KAIPING CHEN & TANJA AITAMURTO
Barriers for Crowd’s Impact in Crowdsourced Policymaking: Civic Data Overload and Filter Hierarchy
While crowdsourcing is an increasingly common method of open-government practices to strengthen participatory democracy, its impact on governance is unclear. Using data from a crowdsourced city-plan update by the City of Palo Alto, California, this article examines the impact of a crowd’s input on policy changes. We used an enacted policy change to quantify government’s response to crowd suggestions, whether crowd suggestions are adopted in the policy changes or not. While the city responded to less than half of the crowd’s suggestions, the likelihood of its doing so increased by 51.42 percentage points when the crowd’s ideas were amplified by a citizen advisory committee (CAC), a panel of residents working with the city in the policy update. We also found that the government is more likely to respond to crowd suggestions that are perceived as actionable. These two factors—CAC and the perceived data quality—constitute a filter which the crowd’s suggestions have to pass to make into the policy. This filter created a hierarchy in the participatory practice. Although crowdsourcing intends to create equality and inclusiveness in policymaking, our findings reveal that the civic data overload and the filter hierarchy complicate the adoption of crowdsourcing as a democratic innovation in governance.