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Pricing of display advertising as indication of the roots of market power of the digital platforms
https://doi.org/10.32609/0042-8736-2024-12-110-130
Abstract
Multi-sided digital platforms such as GAFAM (Google, Apple, Facebook, Amazon, Microsoft) provide services at zero cost by monetizing user attention and data through advertising. Testing the hypotheses on the determinants of display advertising price set by three largest digital platforms — Google, YouTube and Facebook, — contributes to the explanations of the roots of platforms market power. During the period from January 2018 to March 2024 cost per mile (CPM) increases with the number of unique visitors and traffic (total visits), as well as with the time that the user spends on the platform. However, the second effect is more pronounced. We interpret this result as an evidence that the market power in digital advertising depends more on the data of particular user’s preferences (digital footprint) and therefore on the ability to personalize advertising messages than on the number of users and data traffic on the platforms. The results high-light the economic value of personal data under monetization of cross-platform externalities through digital advertising.
Keywords
market power,
digital platforms,
digital advertising,
attention markets,
online advertising,
multi-sided markets,
network effects
JEL: L11, L26
About the Authors
S. V. Bovt
HSE University
Russian Federation
Russian Federation
Svetlana V. Bovt.
Moscow
S. B. Avdasheva
HSE University
Russian Federation
Russian Federation
Svetlana B. Avdasheva.
Moscow
References
1. Gribanov A. A. (2018). General Data Protection Regulation: Ideas for improving Russian legislation. Zakon, No. 3, pp. 149—162. (In Russian).
2. Levakov P. A., Pavlova N. S. (2024). Big data as a source of market power of digital platforms. Social Sciences and Contemporary World, No. 1, pp. 74—91. (In Russian). https://doi.org/10.31857/S0869049924010066
3. Shastitko A. E., Morosanova A. A. (2024). Expensive free. Voprosy Teoreticheskoy Ekonomiki, Vol. 23, No. 2, pp. 56—72. (In Russian). https://doi.org/10.52342/2587-7666VTE_2024_2_56_72
4. Abou Nabout N., Lilienthal M., Skiera B. (2014). Empirical generalizations in search engine advertising. Journal of Retailing, Vol. 90, No. 2, pp. 206—216. https://doi.org/10.1016/j.jretai.2014.03.002
5. Armstrong M. (2006). Competition in two-sided markets. RAND Journal of Economics, Vol. 37, No. 3, pp. 668—691. https://doi.org/10.1111/j.1756-2171.2006.tb00037.x
6. Baslandze S., Greenwood J., Marto R., Moreira S. (2023). The expansion of varieties in the new age of advertising. Review of Economic Dynamics, Vol. 50, pp. 171—210. https://doi.org/10.1016/j.red.2023.07.008
7. Çınar N., Ateş S. (2022). Data privacy in digital advertising: Towards a post-third-party cookie era. In: M. Filimowicz (еd.). Privacy: Algorithms and society. London: Routledge, pp. 55—77. https://doi.org/10.4324/9781003173335-3
8. Chen Y. J., Chen Y. M., Hsu Y. J., Wu J. H. (2019). Predicting consumers’ decisionmaking styles by analyzing digital footprints on Facebook. International Journal of Information Technology & Decision Making, Vol. 18, No. 2, pp. 601—627. https://doi.org/10.1142/S0219622019500019
9. Dakers M. (2016). Uber knows customers with dying batteries are more likely to accept surge pricing. The Telegraph, October 30.
10. Dorfman R., Steiner P. O. (1954). Optimal advertising and optimal quality. American Economic Review, Vol. 44, No. 5, pp. 826—836.
11. Eeckhout J., Veldkamp L. (2024). Data and markups: A macro-finance perspective. Available at SSRN: https://doi.org/10.2139/ssrn.4775852
12. Evans D. S. (2013). Attention rivalry among online platforms. Journal of Competition Law & Economics, Vol. 9, No. 2, pp. 313—357. https://doi.org/10.1093/joclec/nht014
13. Evans D. S. (2019). Attention platforms, the value of content, and public policy. Review of Industrial Organization, Vol. 54, No. 4, рр. 775—792. https://doi.org/10.1007/s11151-019-09681-x
14. Fletcher A., Ormosi P. L., Savani R. (2023). Recommender systems and supplier competition on platforms. Journal of Competition, Law & Economics, Vol. 19, No. 3, рр. 397—426. https://doi.org/10.1093/joclec/nhad009
15. Geradin D., Katsifis D., Karanikioti T. (2021). Google as a de facto privacy regulator: Аnalysing the Privacy Sandbox from an antitrust perspective. European Competition Journal, Vol. 17, No. 3, pp. 617—681. https://doi.org/10.1080/17441056.2021.1930450
16. Libert T., Nielsen R. (2018). Third-party web content on EU news sites: Potential challenges and paths to privacy improvement. Reuters Institute for the Study of Journalism. https://doi.org/10.60625/risj-fr2x-ax36
17. Mattioli D. (2012). On Orbitz, Mac users steered to pricier hotels. Wall Street Journal, June 26.
18. Poort J., Borgesius F. J. Z. (2019). Does everyone have a price? Understanding people’s attitude towards online and offline price discrimination. Internet Policy Review, Vol. 8, No 1. https://doi.org/10.14763/2019.1.1383
19. Prat A., Valletti T. (2022). Attention oligopoly. American Economic Journal: Microeconomics, Vol. 14, No. 3, pp. 530—557. https://doi.org/10.1257/mic.20200134
20. Prüfer J., Schottmüller C. (2021). Competing with big data. Journal of Industrial Economics, Vol. 69, No. 4, pp. 967—1008. https://doi.org/10.1111/joie.12259
21. Rekettye G., Pranjić G. (2020). Price personalization in the Big Data and GDPR context. Marketing & Menedzsment, Vol. 54, No. 3, pp. 5—14. https://doi.org/10.15170/MM.2020.54.03.01
22. Rochet J. C., Tirole J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, Vol. 1, No. 4, pp. 990—1029. https://doi.org/10.1162/154247603322493212
23. Rochet J. C., Tirole J. (2006). Two-sided markets: А progress report. RAND Journal of Еconomics, Vol. 37, No. 3, pp. 645—667. https://doi.org/10.1111/j.1756-2171.2006.tb00036.x
24. Romero Leguina J., Cuevas Rumín Á., Cuevas Rumín R. (2020). Digital marketing attribution: Understanding the user path. Electronics, Vol. 9, No. 11, article 1822. https://doi.org/10.3390/electronics9111822
25. Schepp N. P., Wambach A. (2016). On Big Data and its relevance for market power assessment. Journal of European Competition Law & Practice, Vol. 7, No. 2, pp. 120—124. https://doi.org/10.1093/jeclap/lpv091
26. Tian G., Wu W. (2023). Big data pricing in marketplace lending and price discrimination against repeat borrowers: Evidence from China. China Economic Review, Vol. 78, article 101944. https://doi.org/10.1016/j.chieco.2023.101944
27. Townley C., Morrison E., Yeung K. (2017). Big data and personalized price discrimination in EU competition law. Yearbook of European Law, Vol. 36, pp. 683—748. https://doi.org/10.1093/yel/yex015
28. Wu T. (2017). The crisis of attention theft — ads that steal your time for nothing in return. Wired, April 14. https://www.wired.com/2017/04/forcing-ads-captive-audience-attention-theft-crime/
29. Yoganarasimhan H. (2020). Search personalization using machine learning. Management Science, Vol. 66, No. 3, pp. 1045—1070. https://doi.org/10.1287/mnsc.2018.3255
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For citations:
Bovt S.V., Avdasheva S.B. Pricing of display advertising as indication of the roots of market power of the digital platforms. Voprosy Ekonomiki. 2024;(12):110-130. (In Russ.) https://doi.org/10.32609/0042-8736-2024-12-110-130
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