Abstract
An important trend in knowledge generation and diffusion is that the co-authorship of research publications has become remarkably more frequent. In this paper we study the role of co-authorship networks for starting and maintaining research collaborations. Relying on the network of the IMF’s Working Papers—which reflects well the endogenous nature of research collaborations—we document the presence of many authors with few direct co-authors, yet indirectly connected through short co-authorship chains. Two researchers are more likely to team up if their distance in the existing network is shorter, arguably reflecting reduced matching frictions. In addition, productive authors and authors with different co-author network sizes collaborate more, because of synergies between senior and junior researchers. Being employed in the same department and having citizenship of the same region also influence the decision to collaborate. We argue that incentives should be directed to researcher pairs that are initially more distant from each other in the co-authorship network.
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Notes
Throughout the paper we will use co-authorship as a proxy for research collaboration. Besides co-authorship, research papers embed other forms of (informal) collaboration, as captured by citations of other papers (Iaria et al. 2018; Solomon et al. 2019) and acknowledgements (Rose and Georg 2021), but these do not necessarily entail the same level of engagement. Note that our dataset, like others, underestimates the extent of research collaboration, since some research projects that are initiated will fail to deliver a finished working paper.
IMF Working Papers do undergo an internal review process but the time between when the paper is finalized and when it is published is typically much shorter and homogeneous than in the case of articles in peer-reviewed journals.
See https://www.imf.org/en/publications/search/series=IMF+Working+Papers, accessed June 2020. We excluded 44 working papers published in the early 1990s for which no authors were listed or that were not indexed on the IMF website.
We classified also IMF visiting and resident scholars as “non-IMF” authors and looked up their main affiliation.
For similar reasons, Ravallion and Wagstaff (2012) use Google Scholar to collect citations for the World Bank’s research publications.
Area departments: African Department (AFR), Asia and Pacific Department (APD), European Department (EUR), Middle East and Central Asia Department (MCD), and Western Hemisphere Department (WHD). Functional departments: Fiscal Affairs Department (FAD), Institute of Capacity Development (ICD), Money and Capital Markets Department (MCM), Research Department (RES), Statistics Department (STA), and Strategy, Policy and Review Department (SPR).
As a reference, the IMF has around 3,000 employees, which implies that if all of these employees were active researchers, there would have been around 4.5 million active pairs (\(n(n-1)/2\)).
For a more in-depth, comparative study of the citation record of IMF Working Papers, see Aizenman et al. (2011).
The JEL codes start with a letter and can be further disaggregated with up to three digits. For the purpose of this analysis, we use only the first letter of the JEL codes. These are: A–General Economics and Teaching; B–History of Economic Thought, Methodology, and Heterodox Approaches; C–Mathematical and Quantitative Methods; D–Microeconomics; E–Macroeconomics and Monetary Economics; F–International Economics; G–Financial Economics; H–Public Economics; I–Health, Education, and Welfare; J–Labor and Demographic Economics; K–Law and Economics; L–Industrial Organization; M–Business Administration and Business Economics; N–Economic History; O–Economic Development, Innovation, Technological Change, and Growth; P–Economic Systems; Q–Agricultural and Natural Resource Economics; R–Urban, Rural, Regional, Real Estate, and Transportation Economics; Y–Miscellaneous Categories; Z–Other Special Topics.
The number of collaborations involved in a paper is given by \(n(n-1)/2\), where n is the number of authors. Most nodes in Fig. 3 are connected by less than 10 edges, as collaborations with “non-core” authors are counted but not shown.
The few non-IMF authors present in the core network are predominantly academics that were regularly visiting or were at some point employed at the IMF.
Another 303 or 7.7 percent of authors have either missing affiliation information, since they were only active prior to 1997, or no unique affiliation category mode, as they spent an equal number of years in different categories of institutions (say, three years at the IMF and three years at a central bank).
These findings are consistent with the literature, which shows that, by reducing search costs, physical proximity (even if temporary, during conferences, workshops, information sessions, etc.) significantly increases the likelihood of initiating scientific collaboration and co-authorship. See e.g., Boudreau et al. (2017), Catalini (2018), Campos et al. (2018), and Chai and Freeman (2019).
Note that, for technical reasons, Goyal et al. (2006) exclude from their analysis of the EconLit network all articles with four or more authors, which represents a reported 1.6 percent of the full sample.
The relative ubiquity of “small world” characteristics in co-authorship networks, especially the high degree of clustering, may be partly a reflection of the tendency of authors to collaborate with other authors that are close to them in the existing network (something we find strong evidence for in Sect. 4), as well as of the (increasing) importance of multi-authored publications (since this raises the average clustering coefficient mechanically). Wallace et al. (2012) find that the “small world” nature of most co-authorship networks does not always carry over to citation networks, and that the propensity to cite co-authors (and co-authors of co-authors) varies widely across research fields.
Unlike Fafchamps et al. (2010), we do not draw random samples. Instead, we use all pair-year observations for active pairs. In the case of initial collaborations, the sample consists of 6,040,424 observations for 2,310,620 pairs, and in the case of subsequent collaborations, the sample reduces to 14,893 observations for 3,325 pairs.
While we control for some time-invariant demographic characteristics of the author pair, there might be other time-invariant characteristics that are relevant for the decision to collaborate. Including fixed effects to control for those characteristics, however, would lead to a drastic reduction in the number of observations as there are many author pairs that have been active and collaborated only for one year, and many author pairs that were active for longer but never collaborated.
This leads to the exclusion of the first nine years of the sample in the regressions.
Our measure of productivity is somewhat different from the one used by Fafchamps et al. (2010) in that we use the number of citations instead of their “journal quality index” and we do not include the number of pages in the numerator of the ratio. These adjustments reflect that we consider working papers instead of published articles and the general idea that working paper length depends more on the subject than on the quality of the paper.
This information is available for all researchers that have been affiliated to the IMF at some point during their career.
The inclusion of employment and demographic characteristics purges many external researchers from the sample. However, those that were IMF staff at some point during their career are still part of the sample.
This result is consistent with Yuret (2020), who finds that many co-authors of top economics journal articles had worked together in the same institution at some point in time before their collaboration.
This result is in line with studies such as Freeman and Huang (2015) and AlShebli et al. (2018), who find strong evidence of positive assortative matching on ethnicity in co-authorship networks. Dropping authors that have a European citizenship (excluding transition countries), which represent more than 30 percent of the total number of authors in our sample, does not affect our results.
As in the case of initial collaborations, dropping authors with a European citizenship (excluding transition countries) does not change the results.
ROC curves visualize the trade-off between the true positive rate—here the ratio of correctly called collaborations by author pairs to the true number of collaborations—and the false positive rate—the ratio of non-collaborating author pairs incorrectly classified as collaborating to the total number of non-collaborating pairs—for all possible probability thresholds. The further the ROC curve lies from the 45 degree line, which represents a non-informative classification strategy of random guessing, the better it is able to discriminate between collaborations and non-collaborations.
The relatively weaker performance of the model for subsequent collaborations may be related to the significant reduction in sample size.
This implies the exclusion of visiting and resident scholars too, since we classified those as external authors affiliated with their home institutions (see Sect. 2.1).
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Acknowledgements
This paper is a substantially extended version of a conference paper included in the Proceedings of the 18th International Conference on Scientometrics and Informetrics (ISSI 2021) (Essers et al. 2021). The views expressed are those of the authors and do not necessarily represent those of the IMF, its Executive Board, its management, the National Bank of Belgium, or the Eurosystem. We thank, without implicating, Roberto Guimaraes-Filho and his team for compiling the IMF employment and demographic data used in this paper, and Joris Wauters, conference participants and two anonymous reviewers for helpful comments.
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Essers, D., Grigoli, F. & Pugacheva, E. Network effects and research collaborations: evidence from IMF Working Paper co-authorship. Scientometrics 127, 7169–7192 (2022). https://doi.org/10.1007/s11192-022-04335-4
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DOI: https://doi.org/10.1007/s11192-022-04335-4