Structural Estimation of Attrition in a Last-Mile Delivery Platform: The Role of Driver Heterogeneity, Compensation, and Experience

Problem definition: We examine how to manage turnover among drivers delivering parcels for last-mile platforms. Although driver attrition in these platforms is both commonplace and costly, there is little understanding of the processes responsible for this phenomenon. Methodology/results: We collaborate with a platform to build a structural model to estimate the effects of key predictors of drivers’ decisions to leave or remain at the platform. For this estimation, we apply a dynamic discrete-choice framework in a two-step procedure that accounts for unobserved heterogeneity among drivers while circumventing the use of approximation or reduction methods commonly used to solve dynamic choice problems in the operations management domain. Drivers are compensated using a combination of regular payments that reward their productivity and subsidy payments that support them as they gain experience on the job. We find that regular pay has a greater effect on drivers’ retention. Furthermore, the marginal effects of both regular and subsidy pay diminish with drivers’ tenure at the platform, but the latter diminishes faster than the former. Additionally, we find significant heterogeneity among drivers in their unobserved nonpecuniary taste for the jobs at the platform and a significantly greater probability of retention among drivers with greater taste for these jobs. Managerial implications: Platforms can leverage our results to improve driver retention and design more profitable payment policies. We perform counterfactual analyses and develop a modeling framework to guide platforms toward this goal. Funding: This study was partially funded by a grant from TForce Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0367 .

• 저널Manufacturing & Service Operations Management · 27(2) · 516–534
• 토픽Transportation and Mobility Innovations · Automotive Engineering
• DOI10.1287/msom.2021.0367
• 저자Lina Wang, Scott Webster, Elliot Rabinovich