Jointly published by Akadémiai Kiadó, Budapest Scientometrics, and Springer, Dordrecht Vol. 63, No. 1 (2005) 87–120 Exploring size and agglomeration effects on public research productivity ANDREA BONACCORSI,a CINZIA DARAIOb a University of Pisa, Pisa (Italy) b IIT-CNR and Scuola Superiore S. Anna, Pisa (Italy) The paper assesses the empirical foundation of two largely held assumptions in science policy making, namely scale and agglomeration effects. According to the former effect, scientific production may be subject to increasing returns to scale, defined at the level of administrative units, such as institutes or departments. A rationale for concentrating resources on larger units clearly follows from this argument. According to the latter, scientific production may be positively affected by external economies at the geographical level, so that concentrating institutes in the same area may improve scientific spillover, linkages and collaborations. Taken together, these arguments have implicitly or explicitly legitimated policies aimed at consolidating institutes in public sector research and at creating large physical facilities in a small number of cities. The paper is based on the analysis of two large databases, built by the authors from data on the activity of the Italian National Research Council in all scientific fields and of the French INSERM in biomedical research. Evidence from the two institutions is that the two effects do not receive empirical support. The implications for policy making and for the theory of scientific production are discussed. Introduction In recent years policy making in the field of science and public research has been influenced by the attempt to apply economic concepts. The pressure on public budgets in almost all industrialised countries has lead governments to pursue (or at least to declare they pursue) efficiency in the allocation and management of resources in the public research sector. The increasing societal demand for accountability and transparency of science also makes it important to demonstrate that public funding follows clear rules. A clear manifestation of this trend is the effort to apply to public scientific research two very fundamental concepts drawn from economic analysis, that are, increasing returns to scale or economies of scale, and external economies or economies of agglomeration. Received November 2, 2004 Address for correspondence: CINZIA DARAIO Institute for Informatics and Telematics (IIT), Consiglio Nazionale della Ricerche (CNR) Area della ricerca di Pisa , Via G. Moruzzi, 1; I-56127 Pisa, Italy E-mail: [email protected], [email protected] 0138–9130/US $ 20.00 Copyright © 2005 Akadémiai Kiadó, Budapest All rights reserved A. BONACCORSI, C. DARAIO: Size and agglomeration effects If these two forces were at play in scientific research, then a sound policy implication would be that in order to improve the efficiency of public research resources should be concentrated into larger institutions and/or into geographically agglomerated areas. This paper explores scale and agglomeration effects in scientific research with reference to two large European public research institutions, the Italian National Research Council (CNR) in several research areas, and the French INSERM in the biomedical field. Scale and agglomeration economies in scientific research In the attempt to apply economic concepts to science by means of analogy, it is assumed that institutes and departments are analogous to firms, using production factors or inputs in order to obtain scientific output. This analogy raises several problems. First of all, there is an important identification problem: what is the unit of production in scientific research? On one hand, it has been argued that the appropriate unit of analysis for production is the laboratory or team (LAREDO & MUSTAR, 2001). Researchers are members of several projects, that cut across administrative boundaries of institutes. At the same time, it is still true that all researchers are generally members of an institute or department defined by discipline or thematic field. While direct production takes place in laboratories and within teams, still the institutional level of institutes and departments makes sense. In general, it must be recognized that organizational arrangements may differ across scientific disciplines (SHINN, 1979; WHITLEY, 1984) and that empirical research should try to keep these differences into account. As an example, in this paper we provide data for several disciplines in the Italian case of CNR; furthermore, within a single large and diversified field for which data are available (i.e. biomedicine) we also provide comparative results between a set of institutes in two national institutions (INSERM in France and CNR in Italy). Second, there are several measurement problems for both inputs and outputs. Among inputs to scientific production the following are considered: (i) number of researchers, possibly classified by category (i.e. directors, senior researchers, junior researchers, post-doc. and Ph.D. students), age, seniority (i.e. number of years in the field), disciplinary background, and quality (i.e. cumulated number of publications, or citations, or impact factor); (ii) stock of capital equipment; (iii) research funds; (iv) stock of past knowledge (as measured for example by cumulated number of publications at the level of institute). 88 Scientometrics 63 (2005) A. BONACCORSI, C. DARAIO: Size and agglomeration effects A number of severe measurement and practical problems make the complete analysis almost impossible. In practice, it is enormously difficult to collect data on all these items for a sufficiently long period of time. Within relatively homogeneous research areas it is considered acceptable to utilise a subset of inputs such as number and category of researchers, or number of researchers and research funds. Data on the stock of capital equipment are not easily available. On the side of research outputs, other problems are at play. For most purposes, especially within relatively homogeneous research areas, a simple count of publications is considered acceptable. A more complete treatment, however, should distinguish between quantity of output, its quality and impact (as measured by citations received) and its relevance (as measured by subjective evaluations of experts in the field). In addition, relevant output of scientific production also include teaching, applied research and consultancy for industry and third parties, patenting, and the like. Consequently, not only scientific production is inherently multi-input multi-output, but all inputs and outputs are heterogeneous and cannot be easily measured using commensurable variables. Finally, the specification of the relation between inputs and outputs is another difficult conceptual problem. This relation is likely to be non-deterministic, have a lagged structure, and have a time sequence which is variable over time and across sectors. In the light of these characteristics, any meaningful measure of productivity should be generated by a model of multi-input multi-output production without a fixed functional specification. Despite these severe identification, measurement and specification problems and the resulting difficulties in testing specific predictions, the idea that scientific production must exhibit some relation between the resources employed and the output produced is generally accepted. For practical and policy objectives simple measures of the ratio of output to input are considered an indicator of scientific productivity. As an example, the crude number of paper per researcher, within relatively homogeneous fields, is considered an acceptable indicator of productivity across large numbers. Having established the analogy between scientific research and production, and apart from the methodological problems discussed above, two questions can legitimately arise. Let us state them as follows: (a) does the concentration of resources over large institutions or institutes improve scientific productivity? In other words, is there in the economics of science the same phenomenon called economies of scale in production? (b) does the territorial concentration of scientists improve scientific productivity? In some countries a policy of locating laboratories and research institutes in the same The use of simple ratios within a context of multi-input multi-output production, of course, can be criticized. See LINK (1996) for a discussion of limitations of any production function approach in science. Scientometrics 63 (2005) 89 A. BONACCORSI, C. DARAIO: Size and agglomeration effects territorial area has been actively pursued, with a view of creating so called economies of agglomeration. Does this policy improve the production of scientific publications? Economies of scale in scientific production In the context of manufacturing production, economies of scale refer to the fact that an increase of k times in all factors of production determines an increase in output of more than k times. Therefore the larger the scale of production (i.e. productive capacity of plants), the lower the unit or average cost in the long run. To claim that increasing returns to scale are at play one must increase simultaneously all factors of production, not only the variable ones (i.e. work). It is useful to distinguish between economies of scale at the level of plant and at the level of firm. The latter may be limited to manufacturing costs for several plants or include