The Transition from Stagnation to Growth: An Adaptive Learning Approach |
| |
Authors: | Jasmina Arifovic James Bullard John Duffy |
| |
Affiliation: | (1) Simon Fraser University, USA;(2) FRB–St. Louis, USA;(3) University of Pittsburgh, USA |
| |
Abstract: | This article develops the first model in which, consistentwith the empirical evidence, the transition from stagnation toeconomic growth is a very long endogenous process. The modelhas one steady state with a low and stagnant level of incomeper capita and another steady state with a high and growing levelof income per capita. Both of these steady states are locallystable under the perfect foresight assumption. We relax the perfectforesight assumption and introduce adaptive learning into thisenvironment. Learning acts as an equilibrium selection criterionand provides an interesting transition dynamic between steadystates. We find that for sufficiently low initial values of humancapital—values that would tend to characterize preindustrialeconomies—the system under learning spends a long periodof time (an epoch) in the neighborhood of the low-income steadystate before finally transitioning to a neighborhood of the high-incomesteady state. We argue that this type of transition dynamic providesa good characterization of the economic growth and developmentpatterns that have been observed across countries. |
| |
Keywords: | growth human capital development transition learning genetic algorithm |
本文献已被 SpringerLink 等数据库收录! |