Summer School Theory and Practice of Efficiency & Productivity Measurement - Fortaleza

Productivity growth entails changes in scale, efficiency gains and technological change. Innovations are needed to keep pushing the competitive envelope, and efficiency gains are needed to ensure that implemented technologies achieve their potential. Conventional economic approaches assume that all firms operate rationally and efficiently. This summer school, however, challenges this assumption and presents concepts, models and tools needed to analyze and quantify the levels of inefficiency and productivity at a point in time and their movement over time.

Organised by Wageningen School of Social Sciences (WASS)

Mon 8 January 2024 until Thu 18 January 2024

Venue Faculty of Economics, Management, Actuarial Science and Accounting (FEAAC), Fortaleza

Course organised by Universidade Federal do Ceara and Wageningen

The summer school is designed to bridge the gap between theory and practice. It is organised into distinct parts: “Parametric, Static Approaches” (Week 1) and “Dynamic Approaches” (Week 2). Participants may enrol for either week 1 or 2, or both weeks. Although each week is independent, participants are encouraged to take both weeks.

Week 1 (8-12 January 2024) Parametric Efficiency and Productivity Analysis


  • Chris Parmeter (Associate Professor of Economics, University of Miami, USA)
  • Greg Emvalomatis (Associate Professor of Economics, University of Crete, Greece)

The parametric course uses Stochastic Frontier Analysis and semi-parametric techniques to measure efficiency and productivity by letting the data span the frontier to establish best practice. This approach coupled with the microeconomic theory of the firm provides firm-specific measurements of efficiency and best practice role models for improving performance.

Week 2 (15-19 January 2024): Data Envelopment Analysis: Static and Dynamic Efficiency and Productivity Analysis


  • Alfons Oude Lansink (Professor of Business Economics, Wageningen University)
  • Frederic Ang (Assitant Professor of Business Economics, Wageningen University)

The second week introduces the students into Data Envelopment Analysis (DEA) and the dynamic perspective to measuring efficiency and productivity. DEA is a nonparametric technique for measuring efficiency and productivity. The technique does not require distributional assumptions on the efficiency term and is a flexible approach that can be applied to many situations. Dynamic efficiency and productivity analysis is a relatively new approach that has found a more wide application the economics literature. The approach explicitly accounts for the role of adjustment costs in investments.

Course activities

The course consists of theory and method sessions in the morning followed by an afternoon practicum session.  The computer lab will include applications of the theory, computer analyses with actual data sets, and interpretations in practice.  Applications to various economic sectors will be considered such as agriculture, banking and finance, chain management, health, electrical power generation, and sports.  Extensions of these models will be addressed that measure input-specific technical efficiency and characterize the dynamic linkages in decision making, and introduce hybrid nonparametric-parametric approaches.


Participants will learn the theories concerning efficiency and productivity measurement and will develop proficiency with software to facilitate the initiation of their own research in efficiency and productivity measurement. The course deals with both conceptual and methodological issues. 

In particular, after successful completion (of either module) participants are expected to be able to:

  • Understand sources of efficiency from the perspective of technical feasibility, allocating scarce resource among competing ends, and the firm scale of operations;
  • Understand the input and output perspectives of technical and allocative efficiency;
  • Characterize efficiency and productivity growth from a primal, dual and distance function perspective;
  • Decompose productivity growth that explicitly accounts for the presence of inefficiency;
  • Use DEA models to measure technical, allocative, and scale efficiency levels and productivity growth;
  • Characterize definitions of variables of interest to be employed (goods and services; inputs, outputs, environmental, nonmarket goods/services);
  • Assess the appropriate use of parametric and nonparametric approaches given the data and problem setting (understanding the advantages and disadvantages of both perspectives);
  • Use these approaches to articulate the forces driving efficiency gains and productivity growth;
  • Use these approaches for benchmarking, identifying best practice and role models to plan for performance enhancement/gains;

The Dynamic Analysis course will further cover:

  • Delineation of variable and quasi-fixed factors and their treatment in efficiency and productivity;
  • Use of econometric approaches to address efficiency and productivity change measurement over time.

    Target group

    The course is oriented toward PhD candidates, postdoctoral researchers and others with background in economics.

    Assumed prior knowledge

    Microeconomic theory at the graduate level such as the treatment in H. Varian, Microeconomic Analysis, W.W. Norton. Completion of a course in dynamic optimization is strongly recommended.  Econometric theory and applications at the graduate level to include topics in Maximum Likelihood Estimation and System Estimation are required and some exposure to panel data econometrics is desirable.

    Course materials:

    Week 1:

    • Kumbhakar, S. and C.A.K. Lovell, Stochastic Frontier Analysis, Cambridge University Press, 2000.
    • Subal C. Kumbhakar, Christopher F. Parmeter and Valentin Zelenyuk, `Stochastic Frontier Analysis: Foundations and Advances I, Handbook of Production Economics, edited by R. Chambers, S. C. Kumbhakar and S. Ray, Springer, 2022.
    • Subal C. Kumbhakar, Christopher F. Parmeter and Valentin Zelenyuk, `Stochastic Frontier Analysis: Foundations and Advances II,' Handbook of Production Economics}, edited by R. Chambers, S. C. Kumbhakar and S. Ray, Springer, 2022.
      • Koop, G, and MFJ Steel. "Bayesian Analysis of Stochastic Frontier Models." A companion to Theoretical Econometrics 1 (2001): 520-73.

    Week 2:

    • Silva, E., S. Stefanou and A. Oude Lansink (2021). Dynamic Efficiency and Productivity Measurement. Oxford University Press, 28 Jan 2021, Oxford University Press. 248 p.

    Participants should make sure they have the books of Kumbhakar et al. (2000) and Silva et al. (2021) before the course starts; the costs of these books are not included in participation fee. The two chapters from the handbooks of production economics (Kumbhakar et al. (2022)) and other accompanying materials will be distributed during the course.

    Course fees

    The course fee for each week is 650 euro for those registering for one week and 1100 euro for those registering for both weeks. The course fee does not include books. It includes additional training material, coffee / tea, lunches and an informal dinner.

    Outline of the course in hours

    Participants can choose to participate in the course and receive 1.5 ECTS per week, or write a paper (one for each week) and receive 3 ECTS per week. 1 ECTS is equivalent to 28 hours of work load.  For participation in the full 2-week programme, which entails 168 hours of preparation, attendance and two papers, 6 ECTS can be obtained.

    Cancellation conditions

    Participants can cancel their registration free of charge 1 month before the course starts. A cancellation fee of 100% applies if a participant cancels his/her registration less than 1 month prior to the start of the course.

    The organisers have the right to cancel the course no later than one month before the planned course start date in the case that the number of registrations does not reach the minimum.

    The participants will be notified of any changes at their e-mail addresses.