Discussant: Wen Wen Univ. of Texas at Austin Review Rating-based Platform Screening and New Complementor Entry: Evidence from Natural Experiment and Machine Learning of a Sharing Economy By Xueming Luo, Zhijie Lin, and Tedi Skiti Discussant: Wen Wen Univ. of Texas at Austin

Summary This study examines: Major findings: A platform design that ranks incumbent complementors based on review ratings How does such design affect new complementor entry and the overall quality of complementors? Major findings: The rating-based platform screening reduces new complementor entry; has stronger effect when incumbent complementors have higher ratings increases complementors’ overall quality

Key Features Undoubtedly a fascinating topic! A variety of mechanisms are used by platforms to reduce information asymmetry and user search cost (the demand side), but little understanding on their effects on new complementors (the supply side)

Key Features Impressive data & interesting empirical setting! Detailed complementor-level data on a home-cooked food delivery platform (2 million+ obs., 9k+ complementors) A platform-wide change on complementor ranking during the sample period Before: recommendation not based on rating After: recommendation based on rating (higher on the top)

Comments and Suggestions Central hypothesis: A rating-based ranking system reduces new complementor entry What are the underlying mechanisms? Makes it harder for users to find new complementors, thereby discouraging new entry Increases competition among existing complementors, thereby discouraging new entry Should we also consider the demand effect? The new ranking mechanism reduces user search cost and increases demand, which may encourage new entry

Comments and Suggestions A great opportunity to paint a full picture of complementor responses to a new platform screening design Current focus: new complementor entry Why not also look at: Whether it has different effects on the entry of high-quality vs. low-quality new complementors How it affects the exit rate and survival chance of incumbent complementors How it changes the competitive strategies (e.g., price/quality differentiation) by incumbent and new complementors

Comments and Suggestions The empirics Current baseline analysis: complementor level Alternative analysis: At the district level (16 districts) At the district-product category level (assuming the competition mostly happens within each product category) # of new complementors located in complementor i’s district platform screening change dummy

Comments and Suggestions The empirics May need to address concerns such as It may pick up some general time trend The platform chose to implement the new screening system because of a reduction in new complementor entry Identify a control group for diff-in-diff estimation? Using data from a competing food delivery platform # of new complementors located in complementor i’s district platform screening change dummy

Very Promising Research! Very interesting research question Unique and rich data Exogenous platform design change offers potential for compelling identification