Search

Online publication; International audience; We investigate the effect of both research and consulting on teaching quality in higher education, at the individual level. We propose a theoretical model in which academics allocate effort between the three activities, over a two period time horizon, under the assumption of positive spillovers from research to both consulting opportunities and teaching, and of life-cycle effects on incentives. Propositions from the model are tested against data from a mid-sized Italian engineering faculty. We find that teaching quality is negatively related to consulting and positively related to research experience. However, both relationships are not linear, due to the importance of several mediating factors, such as seniority and the role of scientific publications as a signal for attracting consulting opportunities.; On étude l'effet des activités de recherche ainsi que de conseil sur la qualité de l'enseignement universitaire au niveau individuel. Nous proposons un modèle théorique dans lequel les universitaires consacrent du temps limité entre les trois activités, sur un horizon de deux périodes, dans l'hypothèse de retombées positives de la recherche sur la demande individuelle de conseil et sur la qualité de l'enseignement, en tenant compte des effets du cycle de vie sur les incitations. Les propositions du modèle sont testées en utilisant les données l'évaluation des enseignants d'une école italienne d'ingénierie de taille moyenne. Nous constatons que l'expérience de recherche améliore la qualité de l'enseignement, mais seulement si elle ne se traduit pas par la croissance des occasions de conseil. Dans ce cas, l'expérience de recherche a un effet dissuasif trop forte à investir du temps dans l'enseignement, et en détériore la qualité.

Summary The contribution of this paper is twofold. First, it presents the results of a “history-friendly” simulation model of evolution of the pharmaceutical industry. Second, it aims at contributing to a more general methodological discussion about agent-based models by proposing an econometric analysis of the results of the simulations. The case of the pharmaceutical industry has been studied extensively by scholars because, despite the high level of R&D intensity, the industry has been characterized by a relatively low levels of concentration. The model is able to reproduce the main stylized facts of the industry in an evolutionary perspective. In this paper we extend the analysis conducted in two previous works (Garavaglia et al. 2012, 2013) by further qualifying the findings with an extensive econometric investigation of the model outputs. The paper focuses the attention on the determinants of market structure, the innovative performance of the industry, the diversification in multiple submarkets and the level of prices. We find that the properties of the technological and demand regimes are key determinants of the patterns of industry evolution and that the main mechanisms driving the model are the random processes of search, the discovery of new submarkets as well as the interactions between patent protection, imitation and price competition. In addition, this paper emphasizes how the emerging leaders in the industry are those innovative early entrants which entered in large submarkets, showing the importance of the first mover advantage and of the size of the “prize” accruing to innovators when they discover a new rich submarket.

This paper examines the evolution of the pharmaceutical industry. After a brief discussion of the main stylised facts about the industry, we present a history-friendly model that aims at capturing the underlining mechanisms and the logic that guides the evolution of this industry. Simulation results show the mechanisms and dynamic processes linking the patterns of innovation, demand structure, and concentration.

This paper examines how the nature of the technological regime governing innovative activities and the structure of demand interact in determining market structure, with specific reference to the pharmaceutical industry.The main results are that, while technological regimes remain fundamental determinants of the patterns of innovation, the demand structure plays a crucial role in preventing the emergence of concentration through a partially endogenous process of discovery of new submarkets. However, it is not simply market fragmentation as such that produces this result, but rather the entity of the “prize” that innovators can gain relative to the overall size of the market. Further, the model shows that emerging industry leaders are innovative early entrants in large submarkets. The key question concerns the observation that—despite high degrees ofR&D and marketing-intensity—concentration has been consistently low during the whole evolution of the industry. Standard explanations of this phenomenon refer to the random nature of the innovative process, the patterns of imitation, and the fragmented nature of the market into multiple, independent submarkets. We delve deeper into this issue by using an improved version of our previous “history-friendly” model of the evolution of pharmaceuticals. Thus, we explore the way in which changes in the technological regime and/or in the structure of demand may generate or not substantially higher degrees of concentration.