Network analysis to measure academic performance in economics

  • José Alberto MolinaEmail author
  • Alfredo Ferrer
  • David Iñiguez
  • Alejandro Rivero
  • Gonzalo Ruiz
  • Alfonso Tarancón


Network analysis allows us to introduce different metrics that complement the traditional indicators to measure academic performance, generally based on individual production. In this paper, we show how the use of these techniques provides a more global point of view, introducing indicators that, beyond individual merits, measure the capacity of researchers to generate more intangible assets. We focus on collaboration among groups that can enrich the potential of the research ecosystem as a whole. We present not only numerical indicators, but also several visualisation schemes to see how this approach can help in the academic evaluation and decision-making process of research managers. We have used, as a case study, the research ecosystem formed by more than five thousand economists from Spanish institutions.


Academic performance Co-authorship Economists Interaction maps Complex networks 

JEL Classification

A11 A30 O30 



This paper was partially written while Jose Alberto Molina was Visiting Fellow at the Department of Economics of Boston College (US), to which he would like to express his thanks for the hospitality and facilities provided. Kampal Data Solutions S.L. thanks Web of Science for permission to publish the analysis of these data on its web page ( This paper has benefited from funding from the Spanish Ministry of Economics (Projects ECO2012-34828 and FIS2015-65078-C2-2-P), and it has been dedicated to A. Calvo-Armengol, an expert in social networks, who died prematurely in 2007.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


  1. Alvarez R, Cahué E, Clemente-gallardo J, Ferrer A, Iñíguez D, Mellado X, Rivero A, Ruiz G, Sanz F, Serrano E, Tarancón A, Vergara Y (2015) Analysis of academic productivity based on complex networks. Scientometrics 104:651–672CrossRefGoogle Scholar
  2. Andrikopoulos A, Economou L (2016) Coauthorship and subauthorship patters in financial economics. Int Rev Financ Anal 46:12–19CrossRefGoogle Scholar
  3. Araujo T, Fontainha E (2017) The specific shapes of gender imbalance in specific authorships: a network approach. J Informetr 11:88–102CrossRefGoogle Scholar
  4. Arroyo L, Gallardo-Gallardo E, Gallo P (2017) Understanding scientific communities: a social network approach to collaborations in talent management research. Scientometrics 113:1439–1462CrossRefGoogle Scholar
  5. Bergantiños G, Da Rocha JM, Polomé P (2002) La investigación española en Economía, 1995–1999. Investig Econ 26:373–392Google Scholar
  6. Bergé L, Scherngell T, Wanzenböck I (2017) Brinding centrality as an indicator to measure the “brinding role” of actors in networks: an application to the European Nanotechnology co-publication network. J Informetr 11:1031–1042CrossRefGoogle Scholar
  7. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU (2006) Complex networks: structure and dynamics. Phys Rep 424:175–308CrossRefGoogle Scholar
  8. Bordons M, Aparicio J, González-Albo B, Díaz-Faes A (2015) The relationship between the research performance of scientists and their position in co-authorship networks in three fields. J Informetr 9:135–144CrossRefGoogle Scholar
  9. Bornmann L, Stefaner M, de Moya F, Mutz R (2016) Excellence networks in science: a Web-based application based on Bayesian multilevel logistic regression (BMLR) for the identification of institutions collaborating successfully. J Informetr 10:312–327CrossRefGoogle Scholar
  10. Card D, DellaVigna S (2013) Nine facts about top journals in economics. J Econ Lit 51(19):144–161CrossRefGoogle Scholar
  11. Carrasco R, Ruiz-Castillo J (2014) The evolution of the scientific productivity of highly productive economists. Econ Inq 52:1–16CrossRefGoogle Scholar
  12. Clauset A, Shalizi CR, Newman MEJ (2009) Power law distributions in empirical data. SIAM Rev 51:661–703CrossRefGoogle Scholar
  13. Clerides S, Pashardes P, Polycarpou A (2011) Peer review vs metric-based assessment: testing for bias in the RAE ratings of UK economics departments. Economica 78(311):565–583CrossRefGoogle Scholar
  14. Cobo MJ, López-Herrera AG, Herrera-Viedma E, Herrera F (2011) Science mapping software tools: review, analysis, and cooperative study among tools. J Am Soc Inf Sci Technol 62(7):1382–1402CrossRefGoogle Scholar
  15. Colavizza G (2017) The structural role of the core literature in history. Scientometrics 113:1787–1809CrossRefGoogle Scholar
  16. Costa L, Siqueira M, Alves L, Motta E (2017) Growth patterns of the network of international collaboration in science. Scientometrics. Google Scholar
  17. de Price DJS (1965) Networks of scientific papers. Science 149(3683):510–515CrossRefGoogle Scholar
  18. Ding Y, Yan E, Frazho A, Caverlee J (2009) PageRank for ranking authors in co-citation networks. J Am Soc Inf Sci Technol 60(11):2229–2243CrossRefGoogle Scholar
  19. Ductor L (2016) Does co-authorship lead to higher academic productivity? Oxf Bull Econ Stat 77(3):385–407CrossRefGoogle Scholar
  20. Dusansky R, Vernon CJ (1998) Rankings of U.S. economics departments. J Econ Perspect 12:157–170CrossRefGoogle Scholar
  21. Fruchterman TMJ, Reingold EM (1991) Graph drawing by force-directed. Softw Pract Exp 21(11):1129CrossRefGoogle Scholar
  22. Goyal S, Van Der Leij MJ, Moraga-González JL (2006) Economics: an emerging small world. J Polit Econ 114(2):403–412CrossRefGoogle Scholar
  23. Hamermesh DS (2015) Citations in economics: measurement, uses and impacts. IZA DP no. 9593Google Scholar
  24. Inzelt A, Schubert A, Schubert M (2009) Incremental citation impact due to international co-authorship in Hugarian higher education institutions. Scientometrics 78:37–43CrossRefGoogle Scholar
  25. Kalaitzideakis P, Mamuneas TP, Stengos T (1999) European economics: an analysis based in publications in the core journals. Eur Econ Rev 43:1150–1168CrossRefGoogle Scholar
  26. Kalaitzideakis P, Mamuneas TP, Stengos T (2003) Rankings of academic journals and institutions in economics. J Eur Econ Assoc 1(6):1343–1366Google Scholar
  27. Kalaitzideakis P, Mamuneas TP, Stengos T (2011) An updated ranking of academic journals in economics. Can J Econ 44(4):1525–1538CrossRefGoogle Scholar
  28. Katranidis S, Panagiotidis T, Zontanos C (2014) An evaluation of the Greek universities’ economics departments. Bull Econ Res 66(2):173–182CrossRefGoogle Scholar
  29. Katranidis S, Panagiotidis T, Zontanos C (2017) Economists, research performance and national inbreeding: north versus south. Econ Notes 46(1):145–163CrossRefGoogle Scholar
  30. Kumar S (2015) Co-authorship networks: a review of the literature. Aslib J Inf Manag 67(1):55–73CrossRefGoogle Scholar
  31. Kyvik S, Reymert I (2017) Research collaboration in groups and networks: differences across academic fields. Scientometrics 113:951–967CrossRefGoogle Scholar
  32. Letina S (2016) Network and actor attribute effects on the performance of researchers in two fields of social science in a small peripheral community. J Informetr 10:571–595CrossRefGoogle Scholar
  33. Levenshtein I (1996) Binary codes capable of correcting deletions, insertions and reversals. Cybern Control Theory 10:7076710Google Scholar
  34. Liebowitz SJ, Palmer JP (1984) Assessing the relative impacts of economic journals. J Econ Lit 22:77–88Google Scholar
  35. Macri M, Sinha D (2006) Rankings methodology for international comparisons of institutions and individuals: an application to economics in Australia and New Zealand. J Econ Surv 20:111–156CrossRefGoogle Scholar
  36. Moosa IA (2016) Citations, journal ranking and multiple authorships: evidence based on the top 300 papers in economics. Appl Econ Lett. Google Scholar
  37. Newman MEJ (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):036104CrossRefGoogle Scholar
  38. Newman MEJ (2010) Networks: an introduction. Oxford University Press, OxfordCrossRefGoogle Scholar
  39. Nowell C, Grijalva T (2011) Trends in co-authorship in economics since 1985. Appl Econ 43:4359–4375CrossRefGoogle Scholar
  40. Oswald AJ (2007) An examination of the reliability of prestigious scholarly journals: evidence and implications for decision-makers. Economica 74:21–31CrossRefGoogle Scholar
  41. Padial AA, Nabout JC, Wiqueira T, Bini LM, Diniz-Filho JAF (2010) Weak evidence for determinants of citation frequency in ecological articles. Scientometrics 85:1–12CrossRefGoogle Scholar
  42. Parreira M, Machado K, Logares R, Diniz-Filho J, Nabout JC (2017) The roles of geographic distance and socioeconomic factors on international collaboration among ecologists. Scientometrics 113:1539–1550CrossRefGoogle Scholar
  43. Perc M (2010) Growth and structure of Slovenia’s scientific collaboration network. J Informetr 4:475–482CrossRefGoogle Scholar
  44. Perianes-Rodriguez A, Ludo Waltman L, van Eck NJ (2016) Constructing bibliometric networks: a comparison between full and fractional counting. J Informetr 10:1178–1195CrossRefGoogle Scholar
  45. Polyakov M, Polyakov S, Iftekhar S (2017) Does academic collaboration equally benefit impact of research across topics? The case of agricultural, resource, environmental and ecological economics. Scientometrics 113:1385–1405CrossRefGoogle Scholar
  46. Pons P, Latapy M (2006) Computing communities in large networks using random walks. J Graph Algorithms Appl 10(2):191–218CrossRefGoogle Scholar
  47. Rainho O, Cointet J, Cambrosio A (2017) Oncology research in late twentieth century and turn of the century Portugal: a scientometric approach to its institutional and semantic dimensions. Scientometrics 113(2):867–888CrossRefGoogle Scholar
  48. Rath K, Wohlrabe K (2015) Recent trends in co-authorship in economics: evidence from RePEc. Appl Econ Lett. Google Scholar
  49. Robert C, Arreto C, Azerad J, Gaudy J (2004) Bibliometric overview of the utilization of artificial neural networks in medicine and biology. Scientometrics 59:117–130CrossRefGoogle Scholar
  50. Ruiz-Castillo J, Carrasco R, Albarrán P (2014) The elite in economics. UC3M Working papers. Economics we1414, Universidad Carlos III de Madrid. Departamento de Economía.Google Scholar
  51. Schubert A (2014) Sentences to remember from the first 100 volumes of the journal scientometrics. Scientometrics 100:1–13CrossRefGoogle Scholar
  52. Sutter M, Kocher M (2004) Patterns of co-authorship among economics departments in the USA. Appl Econ 36(4):327–333CrossRefGoogle Scholar
  53. Tang M, Cheng Y, Chen K (2017) A longitudinal study of intellectual cohesion in digital humanities using bibliometric analysis. Scientometrics 113:985–1008CrossRefGoogle Scholar
  54. Waltman L, van Eck NJ, Wouters P (2009) Counting publications and citations: is more always better? J Informetr 7:635–641CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Economics, Faculty of EconomicsUniversity of ZaragozaZaragozaSpain
  2. 2.Department of Theoretical PhysicsUniversity of ZaragozaZaragozaSpain
  3. 3.ARAID Foundation, Government of AragónZaragozaSpain
  4. 4.Institute for Biocomputation and Physics of Complex Systems (BIFI)ZaragozaSpain
  5. 5.Kampal Data Solutions S.L.ZaragozaSpain
  6. 6.Institute for the Study of Labor-IZABonnGermany

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