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Hiding in plain sight: Surge pricing and strategic providers

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  • Jiaru Bai
  • H. Sebastian Heese
  • Manish Tripathy

Abstract

Many on‐demand service platforms employ surge pricing policies, charging higher prices and raising provider compensation when customer demand exceeds provider supply. There is increasing evidence that service providers understand these pricing policies and strategically collude to induce artificial supply shortages by reducing the number of providers showing as available online. We study a stylized mathematical model of a setting in which an on‐demand service platform determines its pricing and provider compensation policies, anticipating their impact on customer demand and the participation of strategic providers, who might collectively decide to limit the number of providers showing online as available. We find that collusion can substantially harm the platform and customers, especially when the potential demand is large, and the supply of providers in nearby regions is limited. We explore two pricing policies that a platform could employ in the presence of (potential) provider collusion: a bonus pricing policy that offers additional provider payments on top of the regular compensation and the optimal pricing policy that maximizes the platform's expected profit while taking strategic provider behavior fully into consideration. Both policies feature a compensation structure that ensures that total provider earnings increase in the number of providers available, thereby encouraging all providers to offer their service. We show that both policies can effectively mitigate the impact of potential provider collusion, with the bonus pricing policy often performing near‐optimally. As it might be difficult for a platform to accurately estimate the propensity of providers to collude, we numerically evaluate how platform profits are affected if the pricing policy is designed based on possibly incorrect estimates of the providers' propensity to collude. Our observations suggest that a platform should design its pricing policy under the assumption that all providers are strategic and consider collusion, as the losses associated with implementing such a policy in settings with minor risk of collusion are limited, while the potential losses from failing to consider rampant collusion can be significant.

Suggested Citation

  • Jiaru Bai & H. Sebastian Heese & Manish Tripathy, 2023. "Hiding in plain sight: Surge pricing and strategic providers," Production and Operations Management, Production and Operations Management Society, vol. 32(12), pages 3837-3855, December.
  • Handle: RePEc:bla:popmgt:v:32:y:2023:i:12:p:3837-3855
    DOI: 10.1111/poms.14064
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    References listed on IDEAS

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    1. Gérard P. Cachon & Kaitlin M. Daniels & Ruben Lobel, 2017. "The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 368-384, July.
    2. Jean Tirole, 1988. "The Theory of Industrial Organization," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262200716, December.
    3. Harish Guda & Upender Subramanian, 2019. "Your Uber Is Arriving: Managing On-Demand Workers Through Surge Pricing, Forecast Communication, and Worker Incentives," Management Science, INFORMS, vol. 67(5), pages 1995-2014, May.
    4. Che, Yeon-Koo & Condorelli, Daniele & Kim, Jinwoo, 2018. "Weak cartels and collusion-proof auctions," Journal of Economic Theory, Elsevier, vol. 178(C), pages 398-435.
    5. Green, Edward J & Porter, Robert H, 1984. "Noncooperative Collusion under Imperfect Price Information," Econometrica, Econometric Society, vol. 52(1), pages 87-100, January.
    6. Bernard Caillaud & Philippe Jehiel, 1998. "Collusion in Auctions with Externalities," RAND Journal of Economics, The RAND Corporation, vol. 29(4), pages 680-702, Winter.
    7. Richard N. Clarke, 1983. "Collusion and the Incentives for Information Sharing," Bell Journal of Economics, The RAND Corporation, vol. 14(2), pages 383-394, Autumn.
    8. Ross, Thomas W., 1992. "Cartel stability and product differentiation," International Journal of Industrial Organization, Elsevier, vol. 10(1), pages 1-13, March.
    9. Nicholas Buchholz, 2022. "Spatial Equilibrium, Search Frictions, and Dynamic Efficiency in the Taxi Industry," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(2), pages 556-591.
    10. John S. Heywood & Zheng Wang, 2020. "Profitable collusion on costs: a spatial model," Journal of Economics, Springer, vol. 131(3), pages 267-286, December.
    11. Yiwei Chen & Ming Hu, 2020. "Pricing and Matching with Forward-Looking Buyers and Sellers," Manufacturing & Service Operations Management, INFORMS, vol. 22(4), pages 717-734, July.
    12. Fraas, Arthur G & Greer, Douglas F, 1977. "Market Structure and Price Collusion: An Empirical Analysis," Journal of Industrial Economics, Wiley Blackwell, vol. 26(1), pages 21-44, September.
    13. Harrington, Joseph Jr., 1989. "Collusion and predation under (almost) free entry," International Journal of Industrial Organization, Elsevier, vol. 7(3), pages 381-401.
    14. Davidson, Carl, 1984. "Cartel stability and tariff policy," Journal of International Economics, Elsevier, vol. 17(3-4), pages 219-237, November.
    15. Nikhil Garg & Hamid Nazerzadeh, 2022. "Driver Surge Pricing," Management Science, INFORMS, vol. 68(5), pages 3219-3235, May.
    16. McAfee, R Preston & McMillan, John, 1987. "Auctions and Bidding," Journal of Economic Literature, American Economic Association, vol. 25(2), pages 699-738, June.
    17. ,, 2008. "Auction design in the presence of collusion," Theoretical Economics, Econometric Society, vol. 3(3), September.
    18. Dingwei Gu & Zhiyong Yao & Wen Zhou & Rangrang Bai, 2019. "When is upstream collusion profitable?," RAND Journal of Economics, RAND Corporation, vol. 50(2), pages 326-341, June.
    19. Omar Besbes & Francisco Castro & Ilan Lobel, 2021. "Surge Pricing and Its Spatial Supply Response," Management Science, INFORMS, vol. 67(3), pages 1350-1367, March.
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