Abstract
This paper uniquely demonstrates the scope for profitable collusion on transport costs under delivered pricing. In addition to being profitable, such collusion is shown to be more stable than price collusion and harder to detect as it presents to authorities as continued Bertrand price competition. Such collusion generates endogenous duopoly locations outside the quartiles with less stable but more profitable collusion happening toward the endpoints. These results emerge in both the traditional model of inelastic demand and an extended model of elastic demand.
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Notes
As a potential illustration, firms engaged in delivered pricing in China face a well-known and routine increase in shipping costs around specific holidays when demand is greater. These increases involve multiple competing firms (see China Daily 2017).
The school milk markets case in Ohio is discussed in detail by Porter and Zona (1999).
They identify price fixing as the first type and concerted action against rivals as the second type.
These two cases and many others are described in more detail by Lande and Marvel (2000).
In other contexts, increased transport cost also increases profit under spatial price discrimination. Gupta et al. (1995) show that the downstream firm can profitably increase transport costs as the upstream firm fears losing market demand and so accommodates by lowering the input price. Similarly, Heywood and Pal (1996) show that a spatial monopoly playing a game against a tax authority can likewise increase its transport cost and so reduce taxes while increasing profitability.
Introducing a fixed cost associated with the one-time location decision does not change our results. Similarly, modeling a case with locations that change without cost for the cheating and punishment phase (see Andree et al. 2018) generates very similar results - available upon request. Left not modeled is the possibility of such relocation in the face of relocation costs.
Alternative assumptions would change the competitive locations known to be at the quantiles with a uniform distribution (Hurter and Lederer 1985). Thus, a normal distribution around the middle would tend to bring competitive location choices toward the middle and bi-modal distributions could drive competitive locations toward the corners.
This conclusion that independent adoption of endogenous costs minimizes costs carries over to the elastic demand case of Gupta and Venkatu (2002).
While the most common, see Miklós-Thal (2008) for alternative punishment strategies. Also recognize that we ignore other factors that might influence cartel stability such as detection lags in identifying the cheater and expected antitrust costs of cartel prosecution.
We recognize that the greater stability of colluding on costs may be irrelevant if the actual discount rates are never above those required for price collusion. In this case price collusion would simply be anticipated.
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Acknowledgements
Zheng Wang acknowledges financial support from the National Natural Science Foundation of China [grant numbers 71803137 and 71733001].
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Appendices
Appendix 1: the expression of \(\delta^{*}\) for inelastic demand
Appendix 2: deriving unit transport cost under competition with elastic demand
Due to the symmetry, we impose \(L_{2} = 1 - L_{1}\) and \(t_{2} = t_{1}\) to \(\partial \pi_{1}^{C} /\partial t_{1}\), where \(\pi_{1}^{C}\) is shown in (15). This yields: \(\frac{{\partial \pi_{1}^{C} }}{{\partial t_{1} }} = \left( { - \frac{4}{3}L_{1}^{3} + \frac{3}{2}L_{1}^{2} - \frac{1}{2}L_{1} + \frac{1}{12}} \right)t_{1} - L_{1}^{2} + \frac{{L_{1} }}{2} - \frac{1}{8} { \gtrless }0\) if \(t_{1} { \gtrless } - \frac{{3\left( {8L_{1}^{2} - 4L_{1} + 1} \right)}}{{2\left( {16L_{1}^{3} - 18L_{1}^{2} + 6L_{1} - 1} \right)}}\). It can be shown that \(- \frac{{3\left( {8L_{1}^{2} - 4L_{1} + 1} \right)}}{{2\left( {16L_{1}^{3} - 18L_{1}^{2} + 6L_{1} - 1} \right)}} > 0.9\) for \(L_{1} < 1/2\). Thus, the optimal unit transport costs for both firms are: \(t_{1}^{C} = t_{2}^{C} = \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{t}\).
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Heywood, J.S., Wang, Z. Profitable collusion on costs: a spatial model. J Econ 131, 267–286 (2020). https://doi.org/10.1007/s00712-020-00709-5
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DOI: https://doi.org/10.1007/s00712-020-00709-5