Benchmarking with DEA, SFA, and R

BAN423 Benchmarking with DEA, SFA, and R

Autumn 2022

  • Topics

    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.

  • Learning outcome

    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:

    Knowledge: Upon completing the course students will

    • Be provided with foundations of efficiency analysis: concepts from production theory, efficiency and productivity
    • Understand how Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) work, how they are estimated, and which advantages and disadvantages they offer
    • Know how to setup meaningful benchmarking models with DEA and SFA, which data is required, and how results can be interpreted

    Skills: Upon completing the course students will have

    • The 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

    General competences: Upon completing the course students

    • Are familiar with team work organization
    • Can search for and analyze data and information for an academic research project
    • Understand how economic models are related to real world problems
    • Are able to present and discuss academically, with (self-)criticisms

  • Teaching

    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.

    In the Autumn semester we will return to physical lectures!

  • Recommended prerequisites

    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.

  • Compulsory Activity

    A group project (3 to 4 group members) including a short presentation, which may take place in class or in an online meeting.

    Compulsory activities (work requirements) from BUS462 is valid for BAN423.

  • Assessment

    Group term paper, based on the group project. Deadline (approximately) at the end of September.

    This course is a continuation of BUS462 and the total number of attempts applies to the course (not the course code).

  • Grading Scale

    Pass - Fail

  • Computer tools

    R (https://cran.r-project.org/) with the packages Benchmarking and frontier. Additionally, a user interface such as RStudio is recommended.

  • Literature

    Advised:

    Bogetoft, P. and Otto, L. (2011) Benchmarking with DEA, SFA, and R, Springer.

    Further reading:

    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.

Overview

ECTS Credits
2.5
Teaching language
 English
Semester

Autumn. Offered Autumn 2022 (first week of the semester).

Course responsible

 Dr. Stefan Seifert, University of Göttingen.