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Measuring Economic Journals' Citation Efficiency: A Data Envelopment Analysis Approach

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  • George Emm. Halkos
  • Nickolaos G. Tzeremes

Abstract

This paper by using Data Envelopment Analysis (DEA) and statistical inference evaluates the citation performance of 229 economic journals. The paper categorizes the journals into four main categories (A to D) based on their efficiency levels. The results are then compared to the 27 “core economic journals” as introduced by Dimond (1989). The results reveal that after more than twenty years Diamonds’ list of “core economic journals” is still valid. Finally, for the first time the paper uses data from four well-known databases (SSCI, Scopus, RePEc, Econlit) and two quality ranking reports (Kiel Institute internals ranking and ABS quality ranking report) in a DEA setting and in order to derive the ranking of 229 economic journals. The ten economic journals with the highest citation performance are Journal of Political Economy, Econometrica, Quarterly Journal of Economics, Journal of Financial Economics, Journal of Economic Literature, American Economic Review, Review of Economic Studies, Journal of Econometrics, Journal of Finance, Brookings Papers on Economic Activity.

Suggested Citation

  • George Emm. Halkos & Nickolaos G. Tzeremes, 2011. "Measuring Economic Journals' Citation Efficiency: A Data Envelopment Analysis Approach," EERI Research Paper Series EERI_RP_2011_13, Economics and Econometrics Research Institute (EERI), Brussels.
    • Handle: RePEc:eei:rpaper:eeri_rp_2011_13
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    Cited by:

    1. Halkos, George & Tzeremes, Nickolaos, 2012. "Ranking agricultural, environmental and natural resource economics journals: A note," MPRA Paper 36233, University Library of Munich, Germany.
    2. Delbianco, Fernando & Fioriti, Andrés & Hernandez-Chanto, Allan & Tohmé, Fernando, 2020. "A Markov-switching approach to the study of citations in academic journals," Journal of Informetrics, Elsevier, vol. 14(4).
    3. Wohlrabe, Klaus, 2016. "Taking the Temperature: A Meta-Ranking of Economics Journals," MPRA Paper 68933, University Library of Munich, Germany.
    4. Biresh K Sahoo & Ramadhar Singh & Bineet Mishra & Krithiga Sankaran, 2015. "Research Productivity in Management Schools of India: A Directional Benefit-of-Doubt Model Analysis," Working Papers id:7139, eSocialSciences.
    5. Eleftheriou, Konstantinos & Polemis, Michael, 2020. "One list to fit them all: What do we learn from journal ranking?," Finance Research Letters, Elsevier, vol. 35(C).
    6. Marcel Clermont & Julia Schaefer, 2019. "Identification of Outliers in Data Envelopment Analysis," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 71(4), pages 475-496, October.
    7. Petridis, Konstantinos & Malesios, Chrisovalantis & Arabatzis, Garyfallos & Thanassoulis, Emmanuel, 2013. "Efficiency analysis of forestry journals: Suggestions for improving journals’ quality," Journal of Informetrics, Elsevier, vol. 7(2), pages 505-521.
    8. Rosenthal, Edward C. & Weiss, Howard J., 2017. "A data envelopment analysis approach for ranking journals," Omega, Elsevier, vol. 70(C), pages 135-147.
    9. Johan Lyhagen & Per Ahlgren, 2020. "Uncertainty and the ranking of economics journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2545-2560, December.
    10. Frederic S. Lee & Xuan Pham & Gyun Gu, 2013. "The UK Research Assessment Exercise and the narrowing of UK economics," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 37(4), pages 693-717.
    11. David I. Stern, 2013. "Uncertainty Measures for Economics Journal Impact Factors," Journal of Economic Literature, American Economic Association, vol. 51(1), pages 173-189, March.
    12. Lutz Bornmann & Alexander Butz & Klaus Wohlrabe, 2018. "What are the top five journals in economics? A new meta-ranking," Applied Economics, Taylor & Francis Journals, vol. 50(6), pages 659-675, February.
    13. Seiler, Christian & Wohlrabe, Klaus, 2013. "Archetypal scientists," Journal of Informetrics, Elsevier, vol. 7(2), pages 345-356.
    14. Tüselmann, Heinz & Sinkovics, Rudolf R. & Pishchulov, Grigory, 2016. "Revisiting the standing of international business journals in the competitive landscape," Journal of World Business, Elsevier, vol. 51(4), pages 487-498.
    15. Chen, Kun & Ren, Xian-tong & Yang, Guo-liang, 2021. "A novel approach for assessing academic journals: Application of integer DEA model for management science and operations research field," Journal of Informetrics, Elsevier, vol. 15(3).
    16. Campanario, Juan Miguel, 2014. "Analysis of the distribution of cited journals according to their positions in the h-core of citing journal listed in Journal Citation Reports," Journal of Informetrics, Elsevier, vol. 8(3), pages 534-545.
    17. Antonio García-Romero & Daniel Santín & Gabriela Sicilia, 2016. "Another brick in the wall: a new ranking of academic journals in Economics using FDH," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 91-101, April.
    18. Laura Vana & Ronald Hochreiter & Kurt Hornik, 2016. "Computing a journal meta-ranking using paired comparisons and adaptive lasso estimators," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 229-251, January.
    19. Sahoo, Biresh K. & Singh, Ramadhar & Mishra, Bineet & Sankaran, Krithiga, 2017. "Research productivity in management schools of India during 1968-2015: A directional benefit-of-doubt model analysis," Omega, Elsevier, vol. 66(PA), pages 118-139.
    20. Tüselmann, Heinz & Sinkovics, Rudolf R. & Pishchulov, Grigory, 2015. "Towards a consolidation of worldwide journal rankings – A classification using random forests and aggregate rating via data envelopment analysis," Omega, Elsevier, vol. 51(C), pages 11-23.

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    More about this item

    Keywords

    Ranking journals; Data Envelopment Analysis; Indexing techniques; Nonparametric analysis.;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models

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