A Time to Sow and a Time to Reap: Growth Based on General Purpose Technologies

Abstract

We develop a model of growth driven by successive improvements in 'General Purpose Technologies' (GPT's), such as the steam engine, electricity, or micro-electronics. Each new generation of GPT's prompts investments in complementary inputs, and impacts the economy after enough such compatible inputs become available. The long-run dynamics take the form of recurrent cycles: during the first phase of each cycle output and productivity grow slowly or even decline, and it is only in the second phase that growth starts in earnest. The historical record of productivity growth associated with electrification, and perhaps also of computerization lately, may offer supportive evidence for this pattern. In lieu of analytical comparative dynamics, we conduct simulations of the model over a wide range of parameters, and analyze the results statistically. We extend the model to allow for skilled and unskilled labor, and explore the implications for the behavior over time of their relative wages. We also explore diffusion in the context of a multi-sector economy.