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Targeted incentives, broad impacts: Evidence from an E-commerce platform

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Abstract

Digital platforms sometimes offer incentives to a subset of sellers to nudge behavior, possibly affecting the behavior of all sellers in the equilibrium. In this paper, we study a policy change on a large e-commerce platform that offers financial incentives only to platform-certified sellers when they provide fast handling and generous return policies on their listings. We find that both targeted and non-targeted sellers become more likely to adopt the promoted behavior after the policy change. Exploiting a large number of markets on the platform, we find that in markets with a larger proportion of the targeted population—hence more affected by the policy change—non-targeted sellers are more likely to adopt the promoted behavior and experience a larger increase in sales and equilibrium prices. This finding is consistent with our key insight that a targeted incentive may increase demand for non-targeted sellers when both platform certificates and the promoted behaviors are quality signals. Our results have implications for digital platforms that use targeted incentives.

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Notes

  1. https://www.cnet.com/news/to-power-prime-one-day-shipping-amazon-asks-sellers-to-send-it-more-stuff/; https://www.uber.com/blog/los-angeles/consecutive-trips-earnings/; (9/14/2020)

  2. This is different from targeted subsidies which are monetary transfers to sellers of a given type regardless of their behavior. Examples of targeted subsidies are government subsidies for small firms and tax cuts for a sector of the economy. With targeted subsidies, the demand for non-targeted (thus non-subsidized) firms will be unambiguously lower because the prices of their subsidized competitors will decrease. As we show later, targeted incentives do not necessarily lead to such equilibrium outcomes.

  3. See Rafieian and Yoganarasimhan (2021) for a discussion of the pros and cons of behavioral and contextual targeting from the perspective of an ad network.

  4. We do not allow for entry and exit in this model, to keep it tractable. In Table A1 in the online appendix, we show that the policy change did not change the number of eTRS or non-eTRS sellers.

  5. Refer to the example given by Tirole (1988) (Section 2.1.1, Chapter 2) for the intuition of this assumption.

  6. We note that there are points of departure from the Tirole (1988) model. The key difference with Tirole’s model is that we allow sellers to endogenously select the quality (PS vs. non-PS). The reason is to fit with our research objective, which is to study how targeted incentives affect sellers’ quality choice and thus the equilibrium outcomes.

    The vertical structure of the model restricts that there is only direct substitution between sellers in adjacent segments. In our context, it means that, for example, eTRS-PS listings only compete against eTRS-non-PS listings but not non-eTRS-PS listings. This could be a limitation of Tirole’s model applying to our context. However, we note that non-adjacent listings can still affect each other indirectly at the market equilibrium.

    We note that, because of the simplifying assumptions, Tirole’s model is used to illustrate the mechanism driving our findings. It is not meant to form a basis for the structural model of which researchers can apply the data to estimate the primitives of the model.

  7. The eTRS badge is beneficial for sellers as it increases sales probability by 7% on the eBay U.K. site (Elfenbein et al. , 2015) and increases sales price by 3% on the eBay U.S. site (Hui et al. , 2016).

  8. The sample period ends at Week 13 because eBay announced a new policy in Week 14. This policy was designed to further incentivize sellers to improve on shipping and handling. Specifically, eTRS sellers would lose the 20% discount on the commission fee for listings without PS.

  9. We have also plotted the figure using 33 weeks after the policy change in Fig. A3 in the online appendix. Both time series became relatively stable in the extended period. This suggests that sellers did not shift forward some inventory because of the targeted incentive. However, this is a possibility that we cannot fully rule out.

  10. One may argue that otherwise identical listings that only differ in PS may render the one without PS inferior and thus not able to sell. However, due to market friction, the matched listings are likely not perfect substitutes. Because of search cost, consumers may not be able to find and compare the two otherwise identical listings. Therefore, the listings without PS may still generate sales in equilibrium.

  11. Using the share of eTRS sellers as an alternative policy exposure measure does not change the results qualitatively.

  12. Specifically, under the targeted incentive, eTRS sellers pay 75% of the normal commission for PS listings and 80% for their non-PS listings. Therefore, the discount for PS listings is 75/80 = 15/16.

  13. Specifically, the targeted incentive only lasted for three months, meaning that eTRS sellers who offered PS after the three-month period do not get the reduced commission rate. Given this, the platform commission growth reduces to \((C_1 - C_0)/C_0 = \beta \).

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Acknowledgements

We thank Seth Benzell, Avi Collis, Oren Reshef, Song Yao, and seminar and conference participants at Washington University in St. Louis, CIST, CODE, UTD-FORMS, ISMS Marketing Science for their helpful comments. We gratefully acknowledge the guidance provided by the editor and two anonymous referees. We are also grateful to eBay for providing access to the data.

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  1. Washington University, St. Louis, MO, USA

    Xiang Hui, Meng Liu & Tat Chan

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  1. Xiang Hui
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Correspondence to Xiang Hui.

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None of the authors has a current financial relationship with eBay. Xiang Hui did an internship at eBay from May 2015 to August 2015, and received a \( \$ \)16,000 compensation.

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Hui, X., Liu, M. & Chan, T. Targeted incentives, broad impacts: Evidence from an E-commerce platform. Quant Mark Econ 21, 493–517 (2023). https://doi.org/10.1007/s11129-023-09267-8

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