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Welfare Evaluation of Shop-Type Strategies on Digital Food Delivery Platforms

Author

Listed:
  • Xiaolan Zhou

    (School of Economics, Shandong University)

  • Yasuyuki Sawada

    (Asian Development Bank Institute and Faculty of Economics, The University of Tokyo)

  • Elaine S. Tan

    (Economic Research and Development Impact Department, Asian Development Bank)

Abstract

Using proprietary data from Alibaba, we estimate a structural model for the digital food delivery platform and quantify the welfare effects of shop-type strategies. We find that shops with higher net gross merchandise value (GMV), multi-app stores, and chain stores exhibit larger cross-network effects on both consumers and couriers. These types of shops are more effective in driving the expansion of the platform’s market size and contribute more significantly to the platform’s net GMV. The magnitudes of these effects are amplified in a dynamic setting due to the positive direct network effects at the platform level. Specifically, platforms' strategy of prioritizing top merchants over supporting a multitude of mid-tier merchants proves more effective in the fresh food sector than in the cooked food sectors characterized by greater product heterogeneity.

Suggested Citation

  • Xiaolan Zhou & Yasuyuki Sawada & Elaine S. Tan, 2026. "Welfare Evaluation of Shop-Type Strategies on Digital Food Delivery Platforms," CIRJE F-Series CIRJE-F-1270, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2026cf1270
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    References listed on IDEAS

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