Attrition-Aware Causal Inference with GPS Tracking Data: A Study of Germany's 9-Euro-Ticket

Abstract

Germany’s 9-Euro-Ticket, implemented from June to August 2022, attracted exceptional international attention because of both the magnitude of the fare discount and the scale of its implementation. It also sparked intense debate over whether the policy mainly generated additional public transport demand or shifted travel away from private cars. This paper estimates the causal impact of the policy on travel behaviour using a longitudinal smartphone-based GPS panel covering May to August 2022. GPS data offer rich, high-frequency, user-level information on trips, mode use, and distance travelled, making them especially valuable for policy evaluation. At the same time, they pose important challenges for causal inference: some users stop contributing data, reporting may be intermittent, and certain outcomes may be observed selectively in ways that are related to travel behaviour itself. If left unaddressed, these features of the data-generating and measurement process can bias estimated policy effects and obscure their dynamics. To address this problem, we develop an estimation framework that treats panel dropout and selective outcome observation as explicit features of the measurement system. The approach constructs stabilised inverse-probability weights on a balanced user-time panel and combines them with a sharp regression discontinuity in time design. Applied to the 9-Euro-Ticket, the results show that conclusions depend strongly on how travel demand is measured. Public transport mode share rises substantially, but the frequency of car use does not fall in any trip-purpose or distance-band category. Although public transport distance increases while car kilometres decline, a temporal decomposition shows that these changes do not occur at the same time, providing little evidence of direct mode shift. More broadly, the paper contributes a practical weighting and diagnostics pipeline that can be embedded in other causal designs using smartphone travel data, helping support more credible policy evaluation.