This paper studies the dynamics of fundamental research. We develop a simple model where researchers allocate their effort between improving existing fields and inventing new ones. A key assumption is that scientists derive utility from recognition from other scientists. We show that the economy can be either in a regime where new fields are constantly invented, and then converges to a steady state, or in a cyclical regime where periods of innovation alternate with periods of exploitation. Our analysis provides a rigorous foundation to the Kuhnian theory of scientific evolution. We show that scientists' care for reputation has a strong impact on research dynamics and tends to favor innovation. Especially, innovation fads may emerge. We also study welfare and find that the academic reputational reward system can help align scientists' short-term incentives with society's long-term interests.

We study the influence of social networks on labor market transitions. We develop the first model where social ties and job status coevolve through time. Our key assumption is that the probability of formation of a new tie is greater between two employed individuals than between an employed and an unemployed individual. We show that this assumption generally generates negative duration dependence of exit rates from unemployment. Our model has a number of novel testable implications. For instance, we show that a higher connectivity among unemployed individuals reduces duration dependence and that exit rates depend positively on the duration of the last job held by the unemployed worker.

We provide new results regarding the identification of peer effects. We consider an extended version of the linear-in-means model where interactions are structured through a social network. We assume that correlated unobservables are either absent, or treated as network fixed effects. We provide easy-to-check necessary and sufficient conditions for identification. We show that endogenous and exogenous effects are generally identified under network interaction, although identification may fail for some particular structures. We use data from the Add Health survey to provide an empirical application of our results on the consumption of recreational services (e.g., participation in artistic, sports and social activities) by secondary school students. Monte Carlo simulations calibrated on this application provide an analysis of the effects of some crucial characteristics of a network (i.e., density, intransitivity) on the estimates of peer effects. Our approach generalizes a number of previous results due to Manski [Manski, C., 1993. Identification of endogenous social effects: The reflection problem. Review of Economic Studies 60 (3), 531-542], Moffitt [Moffitt, R., 2001. Policy interventions low-level equilibria, and social interactions. In: Durlauf, Steven, Young, Peyton (Eds.), Social Dynamics. MIT Press] and Lee [Lee, L.F., 2007. Identification and estimation of econometric models with group interactions, contextual factors and fixed effects. Journal of Econometrics 140 (2), 333-374]. © 2009 Elsevier B.V. All rights reserved.

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