Evaluating Precision, Privacy, and Representation with Cell Phone Data

While mobility data has emerged as a promising alternative for assessing the economic value of recreation, their reliability depends critically on how data are processed and protected. This paper systematically evaluates how key data-handling practices affect the accuracy of recreation demand estimation. Using a random utility model to analyze recreation visits at Cape Cod beaches from 2019-2022, we evaluate how methodological choices influence the marginal willingness to pay (MWTP) for avoiding fecal bacteria contamination. We find an average MWTP of 8. 92 per visit when using the proposed practices, such as refined visit definitions, sampling weights, and long-term choice sets. Deviations from these practices can introduce significant biases: relaxing the minimum dwell time and applying differential privacy reduce MWTP by 57% and 65%, respectively, while short-term choice set definition inflates it by 10%. By demonstrating the sensitivity of welfare estimates to data-processing decisions, this study highlights the importance of transparent and judicious mobility data practices for credible environmental valuation and evidence-based policymaking.