Research School for Socio-Economic and
Natural Sciences of the Environment
Research School for Socio-Economic and
Natural Sciences of the Environment

Summer School: Theory and Practice of Efficiency & Productivity Measurement: static & dynamic analysis

Date: 08 July 2019 - 19 July 2019
Location: Wageningen

The summer school is designed to bridge the gap between theory and practice. It is organized 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.

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 analyse and quantify the levels of inefficiency and productivity at a point in time and their movement over time. Course activities

The course consists of theory and method sessions in the morning followed by an afternoon practicum session. The practicum 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 agricultural and applied economics.

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