INTRODUCTION

This chapter presents a “history-friendly” model of the evolution of the pharmaceutical industry, and in particular of the so-called golden age. This industry is an ideal subject for such an analysis, especially because it has characteristics and problems that provide both contrasts and similarities with the computer industry. Like computers, the pharmaceutical industry has traditionally been a highly R&D and marketing-intensive sector and it has undergone a series of radical technological and institutional “shocks.” However, despite these shocks, the core of the leading innovative firms and countries has remained stable for a very long period of time. Entry by new firms has been a rather rare occurrence until the advent of biotechnology. However, while the evolution of computers coincides largely with the history of very few firms, that of pharmaceuticals involves at least a couple of dozens of companies. Further, the degree of concentration has been consistently low at the aggregate level and the industry has never experienced a shakeout of producers.

We argue that the observed patterns of the evolutionary dynamics were shaped by three main factors, related both to the nature of the relevant technological regime and the structure of demand:

WHAT THIS BOOK IS ABOUT

This book is about technological progress and its relationships with competition and the evolution of industry structures. It presents a new approach to the analysis of these issues, which we have labeled “history-friendly” modeling. This research stream began more than a decade ago and various papers have been published over the years. Here, we build on those initial efforts to develop a comprehensive and integrated framework for a systematic analysis of innovation and industry evolution.

The relationships among technological change, competition and industry evolution are old and central questions in industrial economics and the economics of innovation, a subject matter that dates back to Marshall and of course to Schumpeter. We authors are indeed Schumpeterians in that we believe the hallmark feature of modern capitalism is that it induces, even compels, firms to be innovative in industries where technological opportunities exist and customers are responsive to new or improved products. The evolution of these industries – like computers or semiconductors – is often characterized by the emergence of a monopolist or of a few dominant firms. The speed at which concentration develops varies drastically, however, across sectors and over time, and, often, monopoly power is not durable. In other significant industries – e.g. pharmaceuticals – no firm actually succeeded in achieving such an undisputed leadership. In some cases, the characteristic drift toward concentration is interrupted by significant exogenous change, such as new technologies appearing from outside the sector.

Long ago, Schumpeter proposed that the turning-over of industrial leadership was a common feature in industries where technological innovation was an important vehicle of competition. In recent years economists studying technological change have come to recognize a number of other important connections between the evolution of technologies and the dynamics of industries’ structure. Progress in this area has come from different sources. The availability of large longitudinal databases at a very high level of disaggregation has allowed researchers to unveil robust stylized facts in industrial dynamics and to conduct thorough statistical analyses, which show strong inter-industry regularities, but also deep and persistent heterogeneity across and within industries. New sophisticated models have been created that attempt to explain the regularities.

The formal representation of the history-friendly models presented some notable issues, first of all because of the huge amount of variables and parameters defining the models: some of these elements were common or at least analogous across the models, while others referred to completely different domains. In order to reduce the number of the main symbols to a manageable size, we adapted from computer programming languages the idea of overloading notation: a main symbol can have slightly different meanings according to the presence or absence of further details, such as superscripts and subscripts. For example, the symbol T indicates the total number of periods of a simulation, Tk indicates the period of introduction of technology k, and TI the minimum number of periods a firm has to stay integrated after its decision to switch to internal production of components. In general, we use as subscripts the indices for elements (products, firms, markets, technologies) that take different values, without changing the meaning of the main symbol. Instead, we use as superscripts further identifiers of the main symbols that are not instances of a general category: for example, PT is the symbol of patents and E is the symbol of exit. In a very limited number of cases an identifier can be used both as a subscript identifier (TR and MP in most of the cases are used as instances of component technology k) and as a superscript identifier (TR and MP are used as superscripts of the main symbol α, as they refer to different parts of the same equation).

Upper and lowercase letters are considered as different, although whenever it is possible they take related meanings: for example, i indicates the propensity to integrate and I the corresponding probability. The symbols used for specific variables and parameters are not used across models, unless these variables and parameters have the same or a very similar meaning and role in the different models. The values that parameters take and the range of values that heterogeneous parameters and variables can take are indicated in the tables in the Appendices.