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Measuring Deterrence Motives in Dynamic Oligopoly Games

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Nathan Chi-Chung Yang presents a novel decomposition approach for measuring deterrence motives in dynamic oligopoly games.

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This video video discusses the phenomenon of market saturation, where multiple stores of the same brand, such as Starbucks or 7-Eleven, are found in close proximity. It poses the question of whether this business strategy, often seen as cannibalization of profits, can have economic benefits. The speaker, drawing from an economics background, explores the idea that having multiple locations deters competitors from entering the market, providing a strategic advantage. An extensive review of literature over several years revealed that while it is challenging to quantify deterrence motives without direct data (like internal company discussions), existing economic models, particularly a dynamic oligopoly model, can be adapted to analyze this behavior. The research involves developing a framework to understand the incentives for firms to expand strategically, considering both competitive dynamics and forward-looking behavior. The study also highlights a movement toward more standardized measurement of preemptive market strategies. The findings suggest that a notable percentage of Starbucks' expansion motivations can be attributed to preemptive considerations, with estimates ranging from 5% to 30%. This video video research aims to provide valuable insights for future studies and practical applications in fields like antitrust and corporate finance, as understanding these motivations could help in predicting firm behavior and assessing risks related to expansion strategies. This video video concludes by linking preemptive motives to broader discussions of retail success and failure, suggesting that overexpansion—especially when financed through debt—can contribute to the struggles faced by many retail chains.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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Probably many of us have encountered,

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and in fact, I've seen like comedians kind

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of quip about this, in their acts where, you know,

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you drive down along a street

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or you walk, you know, down a, in downtown.

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And you know, where, what you see is you see a Starbucks

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across the street from another Starbucks, I think in,

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a subway station in Tokyo.

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You know, there are four, seven elevens in the same station.

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And, you know, coming from an economics background,

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this type of observation, what seemed a little,

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counterintuitive to me,

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because, you know, there's this notion of cannibalization

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where, you know, if you set up a bunch of shops

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and close proximity,

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they're gonna eat each other's kind of profits, if you will.

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And so that kind of led to kinda a, a rabbit hole,

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if you will, of me kind of spending the past decade

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or so, kind of looking several years looking into this

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phenomena across various papers.

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And, and so the phenomena I was particularly interested in

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is whether there's actually an economic benefit of them kind

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of saturating a market.

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And in fact, there could be, if,

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there is a benefit associated with deterrence where,

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you know, you take up that spot,

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if McDonald's takes up a spot,

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that means Burger King can't, can't do that.

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And in fact, you know, if you read the autobiography

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of I think Walmart, the, the book by one of the Waltons,

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you know, they, they, they even allude to the fact

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that they will kind of, they, they want to,

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they set up a Walmart in close proximity

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of another Walmart just to keep away

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their competitor at the time, which was Kmart.

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Um, even, even though they knew this would,

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potentially eat away at their existing store sales.

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And so this, this kind of behavior, the strategic behavior,

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this economic benefit is, is,

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has been particularly interesting, to me.

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And I think that the, the thing

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that makes it intellectually, satisfying to study is,

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is actually how challenging it is to, quantify this.

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Because if you think about things like deterrence motive,

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it's hard to quantify this.

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There's no kind of observable data,

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directly observable data that indicates, you know,

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that they are doing this

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because they want to keep competitors away.

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Unless you, you have a subpoena on these companies

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and you actually can wiretap their,

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internal conversations, which, you know,

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typically people don't have in this paper that,

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we're talking about.

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Uh, we use a fairly standard, model of

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how firms kind of, open, expand.

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Basically it's, you could think

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of it like a firm investment model where there, there's a,

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some costs associated with, opening a store.

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There could be a, there could be costs associated

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with closing a store.

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And so what this model does is that it, it captures the,

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what it may me, what the model aims to do is

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to internalize, the incentives these firms have

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for these strategic investment decisions.

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And so there's essentially two important ingredients,

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if you will, or dimensions that this,

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these models in general need to capture.

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If one wants to study, preemption, the first is that,

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there has to be a strategic component, right?

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And so these firms need to be making decisions knowing

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that their decisions have implications on competitors.

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And the same thing, you know, they also expect competitors

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to be equally or reasonably strategic as well, right?

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And so it's, you, you kind of study in, in what we call kind

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of a game type of setting.

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And the second kind of dimension of, of that importance is,

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is this notion of forward looking behavior

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or your investment decision today is not only,

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aimed at optimizing your benefit today,

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but you also are trying to look ahead

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and see, okay, you know, if we open a store here, the,

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this is the implication it'll have on a market structure,

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not just today, but maybe tomorrow and date, you know, year

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after, and so on and so forth.

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And so, the model essentially needs

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to have these dimensions.

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And once you kind of combine these dimensions together,

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then it, it, it, it kind of offers us this kind

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of canonical, dynamic oligo model.

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And so the earliest, what among the earliest versions

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of these models were kind created in the eighties,

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I think the very, kind

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of the workhorse model is the Ericsson peku,

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dynamic oligopoly model, which, many, many of,

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you know, a lot of the modern research today still,

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continues to use.

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And so, yeah, so we, we were, we're essentially kind of,

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building off of, a a lot of the great kind of,

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at least analytical infrastructure

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that has already been, developed by,

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the past scholars.

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Our contributions are multi multifold.

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So the first is, you know,

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a little more theoretical analytical, where a lot

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of our results are actually kind of using,

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you know, pen and paper.

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Uh, not, not literally, you know, most of us now use iPads

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and and whatnot.

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But you know, we, we, we kind

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of build out the results on paper, if you will, so

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that we have a fairly

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concrete analytical notion about what is the value

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of preemption, right?

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And so we essentially need a moral framework in order

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to kind of decompose the various incentives firms have

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for expanding into the new markets.

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And by doing so, by kinda writing it out explicitly,

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which is not trivial,

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because, you know, you have all these different dimensions

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of strategy and forward looking behavior.

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And so when you're trying to decompose things like bill in

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equations, it becomes a fairly thorny task.

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And which is why we think there's a, a theoretical

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contribution to this paper where, you know, once you kind

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of, do this accounting, if you will,

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of all these different incentives, you can then say, aha,

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here is the specific benefit the firms have from

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expanding and as a result of this expansion,

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the added profits they get in the future

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because their competitors chose not to set up shop

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because of them, right?

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And so you, you could, you could tell just this, I,

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I felt like this is the, the, the, the simplest way

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of kind of characterizing this in simple words.

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And so you can imagine to translate this into kind

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of the analytical components, it's a,

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it's a little bit challenging.

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But the thing is, once you have, once we, we,

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we developed this framework,

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and once we had done this, decomposition on paper,

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it then makes the kind of empirical exercise a lot simpler

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because we, you know, you basically have a formula,

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if you will, for how to calculate this.

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Uh, it's, it's kind of hidden this intrinsic motivation

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for this intrinsic value of preempting the markets,

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which you can then kind of put to the data, right?

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So once you, once you have this kind of quantity written out

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analytically, it's really just a matter of estimating

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and fitting a dynamic oligo model with the data,

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which is the, in our empirical application,

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it's the coffee chain data.

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And once you have the model fitted to the data,

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you can then just plug in a bunch of values

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and just calculate, okay, you know, like 20, 35%

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of the motivation for Starbucks is driven by preemptive,

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considerations.

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And so we, as, I guess as the authors of the paper, we,

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our hope was we did the hard work so

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that future researchers can just plug it and play

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and kind of use a lot of these kind analytical insights in,

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you know, subsequent work.

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So that, it's, it's a little more concrete.

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Starbucks, of course, is perhaps the retailer that we,

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that the coffee chain that we notice, has the strongest,

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I guess motivation for, preempting the market.

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And so the, the beauty about our measures

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that we can actually, it's kind of a relative term,

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so we can actually say, okay, you know, X percent

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of their motivation is driven by preemptive considerations,

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and then 95% is just from all other kind of incentives.

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And so we, we actually find a bit of a range, you know, I,

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I think it, it could range from like around 5% to 30%,

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something in that ballpark.

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And so, you know, it, it, it, it's, it's, I believe it's

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among the first papers out there that actually provides kind

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of a percentage, measurement.

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In my earlier work.

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You know, the way we,

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I would've measured this would've been, like in,

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in a paper back in 2016 in quantitative economics

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with a different co-author.

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Um, you know, we had, in order to quantify this incentive,

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we did it in a more indirect way

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where we said, okay, here's a model.

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And it's a very similar model to what this,

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this current paper, this more recent paper uses.

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Uh, but in this other paper, what we do is

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what we economists call counterfactual based analysis, where

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what we, we essentially do is we say, okay, we hypothesized,

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we kind of conjecture that this specific part

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of the model corresponds to the preemptive motives.

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And so we just kind of shut this off, you know,

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assign some arbitrary value to this so

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that it's no longer apparent.

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Uh, we can effectively shut down this.

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So-called incentive behind a preemption,

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and then we just kind of simulate the expansion rates for,

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these retail chains, with and without this incentive

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and just compare the growth rates of the, the firms.

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And so in that 2016 study, we showed that

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preemptive motives are actually associated with,

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a faster growth of, in

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that case it was looking at McDonald's like

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the fast food chains.

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The main difference is, is is actually more in terms

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of, implementation.

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And so when we think about counterfactual based approaches,

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you, you could, you could probably sense,

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just when I was talking about the 2016 paper,

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there's a little bit of a judgment call in terms of, okay,

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you know, we, that's why I used the term conjecture.

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You know, I, I said, you know, our, my, my, in

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that 2016 study, we kind of, we had this intuition

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where we said, okay, you know, this part of the model seems

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to correspond to the preemptive motives.

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And so we, once you have kind of made that decision,

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then it, then it becomes easy, right?

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But the, the, the, the challenge is that, you know,

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our study did this counterfactual in this way,

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but then, you know, there,

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there were certainly a few subsequent papers not written

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by me, but by other researchers,

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and they can use different, you know, assumptions

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or different conjectures as to what is their belief,

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of the mechanism behind this.

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And so they, they will kind

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of implement the counteract differently,

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even if the model is more

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or less very similar, you know,

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albeit maybe different empirical settings.

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But you know, the model is all these models

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are more or less the same.

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And so, you know, you'll have different studies

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and the, these different studies will

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have different implementations.

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Some might even, are,

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use different equilibrium concepts, you know, in order

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to kind of run the counterfactuals.

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And at that point, you're, you're, the, the challenge is, is

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that it becomes hard to compare

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the measurements from one study to another.

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So I, I wouldn't say this means that you know, you know,

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study A is superior to study B or, or, or,

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or vice versa, simply

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because of the implementation assumptions.

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I think the bigger issue with, with kind

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of the counter factor based approaches is that it,

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it makes it hard for us to actually say, okay,

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there is a study in this particular industry

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and they found preemptive motives to be

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quantified at this value.

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And there's another study that showed a different industry

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quantify the preemptive motives at this other value,

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double the value or, or whatever.

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Uh, but you, you, you can't, it's hard to kind

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of compare these numbers.

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And so we hope that with this newest study, we hope

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that this is as close as one can get to the final word on,

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on this topic, topic where, you know,

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if there's a little bit of more standardization.

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So how we kind of measure these things, and you know, as,

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because industrial organization research

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tends to be case-based.

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You know, you, you, you know, in that paper, you know,

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normally we focus on one industry

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or even one sub-industry for each paper.

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And so I think there could be, there could be some merit

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of being able to do some type

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of meta-analysis in the future.

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You know, as more and more researchers study this,

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and there's more and more measurements across different

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empirical settings, across different, time periods,

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I think is valuable to be able to say, okay,

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here's this reasonably standardized measure,

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at least under the context of the standard model

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that most people use and, you know, applying this measure,

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but to different settings.

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So I think there could be some value from a kind

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of a broader perspective and,

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and perhaps in, in a practical sense, you know, if,

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if antitrust wants to, you know, come up with measures

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to determine whether they need to subpoena a company

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or not, measurement of, of, of deterrent modus,

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at least from a circumstantial evidence perspective,

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has always been a huge challenge for, antitrust.

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And so, not surprisingly, if you look in the, in,

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in the past, antitrust cases, there's very few

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that have, you know, been successful at, you know,

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prosecuting firms for, entry deterrents.

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It's, it's a, it's a, it's a difficult thing to, to show,

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like of, of course if you can get a subpoena

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and you can wiretap companies,

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then you can a lot more details,

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and that's probably where you can actually get a conviction.

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But in general, it's hard to build up

14:24.045 --> 14:25.805
that circumstantial evidence, right?

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And so I think the immediate implication is, you know,

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thinking about our policy, I think another

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interesting implication, and,

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and this is something, I'm exploring with the, the,

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the same co-author of this study we're talking about,

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is an implication on how we think about firm value, right?

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And so, if you look at corporate finance, a branch

14:49.725 --> 14:54.125
of corporate finance interested in looking at how

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to predict whether firms fail or not, right?

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And so you, you, you see a lot of retailers now are not,

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some retailers don't do very well.

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They close stores on mass, you know, and,

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and so if, if there's a way to develop some sort

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of prediction as to, whether their kind

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of entry strategies

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or expansion strategies are considered to be risky,

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if there's a way to kind of predict this,

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there could be some value for,

15:22.545 --> 15:25.005
for people in corporate finance, especially those

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that are trying to come up with kind of valuations of firms

15:28.475 --> 15:30.325
that they want to invest in.

15:30.505 --> 15:33.325
And so in this kind of follow up work, that li

15:33.425 --> 15:36.445
and I are kinda working on, we're trying

15:36.445 --> 15:40.365
to find a connection between this preemptive

15:41.035 --> 15:44.325
measure, you know, how incentivized they are to

15:45.385 --> 15:47.805
expand aggressively for the purpose

15:47.805 --> 15:49.125
of keeping competitors away.

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And, and in using this reasonably standardized measure,

15:52.345 --> 15:54.525
we want to see is there a connection between

15:55.055 --> 15:56.765
these preemptive motives

15:57.225 --> 16:00.765
and ex post failure of firms, right?

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So are they doing this?

16:03.505 --> 16:06.885
Um, so one of the assumptions we, we will make is,

16:06.945 --> 16:10.685
of course, firms are acting rationally and optimally,

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but as a, let's say you're an outside investor,

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you might still care, you know, to see, at least

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for risk analysis, you might want to see

16:21.065 --> 16:23.685
and assess, you know, whether, okay, you know,

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we can assume firms are all kinda being as strategic

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and as optimal as possible,

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but perhaps there's even a way to rank, you know,

16:32.645 --> 16:35.565
equally optimal firms in terms of their strategies

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and rank them based on their risk profiles as to, okay,

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is this entry strategy going to lead

16:41.345 --> 16:43.405
to a new bed bath and beyond?

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Which we know, you know, you know, a lot

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of these mall brands are just, you know, closing.

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And, and I think it, from a substative perspective,

16:50.985 --> 16:52.125
it could also be interesting

16:52.125 --> 16:55.805
because, most of us, especially in the marketing side

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of things, has often thought of digital commerce has kind

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of the thing that kills off a lot of brick

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and mortar retail chains.

17:03.385 --> 17:06.605
Uh, but you know, there's a smaller branch

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or a smaller number of people,

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or in particular very few people, perhaps just a, a handful

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of us who think that actually a lot of the, the,

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the issues is more about over expansion,

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especially if this expansion is fueled by debt, right?

17:21.765 --> 17:25.525
Which you've seen a lot of these retailers, of,

17:26.425 --> 17:29.045
you know, suffer from, expos, you know, when it,

17:29.105 --> 17:31.165
it could be okay, you know, when the times are good,

17:31.185 --> 17:34.525
but when the times are bad, it, it really amplifies,

17:34.525 --> 17:36.485
the pain for, for some of these retailers.

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And so I think down the road, I, I, I hope to be able

17:40.645 --> 17:42.965
to kind of integrate some of these insights more closely.

17:43.475 --> 17:45.005
Also to, you know, a lot

17:45.005 --> 17:47.165
of the discussions we have about retail

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apocalypse, although I think

17:48.705 --> 17:50.975
The discussions haven't really been

17:50.975 --> 17:53.535
as active recently about retail apocalypse,

17:53.535 --> 17:54.815
but you know, it's always there, you know,

17:54.815 --> 17:56.735
certainly if you follow retail

17:57.435 --> 18:01.535
expert gurus on the LinkedIn kind of crowds, I.

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