@article{Instrumental:2224,
      recid = {2224},
      author = {Lin, Xiaocai},
      title = {Valuing Intrinsic and Instrumental Preferences for  Privacy},
      publisher = {The University of Chicago},
      school = {Ph.D.},
      address = {2020-06},
      pages = {86},
      abstract = {In this paper, I separately measure two motives for  consumers to protect privacy: an intrinsic motive, which is  a “taste” for privacy; and an instrumental motive, which  reflects the expected economic loss from revealing one’s  private information to the firm. While the intrinsic  preference is a utility primitive, the instrumental  preference arises endogenously from a firm’s usage of  consumer data. Combining a two-stage experiment and a  structural model, I find that consumers’ intrinsic  preferences for privacy range from 0 to 5 dollars per  demographic variable, exhibiting substantial heterogeneity  across consumers and categories of personal data. This rich  heterogeneity in intrinsic preferences leads to a selection  pattern that deviates from the “nothing-to-hide” argument  predicted by a model with pure instrumental preferences. I  then propose three strategies that firms and researchers  can adopt to improve data-driven decisions when shared data  are influenced by consumers’ dual privacy concerns. First,  by using an experiment to measure the joint distribution of  privacy preferences, firms can extrapolate selection  patterns to cases where the data utilization method  changes. Second, when the joint privacy preference  distribution is unknown, data collection should focus on  representativeness over quantity, especially when  information externality is present. Lastly, firms can learn  about the selection pattern in the shared data by  leveraging information contained in consumers’ data-sharing  decisions.},
      url = {http://knowledge.uchicago.edu/record/2224},
      doi = {https://doi.org/10.6082/uchicago.2224},
}