Identification of Causal Effects in the Context of Mass Collaboration

Chapter
Part of the Computer-Supported Collaborative Learning Series book series (CULS, volume 16)

Abstract

Several instances of successful online mass collaboration have recently generated large amounts of data. These datasets are very appealing for empirical research on patterns and drivers of mass collaboration in a wide range of social science disciplines. However, their complexity, the presence of network effects, and multidirectional nature of the causal mechanisms at play often raise substantial challenges to empirical researchers. In this chapter, we discuss the econometric approach to mass collaboration, focusing on the methodological challenges of causal identification and the interpretation of how some factors affect others. Our chapter provides methodological tools for causal identification of effects in observational data from mass collaboration platforms. Specifically, we present two quasi-experimental methods, natural experiments and instrumental variables, in detail and show applications using examples from our own research.

Keywords

Mass collaboration Collaboration Causal effects Natural experiments Econometric research 

References

  1. Algan, Y., Benkler, Y., Morell, M. F., & Hergueux, J. (2013, July). Cooperation in a peer production economy experimental evidence from Wikipedia (Working paper). In NBER Workshop on the Economics of IT and Digitization, Milan, Italy.Google Scholar
  2. Angrist, J., & Pischke, J.-S. (2009). Mostly Harmless Econometrics. Princeton, NJ: Princeton University Press.Google Scholar
  3. Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106, 21544–21549.CrossRefGoogle Scholar
  4. Baldwin, C., & von Hippel, E. (2011). Modeling a paradigm shift: From producer innovation to user and open collaborative innovation. Organization Science, 22, 1399–1417.CrossRefGoogle Scholar
  5. Benkler, Y. (2002). Coase’s Penguin or Linux and the nature of the firm. The Yale Law Journal, 112, 369–446.CrossRefGoogle Scholar
  6. Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150, 41–55.CrossRefGoogle Scholar
  7. Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally College.Google Scholar
  8. Coase, R. (1937). The nature of the firm. Economica, 4, 386–405.CrossRefGoogle Scholar
  9. Demsetz, H. (1967). Toward a theory of property rights. The American Economic Review, 57(2), 347–359.Google Scholar
  10. Goldfarb, A., & Tucker, C. (2014). Conducting research with quasi-experiments: A guide for marketers (Working paper No. 2420920). Rotman School of Management/SSRN. Retrieved from http://ssrn.com/abstract=2420920.
  11. Goldsmith-Pinkham, P., & Imbens, G. W. (2013). Social networks and the identification of peer effects. Journal of Business & Economic Statistics, 31, 253–264.CrossRefGoogle Scholar
  12. Graham, B. S. (2015). Methods of identification in social networks. Annual Reviews of Economics, 7, 465–485. doi:10.1146/annurev-economics-080614-115611.CrossRefGoogle Scholar
  13. Heckman, J. (2000). Causal parameters and policy analysis in economics: A twentieth century retrospective. Quarterly Journal of Economics, 115, 45–97.CrossRefGoogle Scholar
  14. Hess, C., & Ostrom, E. (2003). Ideas, artifacts, and facilities: Information as a common-pool resource. Law and Contemporary Problems, 66, 111–145.Google Scholar
  15. Jackson, M. O. (2014). Networks in the understanding of economic behaviors. The Journal of Economic Perspectives, 28, 3–22.CrossRefGoogle Scholar
  16. Kummer, M. (2013). Spillovers in networks of user generated content—Evidence from 23 natural experiments on Wikipedia (ZEW Discussion Paper No. 13-098). Mannheim, Germany: SSRN.Google Scholar
  17. Lerner, J., & Tirole, J. (2002). Some simple economics of open source. The Journal of Industrial Economics, 50, 197–234.CrossRefGoogle Scholar
  18. Matthews, R. (2000). Storks deliver babies (p = 0.008). Teaching Statistics, 22, 36–38.CrossRefGoogle Scholar
  19. Osterloh, M., & Rota, S. (2007). Open source software development—Just another case of collective invention? Research Policy, 36, 157–171.CrossRefGoogle Scholar
  20. Shriver, S. K., Nair, H. S., & Hofstetter, R. (2013). Social ties and user-generated content: Evidence from an online social network. Management Science, 59, 1425–1443.CrossRefGoogle Scholar
  21. Slivko, O. (2014). Peer effects in collaborative content generation: The evidence from German Wikipedia (ZEW Discussion Paper No. 14-128). Mannheim, Germany.Google Scholar
  22. Stock, J. H., & Trebbi, F. (2003). Retrospectives: Who invented instrumental variable regression? Journal of Economic Perspectives, 17, 177–194.CrossRefGoogle Scholar
  23. Tapscott, D., & Williams, A. D. (2010). Wikinomics: How mass collaboration changes everything. New York, NY: Portfolio.Google Scholar
  24. VanderWeele, T. J. (2011). Sensitivity analysis for contagion effects in social networks. Sociological Methods & Research, 40, 240–255.CrossRefGoogle Scholar
  25. Wooldridge, J. M. (2013). Introductory econometrics: A modern approach (5th ed.). Mason, OH: Cengage Learning.Google Scholar
  26. Zhang, X. M., & Zhu, F. (2011). Group size and incentives to contribute: A natural experiment at Chinese Wikipedia. The American Economic Review, 101, 1601–1615.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Centre for European Research (ZEW)MannheimGermany

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