Many platforms host User Generated Content (UGC) and content developed by professionals side by side. However, so far their impact on platform ecosystems has been mostly studied in isolation. In this paper, we use data from a network of 122 local news outlets hosted by an online news platform to study the spillover effects from UGC developed by citizen journalists to the content developed by professional journalists. We use the removal of a status index associated with citizen journalists as an exogenous shock to their supply of UGC to identify these spillover effects. We find that experienced citizen journalists reduce their production of content when this status index is removed. We then find that inexperienced professional journalists increase their output in response to this behavior. However, as a result of these changes, we find a reduction in the overall content hosted by the platform, especially in the case of local news, and in more isolated regions. We further show that this is likely to have detrimental effects for the platform. In particular, there is a decline in overall viewership and the platform may need to hire and pay salaries to more professional journalists to produce enough articles to close the gap left by the departing citizen journalists. Our work contributes to the literature on UGC and online platforms, as well as to the literature on local news.

Crowdsourcing has evolved as an organizational approach to distributed problem solving and innovation. As contests are embedded in online communities and evaluation rights are assigned to the crowd, community members face a tension: They find themselves exposed to both competitive motives to win the contest prize and collaborative participation motives in the community. The competitive motive suggests they may evaluate rivals strategically according to their self-interest, the collaborative motive suggests they may evaluate their peers truthfully according to mutual interest. Using field data from Threadless on 38 million peer evaluations of more than 150,000 submissions across 75,000 individuals over 10 years and two natural experiments to rule out alternative explanations, we answer the question of how community members resolve this tension. We show that as their skill level increases, they become increasingly competitive and shift from using self-promotion to sabotaging their closest competitors. However, we also find signs of collaborative behavior when high-skilled members show leniency toward those community members who do not directly threaten their chance of winning. We explain how the individual-level use of strategic evaluations translates into important organizational-level outcomes by affecting the community structure through individuals’ long-term participation. Although low-skill targets of sabotage are less likely to participate in future contests, high-skill targets are more likely. This suggests a feedback loop between competitive evaluation behavior and future participation. These findings have important implications for the literature on crowdsourcing design, and the evolution and sustainability of crowdsourcing communities.

The rise of the sharing economy has enabled more efficient use of particular resources and the generation of additional income streams for individuals via collaborative peer-to- peer, platform-mediated transactions. In this paper, we study one of the potential dark sides of this phenomenon: the taxes evaded in those transactions. We integrate detailed data on Airbnb rental activities with individual-level administrative microdata to address two main questions: First, who participates in the sharing economy as a host on Airbnb? Second, how much taxes are potentially evaded on Airbnb transactions, and which hosts are more likely to under-report the income they earn on short-term rentals? We leverage the strict housing regulations in Denmark and detailed data on 27,734 listings and 22,834 unique hosts that were active in Copenhagen and surroundings in 2017-2018 to provide novel evidence on the different types of Airbnb hosts and uncover substantial levels of undeclared income earned on the platform. We find significant associations between individuals’ socio-economic background and their participation on Airbnb as a host. Our analyses furthermore reveal non-negligible effects of these rental activities on undeclared income - back-of-the-envelope calculations suggest that nearly 420 million DKK in income may have been undeclared just in the Copenhagen area in those two years combined. Finally, we identify individual characteristics that significantly predict income under- reporting in this context. Our findings can be informative for policymakers and motivate future research on the impact of platform regulation on individual behavior.