Technical Change and Industrial Dynamics as Evolutionary Processes

This work prepared for B. Hall and N. Rosenberg (eds.) Handbook of Innovation, Elsevier (2010), lays out the basic premises of this research and review and integrate much of what has been learned on the processes of technological evolution, their main features and their effects on the evolution of industries. First, we map and integrate the various pieces of evidence concerning the nature and structure of technological knowledge the sources of novel opportunities, the dynamics through which they are tapped and the revealed outcomes in terms of advances in production techniques and product characteristics. Explicit recognition of the evolutionary manners through which technological change proceed has also profound implications for the way economists theorize about and analyze a number of topics central to the discipline. One is the theory of the firm in industries where technological and organizational innovation is important. Indeed a large literature has grown up on this topic, addressing the nature of the technological and organizational capabilities which business firms embody and the ways they evolve over time. Another domain concerns the nature of competition in such industries, wherein innovation and diffusion affect growth and survival probabilities of heterogeneous firms, and, relatedly, the determinants of industrial structure. The processes of knowledge accumulation and diffusion involve winners and losers, changing distributions of competitive abilities across different firms, and, with that, changing industrial structures. Both the sector-specific characteristics of technologies and their degrees of maturity over their life cycles influence the patterns of industrial organization ? including of course size distributions, degrees of concentration, relative importance of incumbents and entrants, etc. This is the second set of topics which we address. Finally, in the conclusions, we briefly flag some fundamental aspects of economic growth and development as an innovation driven evolutionary process.Innovation, Technological paradigms, Technological regimes and trajectories, Evolution, Learning, Capability-based theories of the firm, Selection, Industrial dynamics, Emergent properties, Endogenous growth

Institutions and Policies Shaping Industrial Development: An Introductory Note

In this work, meant as an introduction to the contributions of the task force on Industrial Policies and Development, Initiative for Policy Dialogue, Columbia University, New York, we discuss the role of institutions and policies in the process of development. We begin by arguing how misleading the "market failure" language can be in order to assess the necessity of public policies in that it evaluates it against a yardstick that is hardly met by any observed market set-up. Much nearer to the empirical evidence we argue that even when one encounters a prevailing market form of governance of economic interactions, the latter are embedded in a rich thread of non-market institutions. This applies in general and is particularly so with respect to the production and use of information and technological knowledge. In this work we build on the fundamental institutional embeddedness of such processes of technological learning in both developed and catching-up countries and we try to identify some quite robust policy ingredients which have historically accompanied the co-evolution between technological capabilities, forms of corporate organisations and incentive structures. All experiences of successful catching-up and sometimes overtaking the incumbent economic leaders – starting with the USA vis-à-vis Britain – have involved “institution building” and policy measures affecting technological imitation, the organisations of industries, trade patterns and intellectual property rights. This is likely to apply today, too, – we argue – also in the context of a “globalised” world economy.Institutions, development, industrial policies, technological catching-up, trade specialisations.

Network effects in a human capital based economic growth model

We revisit a recently introduced agent model[ACS {\bf 11}, 99 (2008)], where economic growth is a consequence of education (human capital formation) and innovation, and investigate the influence of the agents' social network, both on an agent's decision to pursue education and on the output of new ideas. Regular and random networks are considered. The results are compared with the predictions of a mean field (representative agent) model.Comment: to appear in Physica

Schumpeter Meeting Keynes: A Policy-Friendly Model of Endogenous Growth and Business Cycles

a b s t r a c t This paper studies an agent-based model that bridges Keynesian theories of demandgeneration and Schumpeterian theories of technology-fueled economic growth. We employ the model to investigate the properties of macroeconomic dynamics and the impact of public polices on supply, demand and the ''fundamentals'' of the economy. We find profound complementarities between factors influencing aggregate demand and drivers of technological change that affect both ''short-run'' fluctuations and longterm growth patterns. From a normative point of view, simulations show a corresponding complementarity between ''Keynesian'' and ''Schumpeterian'' policies in sustaining long-run growth paths characterized by milder fluctuations and relatively lower unemployment levels. The matching or mismatching between innovative exploration of new technologies and the conditions of demand generation appear to suggest the presence of two distinct ''regimes'' of growth (or absence thereof) characterized by different short-run fluctuations and unemployment levels

Innovation flow through social networks: Productivity distribution

A detailed empirical analysis of the productivity of non financial firms across several countries and years shows that productivity follows a non-Gaussian distribution with power law tails. We demonstrate that these empirical findings can be interpreted as consequence of a mechanism of exchanges in a social network where firms improve their productivity by direct innovation or/and by imitation of other firm's technological and organizational solutions. The type of network-connectivity determines how fast and how efficiently information can diffuse and how quickly innovation will permeate or behaviors will be imitated. From a model for innovation flow through a complex network we obtain that the expectation values of the productivity level are proportional to the connectivity of the network of links between firms. The comparison with the empirical distributions reveals that such a network must be of a scale-free type with a power-law degree distribution in the large connectivity range.Comment: 14 pages, 4 figures, submitted to Phys. Rev.