Working Paper: CEPR ID: DP2474
Authors: Philippe Aghion; Peter Howitt; Giovanni L. Violante
Abstract: This paper develops a theoretical model to analyse how a General Purpose Technology (GPT) shapes within-group wage inequality when workers are ex-ante equal, but their adaptability to new technologies is subject to stochastic factors that are history dependent. It is argued that the diffusion of a GPT leverages the importance of these stochastic factors in three ways. First, a rise in the speed of embodied technological progress raises the market premium to workers adaptable to the leading-edge technology. Second, the generality of the technology raises the ability of adaptable workers to transfer recently acquired knowledge to new machines. Third, the generality of the technology reduces the cost of retooling old machines, which increases the demand for adaptable workers. In the model the rise in within-group inequality is mainly transitory, and is mirrored by a rise in wage instability. The key predictions of the model are shown to be in line with some of the existing empirical evidence.
Keywords: general purpose technology; history-dependence; inequality; skill transferability; technological progress
JEL Codes: E20; J30; O30
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Adaptability (D84) | Wage Levels (J31) |
| Ability to Transfer Skills (J62) | Wage Levels (J31) |
| Reduced Retooling Costs (L68) | Demand for Adaptable Workers (J29) |