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Digital Platforms and Consumer Search Digital Platforms and Consumer Search Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Benjamin Casner, MSc. Graduate Program in Economics The Ohio State University 2020 Dissertation Committee: Huanxing Yang, Co-Advisor James Peck, Co-Advisor Paul J. Healy c Copyright by Benjamin Casner 2020 Abstract In the first chapter I explore why market platforms do not expel low-quality sellers when screening costs are minimal. I model a platform market with consumer search. The presence of low-quality sellers reduces search intensity, softening competition between sellers and increasing the equilibrium market price. The platform admits some low-quality sellers if competition between sellers is intense. Recommending a high-quality seller and this form of search obfuscation are complementary strategies. The low-quality sellers enable the recommended seller to attract many consumers at a high price and the effect of the recommendation is strengthened as low-quality sellers become more adept at imitating high-quality sellers. The second chapter looks at consumer search behavior when consumers are in- experienced with the market. In many consumer search environments, searchers are learning about the distribution of offers in the market. I conduct an experiment exploring a broad class of search problems with learning about the distribution of payoffs. My results support the common theoretical prediction that learning results in declining reservation values, providing evidence that learning may be an explanation for recall seen in field data. While the theory predicts a \one step" reservation value strategy, many subjects instead choose to set a high reservation value in order to learn about the distribution before adjusting based on their observations. I provide evidence that under-searching in search experiments may stem from a reinforcement heuristic. ii In the third chapter I use a model with a platform facilitating interaction between consumers, advertisers, and content creators to explore the effects of introducing a subscription which allows consumers to avoid ads. The subscription increases provision of niche content, but increased advertising and a high subscription price may reduce the welfare of consumers who enjoy mass market content. The effect on total welfare depends on how much the platform increases payments to content creators as a result of the subscription iii Acknowledgments Thank you to my family for always supporting me, Parker Wheatley for teaching me how to study, Soyoung Lee for years of friendship and for being one of the best sounding boards for research ideas, Paul Healy and the micro theory group at Ohio State for guiding me through the softer skills in academia, and most of all to James Peck and Huanxing Yang for teaching me how to recognize when to drop a bad idea, pursue a good one, and how to recognize the difference. iv Vita 2011 ........................................B.A. Economics and Math, Saint John's University 2012 ........................................MSc. Behavioural Economics, Univer- sity of Nottingham 2014 ........................................M.A. Economics, The Ohio State Uni- versity 2014-present . Graduate Teaching Associate, The Ohio State University. Fields of Study Major Field: Economics v Table of Contents Page Abstract....................................... ii Acknowledgments.................................. iv Vita.........................................v List of Tables.................................... ix List of Figures...................................x 1. Introduction..................................1 2. Seller Curation In Platforms.........................2 2.1 Introduction..............................2 2.2 Related Literature...........................5 2.3 The Model...............................9 2.3.1 Equilibrium of the Market Subgame............. 12 2.3.2 Consumer Surplus....................... 19 2.4 Search Obfuscation Via Low Quality Sellers............. 20 2.4.1 Equilibrium Curation Decision................ 24 2.5 Recommending a Seller........................ 28 2.6 Discussion................................ 39 2.7 Conclusion............................... 42 3. Learning While Shopping: An Experimental Investigation into the Effect of Learning on Consumer Search...................... 44 3.1 Introduction.............................. 44 3.2 Related literature............................ 48 vi 3.3 Theory................................. 52 3.4 Design of the Experiment....................... 54 3.4.1 Anatomy of an experimental search spell........... 55 3.4.2 Treatments........................... 58 3.4.3 Supplemental data....................... 60 3.5 Hypotheses............................... 62 3.6 Main Results.............................. 66 3.6.1 The reservation value path.................. 71 3.6.2 The information demand heuristic.............. 78 3.7 Feedback and under-search...................... 82 3.8 Distance from the theoretical optimum................ 88 3.9 High Cost Sessions........................... 92 3.9.1 Results............................. 92 3.10 Conclusion............................... 96 4. Media Provision With Outsourced Content Production.......... 98 4.1 Introduction.............................. 98 4.2 The Baseline Model.......................... 105 4.2.1 Baseline with no subscription................. 105 4.2.2 Baseline with subscription................... 111 4.3 Welfare Comparison.......................... 116 4.3.1 Total Welfare.......................... 119 4.3.2 Platform and Creator Profits................. 121 4.3.3 Consumer Welfare....................... 122 4.3.4 Numerical Example...................... 124 4.4 Extensions............................... 127 4.4.1 Heterogenous Utility...................... 127 4.4.2 Enriched Advertising Sector.................. 133 4.5 Conclusion............................... 137 Appendices 147 A. Omitted Proofs................................ 147 B. Duopoly platforms.............................. 167 C. Experimental Instructions.......................... 173 C.0.1 Learning Last Experimental Instructions........... 173 vii C.0.2 Learning First Instructions.................. 178 D. Media Provision Numerical Example.................... 185 D.0.1 Analytical Solutions for the Triangular Distribution Example. 190 viii List of Tables Table Page 3.1 Summary statistics for each treatment.................. 66 3.2 Fixed effects OLS predicting the reservation value path......... 74 3.3 Random effects ordered probit regression predicting the direction of change in reservation values....................... 76 3.4 Probit predicting whether a subject would be classified as a high information demand type......................... 79 3.5 OLS regression predicting payoffs for the search task in the learning treatment.................................. 80 3.6 Random effects probit predicting the probability of reservation value increasing.................................. 83 3.7 Random effects ordered probit regression predicting the direction of change in initial reservation values.................... 87 3.8 Summary statistics for each treatment in the high cost sessions.... 92 3.9 Random effects ordered probit regression predicting the direction of change in reservation values in the high cost sessions.......... 95 ix List of Figures Figure Page 2.1 The process by which consumers learn about seller quality and their match value................................ 11 2.2 A parameter space showing the values where the platform will admit a positive proportion of low-quality sellers in this numerical example.. 23 2.3 Comparative statics using the uniform numerical example....... 27 2.4 A diagram showing the comparison between α∗ and αR∗ as a function of the parameters............................. 36 2.5 A parameter space showing when the platform will admit a positive proprortion of low-quality sellers when it has the ability to recommend a high-quality seller in this numerical example.............. 38 2.6 The proportion of low-quality sellers with no screening, at the profit maximizing level, if there were 0 screening cost, and if the platform treated prices as independent of the proportion of low quality sellers. 39 3.1 An example of the user interface subjects saw in the full information treatment.................................. 58 3.2 An example of the user interface subjects saw in the learning treatment. 59 3.3 The density plots for two possible distributions in the learning treatment. 60 3.4 Kernel density representation of the length of the search spells..... 67 3.5 The reservation values by depth in search spell and treatment..... 68 x 3.6 Initial reservation values by treatment.................. 69 3.7 The kernel density and approximate paths of the normalized reservation values.................................... 71 3.8 The normalized reservation values in the learning treatment by depth in search spell, broken down by the true mean of the distribution... 73 3.9 The difference between the final observation and the corresponding reservation value by whether they lower, maintain, or raise the initial reservation value in the next search spell................. 85 3.10 The percentage error in reservation values compared to the theoretical optimum for a Bayesian learner by depth in search spell and treatment. 88 3.11 Mean absolute error for the first three reservation values of each search spell....................................
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