Indifference curve technique is definitely an improvement over utility analysis and it has a number of uses and merits. In spite of merits, indifference curve analysis suffers from shortcomings and these are followings:
Criticisms
1. Unrealistic assumptions:
It is based on unrealistic assumptions of rationality, perfect competition, divisibility of goods and perfect knowledge of scale or preference.
The purchases of a consumer arc very much affected by habits, customs and fashion. Therefore, a consumer does not act always rationally. We cannot expect a consumer to know his indifference map. Goods Visible and perfect completion is a myth.
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2. No novelty:
Prof. D.H. Robertson remarked that the indifference curve technique is merely “An old wine in new bottle.”
This technique is similar to the utility analysis because it merely gave new names to old terms. The concept of utility is replaced by scale of preference, tendency of diminishing marginal utility is replaced by diminishing marginal rate of substitution and cordinal numbers such as 1, 2, 3 etc., were labelled as ordinal numbers I, II, III, etc.
The conditions of equilibrium in both analysis are similar. According to utility analysis the consumer is in equilibrium when:
MUX / MUy = Px/ Py
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According to QC, equilibrium is given by:
MRSxy= Px/ Py
Where MRS xy = MUX / MUy
By substituting for MUx/ MUy MRSxy, we get
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MUx/ MUy = Px / Py
Therefore, conditions of equilibrium are similar in both the techniques.
But this criticism is untenable. Prof. Hicks claims, “The replacement of diminishing marginal rate of substitution is not mere translation.
It is a positive change in the theory of consumer demand.” We need not measure utility in fact to know the marginal rate of substitution. The consumer is simply asked to tell how much of if he gives to take an additional unit of X.
3. Indifference curve is non-transitive:
Prof. W.E. Armstrong has argued that a consumer is indifferent between close alternative combinations only because he is not able to perceive the difference between the two.
But as the difference of combinations increases, the difference in the satisfaction of different combinations becomes evident and so the different combinations on the same indifference curve do not yield equal satisfaction.
Thus, if Armstrong argument is accepted different points on an indifference curve give different satisfaction. The indifference curve will become non-transitive.
4. Fails to explain risky choice:
Indifference curve analysis is criticized on the ground that it cannot explain consumer behaviour when he has to choose among alternatives involving risk or uncertainty of expectation.
To make a choice among uncertain alternatives quantitative measurement of utility is needed to decide whether the risk is worth taking. In such situations cordinal system of utility can explain consumer bahaviour.
5. Absurd and unrealistic combinations:
Indifference curve analysis is based on hypothetical combinations. When we consider different combinations of two goods, then there may be some combinations that are meaningless and cannot be possible in real life.
6. Docs not provide behaviouristic explanation of consumer behaviour:
The indifference map is hypothetical in nature and is not based on observed market behaviour. It is subjective in nature instead of objective.
The reason is that it does not set up functions and curves in purely objective terms. Purely objective indifference curves can be possible only if it is possible to obtain quantitative data.
The logical structure of indifference curve theory is such that it is difficult to quantity indifference curves. Though attempts have been made to quantity indifference curve but success is very limited.
7. Based on weak ordering:
Indifference curve analysis is based on the weak ordering hypothesis i.e., a consumer can be indifferent between a large number of combinations.
But, according to Prof. Samulson, it is not possible to find many situations of indifference in real world. The weak ordering makes it subjective in nature.
But ordinal analysis is certainly better than coordinal analysis as it is based on fewer assumptions.
