What this article covers
A comprehensive explanation of the 2024 working paper “Concentrating on Customers: Spending Across Firms and Space” by Christina Patterson and Joseph Vavra (Chicago Booth & NBER) — covering the paper’s central thesis, its new customer-location-based concentration measure, five key empirical findings, implications for antitrust policy and competition analysis, and the link between firm size and size-based market power through customer loyalty patterns.
Traditional measures of market concentration look at where firms are located and how much revenue they capture. A landmark 2024 research paper by economists Christina Patterson and Joseph Vavra argues that this firm-centric view systematically overstates market concentration — and that the correct lens is the customer, not the company. By analysing trillions of dollars of card-level transaction data, the authors propose a new customer-location-based concentration measure that reframes how economists, regulators, and businesses should think about market power and size-based competitive advantage.
Background: the problem with traditional market concentration measures
Market concentration — the degree to which a small number of firms dominate a market — has been central to antitrust analysis and competition economics for decades. The standard tools used to measure it include the Herfindahl-Hirschman Index (HHI), the N-firm concentration ratio, and the Lerner Index.
Traditional approach
Firm-location-based concentration
Measures the distribution of total revenue or sales across firms within a defined geographic boundary. Requires assumptions about market geography — which firms compete with which others — and cannot account for how far customers actually travel or whether they shop online.
Patterson & Vavra’s approach
Customer-location-based concentration
Measures the concentration of spending across firms by all customers living in a particular location — regardless of where those firms are located. Requires no assumptions about market geography because the data directly reveals where customers actually shop.
The fundamental limitation of firm-location-based measures is that they treat market boundaries as fixed geographic areas — a zip code, a city, a metropolitan statistical area — and assume that firms within that area compete with each other while firms outside it do not. In reality, customers routinely cross geographic boundaries, travel for purchases, and increasingly buy online from firms with no local physical presence at all.
Patterson & Vavra, 2024 — core problem statement
Two restaurants located on the same block may draw customers from entirely different distances: one drawing 48% of its customers from the immediate local zip code while the other draws only 8% from the same area. No single fixed geographic boundary can correctly capture the relevant competitive set for both firms simultaneously.
This insight motivates the entire paper: if the geographic assumptions underlying traditional concentration measures are systematically wrong, then decades of market power analysis based on those measures may be systematically biased.
The data: trillions of dollars of card-level transactions
The empirical foundation of the paper is unusually comprehensive. Patterson and Vavra use data from the universe of all credit and debit card transactions processed by a major U.S. card processor between 2018 and 2022. The dataset covers more than 600 million cards per year and more than 4 trillion individual card-merchant transaction pairs.
600M+
Cards observed per year in the dataset
4T+
Individual card-merchant transaction pairs
2018–2022
Time period covered by the primary dataset
For each transaction, the researchers observe the exact amount, the time and date, the unique card identifier, and the merchant identifier. Crucially, because each card is associated with a cardholder location, the data allows the researchers to track which specific customers are shopping at which specific firms — and how far they travel to do so. This is the piece that standard Census firm-revenue data entirely lacks.
Prior scanner datasets such as the NielsenIQ panel could track individual customer behaviour, but sampled only around 60,000 households nationally — too small to reliably measure concentration statistics at the firm level for most businesses. The card-transaction dataset eliminates this sampling limitation.
The new measure: customer-location-based concentration
Patterson and Vavra’s central methodological contribution is a customer-location-based concentration statistic. Rather than measuring the share of total sales captured by large firms within a fixed geographic area, this statistic measures the concentration of spending across firms by all customers who live in a particular location — wherever those customers actually choose to shop.
The measure captures the actual set of shopping options utilised by real customers, including options outside the local area and online options with no physical location at all. This makes the measure geography-agnostic in an important sense: it does not require the researcher to draw a market boundary in advance. The boundary is determined by where customers actually go.
A key conceptual point the paper emphasises is the distinction between an individual customer’s actual spending concentration and the potential options available to them. Even if a customer shops at only one or two firms in a given year, the relevant measure of their market access is the full set of firms where their neighbours shop — because those are the alternatives the customer could realistically switch to if prices changed or a preferred firm closed.
Five key empirical findings
The paper documents five major empirical facts about the distribution of customer spending across firms and space.
Finding 01
Customer-based concentration is much lower than firm-based concentration
Customers face less concentrated markets than firm-location measures suggest. The gap is large even at city level and remains significant across state boundaries — meaning firm-level data substantially overstates market dominance.
Finding 02
Customer concentration varies less across space than firm concentration
Shopping patterns among customers in different locations are much more similar to each other than firm-based concentration data implies. Markets defined by customer behaviour are more homogeneous across geography than markets defined by firm location.
Finding 03
Online shopping reduces concentration and narrows spatial inequality
Online retail lowers concentration overall and is especially powerful in areas where customers have fewer local options. This means online shopping reduces geographic inequality in market access — and suggests traditional census data overstates recent concentration growth.
Finding 04
More potential options leads to more dispersed individual spending
Customers whose neighbours shop at many different firms also distribute their own spending more evenly across firms. This suggests that greater access to potential shopping options raises demand elasticity and increases competitive pressure on incumbent firms.
Finding 05
Large firms systematically attract less price-sensitive customers
Within the same location and product category, large firms’ customers have more concentrated individual spending — meaning they shop at fewer firms — than customers of small firms. This pattern holds after controlling for local market conditions and is the paper’s central insight about size-based market power.
Size-based market power: why large firms have structurally loyal customers
The fifth finding is the most consequential for understanding size-based market power, and deserves detailed explanation. The paper documents that across industries and locations, large firms attract customers who are more loyal — in the sense that those customers concentrate more of their total spending at a single firm — than the customers of smaller competitors operating in the same market.
This is not simply a reflection of local market conditions. The pattern holds even after controlling for customer-location concentration — that is, it is not explained by large firms being in areas where customers have fewer alternatives. Instead, the paper argues it reflects sorting: customers with different preferences and price sensitivities select into different firms, with less price-sensitive, less comparison-shopping customers gravitating toward larger firms.
What this means for market power
If large firms’ customers shop around less than small firms’ customers, then large firms face lower effective demand elasticity — they can raise prices by more before losing a given proportion of their customer base compared to smaller rivals. This is a form of market power that does not appear in traditional concentration ratios at all. It is not captured by the HHI, nor by any measure that looks only at the distribution of total sales across firms. It is a size-based market power that operates through customer composition rather than through market share arithmetic.
Key implication for competition analysis
A large firm and a small firm can have identical market shares and identical local competitive environments yet face fundamentally different competitive pressures — because the large firm’s customers are systematically less likely to switch in response to price changes or new entry. Traditional concentration measures are blind to this distinction.
The role of customer sorting versus firm quality
An important question the paper addresses is whether this loyalty pattern reflects customer sorting (certain types of customers choosing large firms) or firm quality (large firms genuinely offering better products or services that justify the loyalty). The authors control for observable customer and firm characteristics and find that the size-customer loyalty relationship holds within categories and locations, suggesting sorting is the primary driver. However, the paper acknowledges that fully disentangling sorting from quality differences is a challenge for future research — particularly as it relates to understanding the welfare implications of this form of market power.
Implications for antitrust policy and market definition
The paper has significant implications for how competition regulators define markets, assess market power, and evaluate mergers and acquisitions. Current practice in most jurisdictions relies heavily on firm-location-based data and fixed geographic market definitions — often zip codes, counties, or metropolitan areas. Patterson and Vavra’s findings suggest these approaches have consistent and quantitatively large biases.
01
Traditional HHI-based merger analysis may overstate post-merger concentration
If firm-location concentration overstates how concentrated markets actually are from a customer perspective, then HHI calculations based on firm revenue data will systematically overestimate the anticompetitive risk of mergers in many industries — particularly retail and services where cross-boundary shopping and online substitution are significant.
02
Online retail must be integrated into market definition
Finding 3 — that online shopping lowers concentration and reduces geographic inequality in market access — implies that any market definition exercise that excludes online alternatives will systematically overestimate local market power. This has direct implications for antitrust cases involving large brick-and-mortar retailers and their online competitors.
03
Size-based market power requires new measurement frameworks
The finding that large firms attract structurally less price-sensitive customers — independently of local market conditions — implies that market power can accumulate through customer base composition without appearing in conventional concentration statistics. Antitrust frameworks focused only on market share may be missing this channel entirely.
04
Rural vs. urban policy implications may need revision
Because customer-location concentration varies less across space than firm-location concentration, the conventional assumption that rural consumers face systematically more concentrated — and therefore less competitive — markets may be overstated. Online access and cross-boundary shopping provide competitive discipline even in areas with few local firms.
Limitations and ongoing research
Patterson and Vavra are explicit about the limitations of their measure. The customer-location concentration statistic captures the distribution of spending at observed prices — it does not directly measure how market shares would change in response to price changes, which is the more theoretically precise definition of market power. The paper therefore characterises the measure as a closer approximation to market power than traditional firm-based measures, while acknowledging it does not fully resolve the theoretical challenges in market power measurement.
The dataset covers only card transactions and therefore excludes cash purchases, which may represent a non-random subset of spending in some categories. The short time-series of the data limits the paper’s ability to study how concentration evolves over time, which the authors note as a constraint on their ability to study the impact of online retail growth.
The authors note that ongoing research aims to combine customer shopping data with measures of firm entry and exit — allowing them to directly observe how customer switching patterns change when new competitors enter or incumbents exit. This would enable a closer connection between the concentration measure and the price-elasticity interpretation that is central to market power analysis.
Frequently asked questions
What is the paper “Concentrating on Customers” about?
“Concentrating on Customers: Spending Across Firms and Space” is a 2024 working paper by Christina Patterson and Joseph Vavra of Chicago Booth and NBER. It uses data from trillions of card transactions to propose a new customer-location-based measure of market concentration, documents five key empirical facts about how customer spending is distributed across firms and geography, and shows that large firms systematically attract less price-sensitive customers — creating a form of size-based market power that traditional concentration measures cannot detect.
What is a customer-location-based concentration measure?
It is a statistic that measures the concentration of spending across firms by all customers who live in a particular location, regardless of where those firms are physically located. Unlike traditional firm-location measures, it requires no assumptions about market geography because the shopping data directly reveals which firms customers actually use — including firms outside their immediate area and online retailers with no local presence.
Why does firm-location-based concentration overstate market power?
Firm-location measures assume that firms within a defined geographic boundary compete with each other and firms outside do not. In practice, customers regularly shop across boundaries — travelling for purchases and increasingly buying online. Because the typical customer’s actual set of shopping options is much larger than the set of firms located near them, firm-location measures overstate how concentrated — and therefore how uncompetitive — a market appears to be.
What does the paper say about online shopping and market concentration?
The paper finds that online shopping reduces overall market concentration and is especially powerful in areas where customers have fewer local options. This means online retail narrows geographic inequality in market access — customers in low-option areas benefit most from online substitutes. The paper also notes that because standard census data does not capture online spending well, concentration growth over the past decade may have been overstated in traditional data sources.
How does firm size relate to market power in the Patterson-Vavra framework?
The paper finds that within the same location and product category, large firms attract customers with more concentrated individual spending — customers who shop at fewer firms overall and are therefore less likely to switch in response to price changes. This customer composition effect gives large firms a structural advantage in pricing power that is independent of their market share and does not appear in conventional HHI or concentration ratio calculations.
What are the antitrust implications of this research?
The research implies that standard HHI-based merger analysis, which relies on firm-location revenue data and fixed geographic market definitions, may systematically overestimate post-merger concentration in industries where cross-boundary shopping and online substitution are significant. It also implies that size-based market power through customer loyalty is a channel that current antitrust frameworks may be missing entirely, since it does not manifest in market share statistics.
“Concentrating on Customers” represents a significant methodological advance in how economists measure market structure and competitive pressure. By shifting the unit of analysis from the firm’s location to the customer’s location, Patterson and Vavra resolve a long-standing assumption problem in industrial organisation research — and in doing so, reach empirical conclusions that challenge conventional policy narratives about rising market concentration, geographic inequality in market access, and the source of size-based advantages. The finding that large firms attract structurally less price-sensitive customers is particularly important: it suggests that a form of market power accumulates with scale that is invisible to traditional measurement tools, operates through customer sorting rather than entry barriers alone, and may persist even in markets where concentration ratios appear moderate. For economists, antitrust practitioners, and business strategists alike, this reframing — from where firms are to where customers go — is a durable contribution to how market power is understood and measured.
Abdullah Zulfiqar is Co-founder and Client Success Manager at RankWithLinks, an SEO agency helping businesses grow online. He specializes in client relations and SEO strategy, driving measurable results and maximizing ROI through effective link-building and digital marketing solutions.
