Price-Matching Advertisements and Consumer Search

The only programs I watch on TV with any regularity are cooking competitions like Chopped and American Football, so I find it somewhat odd that I have seen the same Toys “R” Us commercial advertising the toy retailer’s price-matching guarantee as many times as I have.  Perhaps I have been watching too much Chopped Junior.  In the commercial, a creepy children’s toy informs Optimus Prime that if potential consumers find him for a lower price at a competing retailer, Toys “R” Us will match the lower listed price (see it here).  Optimus Prime is impressed not by the seemingly great deal, but by the existential realization that as a toy, he is not unique in our world.

When I first saw that commercial, my first though was, “I have a publication coming out about the practice of price-matching guarantees, AWWWESOME!!!” (this sentence is funnier after watching the commercial).  My next though was, “it would benefit consumers more if retailers competed by lowering prices than pretended to compete by using price-matching guarantees.” I explain below.

As my co-author, Brady Vaughan, and I show in our paper, “Price-Match Announcements in a Consumer Search Duopoly,” forthcoming in the Southern Economic Journal, although advertisements emphasizing retailers’ price-matching guarantees appear to be pro-competitive, price-matching guarantees actually tend to lessen firms’ incentives to lower price.  The intuition behind our main result is as follows.  Suppose that consumers vary in their propensity to shop around for price.  We might for instance think of individuals as valuing their scarce time differently, leading some to feel that their incremental cost of uncovering an additional sample price (i.e., by visiting an additional store), is higher than that of others.  In such a setup, firms face two competing forces when setting their prices: they are inclined to lower prices to attract consumers who tend to shop around for the lowest price, and to raise them in an attempt to take advantage of consumers who find the activity of price comparison too time consuming to bother with (see Varian 1980, Stahl 1989 for the mathematical details behind this outcome).  Price dispersion ensues: firms run sales of different magnitudes in an effort to maximize profits.

Now consider what happens when firms offer price-matching guarantees.  Suppose that those consumers who find it worthwhile to shop around literally go store to store in search of price (this is not the only way to explain our results, but we find it to be one that aides intuition).  Some of these consumers will end their search at a store that does not list the lowest price.  Without a price-matching guarantee, if these consumers wish to procure the good at the lowest possible price observed, they will have to go back to the store with the best offer.  But if the last store they visit offers a price-matching guarantee, they can get the lowest price there instead.  Knowing this, firms realize that they won’t be able to win over as many price conscious consumers with deep discounts, so their incentive to run sales diminishes, leading to higher average prices.

Sound like a roundabout explanation?  Brady and I are far from the first to suggest that price-matching guarantees can diminish competition and some of the earliest explanations seem downright obvious: price-matching guarantees keep firms from lowering prices because rival firms immediately match price-cuts (see Hay 1982, Salop 1986, Doyle 1988).  However, I think that Brady and I have made a fairly cogent argument that takes into consideration how consumers behave and also accounts for the myriad advertisements firms undertake to inform consumers about their policies (here is one from Walmart, another from Toys “R” Us, and one from Staples).

This result begs the question, (i) have price-matching guarantees always been found to be anti-competitive and (ii) if so, can the anti-trust authorities reasonably do anything to prevent them?  The answer to question (i) is a no.  Although the bulk of the literature lends support to our findings, there are some notable explanations suggesting the contrary.  One that I find somewhat convincing when firms are differentiated is that price-matching guarantees can be a signal that a firm generally has lower underlying costs (perhaps it has negotiated better deals with merchants or doesn’t spend as much on its service quality) and consequently sets lower prices (see Moorthy and Winter 2006, Moorthy and Zhang 2006 for the details).  That is a theoretical argument.  The empirical literature is somewhat mixed, but typically strays to answer the question, “do firms that price-match have higher prices than those that do not,” instead of “are prices in general lower or higher when price-matching guarantees are used by some firms in the market?” More empirical research is neToyseded to settle the issue.

As for question (ii), the answer may be no as well.  The anti-trust laws, stemming from the Sherman and Clayton Acts are generally focused on restraints of competition between firms, but price-matching guarantees are effectively standing offers by firms to contract with a consumer by referencing another firm’s price (see Edlin 1997).  Contracts that reference rivals are assuredly of concern to anti-trust practitioners (Scott Morton 2013), but when the contract does not impose any restriction on any party except a commitment to lower price by the firm offering it in response to publicly available information, it would seem (without undertaking a very rigorous empirical examination of the case at hand) rather difficult to make a case that competition is being restrained.

All of this comes with a major caveat.  Although I believe that price-matching guarantees have the potential to lead to higher prices in the market as a whole, if as a consumer, you find yourself in a situation where you can use a price-matching guarantee to save money, by all means do!  Unless all consumers can coordinate with all other consumers to bring about a better situation for themselves, they should do what is in their individual best interest.  I recently visited my family for the holidays and we decided to buy a board game to spend the time.  My brother reminded me to put my research to work.  I saved 20 bucks!

Primary takeaways

  • Digital inequality shows larger impacts on youth academic performance as compared to time spent on screens.

  • Digital skills play a significant role in mediating unstructured online engagement (social media use, playing video games, browsing the web) and youth academic, social, and psychosocial development.

  • Unstructured online engagement and face-to-face social interaction are complementary and continuously interact to create and enhance youth capital outcomes.


A familiar story: concerns of screen time

Today’s discussions of adolescent well-being have coalesced around a clear narrative: teenagers spend too much time online, and their academic performance, mental health, and social lives are deteriorating as a result. A steady stream of academic papers, books, and op-eds, alongside a growing number of policy proposals––school phone bans, age-gated social media use, restrictive screen-time limits––rest on the same underlying claim, aligning with a contemporary, digitized version of the displacement hypothesis:

Screen time, particularly the unstructured, free-time spent on social media, gaming, watching video content, or browsing the web, is said to displace the productive face-to-face activities that build adolescents into capable adults.

The implied and often practiced solution is restriction. In response, this dissertation tested this claim directly, and placed it within the broader context of adolescence.

Across three years, I followed 653 Michigan adolescents from early through late adolescence: in grades 8 or 9 (survey one, 2019) to grades 11 or 12 (survey two, 2022). Notably, these students, studied over time, were part of a broader pooled sample of 5,825 students across the same eighteen highschools. The study window captured the year before and the year after the peak of the COVID-19 pandemic and related lockdown orders, functioning as an unprecedented stress test for theories of adolescent social, academic, and digital life and, importantly, as a benchmark to compare the effects of pandemic-related change and inequality to those effects from screen time alone.

Across four studies of adolescents, consisting of six cross-sectional and longitudinal analyses, findings are not consistent with the displacement narrative, nor the broader concerns about the time youth spend on screens.

Findings are, however, consistent with something the current public and (most) academic discussions have largely overlooked or ignored: the gaps and inequalities that determine whether adolescents can access and use the internet meaningfully in the first place.

What the displacement hypothesis overlooks

Displacement and related research and policy concerning the time young people spend online assumes a “zero-sum” model of adolescent day-to-day time. An hour online is an hour not spent studying, reading, sleeping, or interacting face-to-face (i.e., time spent on more productive or developmentally “better” activity).

Indeed, this makes sense logically. However, as an empirical claim, this model requires time spent online to behave differently from all other ways adolescents allocate time; it must produce uniquely negative outcomes and be inherently harmful across digital contexts, rather than the typical mix of trade-offs corresponding to, and often overlooked among any other social or developmental context.

Yet, online time does not differ from other youth activity. Instead, I find it has a mix of pros, cons, and even some “uniquely digital” benefits which youth utilize for social and academic gains. When I compared unstructured digital media use against traditional face-to-face interaction and activities, both produced similar patterns: some negative associations with academic outcomes, some null, and some positive.

Trade-offs within traditional face-to-face activity (for example, social time with friends and family, or time spent in after-school extracurriculars) are treated as ordinary developmental experiences that must be experienced for the betterment of development. The identical trade-offs involving digital time tend to be overlooked or ignored, and online engagement is perceived as altogether harmful.

A growing body of evidence, including this dissertation, do not support that distinction. Indeed, the developmental context is routinely misread, leaving out the context of the experiences and time spent on digital, as well as face-to-face activities, interactions, existing inequalities, and changes inherent to development. As such, I proposed a novel framework to understand these contexts:

Digital capital exchange

Rather than treating screen time as a unified harm, this dissertation advances an exchange”-based framework, grounded in James Coleman’s theories of youth capital and digital inequality scholarship, particularly following Eszter Hargittai, Jan van Dijk, and Alexander van Deursen (see this list of all dissertation references for full works).

The core proposition is that adolescents’ online engagement is not an alternative to developmental activity but another, albiet modern domain through which young people accumulate and mobilize online resources––particularly digital skills––that work alongside existing social networks and experiences to be exchanged for human capital (measured as: academic achievement, aspirations, STEM interest) and social capital (peer networks, community participation, extracurricular involvement).

Online time is not the mechanism; instead, it is digital skills that I find to be the most vital component in youth capital exchange and enhancement. Unstructured online engagement contributes to online skills; those skills, accumulated and mobilized alongside existing peer, family, and community networks, translate into the outcomes researchers and parents care about, i.e., academic achievement, aspirations, and face-to-face interaction and social networks.

This digital capital framework treats online and in-person contexts as complementary rather than antagonistic, and it situates adolescents’ digital lives within the structural conditions––connectivity quality, device reliability, autonomy of use––that determine whether exchange can occur at all.


Methods (in brief)

Paper-and-pencil surveys were administered to students in classrooms at two time-points: spring 2019 (N=2,876) and spring 2022 (N=2,949), across the same eighteen predominantly rural Michigan schools, grades 8–12. Official, nationally-ranked standardized reading, writing, and math test scores (PSAT 8/9, PSAT 10, SAT; College Board) were then anonymously linked to students’ survey responses with the help of participating districts.

Cross-sectional path analyses modeled pooled and wave-specific samples (pooled N=5,825); two-wave cross-lagged panel models tested reciprocal, longitudinal relationships on the 653 students who completed both surveys. Multi-group analyses of the cross-lagged panel models compared relationships between girls (N=345) and boys (N=308). All longitudinal models included time-invariant socioeconomic covariates as well as time-varying covariates to reduce omitted-variable bias.

Key findings: an overview

To summarize, to the best of my ability, eight chapters across 376 pages, I present two primary findings:

First: digital inequality predicted larger and more consistent declines in human capital than screen time did.

Unreliable home internet and technology maintenance problems––experiencing and/or dealing with broken or outdated devices and software, restrictive school-issued hardware, issues with connecting to or maintaining internet access––decreased youth GPA and standardized test achievement. And, these effect sizes were substantially larger than any negative direct effect from unstructured digital media use.

Across all four empirical studies, digital inequality emerged as the most substantial predictor of academic and developmental decline.

Second: digital skills mediated the relationship between online time and adolescent academic and social outcomes.

Unstructured digital media use, particularly online gaming and web browsing, predicted higher internet and social media skills for adolescents, which in turn predicted stronger academic achievement and self-efficacy (human capital), and social interaction and extracurricular participation (social capital). The positive indirect effect of screen time through skills offset or exceeded any small negative direct effects across several outcomes (supporting our existing peer-reviewed work: Hales & Hampton, 2025, and which you can read more about here).

These exchange processes were amplified when peer and family networks were modeled alongside digital skills, consistent with the premise that online and offline contexts operate together rather than in competition. The effect was not universal: social media skills amplified rather than offset a negative association with consistency of interest, one of the two subscales of grit. The exchange framework describes a contextual and conditional, domain-specific mechanism, not a blanket defense of time spent online.

Implications

If digital inequality, and not screen time, is the primary predictor of adolescent academic and developmental decline, and still warrants concern regarding access quality and experience even with the broader adoption of digital devices across the United States, the current policy emphasis on restriction is pointed at the wrong target. The evidence supports a different set of priorities.

Stable, reliable home (fast) broadband should be treated as an educational prerequisite rather than a consumer amenity. Unreliable connectivity exerted larger downward pressure on human capital than any measure of screen time, and that pressure intensified during the pandemic-era reliance on digital infrastructure. Technology maintenance, device repair, replacement, technical support, and the flexibility to install software and explore the web autonomously, matters as much as initial access, and school-issued devices that restrict autonomous use appear to hinder skill accumulation rather than support it.

Restrictive parental mediation of internet use was negatively associated with grit and self-efficacy at magnitudes comparable to the positive contributions of face-to-face activity. This challenges the assumption that digital restriction functions protectively. Instructive mediation, teaching adolescents to verify information, navigate platforms critically, and mobilize online resources toward meaningful ends, is the posture the data supports.

Finally, the technical skill-building that occurs through gaming, self-directed exploration, and deep web use is skill-building, not wasted time. Closing the persistent gender gap in these domains likely requires legitimizing technical play for girls, rather than restricting it for everyone.

None of the above is an argument that screen time is benign. It is an argument that screen time is the wrong focus, particularly when studied mostly in isolation. Context matters substantially, whether that is time spent on other activities during adolescence, the period of adolescence itself, digital inequality, resources gained from such online use, and how all such factors interact. The factor that predicts whether a given adolescent can convert online engagement into capital outcomes is structural: access, infrastructure, skills, and the autonomy to use them. These factors are distributed unevenly, and its uneven distribution, not hours logged, is what separates adolescents who thrive from those who fall behind.

The full dissertation is available through Michigan State University’s ProQuest archive, or see the embedded full-text PDF below. I’m happy to share papers, preprints, or the underlying framework with anyone interested and working in this area––don’t hesitate to reach out via my contact form. Thanks for reading.

Price-Matching Advertisements and Consumer Search