Productive efficiency analysis of quantitative economics journals through Stochastic Frontier Analysis using panel data
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.3496Keywords:
production, productivity, efficiency, scientific production, frontier production models, panel data modelsAbstract
The main goal of a scientific journal is to diffuse new knowledge. The number of citations received by a journal can be considered as a measure of this objective and, in turn, as a measure of productivity in relation to the production process in which the journals are involved. In order to assess this production process, in this paper econometric models using data panel are employed to obtain measures of efficiency for those journals belonging simultaneously to the areas of “economics” and “social science, mathematical methods” in the Web of Science database. This efficiency is measured in terms of the distance between the actual production of the journals and their estimated maximum achievable number of citations based on their available resources.
Downloads
References
Abbott, M., & Doucouliagos, C. (2003). The efficiency of Australian universities: A data envelopment analysis approach. Economics of Education Review, 22, 89-97.
Abbott, M., & Doucouliagos, C. (2009). Competition and efficiency: overseas students and technical efficiency in Australian and New Zealand universities. Education Economics, 17, 31-57.
Aigner, D.J., Lovell, C.A.K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6, 21-37.
Agasisti, T., Catalano, G., Landoni, P., & Varganti, R. (2012). Evaluating the performance of academic departments: an analysis of research-related output efficiency. Research Evaluation, 21, 2-14.
Barros, C.P., Garcia-del-Barrio, P., & Leach, S. (2009). Analysing the technical efficiency of the Spanish Football League First Division with a random frontier model. Applied Economics, 41, 3239-3247.
Basulto, J., & Ortega, F.J. (2005). Modelling citation age data with right censoring. Scientometrics, 62, 329-342.
Battese, G.E., & Coelli, T.J. (1992). Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis, 3, 153-169.
Bogetoft, P., & Otto, L. (2011). Benchmarking with DEA, SFA, and R. Springer-Verlag.
Bonaccorsi, A., & Daraio, C. (2003). A robust nonparametric approach to the analysis of scientific productivity. Research Evaluation, 12, 47-69.
Bonaccorsi, A., Daraio, C., & Simar, L. (2006). Advanced indicators of productivity of universities. An application of robust nonparametric methods to Italian data. Scientometrics, 66, 389-410.
Brissimis, S.N., Delis, M.D., & Tsionas, E.G. (2010). Technical and allocative efficiency in European banking. European Journal of Operational Research, 204, 153-163.
Callon, M., Courtial, J.P., & Penan, H. (1995). Cienciometría. La medición de la actividad científica: de la Bibliometría a la vigilancia tecnológica. Gijón: Ediciones Trea (in Spanish).
Coelli, T.J. (1995). Estimators and hypothesis tests for a stochastic frontier function: A Monte Carlo analysis. Journal of Productivity Analysis, 6, 247-268.
Coelli, T.J. (1996). A guide to FRONTIER version 4.1: a computer program for frontier production function estimation. CEPA Working Paper 96/07, Department of Econometrics, University of New England, Armidale, Australia.
Coelli, T.J., & Henningsen, A. (2013) Frontier: Stochastic Frontier Analysis. R package version 1.1-0. http://CRAN.R-Project.org/package=frontier.
Coelli, T.J., Rao, D.S.P., & Battese, G.E. (1998). An introduction to efficiency and productivity analysis. Boston: Kluver Academic Publishers.
Filippini, M., & Hunt, L.C. (2011). Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach. The Energy Journal, 32, 59-80.
Greene, W. (2004). Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization's panel data on national health care systems. Health Economics, 10, 959-980.
Guan, J., & Chen, K. (2010). Modeling macro-R&D production frontier performance: an application to Chinese province-level R&D. Scientometrics, 82, 165-173.
Gupta, B.M. (1997). Analysis of distribution of the age of citations in theoretical population genetics. Scientometrics, 40, 139-162.
Jondrow, J., Lovell, C.A.K, Materov, I.S., & Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19, 233-238.
Kumbhakar, S.C. (1990). Production frontiers, panel data, and time-varying technical inefficiency. Journal of Econometrics, 46, 201-211.
Kumbhakar, S.C. (1991). Estimation of technical inefficiency in panel data models with firm- and time-specific effects. Economics Letters, 36, 43-48.
Kumbhakar, S.C., Lien, G., & Hardaker, J.B. (2014). Technical efficiency in competing panel data models: A study of Norwegian grain farming. Journal of Productivity Analysis, 41, 321-337.
Kumbhakar, S.C., & Zhang, R. (2013). Labor-use efficiency and employment elasticity in Chinese manufacturing. Economia e Politica Industriale, 40, 5-24.
Lozano, S., & Salmeron, J.L. (2005). Data Envelopment Analysis of OR/MS journals. Scientometrics, 64, 133-150.
Maietta, O.W. (2002). The decomposition of cost inefficiency into technical and allocative components with panel data of Italian dairy farms. European Review of Agricultural Economics, 27, 473-495.
Meeusen, W., & van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18, 435-444.
Melfou, K., Theocharopoulos, A., & Papanagiotou, E. (2009). Assessing productivity change with SFA in the sheep sector of Greece. Operational Research, 9, 281-292.
Merkert, R., Odeck, J., Brathen, S., & Pagliari, R. (2012). A Review of Different Benchmarking Methods in the Context of Regional Airports. Transport Reviews, 32, 379-395.
O’Donnell, C.J., & Nguyen, K. (2013). An econometric approach to estimating support prices and measures of productivity change in public hospitals. Journal of Productivity Analysis, 40, 323-335.
Ortega, F.J. (2003). Propuesta de mejora del índice agregado de impacto. Aplicación a la valoración de sexenios de investigación. Revista Española de Documentación Científica, 26, 403-417 (in Spanish).
Ortega, F.J., & Gavilan, J.M. (2013). The measurement of production efficiency in scientific journals through stochastic frontier analysis models: application to quantitative economics journals. Journal of Informetrics, 7, 959-965.
Ortega, F.J., & Gavilan, J.M. (2014). A comparison between maximum likelihood and Bayesian estimation of stochastic frontier production models. Communications in Statistics - Simulation and Computation, 43, 1714-1725.
Parinduri, R.A., & Riyanto, Y.E. (2014). Bank Ownership and Efficiency in the Aftermath of Financial Crises: Evidence from Indonesia. Review of Development Economics, 8, 93-106.
Park, I., & Lee, Y.H. (2012). Efficiency Comparison of International Golfers in the LPGA. Journal of Sports Economics, 13, 378-392.
Petridis, K., Malesios, C., Arabatzis, G., & Thanassoulis, E. (2013). Efficiency analysis of forestry journals: Suggestion for improving journals’ quality. Journal of Informetrics, 7, 505-521.
Rousseau, S., & Rousseau, R. (1998). The scientific wealth of European nations: Taking effectiveness into account. Scientometrics, 42, 75-87.
Ruiz, C. F., Bonilla, R., Chavarro, D., Orozco, L. A., Zarama, R., & Polanco X. (2010). Efficiency measurement of research groups using Data Envelopment Analysis and Bayesian networks. Scientometrics, 83, 711-721.
Sav, G.T. (2011). Cost Efficiencies and Rankings of Flagship Universities. American Journal of Economics and Business Administration, 3, 596-603.
Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall: London.
Stern, D.I. (2012). Modeling international trends in energy efficiency. Energy Economics, 34, 2200-2208.
Wang, G.W., Knox, K.J., & Lee, P.T.-W. (2013). A study of relative efficiency between privatised and publicly operated US ports. Maritime Policy and Management, 40, 351-366.
Zhou, X., Li, K.-W., & Li, Q. (2011). An analysis on technical efficiency in post-reform China. China Economic Review, 22, 357-372.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Journal of Quantitative Methods for Economics and Business Administration
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data).
All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.
Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.
