A core question in industrial policy design is how to stimulate technological advancements, particularly those with positive externalities. The automobile industry provides an ideal setting for studying this question, as it has accelerated toward electrification propelled by rapid technological breakthroughs in electric vehicle (EV) driving range backed by an unprecedented wave of supportive policies. To evaluate how policies shape automakers’ forward-looking investments in R&D and charger construction and to quantify the resulting social welfare effects, including consumer surplus and greenhouse-gas emissions, I develop and estimate a dynamic structural model of endogenous investment. The model's state space, tracking heterogeneous firms' technology and charger stocks, grows exponentially. To overcome the long-lasting estimation challenge posed by a large state space, I develop a layered-local-iteration estimator that partitions an acyclic state space into layers and solves for value functions through a series of small and tractable fixed point problems. This method reduces estimation runtime dramatically by enabling within-layer parallel computation, and it can be generalized to broader settings with cyclic state spaces by considering the condensation graph of strongly connected components. Estimates and counterfactuals highlight the importance of an optimally coordinated policy mix that allocates investment to its highest social return by exploiting firm heterogeneity. In addition, the optimal intellectual-property policy shaping innovation dynamics depends on whether and by how much the gains from knowledge spillovers exceed the reduction in private R&D incentives.

The growing availability of big data enables firms to predict consumer search outcomes and outside options more accurately than consumers themselves. This paper examines how a firm can utilize such superior information to offer personalized buy-now discounts intended to deter consumer search. However, discounts can also serve as signals of attractive outside options, potentially encouraging rather than discouraging consumer search. We show that, despite the firm’s ability to tailor discounts across a continuum of consumer valuations, the firm-optimal equilibrium features a simple two-tier discount scheme, comprising a uniform positive discount when the consumer outside option is intermediate and no discount when the outside option is low or high. Furthermore, compared to a scenario where the firm lacks superior information, we find that the firm earns lower profits, consumers search more while their welfare remains unchanged, and total welfare declines.

To address range anxiety, the main obstacle to electric vehicle (EV) adoption, policymakers and firms have pursued two primary strategies: R&D aimed at driving range extension, and public charging network expansion. Using data from the Chinese automobile market and patent filings, this paper examines how infrastructure expansion stimulates or discourages subsequent innovation. I first identify range-improving patents using a fine-tuned large language model and quantify patents' novelty and impact through text similarity metrics. Next, drawing on the persistent regional sales reliance of Chinese automakers, I construct a sales-weighted index of charging infrastructure exposure. The study provides empirical evidence showing that the interplay of two opposing effects leads to innovation redirection: On one hand, readily available chargers make long driving ranges less critical, potentially crowding out breakthrough innovations aimed at driving-range improvements due to the substitution effect. On the other hand, a more extensive public charging network enlarges the EV market, thereby raising the marginal returns to innovations directed toward other vehicle improvements, creating a synergy effect. Specifically, easier access to public chargers significantly reduces breakthrough driving-range innovations but significantly stimulates non‑driving-range innovations. Overall, it significantly increases the total number of patents in the automotive sector. Furthermore, I estimate a structural choice model of automobiles that captures the substitution between EV driving range and access to public charging network, and simulate automakers’ expected marginal return to quality improvements under a series of counterfactual infrastructure availability. The results show that expanding charging infrastructure may shrink marginal return to driving range extension but always results in higher marginal return to engine power improvement, reinforcing the empirical evidences earlier.