Innovation and Endogenous Knowledge Network Dynamics
Abstract
We study innovation and endogenous knowledge-network evolution in a general-equilibrium framework with heterogeneous firms and long-run growth. The empirical examination uses patent-citation data to track the formation of new links in the knowledge network across technological categories. We find that these new links are formed infrequently and tend to connect high-quality innovations and large firms on both ends. Formation of such new links boosts innovation and real performance of firms on both ends with positive spillovers to other firms.We build a tractable model to rationalize these findings and study the aggregate implications of knowledge-network dynamics. In the model, the knowledge network evolves endogenously as each firm searches for others as knowledge-input suppliers and attracts others as users of its own knowledge. We characterize the stationary equilibrium path in closed-form and show that denser networks are associated with faster growth. The calibrated model suggests that cross-category knowledge network dynamics account for more than a half of growth in the US and explain almost two thirds of the growth slowdown.