MS-9

Management Programme

ASSIGNMENT FIRST SEMESTER 2012

MS-9: MANAGERIAL ECONOMICS

School of Management Studies INDIRA GANDHI NATIONAL OPEN UNIVERSITY MAIDAN GARHI, NEW DELHI 110 068

ASSIGNMENT Course Code Course Title Assignment Code Coverage : : : : MS-9 Managerial Economics MS-9/TMA/SEM-I/2012 All Blocks

Note : Answer all the questions and submit this assignment on or before April 30, 2012, to the coordinator of your study center. 1. Why is decision making under uncertainty necessarily subjective? Explain giving examples. Decision theory in economics, psychology, philosophy, mathematics, and statistics is concerned with identifying the values, uncertainties and other issues relevant in a given decision its rationality, and the resulting optimal decision. It is closely related to the field of game theory as to interactions of agents with atleast partially conflicting interests whose decisions affect each other. Normative and descriptive decision theory Most of decision theory is normative or prescriptive, i.e., it is concerned with identifying the best decision to take (in practice, there are situations in which "best" is not necessarily the maximal (optimum may also include values in addition to maximum), but within a specific or approximate range), assuming an ideal decision maker who is fully informed, able to compute with perfect accuracy, and fully rational. The practical application of this prescriptive approach (how people ought to make decisions) is called decision analysis, and aimed at finding tools, methodologies and software to help people make better decisions. The most systematic and comprehensive software tools developed in this way are called decision support systems. Since people usually do not behave in ways consistent with axiomatic rules, often their own, leading to violations of optimality, there is a related area of study, called a positive or descriptive discipline, attempting to describe what people will actually do. Since the normative, optimal decision often creates hypotheses for testing against

actual behaviour, the two fields are closely linked. Furthermore it is possible to relax the assumptions of perfect information, rationality and so forth in various ways, and produce a series of different prescriptions or predictions about behaviour, allowing for further tests of the kind of decision-making that occurs in practice. In recent decades, there has been increasing interest in what is sometimes called 'behavioral decision theory' and this has contributed to a re-evaluation of what rational decision-making requires.Kinds of decisions Choice under uncertainty This area represents the heart of decision theory. The procedure now referred to as expected value was known from the 17th century. The idea of expected value is that, when faced with a number of actions, each of which could give rise to more than one possible outcome with different probabilities, the rational procedure is to identify all possible outcomes, determine their values (positive or negative) and the probabilities that will result from each course of action, and multiply the two to give an expected value. The action to be chosen should be the one that gives rise to the highest total expected value. An example in which a Dutch merchant is trying to decide whether to insure a cargo being sent from Amsterdam to St Petersburg in winter, when it is known that there is a 5% chance that the ship and cargo will be lost. In the above solution, it defines a utility function and computes expected utility rather than expected financial value Intertemporal choice This area is concerned with the kind of choice where different actions lead to outcomes that are realized at different points in time. If someone received a windfall of several thousand dollars, they could spend it on an expensive holiday, giving them immediate pleasure, or they could invest it in a pension scheme, giving them an income at some time in the future. What is the optimal thing to do? The answer depends partly on factors such as the expected rate of interest and inflation , the person's life expectancy, and their confidence in the pensions industry. However even with all those factors taken into account, human behavior again deviates greatly from the predictions of prescriptive decision theory, leading to alternative models in which, for example, objective interest rates are replaced by subjective discount rates. Competing decision makers Some decisions are difficult because of the need to take into account how other people in the situation will respond to the decision that is taken. The analysis of

such social decisions is more often treated under the label of game theory., rather than decision theory, though it involves the same mathematical methods. From the standpoint of game theory most of the problems treated in decision theory are one-player games (or the one player is viewed as playing against an impersonal background situation). In the emerging socio-cognitive engineering, the research is especially focused on the different types of distributed decisionmaking in human organizations, in normal and abnormal/emergency/crisis situations. Complex decisions Other areas of decision theory are concerned with decisions that are difficult simply because of their complexity, or the complexity of the organization that has to make them. In such cases the issue is not the deviation between real and optimal behaviour, but the difficulty of determining the optimal behaviour in the first place. The Club of Rome, for example, developed a model of economic growth and resource usage that helps politicians make real-life decisions in complex situations.

2. Define Point Price Elasticity. Calculate Point Price Elasticity from the price andquantity given below. One way to avoid the accuracy problem described above is to minimize the difference between the starting and ending prices and quantities. This is the approach taken in the definition of point-price elasticity, which uses quantity at any given point on the demand curve:

differential

calculus to calculate the elasticity for an infinitesimal change in price and

In other words, it is equal to the absolute value of the first derivative of quantity with respect to price (dQd/dP) multiplied by the point's price (P) divided by its quantity (Qd).

In terms of partial-differential calculus, point-price elasticity of demand can be defined as follows: let be the demand of goods with respect to price pk is as a be the demand for function of parameters price and wealth, and let good . The elasticity of demand for good

However, the point-price elasticity can be computed only if the formula for the

demand function, Qd = f(P), is known so its derivative with respect to

price, dQd / dP, can be determined.

Price (P) 100 90 80 70 60 50

Quanti ty (Q) 0 30 60 90 120 150

Total Sales Change Revenue Rev. TR 0 2700 4800 6300 7200 7500 TR 0 2700 2100 1500 900 300

Margin in al Revenu e MR 90 35 17 7.5 2

Price Elasticity Ep (arc) 100 44 25 13 4

3. Explain the various types of statistical analyses used for estimation of a production function. In the process of decision-making, a manager should understand clearly the relationship between the inputs and output on one hand and output and costs on the other. The short run production estimates are helpful to production managers in arriving at the optimal mix of inputs to achieve a particular output target of a firm. This is referred to as the least cost combination of inputs in production analysis. Also, for a given cost, optimum level of output can be found if the production function of a firm is known. Estimation of the long run production function may help a manager in understanding and taking decisions of long term nature such as capital expenditure. Estimation of cost curves will help production manager in understanding the nature and shape of cost curves and taking useful decisions. Both short run cost function and the long run cost function must be estimated, since both sets of information will be required for some vital decisions. Knowledge of the short run cost functions allows the decision makers to judge the optimality of present output levels and to solve decision problems of production manager. Knowledge of long run cost functions is important when considering the expansion or contraction of plant size, and for confirming that the present plant size is optimal for the output level that is being produced. In the present Unit, we will discuss different approaches to examination of production and cost functions, analysis of some empirical estimates of these functions, and managerial uses of the estimated functions. ESTIMATION OF PRODUCTION FUNCTION The principles of production theory discussed in Unit 7 are fundamental in understanding economics and provide an important conceptual framework for analyzing managerial problems. However, short run output decisions and long run planning often require more than just this conceptual framework. That is, quantitative estimates of the parameters of the production functions are required for some decisions. Functional Forms of Production Function. The production function can be estimated by regression techniques (refer to MS-8, course on Quantitative Analysis for Managerial Applications to know about regression techniques) using historical data (either time-series data, or cross-section

data, or engineering data). For this, one of the first tasks is to select a functional form, that is, the specific relationship among the relevant economic variables. We know that the general form of production function is, Q = f (K, L) Where, Q = output, K = capital and L = labour. Although, a variety of functional forms have been used to describe production relationships, only the Cobb-Douglas production function is discussed here. The general form of Cobb-Douglas function is expressed as: Q = AKa Lb where A, a, and b are the constants that, when estimated, describe the quantitative relationship between the inputs (K and L) and output (Q). The marginal products of capital and labour and the rates of the capital and labour inputs are functions of the constants A, a, and b and. That is, The sum of the constants (a+b) can be used to determine returns to scale. That is, (a+b) > 1 increasing returns to scale, (a+b) = 1 constant returns to scale, and (a+b) < 1 decreasing returns to scale. Having numerical estimates for the constants of the production function provides significant information about the production system under study. The marginal products for each input and returns to scale can all be determined from the estimated function. The Cobb-Douglas function does not lend itself directly to estimation by the regression methods because it is a nonlinear relationship. Technically, an equation must be a linear function of the parameters in order to use the ordinary least-squares regression method of estimation. However, a linear equation can be derived by taking the logarithm of each term. That is, This function can be estimated directly by the least-squares regression technique and the estimated parameters used to determine all the important production relationships. Then the antilogarithm of both sides can be taken, which transforms the estimated function back to its conventional multiplicative form. We will not be studying here the details of computing production function since there are a number of computer programs available for this purpose. Instead, we will provide in the following section some empirical estimates of Cobb-Douglas production function and their interpretation in the process of decision making. Types of Statistical Analyses

Once a functional form of a production function is chosen the next step is to select the type of statistical analysis to be used in its estimation. Generally, there are three types of statistical analyses used for estimation of a production function. These are: (a) time series analysis, (b) cross-section analysis and(c) engineering analysis. a) Time series analysis: The amount of various inputs used in various periods in the past and the amount of output produced in each period is called time series data. For example, we may obtain data concerning the amount of labour, the amount of capital, and the amount of various raw materials used in the steel industry during each year from 1970 to 2000. On the basis of such data and information concerning the annual output of steel during 1970 to 2000, we may estimate the relationship between the amounts of the inputs and the resulting output, using regression techniques. Analysis of time series data is appropriate for a single firm that has not undergone significant changes in technology during the time span analyzed. That is, we cannot use time series data for estimating the production function of a firm that has gone through significant technological changes. There are even more problems associated with the estimation a production function for an industry using time series data. For example, even if all firms have operated over the same time span, changes in capacity, inputs and outputs may have proceeded at a different pace for each firm. Thus, cross section data may be more appropriate. b) Cross-section analysis: The amount of inputs used and output produced in various firms or sectors of the industry at a given time is called cross section data. For example, we may obtain data concerning the amount of labour, the amount of capital, and the amount of various raw materials used in various firms in the steel industry in the year 2000. On the basis of such data and information concerning the year 2000, output of each firm, we may use regression techniques to estimate the relationship between the amounts of the inputs and the resulting output. c) Engineering analysis: In this analysis we use technical information supplied by the engineer or the agricultural scientist. This analysis is undertaken when the above two types do not suffice. The data in this analysis is collected by experiment or from experience with day-to-day working of the technical process. There are advantages to be gained from and approaching the measurement of the production function from this angle because the range of applicability of the data is known, and, unlike time series and cross-section studies, we are not restricted to the narrow range of actual observations. Limitations of Different Types of Statistical Analysis: Each of the methods discussed above has certain limitations.

1. Both time-series and cross-section analysis are restricted to a relatively narrow range of observed values. Extrapolation of the production function outside that range may be seriously misleading. For example, in a given case, marginal productivity might decrease rapidly above 85% capacity utilization; the production function derived for values in the 70%-85% capacity utilization range would not show this. 2. Another limitation of time series analysis is the assumption that all observed values of the variables pertains to one and the same production function. In other words, a constant technology is assumed. In reality, most firms or industries, however, find better, faster, and/or cheaper ways of producing their output. As their technology changes, they are actually creating new production functions. One way of coping with such technological changes is to make it one of the independent variables. 3. Theoretically, the production function includes only efficient (least-cost) combinations of inputs. If measurements were to conform to this concept, any year in which the production was less than nominal would have to be excluded from the data. It is very difficult to find a time-series data, which satisfy technical efficiency criteria as a normal case. 4. Engineering data may overcome the limitations of time series data but mostly they concentrate on manufacturing activities. Engineering data do not tell us anything about the firms marketing or financial activities, even though these activities may directly affect production. 5. In addition, there are both conceptual and statistical problems in measuring data on inputs and outputs. It may be possible to measure output directly in physical units such as tons of coal, steel etc. In case more than one product is being produced, one may compute the weighted average of output, the weights being given by the cost of manufacturing these products. In a highly diversified manufacturing unit, there may be no alternative but to use the series of output values, corrected for changes in the price of products. One has also to choose between gross value and net value. It seems better to use net value added concept instead of output concept in estimating production function, particularly where raw-material intensity is high. The data on labour is mostly available in the form of number of workers employed or hours of labour employed. The number of workers data should not be used because, it may not reflect underemployment of labour, and they may be occupied, but not productively employed. Even if we use man\ hours data, it should be adjusted for efficiency factor. It is also not advisable that labour should be measured in monetary terms as given by expenditure on wages, bonus, etc. The data on capital input has always posed serious problems. Net investment i.e. a change in the value of capital stock, is considered most appropriate. Nevertheless, there are problems of measuring depreciation in fixed capital, changes in quality of fixed capital, changes in inventory valuation, changes in composition and

productivity of working capital, etc. Finally, when one attempts an econometric estimate of a production function, one has to overcome the standard problem of multi-co linearity among inputs, autocorrelation, homoscadasticity, etc. EMPIRICAL ESTIMATES OF PRODUCTION FUNCTION: Consider the following Cobb-Douglas production function with parameters A=1.01, a = 0.25 and b=0.75,Q = 1.01K0.25 L0.75 The above production function can be used to estimate the required capital and labour for various levels of output. For example, the capital and labour required for an output level of 100 units will be given by100 = 1.01K0.25 L0.75 99 = K0.25 L0.75By substituting any value of L (or K) in this equation, we can obtain the associated value of K (or L). For example, if L=50, the value of K will be given by 99 = K0.25 (50)0.75 log 99 = 0.75 log 50 + 0.25 log K 1.9956 = 0.75 (1.6990) + 0.25 log K1 log K = (1.9956 1.2743) = 2.88520.25 K = antilog 2.8852 = 768Similarly, for any given value of K we can find out the corresponding value of L.As explained in Unit 7, an isoquant for any given output level or an isoquant map for a given set of output levels can be derived from an estimated production function. Consider the following Cobb-Douglas production function with parameters A=200, a = 0.50 and b = 0.50,Q = 200K0.50 L0.50For different combinations of inputs (L and K), we can construct an associated maximum rate of output as given in Table 10.1 For example, if two units of labour and 9 units of capital are used, maximum production is 600 units of output. If K=10 and L=10 the output rate will be 2000. The following three important relationships are shown by the data in this production Table.. Table 1 indicates that there are a variety of ways to produce a particular rate of output. For example, 490 units of output can be produced with anyone of the following combinations of inputs. This shows that there is substitutability between the factors of production. That means the production manager can use either the input combination (k=6 and L=1) or (k=3 and L=2) or (k=2 and L=3) or (k=1 and L=6) to produce the same amount of output (490 units). The concept of substitution is important because it

means that managers can change the input mix of capital and labour in response to changes in the relative prices of these inputs. 2. In the equation given that a = 0.50 and b = 0.50. The sum of these constants is 1 (0.50+0.50=1). This indicates that there are constant returns to scale (a+b=1). This means that a 1% increase in all inputs would result in a 1% increase in output. For example, in Table 10.1 maximum production with four units of capital and one unit of labour is 400. Doubling the input rates to K=8 and L=2 results in the rate of output doubling to Q=800. In Table 10.1, production is characterized by constant returns to scale. This means that if both input rates increase by the same factor (for example, both input rates double), the rate of output also will double. In other production functions, output may increase more or less than in proportion to changes in inputs. In contrast to the concept of returns to scale, when output changes because of changes in one input while the other remains constant, the changes in the output rates are referred to as returns to a factor. In Table, if the rate of one input is held constant while the other is increased, output increases but the successive increments become smaller. For example, from Table it can be seen that if the rate of capital is held constant at K=2 and labour is increased from L=1 to L=6, the successive increases in output are 117, 90, 76, 67, and 60. As discussed in Unit 7, this relationship is known as diminishing marginal returns. We will consider another empirical estimate of Cobb-Douglas production function given as: Q = 10.2K0.194 L0.878 Here, the returns to scale are increasing because a+b=1.072 is greater than 1. The marginal product functions for capital and labour are MPK = aAKa-1Lb = 0.194(10.2)K(0.194-1)L0.878 = 0.194(10.2)K-0.806L0.878 and MPL = bAKaLb-1 = 0.878(10.2)K0.194L(0.878-1) = 0.878(10.2)K0.194L-0.122 Based on the above MPK and MPL equations we can calculate marginal products of capital and labour for a given input combination. For example, suppose we are given that the input combination K=20 and L=30. Substituting these values for the constants A, a, and b gives the following marginal products: MPK = 0.194(10.2)(20)-0.806(30)0.878 = 3.50 and MPL = 0.878(10.2)(20)0.194 (30)-0.122 = 10.58 We can interpret the above marginal products of capital and labour as follows. One unit change in capital with labour held constant at 30 would result in 3.50 unit change in output, and one unit change in labour with capital held constant at 20 would be associated with a 10.58 unit change in output. Empirical estimates of production functions for industries such as sugar, textiles, cement etc., are available in the Indian context. We will briefly discuss some of these empirical estimates here. There are many empirical studies of production functions in different countries. John R. Moroney made one comprehensive study of a number of manufacturing industries in U.S.A. He estimated the production function:

Q = AKa L1/bL2g Where, K = value of capital L1 = production worker-hours L2 = non-production worker-hours A summary of the estimated values of the production elasticities (a, b, and g) and R2, the coefficient of determination, for each industry is shown in Table From Table it can be observed that R2 values are very high (more than 0.951) for all the functions. This means that more than 95% of the variation in output is explained by variation in the three inputs. A test of significance was made for each estimated parameter, a, b, and g, using the standard t-test. Those estimated production elasticities that are statistically significant at the 0.05 levels are indicated with an asterix (*). The sum of the estimated production elasticities (a+b+g) provides a point estimate of returns to scale in each industry. Although, the sum exceeds unity in 14 of the 17 industries, it is statistically significant only in the following industries: food and beverages, apparel, furniture, printing, chemicals, and fabricated metals. Thus, only in those six industries there are increasing returns to scale. For example, in the fabricated metals industry, a 1% increase in all inputs is estimated to result in a 1.027% increase in output.

COST FUNCTION AND ITS DETERMINANTS Cost function expresses the relationship between cost and its determinants such as the size of plant, level of output, input prices, technology, managerial efficiency, etc. In a mathematical form, it can be expressed as, C = f (S, O, P, T, E..) Where, C = cost (it can be unit cost or total cost) S = plant size O = output level P = prices of inputs used in production T = nature of technology E = managerial efficiency Determinants of Cost Function The cost of production depends on many factors and these factors vary from one firm to another firm in the same industry or from one industry to another industry. The main determinants of a cost function are: a) Plant size b) Output level c) Prices of inputs used in production,

d) Nature of technology e) Managerial efficiency We will discuss briefly the influence of each of these factors on cost. a) Plant size: Plant size is an important variable in determining cost. The scale of operations or plant size and the unit cost are inversely related in the sense that as the former increases, unit cost decreases, and vice versa. Such a relationship gives downward slope of cost function depending upon the different sizes of plants taken into account. Such a cost function gives primarily engineering estimates of cost. b) Output level: Output level and total cost are positively related, as the total cost increases with increase in output and total cost decreases with decrease in output. This is because increased production requires increased use of raw materials, labour, etc., and if the increase is substantial, even fixed inputs like plant and equipment, and managerial staff may have to be increased. c) Price of inputs: Changes in input prices also influence cost, depending on the relative usage of the inputs and relative changes in their prices. This is because more money will have to be paid to those inputs whose prices have increased and there will be no simultaneous reduction in the costs from any other source. Therefore, the cost of production varies directly with the prices of production. d) Technology: Technology is a significant factor in determining cost. By definition, improvement in technology increases production leading to increase in productivity and decrease in production cost. Therefore, cost varies inversely with technological progress. Technology is often quantified as capital-output ratio. Improved technology is generally found to have higher capital-output ratio. e) Managerial efficiency: This is another factor influencing the cost of production. More the managerial efficiency less the cost of production. It is difficult to measure managerial efficiency quantitatively. However, a and change in cost at two points of time may explain how organizational or managerial changes within the firm have brought about cost efficiency, provided it is possible to exclude the effect of other factors.

4. Products can be related in production as well as demand.Examine this statement with reference to Pricing of Joint Products.

Products can be related in production as well as demand. One type of production interdependency exists when goods are jointly produced in fixed proportions. The process of producing beef and hides in a slaughterhouse is a good example of fixed proportions in production. Each carcass provides a certain amount of meat and one hide. There is little that the slaughterhouse can do to alter the proportions of the two products. When goods are produced in fixed proportions, they should be thought of as a product package. Because there is no way to produce one part of this package without also producing the other part, there is no conceptual basis for allocating total production costs between the products. Increase in Price of one leads to increase in Demand for other Example: Dove and Pears from HUL The magnitude depends on number of substitutes available from other suppliers Products can be related in production as well as demand Joint Products are two or more products, produced from the same process or operation in a fix proportion F or example, Sooji and Maida Such goods should be consider as Product Package because there is no way to produce Maida without producing Sooji There is no conceptual way for allocating total production cost between two goods

Calculating the Profit-Maximizing Prices for Joint Products Assume a rancher sells hides and beef. The two goods are assumed to be jointly produced in fixed proportions. The marginal cost equation for the beef-hide product package is given by MC = 30 +5Q

The demand and marginal revenue equations for the two products are What prices should be charged for beef and hides? How many units for the product package should produced? Summing the two marginal revenue (MRT) equations gives MRT = 140 6Q The optimal quantity is determined by equating MRT and MC and solving for Q. Thus 140-6Q = 30 +5Q and, hence, Q = 10 Substituting Q =10 into the demand curves yields a price of $50 for beef and $60 for hides. However, before concluding that these prices maximize profits, the marginal revenue at this output rate should be computed for each product to assure that neither is negative. Substituting Q=10 into the two marginal revenue equations gives 40 for each good. Because both marginal revenues are positive, the prices just given maximize profits. If marginal revenue for either product is negative, the quantity sold of that product should be reduced to the point where marginal revenue equals zero. For a firm to be able and willing to engage in price discrimination, the buyers of the firms product must fall into classes with considerable differences among classes in the price elasticity of demand for the product, and it must be possible to identify and segregate these classes at moderate cost. Also, buyers must be unable to transfer the product easily from one class to another, since otherwise persons could make money by buying the product from the low-price classes and selling it to the high price classes, thus making it difficult to maintain the price differentials among classes. The differences among classes of buyers in the price elasticity of demand may be due to differences among classes in income, level, tastes, or the availability of substitutes.

5. What is an Isocost Line? Discuss the shifting of Isocost line. An isocost line is a line showing combinations of inputs that would yield the same cost. An isocost line includes all possible combinations of labor and capital that can be purchased for a given total cost. In equation form the total cost is B = wL + rK, where B = Total cost or budget level, w= the wage rate, L = the amount of labor taken, r = the rental price of capital, and K = the amount of capital taken. This equation can be re-expressed as K = (B/r) - (w/r) L. In economics an isocost line shows all combinations of inputs which cost the same total amount. Although similar to the budget constraint in consumer theory, the use of the isocost line pertains to cost-minimization in production, as opposed to utilitymaximization. For the two production inputs labour and capital, with fixed unit costs of the inputs, the equation of the isocost line is

where w represents the wage rate of labour, r represents the rental rate of capital, K is the amount of capital used, L is the amount of labour used, and C is the total cost of acquiring those quantities of the two inputs. The absolute value of the slope of the isocost line, with capital plotted vertically and labour plotted horizontally, equals the ratio of unit costs of labour and capital. The slope is:

The isocost line is combined with the isoquant map to determine the optimal production point at any given level of output. Specifically, the point of tangency between any isoquant and an isocost line gives the lowest-cost combination of inputs that can produce the level of output associated with that isoquant. Equivalently, it gives the maximum level of output that can be produced for a given total cost of inputs. A line joining

tangency points of isoquants and isocosts (with input prices held constant) is called the expansion path.

The cost-minimization problem The cost-minimization problem of the firm is to choose an input bundle (K,L) feasible for the output level y that costs as little as possible. A cost-minimizing input bundle is a point on the isoquant for the given y that is on the lowest possible isocost line. Put differently, a cost-minimizing input bundle must satisfy two conditions: 1. it is on the y-isoquant

2. No other point on the y-isoquant is on a lower isocost line.The case of smooth isoquants convex to the origin If the y-isoquant is smooth and convex to the origin and the cost-minimizing bundle involves a positive amount of each input, then at a cost-minimizing input bundle an isocost line is tangent to the y-isoquant. Now since the absolute value of the slope of the isocost line is the input cost ratio w / r, and the absolute value of the slope of an isoquant is the marginal rate of technical substitution (MRTS), we reach the following conclusion: If the isoquants are smooth and convex to the origin and the cost-minimizing

input bundle involves a positive amount of each input, then this bundle satisfies the following two conditions: It is on the y-isoquant (i.e. F(K, L) = y where F is the production function), and The MRTS at (K, L) equals w/r.

The condition that the MRTS be equal to w/r can be given the following intuitive interpretation. We know that the MRTS is equal to the ratio of the marginal products of the two inputs. So the condition that the MRTS be equal to the input cost ratio is equivalent to the condition that the marginal product per dollar is equal for the two inputs. This condition makes sense: at a particular input combination, if an extra dollar spent on input 1 yields more output than an extra dollar spent on input 2, then more of input 1 should be used and less of input 2, and so that input combination cannot be optimal. Only if a dollar spent on each input is equally productive is the input bundle optimal.

6. Write short notes on the following:a) Engineering method of Cost Estimation

The discipline of cost engineering can be considered to encompass a wide range of cost related aspects of engineering and programme management, but in particular cost estimating, cost analysis/cost assessment, design-to-cost, schedule analysis/planning and risk assessment. These are fundamental tasks which may be undertaken by different groups in different organizations, but the term cost engineering implies that they are undertaken throughout the project life-cycle by trained professionals utilizing appropriate techniques, cost models, tools and databases in a rigorous way, and applying expert judgment with due regard to the specific circumstances of the activity and the information available. In most instances, the output of a cost engineering exercise is not an end in itself but rather an input to a decision making process. The alternative offered by cost engineering is to have cost information available when design choices are being made, so that they will be made in the knowledge of approximately what the different potential solutions are likely to cost. This awareness of the likely cost is essential to be able to make effective cost/ benefit trade-offs. In other circumstances where cost is a critical factor, this awareness of costs can be applied in a design-to-cost approach whereby the cost influence directly drives the choice of solution. Cost engineering therefore embraces many facets of project management and engineering. The European Aerospace working group on Cost Engineering (EACE), in which ESA actively participates, has developed a Cost Engineering Capability Improvement Model (CECIM) in which 20 domains and more than 120 processes are

identified which can be said to fall within the broad scope of cost engineering. Responsibility for these tasks varies from one organization to another; in ESA responsibility for the schedule analysis/ control and risk-assessment elements usually rests with the function within a project management team known as Project Control.

Cost engineering is a discipline with relatively few full-time practitioners, who are to be found mainly in larger organizations. Therefore, cost-engineering groups and professional bodies like EACE originally known as the ESA/Euro space Working Group on Cost Engineering - are very important in helping to maintain the level

Cost-Estimating Methods - "Rule of thumb" approach - Detailed "grass-root" or "bottom-up" approach - Analogy - Competitive supplier proposals - Parametric approach The Accuracy of Cost Estimates and Cost Assessments It is the mark of an instructed mind to rest satisfied with the degree of precision which the nature of the subject admits and not to seek exactness when only an approximation of the truth is possible. For any cost-estimating or cost-assessment exercise the achievable level of accuracy will be dependent on the level of understanding of the problem, the completeness and the correctness of the information relating to the cost-driving parameters, and the quality of the cost model itself. The desirable level of accuracy is that which is sufficient to allow a correct decisionmaking process. In many situations it will be sensible to give a range of projected costs from a lowest cost, to a most likely cost and a highest cost. This is known as a three-point estimate-with the width of the range or spread being indicative of the perceived degree of uncertainty as determined by a risk-assessment exercise.

b) Price Leadership

Definition Situation in which a market leader sets the price of a product or service, and competitors feel compelled to match that price.

Tacit collusion is best understood in the context of a duopoly and the concept of Game Theory (namely, Nash Equilibrium). Let's take an example of two firms A and B, who both play an advertising game over an indefinite number of periods (effectively saying 'infinitely many'). Both of the firms' payoffs are contingent upon their own action, but more importantly the action of their competitor. They can choose to stay at the current level of advertising or choose a more aggressive advertising strategy. If either firm chooses low advertising while the other chooses high, then the low-advertising firm will suffer a great loss in market share while the other experiences a boost. However if they both choose high advertising, then neither firms' market share will increase but their advertising costs will increase, thus lowering their profits. If they both choose to stay at the normal level of advertising, then sales will remain constant without the added advertising expense. Thus, both firms will experience a greater payoff if they both choose normal advertising (however this set of actions is unstable, as both are tempted to defect to higher advertising to increase payoffs). A payoff matrix is presented with numbers given:

Firm

B

normal Firm

B

aggressive $0 profit

advertising Firm A normal advertising Firm A Each earns $50 profit A: $80 profit

advertising Firm A: Firm B: $80 profit

aggressive Firm

advertising

Firm B: $0 profit

Each earns $15 profit

Notice that Nash's Equilibrium is set at both firms choosing an aggressive advertising strategy. This is to protect themselves against lost sales. In general, if the payoffs for colluding (normal, normal) are greater than the payoffs for cheating (aggressive, aggressive), then the two firms will want to collude (tacitly).

Classical economic theory holds that price stability is ideally attained at a price equal to the incremental cost of producing additional units. Monopolies are able to extract optimum revenue by offering fewer units at a higher cost. An oligopoly where each firm acts independently tends toward equilibrium at the ideal, but such covert cooperation as price leadership tends toward higher profitability for all, though it is an unstable arrangement. In barometric firm price leadership, the most reliable firm emerges as the best barometer of market conditions, or the firm could be the one with the lowest costs of production, leading other firms to follow suit. Although this firm might not be dominating the industry, its prices are believed to reflect market conditions which are the most satisfactory, as the firm would most likely be a good forecaster of economic changes.

c) The Law of Demand

In economics, the law of demand is an economic law that states that consumers buy more of a good when its price decreases and less when its price increases (ceteris paribus). The greater the amount to be sold, the smaller the price at which it is offered must be, in order for it to find purchasers. Law of demand states that the amount demanded of a commodity and its price are inversely related, other things remaining constant. That is, if the income of the consumer, prices of the related goods, and tastes and preferences of the consumer remain unchanged, the consumers demand for the good will move opposite to the movement in the price of the good.

Assumptions Every law will have limitation or exceptions. While expressing the law of demand, the assumptions that other conditions of demand were unchanged. If remain constant, the inverse relation may not hold well. In other words, it is assumed that the income and tastes of consumers and the prices of other commodities are constant. This law operates when the commoditys price changes and all other prices and conditions do not change. The main assumptions are Habits, tastes and fashions remain constant. Money, income of the consumer does not change. Prices of other goods remain constant. The commodity in question has no substitute or is not competed by other. The commodity is a normal good and has no prestige or status value. People do not expect changes in the prices.

Exceptions to the law of demand Generally, the amount demanded of good increases with a decrease in price of the good and vice versa. In some cases, however, this may not be true. Such situations are explained below.

Giffen goods As noted earlier, if there is an inferior good of which the positive income effect is greater than the negative substitution effect, the law of demand would not hold. For example, when the price of potatoes (which is the staple food of some poor families) decreases significantly, then a particular household may like to buy superior goods out of the savings which they can have now due to superior goods like cereals, fruits etc., not only from these savings but also by reducing the consumption of potatoes. Thus, a decrease in price of potatoes results in decrease in consumption of potatoes. Such basic good items (like bajra, barley, grain etc.) consumed in bulk by the poor families, generally fall in the category of Giffen goods. It should be noted that not all inferior goods are giffen goods, but all giffen goods are inferior goods. Commodities which are used as status symbols Some expensive commodities like diamonds, air conditioned cars, etc., are used as status symbols to display ones wealth. The more expensive these commodities become, the higher their value as a status symbol and hence, the greater the demand for them. The amount demanded of these commodities increase with an increase in their price and decrease with a decrease in their price. Also known as a Veblen good. Expectation of change in the price of commodity If a household expects the price of a commodity to increase, it may start purchasing greater amount of the commodity even at the presently increased price. Similarly, if the household expects the price of the commodity to decrease, it may postpone its purchases. Thus, law of demand is violated in such cases. In the above circumstances, the demand curve does not slope down from left to right instead it presents a backward sloping from top right to down left. This curve is known as exceptional demand curve. Law of demand and changes in demand The law of demand states that, other things remaining same, the quantity demanded of a good increases when its price falls and vice-versa. Note that demand for goods changes as a consequence of changes in income, tastes etc. Hence, the demand may sometime expand or contract and increase or decrease. In this context, let us make a distinction between two different types of changes that affect quantity demanded, viz., expansion and contraction; and increase and decrease.

While stating the law of demand i.e., while treating price as the causative factor, the relevant terms are Expansion and Contraction in demand. When demand is changing due to a price change alone, we should not say increase or decrease but expansion or contraction. If one of the non-price determinants of demand, such as the prices of other goods, income, etc. change & thereby demand changes, the relevant terms are increase and decrease in demand. The expansion and contraction in demand are shown in the diagram. You may observe that expansion and contraction are shown on a single DD curve. The changes (movements) take place along the given curve k. Limitation Change in taste or fashion. Change in income Change in other prices. Discovery of substitution. Anticipatory change in prices. Rare or distinction goods.

There are certain goods which do not follow this law. These include Veblen goods and Giffen goods.