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Can temporary subsidies to bundles induce long-run changes in demand due tolearning about the relative quality of one of its constituent goods? This paperprovides theoretical and experimental evidence on the role of this mechanism.Theoretically, we introduce a model where an agent learns about the quality ofan innovation on an essential good through consumption. Our results show thatthe contemporaneous effect of a one-off subsidy to a bundle that contains theinnovation may be decomposed into a direct price effect, and an indirectlearning motive, whereby an agent leverages the discount to increase theinformational bequest left to her future selves. We then assess the predictionsof our theory in a randomised experiment in a ridesharing platform. Theexperiment provided two-week discounts for car trips integrating with a trainor metro station (a bundle). Given the heavy-tailed nature of our data, wefollow \cite{Athey2023} and, motivated by our theory, propose a semiparametricmodel for treatment effects that enables the construction of more efficientestimators. We introduce a statistically efficient estimator for our model byrelying on L-moments, a robust alternative to standard moments. Our estimatorimmediately yields a specification test for the semiparametric model; moreover,in our adopted parametrisation, it can be easily computed through generalizedleast squares. Our empirical results indicate that a two-week 50\% discount oncar trips integrating with train/metro leads to a contemporaneous increase inthe demand for integrated rides, and, consistent with our learning model,persistent changes in the mean and dispersion of nonintegrated rides. Theseeffects persist for over four months after the discount. A simple calibrationof our model shows that around 40\% to 50\% of the estimated contemporaneousincrease in integrated rides may be attributed to a learning motive.

The learning effects of subsidies to bundled goods: a semiparametric approach