Titel: | The Impact of the Agency Model on E-book Prices: Evidence from the UK |
Autor: | Gail, Maximiliam Maurice |
Weitere Verfasser: | Klotz, Phil-Adrian |
Veröffentlicht: | 2021 |
URI: | https://archiv.ub.uni-marburg.de/es/2024/0687 |
URN: | urn:nbn:de:hebis:04-es2024-06873 |
DOI: | https://doi.org/10.17192/es2024.0687 |
ISSN: | 1867-3678 |
DDC: | 330 Wirtschaft |
Publikationsdatum: | 2024-01-19 |
Lizenz: | https://creativecommons.org/publicdomain/mark/1.0 |
Schlagwörter: |
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double machine learning, Amazon, agency, resale price maintenance, e-books, Latent Dirichlet allocation |
Summary:
This paper empirically analyzes the effect of the widely used agency
model on the retail prices of e-books in United Kingdom. Using a
unique cross-sectional data set of e-book prices for a large sample of
book titles across all major publishing houses, we exploit cross-genre
and cross-publisher variation to identify the mean effect of the agency
model on e-book prices. Since the genre information is ambiguous and
even missing for some titles in our original dataset, we use a Latent
Dirichlet Allocation (LDA) approach to determine detailed book genres
based on the book's descriptions. We find that e-book prices for
titles that are sold under the agency model are 36% cheaper than titles
sold under the wholesale model on average. Our results are robust to
different specifications, a Lewbel instrumental variable approach, and
machine learning techniques.
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