  Data generated by users on digital platforms are a crucial resource for
advocates and researchers interested in uncovering digital inequities, auditing
algorithms, and understanding human behavior. Yet data access is often
restricted. How can researchers both effectively and ethically collect user
data? This paper shares an innovative approach to crowdsourcing user data to
collect otherwise inaccessible Amazon purchase histories, spanning 5 years,
from more than 5000 US users. We developed a data collection tool that
prioritizes participant consent and includes an experimental study design. The
design allows us to study multiple aspects of privacy perception and data
sharing behavior. Experiment results (N=6325) reveal both monetary incentives
and transparency can significantly increase data sharing. Age, race, education,
and gender also played a role, where female and less-educated participants were
more likely to share. Our study design enables a unique empirical evaluation of
the "privacy paradox", where users claim to value their privacy more than they
do in practice. We set up both real and hypothetical data sharing scenarios and
find measurable similarities and differences in share rates across these
contexts. For example, increasing monetary incentives had a 6 times higher
impact on share rates in real scenarios. In addition, we study participants'
opinions on how data should be used by various third parties, again finding
demographics have a significant impact. Notably, the majority of participants
disapproved of government agencies using purchase data yet the majority
approved of use by researchers. Overall, our findings highlight the critical
role that transparency, incentive design, and user demographics play in ethical
data collection practices, and provide guidance for future researchers seeking
to crowdsource user generated data.
