BAN423 Benchmarking with DEA, SFA, and R
First, we will discuss why to benchmark, the concepts of key performance indicators (KPIs), and different measures of efficiency and productivity (e.g., technical efficiency, cost efficiency, scale efficiency).
Second, efficiency measurement with the non-parametric and deterministic Data Envelopment Analysis (DEA) is covered. The measurement of technical efficiency, cost efficiency, allocative efficiency, and scale efficiency will be discussed. The underlying assumptions and strength and weaknesses of the method are outlined.
Third, efficiency measurement with Stochastic Frontier Analysis (SFA) is covered. Starting from simple regression approaches based on ordinary least squares (OLS), we introduce models that incorporate noise and inefficiency simultaneously when estimating technical efficiency and cost efficiency. Underlying assumptions regarding functional forms and distributions are discussed, statistical inference and strength and weaknesses of this method are outlined.
Benchmarking is the systematic performance evaluation of decision making units (DMUs), such as, e.g., firms, organizations, industries, or governments. The course will provide students with the following knowledge, skills and general competences:
- Foundations of efficiency analysis: concepts from production theory, efficiency and productivity
- Understanding how Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) work, how they are estimated, and which advantages and disadvantages they offer
- How to setup meaningful benchmarking models with DEA and SFA, which data is required, and how can results be interpreted
- Ability to carry out scientific benchmarking with DEA and SFA using the statistical software R
- Good practices in efficiency analysis, including, e.g., descriptive data analysis, implementation of outlier detection, and meaningful visualization of results
- Team work organization
- Search for and analysis of data and information for an academic research project
- Understanding how economic models are related to real world problems
- Presenting and discussing academically, (self-)criticisms
This course combines lectures, exercises, and programming tutorials. Lectures focus on methodological issues. Pen and paper exercises help to deepen the knowledge obtained in the lectures with illustrative examples. In programming tutorials, students will implement the learned methodologies with real data sets using the free software R.
A basic understanding of microeconomic concepts such as production and cost functions is advised. First experience with linear programming and econometric estimation techniques is advantageous.
Programming skills with R are helpful. However, if necessary, an additional introduction to programming with R will be offered on the first day of the course.
Credit reduction due to overlap
Course identical to BUS462.
Requirements for course approval
A group project (3 to 4 group members), with a presentation during the course. The students will work with the group project on Thursday, and the presentations will take place on Friday.
Course approval from BUS462 is valid for BAN423.
Group term paper, based on the group project. Deadline at the end of October.
This course is a continuation of BUS462 and the total number of attempts applies to the course (not the course code).
Pass - Fail
R (https://cran.r-project.org/) with the packages Benchmarking and frontier. Additionally, a user interface such as RStudio is recommended.
Bogetoft, P. and Otto, L. (2011) Benchmarking with DEA, SFA, and R, Springer.
Coelli, T., Rao, D., O'Donnell, C. and Battese, G. (2005) An Introduction to Efficiency and Productivity Analysis, 2nd edition, Springer.
Färe, R., Grosskopf, S. and Lovell, C.A.K. (1994) Production Frontiers, Cambridge University Press, Cambridge.
Greene, W. H. (2007) The Econometric Approach to Efficiency Measurement. In: Fried, H., Lovell, C.A.K. and Schmidt, S., editors, The Measurement of Productive Efficiency. Oxford University Press, Oxford.
- ECTS Credits
- Teaching language
Autumn. First week of the Autumn Semester. Offered autumn 2019.
Dr. Stefan Seifert, University of Bonn