Externalities in knowledge production: evidence from a randomized field experiment

Hinnosaar, Marit; Hinnosaar, Toomas; Kummer, Michael E.; Slivko, Olga

Abstract

Are there positive or negative externalities in knowledge production? We analyze whether current contributions to knowledge production increase or decrease the future growth of knowledge. To assess this, we use a randomized field experiment that added content to some pages in Wikipedia while leaving similar pages unchanged. We compare subsequent content growth over the next four years between the treatment and control groups. Our estimates allow us to rule out effects on four-year growth of content length larger than twelve percent. We can also rule out effects on four-year growth of content quality larger than four points, which is less than one-fifth of the size of the treatment itself. The treatment increased editing activity in the first two years, but most of these edits only modified the text added by the treatment. Our results have implications for information seeding and incentivizing contributions. They imply that additional content may inspire future contributions in the short-and medium-term but do not generate large externalities in the long term. JEL: L17, L86, C93

Citation

Hinnosaar, M., Hinnosaar, T., Kummer, M. E., & Slivko, O. (2022). Externalities in knowledge production: evidence from a randomized field experiment. Experimental Economics, 25(2), 706-733. https://doi.org/10.1007/s10683-021-09730-x

• Journal Article Type: Article
• Acceptance Date: Aug 23, 2021
• Online Publication Date: Sep 1, 2021
• Publication Date: 2022-04
• Deposit Date: Aug 25, 2021
• Publicly Available Date: Sep 2, 2022
• Journal: Experimental Economics
• Print ISSN: 1386-4157
• Electronic ISSN: 1573-6938
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 25
• Issue: 2
• Pages: 706-733
• DOI: https://doi.org/10.1007/s10683-021-09730-x
• Keywords: knowledge accumulation; user-generated content; Wikipedia; public goods provision; field experiment
• Public URL: https://nottingham-repository.worktribe.com/output/6091385
• Publisher URL: https://link.springer.com/article/10.1007%2Fs10683-021-09730-x
• Additional Information: Data and code for this article are available in Hinnosaar et al. (2021a), Harvard Dataverse, doi: 10.7910/DVN/T4VFCX