Daniel Coronado

Esther Ferrándiz

M Dolores León

Manuel Acosta

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Regional scientific capacities in Europe. Specialization and 
determining factors 
Manuel Acosta, Daniel Coronado, Esther Ferrándiz, M. Dolores León 
 
Address: 
Facultad de Ciencias Económicas y Empresariales 
Duque de Nájera, 8 
11002-CADIZ (SPAIN) 
 
Correspondence to:  
daniel.coronado@uca.es 
(Extended abstract) 
 
1. Introduction and research questions 
Despite the theoretical and empirical relevance of several economic aspects involving 
research activities in universities (see the surveys b  Dasgupta and David, 1994; 
Stephan, 1996), the empirical analysis concerning the production of science for 
universities is very scarce; only a few papers have ddressed this issue from an 
economic view1. While there is some evidence of the short-term usefulness of 
incentives at country level to promote scientific research (Adams and Griliches, 1996; 
1998; Payne and Siow, 2003; Adams et al., 2005; Crespi and Geuna, 2008), regional 
information seems to be largely missing in the litera ure. In this paper we try to fill this 
gap providing some insight on the distribution and academic scientific specialization 
across European regions; additionally, we particularly focus on the role of financial 
funds on the development of university-based research at regional level. More 
specifically our research questions are as follows: 
 
1. How is the production of scientific research distributed across European 
regions? Which regions lead in scientific production by discipline? What are the 
regional scientific specialization patterns across European regions?. 
 
2. What are the effects of academic R&D expenditures in promoting the production 
of scientific research at regional scale? Is there any difference according to the 
level of regional development? What other factors may determine the production 
of science?. 
 
2. Data 
The main data set to capture the regional scientific production consists of a 
regionalization of roughly 1,000,000 papers from 1998 to 2004 obtained from the 
Thomson ISI (Information Sciences Institute) database. The procedure to account for 
university scientific papers at NUTS II level in the EU-15 is described in Acosta et al. 
(2011). 
                                                
1 A stream of literature focused on the individual productivity of researchers has sometimes considered 
R&D funding as an “environmental attribute”, along with other personal characteristics (researcher sex, 
age, education, etc.) and institutional attributes of the institutions for which those researchers work (see 
for a review, Carayol and Matt, 2006). However, this growing literature is far from the view followed in 
our paper. 
 
 2
The descriptive analysis shows that that the production of scientific knowledge is highly 
concentrated in a few regions. For instance, the top ten regions in terms of publications 
accounted for 22.61% of total number of publications. This high concentration holds for 
all disciplines, although the composition of the top scientific regions differs across 
scientific fields.  Addionally, we classified regions into four groups according to if they 
were above or below 75 % of EU-15 average of GDP per capita and HERD per capita. 
This analysis shows an unbalanced picture of the gen ration of academic papers since 
the average capacity for publication of a less developed region is about 45% of the 
capacity of a developed region in the core group. The disparities are rather stronger 
when we consider a classification of regions based on HERD (on average, a region with 
less amount of funding produced 79% fewer papers than a region HERD above 75% of 
EU-15 average HERD per capita). 
 
3. Model and variables 
In order to evaluate the impact of academic R&D expenditures on scientific production, 
we put forward a regional version of the knowledge production function suggested by 
Adams and Griliches (1996) in terms of inputs and outputs. The inputs are academic 
R&D funds; the outputs are scientific publications. The empirical panel model takes the 
form: 
ittiitit urRDSP +++= ηαβ )(  
 
where the dependent variable SPit is the scientific knowledge production proxied by the
logarithm of papers for the region i in the year t. The explanatory variables are as 
follows: 
- RD(r) is the logarithm of a distributed lag function of past academic R&D 
expenditures. 
- Lambda represents regional specific effects.  
- Nu represents time effects;  
- u represents all other unaccounted forces determining this particular measure of 
output.  
 
4. Results 
To study the effects of academic R&D funds on regional scientific, we estimated several 
fixed effects regressions including HERD with different time lags. Models t-1, t-2, t-3, 
t-4, t-5 include HERD data lagged 1, 2, 3, 4, 5 years respectively. Models named weight 
3t and weight 5t include the inverted V-lag for R&D in the previous three and five 
years, respectively.  
 
4.1 Effect of Higher Education R&D expenditures on scientific production 
From the estimation of the models, we obtained that HERD is only significant for the 
two-year lag model and for the three-year weighted model. The higher significance of 
HERD variable in the 2-year lag model than for the 3-year weighted model suggests 
that, for the regions in the sample, the 2-year lag model is the best representation of the 
effects of HERD on publications. This result suggests that HERD take two years in 
promoting scientific production, and then this positive effect disappears. Regional 
effects and time effects are significant for all the models, which confirm the presence of 
regional differences in the scientific capability to transform the input in outputs and the 
existence of a temporal effect, since scientific publications tend to increase over time. 
The Hausman tests show that the fixed effect model is to be preferred in all the 
 3
estimated models. The number of observations and regions vary across the models due 
to the lack of data for HERD in some years. 
 
4.2 Centre-periphery models of scientific production 
In order to test the centre-periphery hypothesis, we divided the regions in our sample 
into objective 1 regions (those with a GPD per capita below the 75% percent of the EU-
15 average), and non objective 1 regions (the others). Then, we estimated separated 
econometric models for each group of regions. From these estimations, we found that 
HERD in more developed regions produced results within the two and three subsequent 
years. This lag is shorter than for less developed regions, in which HERD take five 
years to positively affect the amount of scientific publications. However, the impact of 
such investment is greater for objective 1 regions than for more developed regions. This 
could be explained because the second group of regions is likely to have a ready-to-use 
knowledge infrastructure, which allows them to convert faster HERD into new 
scientific knowledge, but also to be less dependent on new funds than objective 1 
regions. Conversely, objective 1 regions are likely to lag in knowledge pool and 
innovative capacity, which takes time to be developd. This has two main 
consequences: (1) it takes more time to convert HERD into new knowledge in these 
regions and (2) they are more dependent on research funding, which could also explain 
the greater impact of HERD on scientific production n these regions.  For both types of 
regions and for all the estimated models, regional a d time effects are significant. 
    
 
Authors’ note: This is a paper in progress. Future versions will include 
spillover effects. Additionally, other explanatory variables, such as inter-
regional scientific collaboration, may be added to the empirical model. 
 
 
References 
Acosta, M., Coronado, D., Ferrándiz, E., León, M.,(2011). Factors affecting inter-
regional academic scientific collaboration within Europe: the role of economic distance. 
Scientometrics 87, 63-74. 
Adams, J.D., Black, G.C., Clemmons, J.R., Stephan, P.E.,(2005). Scientific teams and 
institutional collaborations: Evidence from U.S. universities, 1981-1999. Research 
Policy 34, 259-285. 
Adams, J.D., Griliches, Z., (1996). Research Productivity in a System of Universities. 
National Bureau of Economic Research, Inc. 
Adams, J.D., Griliches, Z.,(1998). Research Productivity in a System of Universities. 
Annales d'Economie et de Statistique. 
Carayol, N., Matt, M. Individual and collective determinants of academic scientists 
productivity. Information Economics and Policy 18: 55- 72, 2006 
Crespi, G.A., Geuna, A.,(2008). An empirical study of scientific production: A cross 
country analysis, 1981-2002. Research Policy 37, 565-579. 
Dasgupta, P., David, P.,(1994). Towards a new economics of science. Research Policy 
23, 487-521. 
Payne, A., Siow, A.,(2003). Does Federal Research Funding Increase University 
Research Output? Advances in Economic Analysis & Policy 3, 1018. 
Stephan, P.,(1996). The economics of science. Journal of Economic Literature 34, 1199-
1235. 
 


Regional scientific capacities in Europe. Specialization and determining factors

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Regional scientific capacities in Europe. Specialization and determining factors

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