  Can temporary subsidies to bundles induce long-run changes in demand due to
learning about the quality of one of the constituent goods? This paper provides
theoretical support and empirical evidence on this mechanism. Theoretically, we
introduce a model where an agent learns about the quality of an innovation
through repeated consumption. We then assess the predictions of our theory in a
randomised experiment in a ridesharing platform. The experiment subsidised car
trips integrating with a train or metro station, which we interpret as a
bundle. Given the heavy-tailed nature of our data, we propose a semiparametric
specification for treatment effects that enables the construction of more
efficient estimators. We then introduce an efficient estimator for our
specification by relying on L-moments. Our results indicate that a ten-weekday
50\% discount on integrated trips leads to a large contemporaneous increase in
the demand for integration, and, consistent with our model, persistent changes
in the mean and dispersion of nonintegrated app rides. These effects last for
over four months. A calibration of our theoretical model suggests that around
40\% of the contemporaneous increase in integrated rides may be attributable to
increased incentives to learning. Our results have nontrivial policy
implications for the design of public transit systems.
