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      Research funding and academic output: evidence from the Agricultural University of Athens

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            This paper uses detailed data on funding information and research output from the Agricultural University of Athens to examine how each type of funding source is related to the quantity and quality of academic research output. Of special interest are private, Greek government and European Union sources of funding. We find that after controlling for unobserved heterogeneity from each research laboratory, all types of research funding are similarly related to both the count of publications and citations. Further, we find that research laboratories that have filed for at least one patent application produce more publications and citations to their work, indicating that laboratories that are close to industry are also engaged actively in research.


            Author and article information

            Pluto Journals
            1 September 2015
            : 33
            : 3 ( doiID: 10.1080/prometheus.33.issue-3 )
            : 235-256
            Department of Agricultural Economics and Rural Development, Agricultural University of Athens, Athens, Greece
            © 2015 Pluto Journals

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            Computer science,Arts,Social & Behavioral Sciences,Law,History,Economics


            1. Two examples are research into wheat varieties in Mexico and rice at the International Rice Research Institute (Wright, 2012).

            2. The other major concern of those studying university–industry collaboration is the exclusionary control over academic research findings stemming from industry funding (Washburn, 2005). This concern received considerable attention in the literature after a series of high-profile research grants from multinational corporations to academic institutions (Press and Washburn, 2000; Washburn, 2010). This issue, however, is not the focus of this paper. For evidence from the UK, see Lawson (2013) and from California, see Wright et al. (2014).

            3. These are Agricultural Economics and Rural Development; Crop Science; Animal Science and Aquaculture; Agricultural Biotechnology; Food Science and Technology; Natural Resources Management and Agricultural Engineering.

            4. Results are similar if we include these two laboratories in our analysis.

            5. We should also note that an alternative data source would be the Web of Science. Web of Science has a smaller number of journals listed (Chadegani et al., 2013) than Scopus as it focuses more on basic research (Goldfarb, 2008). Since we are also interested in the overall impact of the research and have included citations as a variable in the quantitative analysis, Scopus seems overall more appropriate to our research.

            6. Whenever the id number was not unique (fewer than 10% of cases), we downloaded all the id numbers and their associated publications for each author.

            7. We also excluded duplicate publications. For instance, we took out Greek publications as these were also likely to be published in English. These accounted for approximately 20 publications out of 3000.

            8. In detail, we downloaded the CV of each faculty member from their academic webpage and retrieved all their publications. In addition, we retrieved all their publications from their Scopus profiles. As not all CVs were up to date, we made sure to compare the number of publications up to a certain common year. In all cases, the number of publications in Scopus was very close to the number of publications in CVs. On occasion, the Scopus profile counted a few more publications, mainly because Scopus considers chapters in edited volumes to have been peer reviewed (as they have been in most cases). In conclusion, we did not find pronounced differences between the number of papers reported in Scopus and the number reported in the CVs.

            9. http://worldwide.espacenet.com/advancedSearch?locale=en_EP.

            10. As UNCLASS is a very small portion of total research funding, we exclude it from the rest of the analysis.

            11. Harman and Ollif (2004) show that implementation of public funding in Australia also faces similar problems.

            12. We also collected information about Ph.D. students, postdocs, teaching and research staff by laboratory. However, these data are not accurate for earlier years in our sample. In any case, regressions where we divide research metrics with the entire number of research staff by laboratory provide qualitatively similar results and are available on request.

            13. These are evenly spread across the five departments of AUA, excluding the AGECON department. The most productive laboratories in terms of patent applications are the general chemistry laboratory of the FOOD department and the agricultural engineering laboratory of the NAT department with nine and five applications respectively.


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