Productivity growth in network models: an application to banking during the financial crisis

Kourtzidis, Stavros A; Matousek, Roman; Tzeremes, Nickolaos G.

Abstract

We construct Malmquist Productivity indices for two-stage processes. A two-stage data envelopment analysis model with an additive efficiency decomposition is used for the modeling of the two-stage process. We incorporate prior information into the analysis using the Weight Assurance Region model. This model offers advantages such as the weights representing the contribution of each stage to the overall process are always positive and we also can restrict them into a region given the available prior information. We extend this model from efficiency analysis to productivity analysis and we calculate Malmquist Productivity indices using four alternative decomposition approaches. The model is applied to a panel of banks in Central and Eastern European countries and productivity change is evaluated for three periods of the financial crisis. The alternative decompositions allow us to examine the various sources of productivity change during the financial crisis. Convergence patterns are also examined.

Citation

Kourtzidis, S. A., Matousek, R., & Tzeremes, N. G. (2018). Productivity growth in network models: an application to banking during the financial crisis. Journal of the Operational Research Society, https://doi.org/10.1080/01605682.2017.1421851

• Journal Article Type: Article
• Acceptance Date: Feb 18, 2017
• Online Publication Date: Feb 7, 2018
• Publication Date: Feb 7, 2018
• Deposit Date: Feb 11, 2019
• Publicly Available Date: Feb 11, 2019
• Print ISSN: 0160-5682
• Electronic ISSN: 1476-9360
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1080/01605682.2017.1421851
• Keywords: Data envelopment analysis; two-stage; productivity growth; transition economies; banking efficiency
• Public URL: https://nottingham-repository.worktribe.com/output/1535247
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/01605682.2017.1421851
• Contract Date: Feb 11, 2019