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Rise of multi-authored papers in economics: Demise of the ‘lone star’ and why?

Abstract

This paper builds on previous work by reviewing the key literature relating to the rise in co-authorship in economics and by presenting further new evidence on several features of co-authorship in articles in economic journals. The empirical analysis draws on around 175,000 articles in the top 255 journals, over the period 1996–2014. The rises in quarto-plus and cross-country co-authored papers are striking, as are the differences in citations per article and citations per author. There is evidence of an alphabetical ordering of authors as the standard in co-authored papers in top journals with no downward trend evident over time. A correlation between co-authorship and career stage is observed with young authors publishing significantly more solo-authored articles.

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Fig. 1

Source: own calculations based on Scopus data

Fig. 2

Source: own calculations based on Web of Science data

Fig. 3

Source: own calculations based on Scopus data

Fig. 4

Source: own calculations based on Scopus data

Fig. 5

Source: own calculations based on Scopus data

Fig. 6

Source: own calculations based on Scopus data

Fig. 7

Source: Own calculations based on Scopus data

Fig. 8

Source: Own calculations based on the authors’ on-line CVs and Scopus data

Fig. 9

Source: own calculations based on Scopus data

Notes

  1. 1.

    See for example McDowell and Michael (1983), Barnett et al. (1988), Hudson (1996), Wuchty et al. (2007), and Card and DellaVigna (2013).

  2. 2.

    The main reason for this is that the Scopus data allowed for exploration of other work [see Kuld (2017)] not possible using the RePEc data.

  3. 3.

    It is not clear though that if books and reports no longer count that the total demands on research time, and hence the opportunity cost of time, should have risen. Books and reports in many cases would have taken up a huge amount of research time, time now ‘free’ for journal article research and assistance to colleagues.

  4. 4.

    However, while it is true that there has been a huge increase in the number of journals and articles submitted, there has also been a large increase in the number of economists upon which to draw on for editorial and refereeing purposes [see Osterloh and Frey (2014)], implying no increase in work-load per referee and editor.

  5. 5.

    One wonders would that be the case today. From anecdotal evidence it appears that the return on a co-authored paper today might be considerably more than 1/n times that of a solo-authored paper, which in itself would be a very strong argument for the rise of co-authorship. It is likely that the more cross-country the co-authorship the more likely is the chance that there is no discounting of multi-authored papers (see later).

  6. 6.

    Agrawal and Goldfarb (2008) examined the effect of a decrease in collaboration costs resulting from the adoption of Bitnet (an early version of the Internet) on university research collaboration in engineering, their interest being the broader question of how changes in collaboration costs may affect the structure of knowledge production. They examined 270 universities that published in seven top electrical engineering journals from 1981 to 1991 and found that a Bitnet connection did seem to facilitate a general increase in multi-institutional collaboration (by 40%, on average). Catalini et al. (2016) built on the explanation that links the increase in co-authorship to the drastic reduction in communication costs brought by the internet: as coordination and communication costs go down, scientists are able to sustain collaboration over distance in a more efficient way. In this paper, they test a complementary hypothesis: that the increase in distant collaboration may also be the result of the dramatic reduction in air travel costs that took place within the United States over the last 30 years.

  7. 7.

    The focus of Fafchamps et al. (2010) was linking the extent of co-authorship to networks. The stronger the networks the greater the degree of co-authorship. They also note though that networks maybe are not as important in determining co-authorship given the greatly increased access to the web. They also address a potential problem, namely the time between when collaboration commenced and when it is noted, namely in a publications.

  8. 8.

    Önder and Schweitzer (2016) also examine trends in co-authorship, highlighting the rise in papers with more than two authors, applied to PhD graduates from German-speaking countries.

  9. 9.

    Other data bases include Google Scholar, RePEc and EconLit. Scopus (http://www.scopus.com) has full coverage of the selected journals from 1996.

  10. 10.

    The primary purpose of our data-collection exercise was to obtain information on the career paths of the most published economists in of the last 20 years [see Kuld (2017)].

  11. 11.

    Rath and Wohlrabe (2016) broke the 1615 journals used into quartiles using RePEc impact factors and found that the rises in all quartiles were the same, with the average number of authors in the top three quartiles very similar. The top quartile though includes over 400 journals.

  12. 12.

    It is reassuring that similar trends were found in Rath and Wohlrabe (2016), even though using very different data and categories of authors.

  13. 13.

    It would be interesting to expand this work to other pairwise comparisons [see Henriksen (2016)].

  14. 14.

    See Card and DellaVigna (2013) and Card and DellaVigna (2014), for discussions of trends in article length, but not from a co-authorship perspective.

  15. 15.

    Ordering by contribution can lead to unintentional alphabetical listings. For instance, a random order is alphabetical in 50% of duo-authored articles. The share of non-alphabetical duo-authored papers is, therefore, taken out of half of the sample. Accordingly, trio-authored papers are related to five-sixths of the sample and quarto-authored papers to 23/24.

  16. 16.

    Rath and Wohlrabe (2016) looked briefly at this issue, from a different perspective. They used the first journal article to examine whether or not the average number of papers by ‘scientific rookies’ is smaller than the overall average but find that this is not the case. The analysis in our paper differs by following authors over their career.

  17. 17.

    The top 20 journals are picked as the reference group as said economists are highly cited. However, taking all journals does not alter the conclusion.

  18. 18.

    \(f(\#A_i,J_i)=\beta _1 {\text{DuoPlus}} + \beta _2 {\text{DuoPlus}} + \beta _3 {\text{TreblePlus:Top20}} + \beta _4 {\text{DuoPlus:Top20}} + \beta _5 {\text{DuoPlus:Top20}} + \beta _6 {\text{TreblePlus:Top20}}.\)

  19. 19.

    The estimated percentage effect for \(\gamma _{2017}=0.485\) is calculated as \(({\text {e}}^{0.485}-1)*100=62.4\).

  20. 20.

    Table 3 indicates that this correlation between co-authorship and citations received weakens over the years. However, this might be caused by the overall compression of citation counts.

  21. 21.

    Sommer and Wohlrabe (2017) and Moosa (2017) examine the relation of co-authorship and citations received. Moosa (2017) studies the 300 most cited articles on RePEc and find no effect of co-authorship on citations received. Sommer and Wohlrabe (2017) repeat this exercise by varying the sample of articles by citations received and conclude that a negative effect of co-authorship is only visible for highly cited articles (though not significant). Taking the full sample of RePEc papers leads to a positive relation between co-authorship and citations received.

  22. 22.

    In addition, we confirm that this correlation holds throughout the distribution of citations received. We estimate the probability that an article has been co-authored based on the citations it received relative to the other articles published in the same year. Using such relative citation brackets, we see that the more citations it received the more likely an article is co-authored. This holds for two to four-plus authors and throughout the distribution of citations received up to the top percentile, that is the 95 most cited articles per year.

  23. 23.

    In a follow-up version of this paper, Sarsons (2017a) finds no such discrimination in sociology, a discipline in which authors are listed according to contribution. Interestingly, sociology articles, though, have fewer authors on average [see Henriksen (2016)].

  24. 24.

    The focus of Fafchamps et al. (2010) was linking the extent of co-authorship to networks. The stronger the networks the greater the degree of co-authorship. They also note though that networks maybe are not as important in determining co-authorship given the greatly increased access to the web. They also address a potential problem, namely the time between when collaboration commenced and when it is noted, namely in a publication.

  25. 25.

    See Osterloh and Frey (2014) for a general discussion on the use of citations and rankings in economics, in particular the randomness of some of the reviewing processes. Even if this always existed to a certain extent, the non-discounting of multi-authored papers would mean that the latter would be a very useful way of countering this randomness without any loss of individual/institutional ranking.

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Acknowledgements

The authors would like to thank two anonymous referees, and in particular the first, for extensive and helpful comments on an earlier draft of this paper. Lukas Kuld also acknowledges the generous support by a Government of Ireland Postgraduate Scholarship from the Irish Research Council.

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Correspondence to John O’Hagan.

Appendix

Appendix

See Tables 1, 2 and 3.

Table 1 Number of pages, references given, citations received, and alphabetical listing
Table 2 The estimates with single-authorship as reference
Table 3 Trends in pages, references, and citations received

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Kuld, L., O’Hagan, J. Rise of multi-authored papers in economics: Demise of the ‘lone star’ and why?. Scientometrics 114, 1207–1225 (2018). https://doi.org/10.1007/s11192-017-2588-3

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Keywords

  • Co-authorship 
  • Individual contribution
  • Academic economic research

JEL Classification

  • A14
  • D85
  • I23
  • O33