The goal of this project is to develop a model in which there is an incentive to adopt a new technology because it increases productivity, but the new technology may require a large adjustment in the level of human capital. The model will be used to explain volatility of growth, growth-rate disasters, counter cyclical human capital accumulations and other empirical observations about economic growth.

Real Business Cycle research is a large and very useful literature on the relationship between business cycles, economic growth and technology shocks. But almost all this research assumes that the technology shocks are exogenous and studies how the economy responds to them. This project develops a model in which technology is chosen, total factor productivity depends on current technology and human capital, and human capital can be upgraded at a cost to better match future technology. In this model technology adoption is risky. The risk is one of possibly having the wrong skills and therefore of bearing large costs of retooling and there are additional risks. So choosing a better technology increases tomorrow's productivity directly, but increases the risk that resources will have to be spent to upgrade human capital to keep pace. If growth imposes risks, then it seems like we should observe a positive relation between the growth rate and its volatility. Yet the data show a negative relation between the two over time. This project develops a model that explains this, in that higher volatility leads to a greater mismatch between future technologies and skills and greater retooling costs. The model can be decentralized and extended so that it fits the distribution of growth rates of output, consumption, investment, stock prices, and interest rates observed in post-war U.S. data and elsewhere. Business cycles are partly an outcome of technology choices. This project studies how policy implications hinge on external effects in the use of technology and in the adjustment of human capital.

National Science Foundation (NSF)
Division of Social and Economic Sciences (SES)
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Daniel H. Newlon
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New York University
New York
United States
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