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
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- 1.
If information is around, it is impossible to exclude anyone completely from its use, though partial exclusion, e.g., through patents, is possible.
- 2.
Many people can use the same piece of information at the same time.
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Slivko, O., Kummer, M., Saam, M. (2016). Identification of Causal Effects in the Context of Mass Collaboration. In: Cress, U., Moskaliuk, J., Jeong, H. (eds) Mass Collaboration and Education. Computer-Supported Collaborative Learning Series, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-13536-6_19
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DOI: https://doi.org/10.1007/978-3-319-13536-6_19
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