The Origin of Name Brands and Generics

By Joseph Johnson

Advertising is essential in a free market system. In fact, U.S. companies spent 238.8 billion dollars on advertising in 2019 alone [1]. However, existing literature still debates the beneficial effects of advertising. Does advertising increase profit or promote positive outcomes for consumers? What is the expected distribution? 

According to Ad Age, the top 200 U.S. companies who spent the most money in advertising in 2019 were responsible for 73 percent of total U.S. advertising expenditures [1]. This figure implies that companies fall into one of two classes: those that spend massive amounts on advertising and those that spend relatively little.

To gain a qualitative understanding of the expected shape of advertising distribution under monopolistic competition, my collaborators—Daniel Abrams and Adam Redlich—and I aimed to develop a dynamical systems model. Monopolistic competition occurs when a market has many suppliers and the products are distinguished by either branding or quality. We make the following assumptions within our model:

1. Companies’ products are indistinguishable (except for brand label)
2. The relationship between demanded quantity and product price is linear
3. Demand for a product increases when advertising is above the mean advertising level and decreases when advertising is below the mean (see Figure 1)
4. Companies set their prices to the profit-maximizing value
5. Companies shift advertising levels in the direction that increases profit.

These assumptions yield a system of differential equations that outlines the dynamics of advertising expenditures for each company. We find that when the marginal advertising cost—the cost for an additional unit of exposure, assuming linearity—is low enough, companies can split into two groups. The “name brand” group spends a significant amount of money on advertising while the “generic” group abstains from advertising (see Animation 1 for an explanation of the dynamics). We call this equilibrium the “differentiated state.” If the costs of marginal advertising are too high, all of the companies will eventually choose to abstain from advertising; we call this result the “undifferentiated state.” There is also a bistable region where marginal advertising costs are not too low or too high, meaning that both the differentiated and undifferentiated states are stable equilibria.

Because the demand changes with mean advertising, one must develop self-consistency conditions for the system’s equilibrium. After developing such conditions, we discovered that the feasible proportion of name-brand companies is governed by factors such as the marginal cost of advertising, maximum gain in demand, and consumer price sensitivity. 

We further analyzed the benefit of the differentiated state compared to the undifferentiated state by measuring benefit in three ways: consumer welfare (the net value of products that consumers purchase after subtracting price), total profit (sum of the profit for all companies), and total welfare (sum of consumer welfare and profit). 

Surprisingly, consumer welfare was always better for the differentiated state than the undifferentiated state in our experiments. One might hypothesize that this result is due to advertisements that “increase” the value of name-brand products. However, total profit was larger for the differentiated state only when marginal advertising cost was small; otherwise, the undifferentiated state was more profitable for the whole. Total welfare was similar to total profit; it was larger for the differentiated state with small values of marginal advertising cost and smaller for large values of marginal advertising cost.

We also demonstrated our model’s ability to match its generated price distributions to price data from Nielsen’s Consumer Panel dataset. The dataset holds information on consumer expenditures from thousands of American households and contains more than 64 million transactions. We generated model fits for the 500 most populous products in the dataset (see Figure 2). Use of two statistical tests—the Kolmogorov-Smirnov test and Hartigan’s dip test [2]—showed that 58 percent of our 500 model fits matched well. 46 percent of the dataset was bimodal in nature.

Although it is simple, our model generates a robust prediction: when the marginal cost of advertising is low enough, companies will split into “name brand” and “generic” groups. This prediction is surprising because many economic quantities are assumed to be unimodal in nature. We have also conducted numerical experiments that compare the benefits of advertising for companies and consumers. In all cases, consumer welfare was larger in the presence of advertising (though whether that is due to an artificially high value induced by advertising is up for debate). However, profit and overall welfare were only larger in the presence of advertising if the marginal advertising cost was low. 

We hope that our analysis will inspire further research into advertising’s effects on competition and consumer welfare, given the structure of the advertising distribution.

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Daniel Abrams presented this research during a minisymposium at the 2021 SIAM Conference on Applications of Dynamical Systems, which took place virtually in May 2021. 

References
[1] Ad Age. (2020, July 13). Leading national advertisers 2020 fact pack. Crain Communications Inc. Retrieved from https://s3-prod.adage.com/s3fs-public/2020-07/lnafp_aa_20200713_locked.pdf.
[2] Hartigan, J.A., & Hartigan, P.M. (1985). The dip test of unimodality. Ann. Statist., 13(1), 70-84.

Joseph Johnson is a postdoctoral research fellow at the University of Michigan’s Center for the Study of Complex Systems. His research centers on the application of dynamical systems theory to social and biological systems, ranging from bimodality in sex cells to the development of name brand and generic companies.