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<p>This paper empirically analyzes the relation between the widely used agency model and retail prices of e-books sold in the UK. Using a unique cross-sectional data set of e-book prices for a large number of book titles across all major publishing houses, we exploit cross-genre and cross-publisher variation to examine the interplay between the agency model and e-book prices. Since the genre information is ambiguous and even missing for some titles in our original data set, we also apply a latent Dirichlet allocation (LDA) approach to determine detailed book genres based on the book’s descriptions. Using propensity score matching, we find that retail prices for e-books sold under the agency model tend to be systematically lower than book titles with similar characteristics sold under the wholesale model, approximately 20%. This result varies with the exact sales rank of a book and is driven by the so-called <i>long tail</i> books. Our results are robust across various regression specifications and double machine learning techniques.</p>

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E-book Pricing Under the Agency Model: Lessons from the UK

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月1日

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  • Maximilian Maurice Gail,
  • Phil-Adrian Klotz

摘要

This paper empirically analyzes the relation between the widely used agency model and retail prices of e-books sold in the UK. Using a unique cross-sectional data set of e-book prices for a large number of book titles across all major publishing houses, we exploit cross-genre and cross-publisher variation to examine the interplay between the agency model and e-book prices. Since the genre information is ambiguous and even missing for some titles in our original data set, we also apply a latent Dirichlet allocation (LDA) approach to determine detailed book genres based on the book’s descriptions. Using propensity score matching, we find that retail prices for e-books sold under the agency model tend to be systematically lower than book titles with similar characteristics sold under the wholesale model, approximately 20%. This result varies with the exact sales rank of a book and is driven by the so-called long tail books. Our results are robust across various regression specifications and double machine learning techniques.