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The determinants of the research output of universities: specialization, quality and inefficiencies

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Abstract

The analysis of productivity in Higher Education Institutions (HEIs) at a European level reveals enormous differences in output per researcher across countries. This study develops a 5-step methodology that explicitly considers the quality of scientific output in EU universities and its specialisations to explain and decompose the differences in output per university researcher in terms of (a) differences in efficiency within each field of science (FOS), (b) differences in FOS specialisation of HEIs in each country, (c) differences in quality, and (d) differences in allocation of resources per researcher. The inefficiency levels estimated show that across the EU as a whole there is a substantial margin for increasing research output without having to spend more resources. There are also major differences between countries in terms of inefficiency. The main sources of heterogeneity in scientific output in the HEIs of the EU are the differences in resources allocated per researcher and, to a lesser extent, the differences in efficiency within each knowledge field. The differences in quality and in specialisation also play a smaller role in determining differences in output.

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Fig. 1

Source: SCImago Journal & Country Rank, Eurostat and own elaboration

Fig. 2

Source: SCImago Journal & Country Rank, Eurostat and own elaboration

Fig. 3

Source: Eurostat

Fig. 4
Fig. 5

Source: SCImago Journal & Country Rank and Eurostat

Fig. 6

Source: SCImago Journal & Country Rank and own elaboration

Fig. 7

Source: SCImago Journal & Country Rank, Eurostat and own elaboration

Fig. 8

Source: SCImago Journal & Country Rank, Eurostat and own elaboration

Fig. 9
Fig. 10

Source: SCImago Journal & Country Rank, Eurostat and own elaboration

Fig. 11

Source: SCImago Journal & Country Rank, Eurostat and own elaboration

Fig. 12

Source: Own elaboration

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Notes

  1. Some studies propose the additional use of diverse indicators of the quality of university teaching, such as the drop-out rate, the performance rate, the student-teacher ratio, expenditure per student, the number of information technology (IT) and library staff per student, expenditure per student, etc. See Pérez et al. (2015a, b).

  2. Data provided by SCIMAGO Journal & Country Rank refer to the total number of scientific publications produces by a country. 99 % of the EU-28’s scientific output comes from universities (64.3 %), Public research centres (22.8 %) and Hospitals (11.8 %). For this reason the data on patents, publications, citations, R&D expenditure and R&D personnel provided throughout this paper refer to Higher Education (universities) and Government sector (Public Research Centres and Hospitals) as a whole.

  3. The information is available on the following website: http://www.scimagojr.com/countryrank.php

  4. R&D expenditures will cover also expenditures for R&D personnel. The data do not allow to disentangle personnel expenditure from other R&D expenditure by FOS. If we want to take into account the specialization effect, as we do, we have to use total R&D expenditures as input (which include both non-personnel R&D expenditure and research wages, in fact a measure of abundance of resources for researchers). It is also important to consider research as an output coming from more than one input. Labour is a very important input and other types of R&D expenditure are also very relevant. HEIs have the option of allocating more resources to their researchers, employing better qualified and paid researchers or using more researchers. We think that this is an important fact that needs to be considered. It is possible to disentangle personnel expenditure from other R&D expenditure but only without taking into account FOS specialization. We have carried out that exercise using two inputs: only the non-personnel R&D expenditure and R&D personnel. This analysis avoids any potential issue of double accounting. The coefficients of correlation between the inefficiency indicators obtained from this new exercise and the comparable inefficiency indicators obtained and discussed in the paper range between 0.96 and 0.98. Therefore, the results are maintained, showing robustness to this potential issue.

  5. The positive impacts of universities on the economic growth of their countries’ economies have been widely demonstrated in the literature, especially in the case of North American universities (Pastor et al. 2013).

  6. Alternatively, Farrel also proposed to measure efficiency from the perspective of the potential reduction of inputs given a vector of outputs (Farrell 1957).

  7. Maudos et al. (2000) use a similar methodology to analyse the regional output differences.

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Acknowledgments

The authors would like to thank a reviewer for the comments that helped to improve an earlier version of the manuscript. This paper was developed as part of the SPINTAN project funded by the European Commission. This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under Grant Agreement No: 612774. The authors are grateful for funding from Research Project ECO2015-70632-R (Ministry of the Economy and Competitiveness).

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Correspondence to José Manuel Pastor.

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Pastor, J.M., Serrano, L. The determinants of the research output of universities: specialization, quality and inefficiencies. Scientometrics 109, 1255–1281 (2016). https://doi.org/10.1007/s11192-016-2102-3

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