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In Discussion Paper No. 1080, Research Fellow Elhanan Helpman and Manuel Trajtenberg develop a model of growth driven by successive improvements in `General Purpose Technologies' (GPTs), such as the steam engine, electricity, or microelectronics. They study the economy-wide dynamics that a GPT may generate, presenting a basic GPT-based growth model and analysing its long-run dynamics, in the form of repetitive cycles. They then describe the consequent behaviour over time of GDP, total factor productivity, real wages and factor shares.A number of important results emerge. First, the immediate impact of the arrival of the new, more productive GPT, is to lower output. Second, a typical cycle contains two distinct phases. During the first phase, output and productivity experience negative growth, the real wage rate stagnates, and the share of profits in GDP declines. The benefits from a more advanced GPT manifest themselves during the second phase, after enough complementary inputs have been developed for it. During this later phase, there is a spell of growth, with rising output, real wages and profits. Over the entire cycle, the economy grows at the rate determined by the rate of advance in the GPT itself. A Time to Sow and a Time to Reap: Growth Based on General Purpose Technologies Elhanan Helpman and Manuel Trajtenberg Discussion Paper No. 1080, December 1994 (IM) |