Nicola De Liso

ON TECHNOLOGY COMPETITION: A FORMAL ANALYSIS OF THE ‘SAILING-SHIP EFFECT’

One of the key features of our economies consists of the coexistence of different technologies supplying similar products and services. We often observe that an old technology is improved when a new one appears; behind this process of improvement often lies an intentional research activity. There thus begins a competition between the two technologies whose performances are improved via R&D. We focus our attention on this competition process and supply a formal model, based on the optimization of R&D expenditure of both technologies, which can describe the dynamics of the delayed overtaking of the new technology over the old one.

On endogenous growth and increasing returns: modeling learning-by-doing and the division of labor

This paper discusses those sources of endogenous growth arising from labor as labor. It uses a production function which models the returns to scale as a function of the division of labor and learning. Smithian analysis of the labor process constitutes the basis upon which we build our own approach. The dynamics, therefore, drawn upon are more classical in style than those suggested by Arrow`s (1962) article.

Charles Babbage, Technological Change and the "National System of Innovation"

I investigate the role played by Charles Babbage in the economic analysis of technological change. His contribution is quite important, for the way in which he carried out his studies anticipated themes that are the centre of economics and policy-making today. He considered the economic forces that shape technology and its change, the importance of scientific knowledge, and the role that institutions may play. Particularly in his Reflections on the Decline of Science in England and Some of its Causes, he anticipated the concept of the national system of innovation.

On delayed technological shifts

When a new technology capable of superseding an existing one appears, we sometimes observe the so-called sailing-ship effect, which consists of the old technologys improvements in response to the emergence of the new one. This helps explain why the old technology does not disappear quickly. However, some more aspects contribute to slowing down the process of substitution of the new for the old technology, such as users’ reluctance to switch to the new one, the degree of diffusion of the old technology, and other forces. In this work, we provide a formal model which takes into account both the technical improvements of the old technology as well as the other forces, where the latter are synthesised in what we define as a memory effect.

Structural Change and Technological Externalities in the Service Sector: Some Evidence from Italy

The economic role of the tertiary sector is already predominant in all major economies. This is also true for the Italian economy, despite the fact that it is lagging behind the EU average. According to the last accounts of the Italian economy (ISTAT, 1999), in 1996 more than 60% of overall value added was produced in the tertiary sector. Around 2.6 million firms3 and 7 million employees4 were active in the market for services. On the whole, the tertiary sector engaged, in the same year, 2.94 million firm5 and 9.69 million employees6. Moreover, the fact should also be taken into account that two sections of services, that is ‘trade’, ‘hotels and restaurants’ and ‘other services’7, are among the sectors in which the ‘non directly observed economy’ concentrates, if we consider the percentage of employees involved, while ‘other services’ are the leading section if we consider the percentage of selfemployed workers9. All of this suggests that official figures tend to underestimate the relative weight of the tertiary sector in the Italian economy — whose overall performance of course depends on, and derives from, the interaction between all sectors of activity.

From mechanical arts to the philosophy of technology

In this paper, I reconstruct some of the key steps which characterise the way in which technology has been considered and studied since the times of ancient Greece. Within this reconstruction, I will highlight the evolution of the mechanical arts into the predominant technology which took centre stage during the First Industrial Revolution. The further step of the alliance between technology and science is also considered.In this paper, I reconstruct some of the key steps which characterise the way in which technology has been considered and studied since the times of ancient Greece. Within this reconstruction, I will highlight the evolution of the mechanical arts into the predominant technology which took centre stage during the First Industrial Revolution. The further step of the alliance between technology and science is also considered.

La globalizzazione in una prospettiva tecnologico-istituzionale

In this paper we look at the process of globalisation according to a technological and institutional perspective. We take as starting point of globalisation in the modern sense the Industrial Revolution of the late 18th century England in which the capitalist institutional set-up met scientific technology. Since then economies have become more and more integrated because of both technologies and institutions. As usual we ask the question of who benefited more from globalisation and growth. Despite the fact that neoclassical theory predicts convergence between economies, empirical evidence if far from giving a final answer. Success of the policies based on privatisations, liberalisation, stabilisation and the broad opennessfosters- growth idea can always be contrasted with counterexamples in which protectionism and the visible hand of the state have actually promoted development.

Increasing Returns, Learning-By-Doing And Neural Networks

In this paper we draw an analogy between the process of learning-bydoing and the learning process which develops in a neural network context. The bridging tool we refer to is a dynamic production function whose only variable input is labour. By concentrating on the ‘neural network production function’ we show that the learning process can lead to increasing returns. The simulations show that when learning is characterized by an upper limit. returns are increasing for some time, while in the long run they go back to the level where they are constant.