.). See also An Act to Amend the Excise Tax Act and the Competition Act, S.C. 2023, c 31, pt 2 (Can.) [hereinafter Bill C-56].

[13] Consolidated Version of the Treaty on the Functioning of the European Union art. 102, May 9, 2008, 2008 O.J. (C 115) 47.

[14] Case 27/76, United Brands Co. v. Comm’n, ECLI:EU:C:1978:22, ¶ 252 (Feb. 14, 1978) (“The questions therefore to be determined are whether the difference between the costs actually incurred and the price actually charged is excessive, and, [if] the answer to this question is in the affirmative, whether a price has been imposed which is either unfair in itself or when compared to competing products.”).

[15] See, e.g., Aline Blankertz et al., The European Commission Can and Must Act on Excessive Pricing, ProMarket (Oct. 8, 2025), https://www.promarket.org/2025/10/08/the-european-commission-can-and-must-act-on-excessive-pricing.

[16] See Org. for Econ. Coop. & Dev., Excessive Prices, OECD Doc. DAF/COMP(2011)18 (Feb. 7, 2012), https://www.oecd.org/content/dam/oecd/en/publications/reports/2012/02/excessive-prices_372e092c/8e1fd82e-en.pdf; Raphaël De Coninck, Excessive Prices: An Overview of EU and National Case Law, Concurrences e-Competitions Bull. (June 2018), https://ecp.crai.com/wp-content/uploads/2018/06/Excessive-Pricing-R.-de-Coninck-e-Competitions-2018.pdf.

[17] David S. Evans & A. Jorge Padilla, Excessive Prices: Using Economics to Define Administrable Legal Rules, 1 J. Competition L. & Econ. 97 (2005).

[18] Massimo Motta & Alexandre de Streel, Excessive Pricing and Price Squeeze Under EU Law, in The Pros and Cons of High Prices 14 (Konkurrensverket ed., 2007), https://www.konkurrensverket.se/globalassets/dokument/informationsmaterial/rapporter-och-broschyrer/pros-and-cons/rapport_pros-and-cons_2007_high_prices.pdf.

[19] Lars-Hendrik Röller, Exploitative Abuses (ESMT Bus. Brief No. BB-107-002, 2007), https://d-nb.info/1012903311/34.

[20] Competition Act, R.S.C. 1985, c C-34, § 103.1(7) (Can.); see also Competition Bureau Can., Information Bulletin on Private Access to the Competition Tribunal (June 2025), https://competition-bureau.canada.ca/en/information-bulletin-private-access-competition-tribunal.

[21] Brunswick Corp. v. Pueblo Bowl-O-Mat, Inc., 429 U.S. 477, 489 (1977) (holding that antitrust injury must be “of the type the antitrust laws were intended to prevent” and “flow[] from that which makes defendants’ acts unlawful”).

[22] See George J. Stigler, A Theory of Oligopoly, 72 J. Pol. Econ. 44 (1964).

[23] Maureen K. Ohlhausen, Acting Chairman, Fed. Trade Comm’n, Should We Fear the Things That Go Beep in the Night? Some Initial Thoughts on the Intersection of Antitrust Laws and Algorithmic Pricing 10 (May 23, 2017), https://www.ftc.gov/system/files/documents/public_statements/1220893/ohlhausen_-_concurrences_5-23-17.pdf.

[24] Zach Y. Brown & Alexander MacKay, Competition in Pricing Algorithms, 15 Am. Econ. J.: Microeconomics 109 (2023), https://www.aeaweb.org/articles?id=10.1257/mic.20210158.

[25] Sophie Calder-Wang & Gi Heung Kim, Algorithmic Pricing in Multifamily Rentals: Efficiency Gains or Price Coordination? (Aug. 16, 2024) (unpublished manuscript), https://ssrn.com/abstract=4403058.

[26] Stephanie Assad et al., Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market, 132 J. Pol. Econ. 723 (2024).

[27] Id. at 726 (finding that algorithmic adoption increased margins by roughly 15% overall and had no meaningful effect in monopoly markets, but increased margins by 38% in duopoly markets where both stations adopted).

[28] Id. at 727 (“Margins start to increase only about a year after market-wide adoption, suggesting that algorithms in this market learn tacitly collusive strategies. . . . [A]lgorithms learn that undercutting will not be profitable, since lower prices will be followed.”).

[29] Id. at 760 (“To the best of our knowledge, this occurs without explicit communication between competitors, making it legal according to current competition laws in many countries.”).

[30] Competition Bureau Can., Anti-Competitive Conduct and Agreements Enforcement Guidelines ¶ 121 (proposed Oct. 31, 2025), https://competition-bureau.canada.ca/en/how-we-foster-competition/consultations/anti-competitive-conduct-and-agreements.

[31] Gibson v. Cendyn Grp., LLC, 148 F.4th 1069 (9th Cir. 2025).

[32] Id.

[33] Cornish-Adebiyi v. Caesars Entm’t, Inc., No. 24-3006, slip op. (3d Cir. July 29, 2026), rev’g 2024 WL 4356188 (D.N.J. Sept. 30, 2024).

[34] See Ken Littlewood, Forecasting and Control of Passenger Bookings, 4 J. Revenue & Pricing Mgmt. 111 (2005) (reprinting a 1972 AGIFORS paper); Barry C. Smith et al., Yield Management at American Airlines, 22 Interfaces 8 (1992).

[35] See Kevin R. Williams, The Welfare Effects of Dynamic Pricing: Evidence from Airline Markets, 90 Econometrica 831 (2022).

[36] See Juan Camilo Castillo, Who Benefits from Surge Pricing?, 93 Econometrica 1811 (2025).

[37] See Andrew Sweeting, Dynamic Pricing Behavior in Perishable Goods Markets: Evidence from Secondary Markets for Major League Baseball Tickets, 120 J. Pol. Econ. 1133 (2012).

[38] See Frank A. Wolak, Do Residential Customers Respond to Hourly Prices? Evidence from a Dynamic Pricing Experiment, 101 Am. Econ. Rev. Papers & Proc. 83, 87 (2011).

[39] See Brian C. Albrecht, Price Competition and the Use of Consumer Data (Working Paper, June 12, 2026), https://briancalbrecht.com/papers/price-competition-data.

[40] See Manne et al., supra note 2, at 11–12 (discussing Nicholas Buchholz et al., Personalized Pricing and the Value of Time: Evidence from Auctioned Cab Rides, 93 Econometrica 929, 930 (2025)); see also John M. Yun, Should We Fear Personalized Pricing?, CPI Antitrust Chron. (July 2025).

[41] See James D. Dana Jr., Advance-Purchase Discounts and Price Discrimination in Competitive Markets, 106 J. Pol. Econ. 395, 395 (1998).

[42] See Joanna Stavins, Price Discrimination in the Airline Market: The Effect of Market Concentration, 83 Rev. Econ. & Stat. 200, 202 (2001).

[43] See Severin Borenstein & Nancy L. Rose, Competition and Price Dispersion in the U.S. Airline Industry, 102 J. Pol. Econ. 653, 672 (1994).

[44] See Ambarish Chandra & Mara Lederman, Revisiting the Relationship Between Competition and Price Discrimination, 10 Am. Econ. J.: Microeconomics 190, 193, 198, 210 (2018).

[45] See Retail Council of Can., The Great Canadian Shopping Trip (2019); see infra Sub-Question 4.

[46] See Eur. Comm’n, Consumer Market Study on Online Market Segmentation Through Personalised Pricing/Offers in the European Union 171 (2018).

[47] See Manne et al., supra note 2, at 9 (quoting OECD); Yun, supra note 40.

[48] See Katie Wells et al., Same Cart, Different Price: Instacart’s Price Experiments Cost Families at Checkout (Groundwork Collaborative & Consumer Reports, Dec. 9, 2025), https://groundworkcollaborative.org/wp-content/uploads/2025/12/Same-Cart-Different-Price.pdf.

[49] See Brian Albrecht, Instacart Didn’t Read Your Mind. It Ran an A/B Test., Truth on the Mkt. (Dec. 10, 2025), https://truthonthemarket.com/2025/12/10/instacart-didnt-read-your-mind-it-ran-an-a-b-test.

[50] See Ginger Zhe Jin et al., Surveillance Pricing: A Cautionary Summary of Potential Harms and Solutions, Competition Pol’y Int’l (July 14, 2025).

[51] See H.B. 895, 2026 Gen. Assemb., Reg. Sess. (Md. 2026) (enacted as 2026 Md. Laws ch. 154) (effective Oct. 1, 2026); Brian Albrecht, How a Panic over Egg Prices Hatched a Bad Law, Wash. Post (May 11, 2026); see infra Sub-Question 3.

[52] See Brian Albrecht, Eliminate ‘Personal Pricing’ and You Risk Harming Consumers, Fin. Times (July 2, 2026).

[53] See U.K. Competition & Mkts. Auth., Loyalty Pricing in the Groceries Sector—Summary (Nov. 27, 2024), https://assets.publishing.service.gov.uk/media/6749f28975bb645366b3a2a9/Executive_summary__2.pdf; U.K. Competition & Mkts. Auth., Loyalty Pricing in the Groceries Sector—Findings Report (Nov. 2024).

[54] See Borenstein & Rose, supra note 43.

[55] See Competition Bureau Can., Competitor Collaboration Guidelines § 1.1 (May 6, 2021), https://competition-bureau.canada.ca/sites/default/files/attachments/2022/CB-BC-CCGs-Eng.pdf.

[56] See id. § 3.7.2, at 42; Competition Bureau Can., supra note 30, ¶ 272.

[57] See Competition Bureau Can., supra note 55, § 2.2.

[58] See Competition Act, R.S.C. 1985, c C-34, §§ 78(1)(h)–(i), 79(1)(b)(ii) (Can.).

[59] See Competition Bureau Can., supra note 30, ¶¶ 171–176.

[60] Gibson, 148 F.4th at 1076, 1083 & n.8, 1088 n.12.

[61] See Cornish-Adebiyi v. Caesars Entm’t, Inc., No. 1:23-cv-02536-KMW-EAP, 2024 WL 4356188 (D.N.J. Sept. 30, 2024), rev’d, No. 24-3006, slip op. (3d Cir. July 29, 2026); Cornish-Adebiyi, No. 24-3006, slip op. at 9, 36–38, 42; In re RealPage, Inc., 709 F. Supp. 3d 478, 494, 512 (M.D. Tenn. 2023).

[62] See Competition Act, R.S.C. 1985, c C-34, § 79(1)(b)(ii) (Can.).

[63] See Competition Bureau Can., supra note 30, ¶ 236.

[64] See Br. of Amici Curiae Int’l Ctr. for Law & Econ. et al. at 2, In re EpiPen (Sanofi-Aventis U.S., LLC v. Mylan Inc.), No. 21-3005 (10th Cir. Sept. 22, 2021).

[65] See id. at 15.

[66] Org. for Econ. Coop. & Dev., Algorithmic Competition: Background Note, OECD Doc. DAF/COMP(2023)3 (June 14, 2023), https://one.oecd.org/document/DAF/COMP(2023)3/en/pdf.

[67] Emek Basker, The Causes and Consequences of Wal-Mart’s Growth, 21 J. Econ. Persp. 177, 188–89 (2007).

[68] Id. at 188.

[69] Jerry Hausman & Ephraim Leibtag, Consumer Benefits from Increased Competition in Shopping Outlets: Measuring the Effect of Wal-Mart, 22 J. Applied Econometrics 1157 (2007).

[70] Fruits, supra note 7.

[71] See Stefano DellaVigna & Matthew Gentzkow, Uniform Pricing in U.S. Retail Chains, 134 Q.J. Econ. 2011 (2019).

[72] See Alberto Cavallo, Are Online and Offline Prices Similar? Evidence from Large Multi-Channel Retailers, 107 Am. Econ. Rev. 283, 290, 292 (2017).

[73] Albrecht et al., supra note 8.

[74] Competition Bureau Can., Algorithmic Pricing and Competition: Discussion Paper (June 10, 2025), https://competition-bureau.canada.ca/en/how-we-foster-competition/education-and-outreach/publications/algorithmic-pricing-and-competition-discussion-paper. See infra Appendix A.

[75] See infra Appendix A.

[76] Competition Bureau Can., supra note 55, at 42–43.

[77] Org. for Econ. Coop. & Dev., supra note 66.

[78] Neth. Auth. for Consumers & Mkts., Position Paper: Oversight of Algorithms (2020), https://www.acm.nl/sites/default/files/documents/position-paper-oversight-of-algorithms.pdf.

[79] Eur. Comm’n, Commission Staff Working Document Accompanying the Final Report on the E-Commerce Sector Inquiry 17, 22, 24, 29–32, SWD(2017) 154 final (May 10, 2017), https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=SWD:2017:154:FIN.

[80] Id.

[81] See Org. for Econ. Coop. & Dev., supra note 66, at 12. In 2020, the Norwegian Competition Authority found that 55% of surveyed firms used monitoring algorithms and 20% used pricing algorithms. In Denmark, 17% of e-commerce companies used pricing algorithms, with automation ranging from basic information provision to full algorithmic control. The Netherlands Authority for Consumers and Markets reported that 36% of firms used competitors’ pricing data. Of those firms, 16%—or 6% of all firms—used pricing algorithms.

[82] Albrecht et al. supra note 8.

[83] Albrecht et al., supra note 6.

[84] Id.

[85] See, e.g., Simon Lermen et al., Large-Scale Online Deanonymization with LLMs, arXiv:2602.16800 (Feb. 18, 2026), https://doi.org/10.48550/arXiv.2602.16800.

[86] Stigler, supra note 22; Fruits, supra note 7. See infra Sub-Question 6 for further discussion of compelled disclosure.

[87] Wells et al., supra note 48.

[88] Albrecht, supra note 49.

[89] H.B. 895, 2026 Gen. Assemb., Reg. Sess. (Md. 2026) (enacted as 2026 Md. Laws ch. 154) (effective Oct. 1, 2026).

[90] Albrecht, supra note 51.

[91] See Org. for Econ. Coop. & Dev., supra note 66 (reporting the European Commission’s mystery-shopping exercise); see also infra Appendix A.

[92] Estimates of the combined market share vary by scope and source. See U.S. Dep’t of Agric., Foreign Agric. Serv., Retail Foods Annual: Canada, GAIN Rep. No. CA2025-0040 (2025), https://www.fas.usda.gov/data/gain-report/2025/12/Retail%20Foods%20Annual_Ottawa_Canada_CA2025-0040.pdf (estimating that five leading retailers held more than 65% of the market in 2024: Loblaws 17%, Costco 15%, Sobeys 14%, Walmart 13%, and Metro 6%); Kevin Grier, Grocery Retail (UFCW Loc. 401, Nov. 2024), https://gounion.ca/wp-content/uploads/2024/11/UFCW-Local-401-Retail-Grier-November-2024-rev.pdf (estimating 2022 shares of 26% for Loblaws, 20% for Sobeys, 14% for Costco, and 12% each for Walmart and Metro).

[93] Competition Bureau Can., supra note 10.

[94] Id.

[95] Karl Littler, Retail Council of Can., Retail Council of Canada Submission to the Competition Bureau’s Market Study: Competition in Canada’s Grocery Sector (Dec. 16, 2022), https://competition-bureau.canada.ca/sites/default/files/attachments/2023/RCC-Market-Study-Submission.pdf (arguing that concentration had not recently increased, big-box retailers and new entrants had gained market share, regional and independent grocers remained significant, and 87% of Canadians regularly shopped at discount grocers).

[96] Ritika Dubey, Discount Is the New Name of the Game. What Does That Mean for Conventional Stores?, Can. Press (Apr. 3, 2026), https://www.thecanadianpressnews.ca/business/discount-is-the-new-name-of-the-game-what-does-that-mean-for-conventional-stores/article_50f71fe5-5410-5c00-ab63-b5d0655397f6.html (quoting Henry Chamber of Sentinel Management Consultants on grocers’ efforts to retain shoppers across their discount and conventional banners); Rebecca Harris, Retailers Are Betting on Discount. Will the Momentum Last?, Can. Grocer (Dec. 2, 2024), https://canadiangrocer.com/retailers-are-betting-discount-will-momentum-last (reporting that more than 62% of grocery shoppers had changed their primary store to secure better deals and nearly 30% chose stores based on in-store discounts and promotions).

[97] Competition Bureau Can., supra note 10.

[98] Id.

[99] Angela Altass, Budget-Conscious Shoppers Drive Increased Growth in Discount Grocery Channel, Grocery Bus. (July 9, 2025) (reporting that discount grocers’ market share rose from 35% in 2008 to roughly 50% and that their same-store sales outpaced conventional grocers 3-to-1 nationally by both dollars and tonnage).

[100] Competition Bureau Can., supra note 10.

[101] Id.

[102] Id.

[103] Id.

[104] U.K. Competition & Mkts. Auth., Loyalty Pricing in the Groceries Sector—Summary, supra note 53 (reporting that 92% of roughly 50,000 products offered through dual-price loyalty promotions yielded genuine savings, with average member savings of 17% to 25%).

[105] See Aljoscha Janssen & Johannes Kasinger, Shrinkflation and Consumer Demand, 45 Mktg. Sci. 142 (2026).

[106] Id. at 142.

[107] Competition Act, R.S.C. 1985, c C-34, § 45(1)(c) (Can.); Competition Bureau Can., supra note 55, § 2.4.3.

[108] See Xibo Wan et al., Shrinkflation, Unit Price Disclosure, and Consumer Welfare: Evidence from Canned Tuna, 142 Food Pol’y 103106 (2026); Press Release, U.S. Dep’t of Just., Former Bumble Bee CEO Sentenced to Prison for Fixing Prices of Canned Tuna (June 16, 2020), https://www.justice.gov/archives/opa/pr/former-bumble-bee-ceo-sentenced-prison-fixing-prices-canned-tuna.

[109] Competition Bureau Can., supra note 10, at 44.

[110] See S. 8563, 2025–2026 Reg. Sess. (N.Y. 2025).

[111] Fruits, supra note 7.

[112] See Antitrust Modernization Comm’n, Report and Recommendations (2007).

[113] Roman Inderst & Tommaso M. Valletti, Buyer Power and the “Waterbed Effect”, 59 J. Indus. Econ. 1 (2011).

[114] See Brian C. Albrecht, Consumer Harm from the Waterbed Effect: A Comment on Inderst and Valletti (2011) (Working Paper, Apr. 22, 2026), https://briancalbrecht.com/Albrecht-IV-Waterbed-Comment.pdf; Brian C. Albrecht, When Does the Waterbed Effect Harm Consumers? (Int’l Ctr. for Law & Econ., July 24, 2026), https://laweconcenter.org/resources/when-does-the-waterbed-effect-harm-consumers.

[115] DeHoog v. Anheuser-Busch InBev SA/NV, No. 1:15-cv-02250-CL, 2016 U.S. Dist. LEXIS 137759 (D. Or. July 22, 2016).

[116] Fruits, supra note 7.

[117] Stigler, supra note 22; Fruits, supra note 7.

[118] Competition Bureau Can., supra note 10.

[119] Albrecht et al., supra note 8.

[120] Competition Bureau Can., supra note 55.

[121] See Competition Bureau Can., supra note 10, recommendations 2 and 4.

[122] Hunt Allcott et al., Food Deserts and the Causes of Nutritional Inequality, 134 Q.J. Econ. 1793 (2019).