Evaluating Precision, Privacy, and Representation with Cell Phone Data
Cristina Connolly et al.
Abstract
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.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.