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Strategic Obfuscation and Retail Pricing

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Abstract

Consumer-product manufacturers—and retailers that sell their products—often sell slightly differentiated items for reasons other than appealing to heterogeneous tastes—different sizes of a popular brand, or different flavors in a common product line for instance. We argue that this practice is a form of strategic obfuscation, which is intended to make price-comparison more difficult, and thereby raise margins on non-comparable products. We test our hypothesis with the use of examples from consumer-packaged good categories in German and French retail scanner data. We find that—after controlling for other explanations for how margins can vary with package size and type—we cannot rule out strategic obfuscation as a feature of our retail sales data.

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Notes

  1. Throughout, we define prices as the price-per-unit-of-volume, or euros per litre in our context.

  2. We define a unique offering as one in which a particular retailer is the dominant supplier. We provide more details on our sample design below.

  3. We confirm our findings from the French data with a similar analysis that is applied to German coffee data.

  4. Chakraborty et al. (2015) identify a third type of obfuscation in which retailers attempt to mask general price changes by making very small changes to highly visible products. In their example, retailers disguise general basket-price increases with small price-reductions (about one cent) for items consumers care particularly about.

  5. Cakır and Balagtas (2014) argue that package downsizing is used as a means of passing along input price increases without losing market share, while Yonezawa and Richards (2016) show that this argument is incomplete as it does not allow for strategic behavior among retailers and manufacturers.

  6. Piccione and Spiegler (2012) argue that their approach provides a new interpretation of differentiation that admits perceptual differentiation through framing complexity.

  7. GfK Panel Services data are not available beyond the 2010 calendar year.

  8. The specific identity of the manufacturer, brands, and retailers in our sample are not disclosed for confidentiality reasons. However, the manufacturer is a major multi-national firm with many brands that are in wide distribution. The retailers are among the top 10 in France.

  9. We estimated a version of the model using a “70% rule” instead and the results were not qualitatively different, although the sample of unique items was larger.

  10. “Fringe sales” of items (in other stores) that are deemed unique to one store by our 80% rule, are responsible for the fact that we have 69 retailer/item combinations from only 11 unique and seven non-unique items. These items are defined as “common” in order to differentiate them from the items that are defined as unique to a dominant store.

  11. These definitions are well-supported in the literature on retailing in France (Bonnet and Bouamra-Mechemache 2016; Turolla 2016).

  12. Consumers do not appear to be absolutely loyal to either a retailer or retailer-item combination: households in our sample return to the same retailer 62.7% of the time, but purchase the same retailer-item pair only 40.6% of the time.

  13. A Wald test for the equality of the two sets of regression coefficients produces a Chi-square statistic of 5613.82, so we easily reject the null hypothesis with 9 degrees of freedom.

  14. An anonymous reviewer pointed out that loyalty to either unique or non-unique items may explain any observed difference in prices. We tested this hypothesis using our hedonic model and the t-statistic on the loyalty variable equals 0.41, so loyalty does not explain the observed unique premium.

  15. The German coffee data exhibits similar variability. Summary tables are available upon request.

  16. We recognize that stockpiling, loyalty, or other forms of state-dependence may be important in determining the frequency with which consumers purchase soft-drinks in general (Wang et al. 2016), but the effect on equilibrium prices is not likely to differ among variants of the same brands of soda. Conceptually, there is an unmodeled category-choice decision-stage before the one that we report here that determines how frequently consumers purchase, which is largely based on need (interpurchase time, lagged purchase quantity, and consumption rate). Purchase incidence is most certainly affected by stockpiling and associated dynamic behaviors, but choice among variants of the same brands is not. Others in the beverage, brand-choice literature (Dubé 2004; Bonnet and Dubois 2010; Bonnet and Réquillart 2013) and in the storable, snack-food literature (Dubois et al. 2017) do not account for stockpiling, and find credible results.

  17. To clarify terminology, we refer to an “item” as a specific product-retailer combination. For example, a 330 ml can of brand 1 at retailer 1 is a separate item from a 330 ml can of brand 1 offered at retailer 2.

  18. Prices and the error term vary by purchase occasion, but we do not subscript by t for clarity sake.

  19. Retailer 5 is our base case. The identities of the sample stores are not disclosed for reasons of confidentiality.

  20. In the estimated form of the model, \(\Sigma\) was constrained to a diagonal matrix.

  21. Others estimate models similar to ours with the use of Bayesian methods; however, Train (2003) argues that random parameter logit models of the type that we estimate here are observationally equivalent to Bayesian models, and are more easily estimated with the use of simulated maximum likelihood. We use 50 Halton draws (Bhat 2003) in order to make the estimation routine more efficient.

  22. The use of a conduct parameter has been criticized in the theoretical literature (Corts 1999), but nonetheless represents a concise way of nesting a wide range of observed price behaviors. Moreover, the criticism that it mis-represents the true dynamics of oligopolistic rivalry can be applied to any static model of firm or consumer behavior.

  23. A Hausman test of the exogeneity of retail margins (Hausman 1978) produces a Chi-square statistic of 5.72, which is greater than the critical Chi-square value of 3.84 with one degree of freedom. Therefore, we reject the null hypothesis of exogeneity.

  24. A reviewer notes that there is an alternative explanation for our findings; that uniqueness creates local monopolies, in which case premia would exist even with zero search costs. However, the fact that our unique items are only slightly differentiated from others makes the local-monopoly story less plausible than our maintained explanation. Choosing products that are nearly identical also rules out the notion that our findings are picking up a “store brand” effect as it is not plausible that consumers identify subtle differences in package with a particular store.

  25. Note that the scale parameter is relatively large compared to the French estimate (10.0848 versus 0.919). This difference is due to the fact that the German demand model was estimated with a log-normal distribution for the marginal utility of income parameter (Hole 2007). While this assumption rules out positive estimates for the price-parameter, the log-normal distribution has notoriously fat tails.

  26. The willingness-to-pay premium is found by dividing the “Unique” parameter estimate by the negative of the mean marginal utility of income estimate.

  27. Note that a full set of input price indices that would be similar to those that are used in French model were not available for Germany. Brand and retailer fixed effects, and indicators of package variants, are used to identify any cost differences that are associated with selling through a different outlet, producing through different manufacturing facilities, or using a different package.

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Acknowledgements

The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013) Grant Agreement No. 340903. Support from the Agricultural and Food Research Initiative (NIFA, USDA) is also gratefully acknowledged. The authors wish to thank seminar participants at the Toulouse School of Economics and at the Agricultural and Applied Economics Association annual meetings (Boston, MA). All remaining errors are our own.

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Authors and Affiliations

  1. Morrison School of Agribusiness, W. P. Carey School of Business, Arizona State University, 7231 E Sonoran Arroyo Mall, Mesa, AZ, 85212, USA

    Timothy J. Richards

  2. University of Münster, Münster, Germany

    Gordon J. Klein

  3. University of Düsseldorf (Research Affiliate), Düsseldorf, Germany

    Gordon J. Klein

  4. INRA, Toulouse School of Economics, University of Toulouse Capitole, Toulouse, France

    Celine Bonnet & Zohra Bouamra-Mechemache

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  1. Timothy J. Richards
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  2. Gordon J. Klein
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  4. Zohra Bouamra-Mechemache
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Correspondence to Timothy J. Richards.

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Richards, T.J., Klein, G.J., Bonnet, C. et al. Strategic Obfuscation and Retail Pricing. Rev Ind Organ 57, 859–889 (2020). https://doi.org/10.1007/s11151-019-09744-z

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