IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v20y2022i3d10.1007_s11129-022-09254-5.html
   My bibliography  Save this article

Distilling network effects from Steam

Author

Listed:
  • José Tudón

    (Río Hondo 1)

Abstract

This paper develops a method to estimate the demand for network goods, using minimal network data, but leveraging within-consumer variation. I estimate demand for video games as a function of individuals’ social networks, prices, and qualities, using data from Steam, the largest video game digital distributor in the world. I separately identify price elasticities on individuals with and without friends with the same game, conditional on individual fixed effects and games’ characteristics. I then use the discrepancies between estimated price elasticities to identify the impact of social networks. I compare my method to “traditional-IV” strategies in the literature, which require detailed network data, and find similar results. A 1% increase in friends’ demands, increases demand by .13%. In counterfactual simulations, I find demand increases by about 5% from a promotional giveaway to “influencers,” those users in the top 1% of popularity in the network.

Suggested Citation

  • José Tudón, 2022. "Distilling network effects from Steam," Quantitative Marketing and Economics (QME), Springer, vol. 20(3), pages 293-312, September.
    • Handle: RePEc:kap:qmktec:v:20:y:2022:i:3:d:10.1007_s11129-022-09254-5
    DOI: 10.1007/s11129-022-09254-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11129-022-09254-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11129-022-09254-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stephen Ryan & Catherine Tucker, 2012. "Heterogeneity and the dynamics of technology adoption," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 63-109, March.
    2. Marianne Bertrand & Erzo F. P. Luttmer & Sendhil Mullainathan, 2000. "Network Effects and Welfare Cultures," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(3), pages 1019-1055.
    3. Becker, Gary S, 1991. "A Note on Restaurant Pricing and Other Examples of Social Influences on Price," Journal of Political Economy, University of Chicago Press, vol. 99(5), pages 1109-1116, October.
    4. Harikesh Nair, 2007. "Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games," Quantitative Marketing and Economics (QME), Springer, vol. 5(3), pages 239-292, September.
    5. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    6. Jean-Pierre H. Dubé & Günter J. Hitsch & Pradeep K. Chintagunta, 2010. "Tipping and Concentration in Markets with Indirect Network Effects," Marketing Science, INFORMS, vol. 29(2), pages 216-249, 03-04.
    7. Giacomo De Giorgi & Anders Frederiksen & Luigi Pistaferri, 2020. "Consumption Network Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(1), pages 130-163.
    8. Arun G. Chandrasekhar & Cynthia Kinnan & Horacio Larreguy, 2018. "Social Networks as Contract Enforcement: Evidence from a Lab Experiment in the Field," American Economic Journal: Applied Economics, American Economic Association, vol. 10(4), pages 43-78, October.
    9. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    10. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    11. Belanche, Daniel & Casaló, Luis V. & Flavián, Marta & Ibáñez-Sánchez, Sergio, 2021. "Understanding influencer marketing: The role of congruence between influencers, products and consumers," Journal of Business Research, Elsevier, vol. 132(C), pages 186-195.
    12. Marc Rysman, 2019. "The reflection problem in network effect estimation," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 28(1), pages 153-158, January.
    13. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
    14. Angrist, Joshua D., 2014. "The perils of peer effects," Labour Economics, Elsevier, vol. 30(C), pages 98-108.
    15. Bruce Sacerdote, 2001. "Peer Effects with Random Assignment: Results for Dartmouth Roommates," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(2), pages 681-704.
    16. Wesley Hartmann & Puneet Manchanda & Harikesh Nair & Matthew Bothner & Peter Dodds & David Godes & Kartik Hosanagar & Catherine Tucker, 2008. "Modeling social interactions: Identification, empirical methods and policy implications," Marketing Letters, Springer, vol. 19(3), pages 287-304, December.
    17. Emily Breza & Arun G. Chandrasekhar, 2019. "Social Networks, Reputation, and Commitment: Evidence From a Savings Monitors Experiment," Econometrica, Econometric Society, vol. 87(1), pages 175-216, January.
    18. Emily Breza & Arun G. Chandrasekhar & Tyler H. McCormick & Mengjie Pan, 2020. "Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data," American Economic Review, American Economic Association, vol. 110(8), pages 2454-2484, August.
    19. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bruno Jullien & Alessandro Pavan & Marc Rysman, 2021. "Two-sided markets, pricing, and network effects," Post-Print hal-03828345, HAL.
    2. Park, Minjung, 2019. "Selection bias in estimation of peer effects in product adoption," Journal of choice modelling, Elsevier, vol. 30(C), pages 17-27.
    3. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    4. Daniel Bjorkegren, 2020. "Competition in Network Industries: Evidence from the Rwandan Mobile Phone Network," Working Papers 2020-04, Brown University, Department of Economics.
    5. de Paula, Aureo & Rasul, Imran & Souza, Pedro, 2018. "Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition," CEPR Discussion Papers 12792, C.E.P.R. Discussion Papers.
    6. Vincent Boucher & Yann Bramoullé, 2020. "Binary Outcomes and Linear Interactions," AMSE Working Papers 2038, Aix-Marseille School of Economics, France.
    7. Xiaohua Zeng & Liyuan Wei, 2013. "Social Ties and User Content Generation: Evidence from Flickr," Information Systems Research, INFORMS, vol. 24(1), pages 71-87, March.
    8. Che, Yuyuan & Feng, Hongli & Hennessy, David, 2021. "Assessing Peer Effects and Subsidy Impacts in Technology Adoption: Application to Grazing Management Choices with Farm Survey Data," 2021 Conference, August 17-31, 2021, Virtual 315123, International Association of Agricultural Economists.
    9. Catherine Tucker, 2011. "Network Stability, Network Externalities and Technology Adoption," NBER Working Papers 17246, National Bureau of Economic Research, Inc.
    10. Christiern Rose & Lizi Yu, 2021. "Identification of Peer Effects with Miss-specified Peer Groups: Missing Data and Group Uncertainty," Papers 2104.10365, arXiv.org, revised May 2022.
    11. Kuersteiner, Guido M. & Prucha, Ingmar R. & Zeng, Ying, 2023. "Efficient peer effects estimators with group effects," Journal of Econometrics, Elsevier, vol. 235(2), pages 2155-2194.
    12. Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2020. "Peer Effects in Networks: A Survey," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 603-629, August.
    13. Julian Hidalgo & Michelle Sovinsky, 2023. "Internet (Power) to the People: How to Bridge the Digital Divide," CRC TR 224 Discussion Paper Series crctr224_2023_461, University of Bonn and University of Mannheim, Germany.
    14. Jose-Alberto Guerra & Myra Mohnen, 2022. "Multinomial Choice with Social Interactions: Occupations in Victorian London," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 736-747, October.
    15. Tat Y. Chan & Jia Li & Lamar Pierce, 2014. "Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth," Marketing Science, INFORMS, vol. 33(4), pages 463-484, July.
    16. Jason M. Fletcher & Stephen L. Ross, 2018. "Estimating the effects of friends on health behaviors of adolescents," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1450-1483, October.
    17. Wesley R. Hartmann, 2010. "Demand Estimation with Social Interactions and the Implications for Targeted Marketing," Marketing Science, INFORMS, vol. 29(4), pages 585-601, 07-08.
    18. Leonardo Bursztyn & Florian Ederer & Bruno Ferman & Noam Yuchtman, 2012. "Understanding Peer Effects in Financial Decisions: Evidence from a Field Experiment," NBER Working Papers 18241, National Bureau of Economic Research, Inc.
    19. Bryan Bollinger & Kenneth Gillingham, 2012. "Peer Effects in the Diffusion of Solar Photovoltaic Panels," Marketing Science, INFORMS, vol. 31(6), pages 900-912, November.
    20. Hvide, Hans K. & Östberg, Per, 2015. "Social interaction at work," Journal of Financial Economics, Elsevier, vol. 117(3), pages 628-652.

    More about this item

    Keywords

    Network goods; Partially observed networks; Direct network externalities; Network effects;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:qmktec:v:20:y:2022:i:3:d:10.1007_s11129-022-09254-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.