ASSIGNMENT ON GINI COEFFICIENTAND LEARNING CURVES

Submitted to:
Dr. Muhammad Ziaulhaq Mamun
Professor
Course Instructor: Operations Management (P-301)
IBA, DU

Submitted by:
Shatabdi Biswas
RH-12, Section-A
BBA 21st Batch

6 September 2015
Institute of Business Administration, University of Dhaka

1. Gini Coefficient
1.1 Definition
The Gini coefficient measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. It is the most commonly used measure of inequality (World Bank).
This coefficient, which ranges between 0 and 1 and is based on residents' net income, helps define the gap between the rich and the poor, with 1 representing perfect inequality where only one household has any income and 0 representing perfect equality, where all households have equal income (U.S. Census Bureau, 2012). It is named after its developer, Corrado Gini (1884-1965), an Italian statistician of the early 20th century.
As with all statistics, when collecting the income data initially, there will always be systematic and random errors. If the data is less accurate, then the Gini coefficient has less meaning. Also, countries may measure the Gini coefficient differently, thus reducing the utility in comparison of the coefficient values between countries (The University of Texas at Austin, 2005).
1.2 Gini Coefficient for income of Bangladesh (as of 2010) Item | Gini Coefficient | National | 0.458 | Rural | 0.430 | Urban | 0.452 |
Table 1: The national, along with the rural and urban Gini Coefficient for income of Bangladesh (Source: Statistical Yearbook of Bangladesh-2010; Bangladesh Bureau of Statistics)
1.3 Gini Coefficient for expenditure of Bangladesh (as of 2010) Item | Gini Coefficient | National | 0.320 | Rural | 0.270 | Urban | 0.340 |
Table 2: The national, along with rural and urban Gini Coefficient for expenditure of Bangladesh (Source: Statistical Yearbook of Bangladesh-2010; Bangladesh Bureau of Statistics)
1.3 Comparison of GINI Coefficient of Bangladesh with USA and Norway Country | GINI Coefficient (2010) | Bangladesh | 0.458 | USA | 0.411 | Norway (Scandinavian Country) | 0.268 | Table- 3 GINI Coefficient Bangladesh with USA and Norway (Source: Statistical Yearbook of Bangladesh-2010; Bangladesh Bureau of Statistics & World Bank, 2015)
2.0 Learning Curves
2.1 Definition
A learning curve, first described by psychologist Hermann Ebbinghaus in 1885 and elaborated by psychologist Arthur Bills in 1934, is a graphical representation of the increase of learning (vertical axis) with experience (horizontal axis). It illustrates the concept that describes how new skills or knowledge can be quickly acquired initially, but subsequent learning becomes much slower (Investopedia ,2015). Learning curve shows the rate of improvement in performing a task as a function of time, or the rate of change in average cost (in terms of time or money) as a function of cumulative output. Learning curve is based on individual learning/organizational leaning. Individual learning is improvement that results when people repeat a process and gain skill or efficiency from their own experience. Organizational learning results from practice as well, but it also comes from changes in administration, equipment, and product design. In organizational settings, we expect to see both kinds of learning occurring simultaneously and often describe the combined effect with a single learning curve. Learning curves can be used in manufacturing to estimate the time for product design and production, as well as costs. Learning curves are also an integral part in planning corporate strategy, such as decisions concerning pricing, capital investment, and operating costs based on experience curves.

2.2 Different Shapes of Learning Curves
Exponential Growth Curve: This learning curve shows that proficiency can increase limitelessly. (Diagram 1)

Diagram 1: Exponential Growth Curve
Exponential Rise or Fall to a Limit Curve: In this learning curve proficiency can exponentially approach a constant level showing that the increase in skill or retention of information is sharpest during the initial attempts, and then gradually flattening which indicates that each repetition does not lead to significant improvement of the subject’s skill, or that less new knowledge is gained over time. (Diagram 2)

Diagram 2: Exponential Rise and Fall to Limit Curve
Power Law Curve: This resembles an Exponential decay function, and is almost always used for a decreasing performance metric, such as cost. (Diagram 3)

Diagram 3: Power Law Curve
S-Curve or Sigmoid Function Curve: Here the increase in proficiency begins gradually, then increases rapidly and finally levels off. (Diagram 4)

Diagram 4: S-Curve or Sigmoid Function
Experience Curve: This form of learning curve is mainly used in industry for cost projections. (Diagram 5)

Diagram 5: Experience Curve
Single Subject learning curve: This form of learning curve shows how learning improves with experience over many trials (Diagram 6).

Diagram 6: Single Subject Experience Curve

2.3 Interpretation of Statements Related to Learning Curves
Learning curves range from 70% to 100%. The implication is that per unit average cumulative cost (in terms of time or money) declines by a constant percentage with doubling of cumulative output.
1. 80% Learning Curve- It denotes that the per unit average cumulative cost (in terms of time or money) falls to 80 percent of the previous per unit average cumulative cost (that means the percentage decrease is 20%) as the cumulative output doubles. (Stevenson, 2009).
2. 90% Learning Curve
It denotes that the per unit average cumulative cost (in terms of time or money) falls to 90 percent of the previous per unit average cumulative cost (that means percentage decrease is 10%) as the cumulative output doubles. (Stevenson, 2009).