NBER Publications by Yaw Nyarko

People at the top of an occupational ladder earn more partly because they have spent time on lower rungs, where they have learned something. But what precisely do they learn? There are two contrasting views: First, the Bandit model assumes that people are different, that experience reveals their characteristics, and that consequently an occupational switch can result. Second, in our Stepping Stone model, experience raises a worker's productivity on a given task and the acquired skill can in part be transferred to other occupations, and this prompts movement. Safe activities (where mistakes destroy less output) are a natural training ground.

We model research as a signal on an unknown parameter of a technology. We distinguish applied from basic research and show that firms in the same industry can optimally choose different research portfolios, and that basic research can seem to have a higher rate of return than applied research, even though it really doesn't -- essentially, firms on a 'fast track' upgrading policy opt for basic research but fast and slow-track upgrading policies can coexist in a long-run equilibrium. We also derive the lag structure for how R&D affects the firm's stock of knowledge. To a first approximation, the lags decay geometrically (as is typically assumed in practice) but the rate of decay is endogenous, and depends on how fast the firm is upgrading its technology.

Most of our productive knowledge was handed down to us by previous generations. The transfer of knowledge from the old to the young is therefore a cornerstone of productivity growth. We study this process in a model in which the old sell knowledge to the young - - old workers train the young, and charge them for this service. We take an information-theoretic approach in which training occurs if a young agent watches an old worker perform a task. This assumption has plenty of empirical support -- in their first three months on the job, young workers spend about five times as long watching others work as they do in formal training programs. Equilibrium is not constrained Pareto optimal. The old have private information, and this gives rise to an adverse selection problem: some old agent...

This paper explores a one-agent Bayesian model of learning by doing and technological choice. To produce output, the agent can choose among various technologies. The beneficial effects of learning by doing are bounded on each technology, and so long-run growth in output can take place only if the agent repeatedly switches to better technologies. As the agent repeatedly uses a technology, he learns about its unknown parameters, and this accumulated expertise is a form of human capital. But when the agent switches technologies, part of this human capital is lost. It is this loss of human capital that may prevent the agent from moving up the quality ladder of technologies as quickly as he can, since the loss is greater the bigger is the technological leap. We analyze the global dynamics. ...