Four Models of Knowledge Diffusion and Growth

Abstract

This paper describes how long-run growth emerges in four closely related models that combine individual discovery with some form of social learning. In a large economy, there is a continuum of long-run growth rates and associated stationary distributions when it is possible to learn from individuals in the right tail of the productivity distribution. What happens in the long run depends on initial conditions. Two distinct literatures, one on reaction-diffusion equations, and another on quasi-stationary distributions suggest a unique long-run outcome when the initial productivity distribution has bounded support.

Idea Flows and Economic Growth

“…This feature of the model with Brownian noise was highlighted by Staley (2011). Staley (2011) and Luttmer (2015) also provide theoretical results for finite populations.…”

Time Allocation, the Dynamics of Interaction, and Technology Adoption

“…Innovation and adoption are, in fact, two sides of a same coin: without innovation, growth dynamics will sooner or later be exhausted; however, without diffusion and adoption, the potential of wealth creation that research results disclose will never be fulfilled. The interplay between innovation and imitation and their joint contribution to productivity growth is a subject of intense research; some meaningful studies on this issue include Mukoyama (2003), Luttmer (2011Luttmer ( , 2015, Davis and Hashimoto (2015), and König et al (2016). In the analysis, we engage on in the following sections, innovation and imitation are also analyzed in an integrated perspective.…”

Reconciling Models of Diffusion and Innovation: A Theory of the Productivity Distribution and Technology Frontier

“…77 Luttmer (2012a, 2015a, 2015b, 2020) provided careful analysis of the role of hysteresis, including the important interaction of the stochastic innovation process with initial conditions. …”