# Data Envelopment Analysis

Data envelopment analysis (DEA) is one of the non-parametric methods for estimating production frontiers. This analysis is used measuring the efficiencies of a set of decision-making units (DMU) using multiple inputs and outputs. By assigning specific weights to the input and output variables and through the allocation of equal weight to each units, DEA is used to derive efficient units. Data envelopment analysis is widely used in the field of finance, economics, business, and marketing.

##### DEA is useful in measuring
• Productivity
• Costs
• Technological capacity
• Resource allocation

There are various software packages like MAX DEA Basic, MAX DEA Ultra, DEAP and DEA frontier which are used for Data envelopment analysis. The major difference arises in terms of the models and speed.

• Distance models such as the estimation of radial, non-radial, revenue, and profit models
• Orientation inputs such as the input-output, generalized and non-generalized orientations
• Approaches for measuring the returns to scale including CRS, VRS, NIRS, NDRS, and GRS.
• Measurement of elasticity of scales.
##### Learning outcomes
1. Learn how technological developments affect the overall efficiency of decision-making units.
2. Examine the factors that affect the productivity of decision-making units over a period of time.
3. Understand returns to scale affecting the overall performance of decision-making units.
4.  The distinction between Malmquist productivity indexes and cost efficiency testing.
5. Comparison of the different scales that affect the resource allocation

## Getting started

This section assesses different case studies wherein DEA can be applied and compare the input variables which to rise or fall in costs. Furthermore, this section also defines the processes of importing data, defining the variables in accordance with the output results of different models.

## Measuring returns to scale

This section comprises of the articles which involve the analysis of variable returns to scale and their interpretation. These articles highlight the radial, slack and the projected values of the VRS DEA analysis. This module also includes a case study to determine the efficiency of the public and private sector banks.

## Time series using MALMQUIST

The next section includes the set of articles that explores the key features and applications of Malmquist DEA using a case study of the Hospitality industry.  Further, these articles include the Malmquist index summaries capturing the technological, scale and factor efficiency.

## Examining costs efficiency

This module includes the articles capturing the effect of cost efficiency including the execution and interpretation of cost efficiency of DEA of banks using DEAP.