Knowledge that Transforms

This paper attempts to integrate traditional models of invention and learning by doing, developing a model that emphasizes the interdependence between research activity in the laboratory and production experience on the factory floor. Learning depends on invention in that learning by doing is viewed as the exploration of the finite and bounded productive potential of invented technologies. At the same time, the profitability of costly invention is dependent on learning in that costs of production depend on cumulative learning experience. The model is a true hybrid, allowing for circumstances in which either the incentives to engage in research and/or the incentives to produce different goods are the binding constraints on growth.

This paper presents a class of models in which agents may devote part of their nonleisure activities to going to school so as to increase the efficiency units of labor they supply to the firms and the wages they receive. The interaction among the technology of human capital accumulation and agents' preferences will determine endogenously the economy's rate of growth. Given a constant returns to scale technology for physical capital accumulation, we characterize the set of steady states as a ray from the origin and show the global convergence of every off-balanced path to some point on this ray. Further properties concerning the dynamic evolution of the state and control variables around the ray of steady states are also established. Our analysis is useful to understand the role played by the technologies of physical and human capital in the process of accumulation and to evaluate the impact of policies geared toward attaining higher levels of capital. Our results highlight the importance of human capital in the dynamics of growth.

The paper examines learning by doing in the context of a production function in which the other arguments are labor, human capital, physical capital, and vintage as a proxy for embodied technical change in physical capital. Learning is further decomposed into organization learning, capital learning, and manual task learning. The model is tested with time-series and cross-section data for various samples of up to 2,150 plants over a 14-year period.

This paper examines bidding in auctions for state highway construction contracts, in order to determine whether bid rigging occurred. Detection of collusion is possible because of limited participation in the collusive scheme. Collusion did not take the form of a bid rotation scheme. Instead, several ring members bid on most jobs. One was a serious bidder, and the others submitted phony higher bids. The bids of noncartel firms, as well as their rank distribution, were related to cost measures. In contrast, the rank distribution of higher cartel bids was unrelated to similar cost measures and differed from that of the low cartel bid.

When harassed users adopt new ways of transaction, supply changes in predictable ways. This linkage between demand and supply can explain a number of puzzling consequences of the decriminalization of marijuana and the war on heroin and cocaine.

This paper investigates the sensitivity of Solow residual based measures of technology shocks to labor hoarding behavior. Using a structural model of labor hoarding and the identifying restriction that innovations to technology shocks are orthogonal to innovations in government consumption, we estimate the fraction of the variability of the Solow residual that is due to technology shocks. Our results support the view that a significant proportion of movements in the Solow residual are artifacts of labor hoarding behavior. Specifically, we estimate that the variance of innovations to technology is roughly 50 percent less than that implied by standard real business cycle models. In addition, our results suggest that existing real business cycle studies substantially overstate the extent to which technology shocks account for the variability of postwar aggregate U.S. output.