MMPC-010 Solved Question Paper

Question 1: Present value and the discounting principle

Reframed question: How can a manager calculate the present value of a future cash flow using the discounting principle, and what does this mean in a practical decision situation?

In managerial economics, most investment and financing decisions involve cash flows spread over time. The discounting principle recognises the time value of money – a rupee to be received in the future is worth less than a rupee in hand today, because today’s rupee can be invested to earn a return. Hence, to compare alternatives properly, managers convert future amounts into their present value (PV).

Present value formula

If a firm expects to receive a single amount R_n after n years, and the appropriate discount (interest) rate is i then the present value is:

$$ PV = \frac{R_n}{(1 + i)^n} $$

Here:

• (PV) = value today of the future receipt
• (R_n) = rupees to be received after (n) years
• (i) = discount rate (opportunity cost of capital)
• (n) = number of years till the cash is received

If there are several cash flows over time (like a series of yearly profits or loan instalments), the present value of each is computed separately and then added. This is the idea behind present value of an annuity in the course material.

Numerical illustration from a manager’s viewpoint

Suppose a project will give a single cash inflow of ₹50,000 three years from now. The firm’s required rate of return (discount rate) is 8% per year. Should the manager be willing to invest ₹40,000 today to get that future amount?

Step 1: Identify the data

• R_n = ₹50,000
• (i = 0.08) (8%)
• (n = 3) years

Step 2: Apply the PV formula

$$ PV = \frac{50{,}000}{(1 + 0.08)^3} $$

Compute the denominator:

$$ (1.08)^3 \approx 1.2597 $$

So:

$$ PV \approx \frac{50{,}000}{1.2597} \approx 39{,}700 \text{ (approximately)} $$

Interpretation: Receiving ₹50,000 after three years is roughly equivalent to receiving about ₹39,700 today if the relevant discount rate is 8%.

Step 3: Decision insight

• If the project requires ₹40,000 today and returns only this single cash flow of ₹50,000 after three years, its present value (~₹39,700) is slightly less than the investment.
• In strict financial terms, this project would not meet the required 8% return; the manager should either negotiate better terms or look for an alternative project.

Managerial applications in real decisions

Managers apply the discounting principle in several routine but critical decisions, for example:

• Capital budgeting: Comparing two machines where one is cheap but has higher running costs and the other is expensive but saves energy. Future running costs and savings are discounted to present value to identify which option maximises firm value.
• Evaluating long-term contracts: Deciding whether to accept a client’s offer to pay in instalments over five years or to insist on a larger one-time payment now.
• Lease vs. buy decisions: Discounting future lease rentals versus the upfront cost and benefits of buying an asset outright.

In summary, the discounting principle converts a stream of future rupees into a single comparable amount today. This allows managers to rank projects, negotiate better terms, and ensure that the firm’s resources are allocated to alternatives that genuinely add to the present value of the firm.

Question 2: Decision-making under risk and risk attitudes

Reframed question: How does a manager take decisions when outcomes are risky but probabilities are known, and how do risk-averse and risk-seeking behaviours differ in such situations?

Decisions under risk – basic idea

The course distinguishes between certainty, risk and uncertainty. Under risk, the manager knows the possible outcomes and can assign probabilities to them, usually from past data or experience. Decisions such as launching a new product, entering a new market or choosing a technology often fall in this category.

For each possible action (strategy), the manager lists:

• Possible states of nature (e.g., high demand, medium demand, low demand)
• The payoff (profit or loss) under each state
• The probability of each state

Expected Monetary Value (EMV)

A common decision rule under risk is to compute the Expected Monetary Value of each alternative:

$$ \text{EMV} = \sum_{i} p_i \times \text{Payoff}_i $$

Here (p_i) is the probability of the (i^{th}) outcome and (\text{Payoff}_i) is the corresponding profit or loss.

Example (simplified):

• If launching a new product could give a profit of ₹10 lakh with probability 0.4, ₹4 lakh with probability 0.4, and a loss of ₹2 lakh with probability 0.2, then: $$ \text{EMV} = 0.4(10) + 0.4(4) + 0.2(-2) = 4 + 1.6 – 0.4 = ₹5.2 \text{ lakh} $$
• The manager can calculate similar EMVs for alternative strategies (for example, launching on a smaller scale or not launching at all) and compare them.

Beyond EMV, the course also stresses that managers should consider risk measures like variance or standard deviation of outcomes to judge how “spread out” the possible results are.

Risk-averse versus risk-seeking behaviour

Risk-averse behaviour

A risk-averse decision-maker prefers a certain outcome to a risky alternative with the same or even slightly higher expected monetary value. In terms of the course, such a person exhibits a concave utility function – additional rupees give diminishing marginal satisfaction.

Managerially, a risk-averse CEO may:

• Prefer a steady project with moderate but reliable cash flows to a highly volatile project with higher EMV but a real chance of large losses.
• Invest in diversification, insurance, hedging or long-term contracts to stabilise earnings.
• Avoid over-leveraging the firm, preferring a safer capital structure.

Real-life flavour: A family-owned manufacturing firm might decline an aggressive expansion into an unfamiliar overseas market, even if projections show a high expected return, because a single bad year could threaten the survival of the firm.

Risk-seeking behaviour

A risk-seeking decision-maker, in contrast, tends to prefer risky alternatives even when the certain outcome has an equal or slightly higher EMV. Here the implicit utility function is more convex – the thrill or potential upside from a big gain outweighs discomfort from possible losses.

From a managerial angle, a risk-seeking manager might:

• Be willing to invest heavily in untested technology or radically new products.
• Prefer “high-variance” strategies where payoffs could be very large or very small.
• Accept big swings in quarterly profits to chase breakthrough opportunities.

Real-life flavour: A tech start-up founder might repeatedly invest in high-risk projects like new apps or platforms where success probabilities are low but the potential payoff could be enormous.

Practical decision process under risk

In practice, managers combine the course tools with their risk attitudes as follows:

1. Construct a payoff table: strategies vs. states of nature, with profits/losses.
2. Assign probabilities: using historical data, market research or expert judgement.
3. Compute EMV and risk measures: expected value, variance, downside risk.
4. Apply a decision rule:
   • Risk-neutral: choose the highest EMV.
   • Risk-averse: may choose an alternative with slightly lower EMV but less downside risk.
   • Risk-seeking: may accept a lower EMV for a chance of very large gains.
5. Review in a broader context: impact on liquidity, reputation, regulatory exposure and long-term strategy.

Thus, the formal framework of decision under risk provides the numerical foundation, while the risk attitude of the decision-maker shapes which option is finally chosen.

Question 3: Income, tastes and preferences as determinants of demand

Reframed question: Briefly explain how (I) consumer income and (II) tastes and preferences influence the demand for a product.

(I) Income as a determinant of demand

In the demand analysis of Block 2, income is identified as one of the most important determinants of demand. For a given price, a change in the consumer’s income shifts the entire demand curve to the right or left, instead of movement along the curve.

The broad patterns are:

• Normal goods: As income increases, demand rises. Branded clothing, restaurant meals, private health care and domestic air travel usually fall in this category for middle-class households.
• Inferior goods: As income increases, demand falls because consumers “trade up” to better alternatives. Examples include low-quality staples, very basic local transport, or cheap unbranded products, which are replaced by higher-quality versions as income rises.

In more formal terms, the course discusses income elasticity of demand to capture the responsiveness of quantity demanded to changes in income:

$$ E_Y = \frac{\%\ \Delta Q_d}{\%\ \Delta Y} $$

• (E_Y > 0) for normal goods; higher income leads to higher demand.
• (E_Y < 0) for inferior goods.

Managerial perspective and experience:

• During an economic boom, firms selling normal or luxury items (cars, premium smartphones, branded apparel) generally see a strong increase in demand and can plan capacity expansion or premium versions.
• Firms selling mainly inferior goods may see their sales stagnate or fall as customers shift to better-quality substitutes when their incomes rise.
• In downturns, the pattern can reverse: demand for inexpensive substitutes rises, while demand for high-end items falls. Managers closely monitor such shifts while planning production, inventory and marketing strategies.

(II) Tastes and preferences as determinants of demand

Block 2 also emphasises that demand is influenced not only by prices and income, but also by tastes, preferences, fashions and habits. These are often shaped by culture, advertising, social media, health awareness and demographic trends.

Some important aspects are:

• Changes in lifestyle: Rising health consciousness can increase demand for low-fat dairy products, sugar-free beverages and gym memberships, while reducing demand for sugary drinks and fast food.
• Advertising and branding: Effective advertising can alter perceptions so that consumers prefer one brand over another even if prices are similar. This produces an outward shift in the demand curve for the favoured brand.
• Social influence: Products that become popular on social media (for example, a new gadget or a fashion trend) can see a sudden jump in demand even with no change in price or income.
• Demographic and cultural factors: Age composition, festivals, and local customs shape tastes. For instance, demand for sweets and decorative items rises during festivals, while demand for certain clothing styles varies across regions.

Managerial experience: A manager who understands that demand is taste-driven will:

• Track trends through market surveys, social media listening and sales data.
• Continuously adjust product design, packaging and communication messages.
• Use promotions and endorsements to influence preferences in favour of the firm’s brand.

Thus, both income and tastes/preferences operate as “shift factors” of demand. A good manager keeps an eye on both when forecasting sales, planning capacity and making pricing decisions.

Question 4: Demand estimation using regression analysis

Reframed question: How can regression analysis be used to estimate a demand function, and how do managers use such estimates in decision-making?

Role of regression in demand estimation

Blocks 1 and 2 introduce regression analysis as a key quantitative tool for estimating relationships between variables. In the context of demand, regression helps the manager to quantify how quantity demanded depends on price, income, advertising and other factors using past data.

A typical linear demand function for a product might be written as:

$$ Q_t = a + b P_t + c Y_t + d A_t + u_t $$

Where:

• (Q_t) = quantity demanded in period (t)
• (P_t) = price of the product in period (t)
• (Y_t) = consumer income (or some proxy) in period (t)
• (A_t) = advertising expenditure or promotional index in period (t)
• (a, b, c, d) = parameters to be estimated from data
• (u_t) = error term capturing other influences

Regression analysis uses historical observations of ((Q_t, P_t, Y_t, A_t)) to estimate the parameters (a, b, c, d) such that the “best-fitting” demand curve is obtained in a statistical sense.

Steps in using regression for demand estimation

1. Specify the demand model: Based on theory and managerial understanding, choose which variables matter: own price, income, advertising, price of substitutes/ complements, etc. Decide whether the relationship is linear, log-linear, etc.
2. Collect data: Use firm records, market reports and official statistics to compile time-series or cross-sectional data on quantity sold and determinants (price, income, advertising, etc.).
3. Estimate parameters using regression: Apply ordinary least squares (OLS) or similar methods to obtain estimates of (a, b, c, d). In practice this is done using statistical software. The course highlights that regression “helps to determine values of the parameters of a function”.
4. Interpret the coefficients:
   • (b) is usually negative for a normal downward-sloping demand curve (higher price → lower quantity).
   • (c) will be positive for normal goods and negative for inferior goods.
   • (d) should be positive if advertising successfully raises demand.
5. Check statistical reliability: Use (R^2), t-statistics, F-tests and diagnostic checks (discussed in the unit) to see if the model fits well and the signs of coefficients are economically sensible.
6. Use the estimated equation for forecasting and simulation: Once the demand function is estimated, managers can plug in alternative values of price, income and advertising to predict future demand or to analyse “what-if” scenarios.

How managers use regression-based demand estimates

From a practical managerial standpoint, an estimated demand function is extremely useful:

• Pricing decisions: With a demand equation, the firm can see how sensitive quantity sold is to price changes. This supports decisions like:
  • How much sales may fall if price is increased by 5%?
  • What price would maximise revenue or profit, given cost data?
• Sales and production forecasting: Given expected income levels and planned advertising, the manager can forecast future sales. This is critical for capacity planning, procurement of raw materials and workforce scheduling.
• Marketing budget allocation: If the coefficient on advertising is large and statistically significant, it indicates that additional advertising is likely to raise demand. The manager can justify higher promotional expenditure in such a case.
• Market segmentation and product strategy: If separate regressions are estimated for different regions or customer segments, differences in price sensitivity and income effects become visible. This helps in designing differentiated pricing and product versions.
• Risk analysis: Regression-based demand forecasts also allow scenario analysis—what happens if income growth is slower than expected, or if competitors cut prices?

In short, regression transforms raw data into a usable demand function that links quantity sold to its key determinants. This supports systematic, data-driven pricing, production and marketing decisions rather than relying solely on intuition.

Question 5: Short-run cost identities – TC = TFC + TVC and ATC = AFC + AVC

Reframed question: In the short run, explain with the help of cost relationships and diagrams (conceptually) why (I) Total Cost equals Total Fixed Cost plus Total Variable Cost, and (II) Average Total Cost equals Average Fixed Cost plus Average Variable Cost.

(I) TC = TFC + TVC in the short run

Block 3 explains that in the short run at least one input (usually capital, plant size) is fixed. Hence, total cost of production can be split into:

• Total Fixed Cost (TFC): Costs that do not change with output in the short run – rent of factory building, salaries of permanent staff, insurance, etc.
• Total Variable Cost (TVC): Costs that vary directly with the level of output – wages of casual labour, cost of raw materials, power, packaging, etc.

The course states the basic relationship:

$$ TC = TFC + TVC $$

Shape of the curves (diagram in words):

• On a graph with output on the horizontal axis and cost on the vertical axis:
  • The TFC curve is a horizontal straight line, since fixed cost is the same at all output levels (including zero output).
  • The TVC curve starts from the origin and rises as output increases. Initially it may rise at a decreasing rate (due to increasing returns to the variable factor) and then at an increasing rate (due to diminishing returns).
  • The TC curve starts at the level of TFC on the vertical axis and is always vertically above the TVC curve by exactly the amount of TFC. The vertical distance between the TC and TVC curves is constant and equal to TFC at all levels of output.

Managerial interpretation:

• Even if the firm produces zero output, it must still bear TFC (commitments like rent, salaries). Hence TC > 0 when Q = 0.
• As output increases, only TVC changes; TFC remains unchanged. Therefore, cost control in the short run is mainly about managing variable inputs efficiently.

(II) ATC = AFC + AVC in the short run

Average costs are obtained by dividing each total cost by the output (Q):

$$ ATC = \frac{TC}{Q}, \quad AFC = \frac{TFC}{Q}, \quad AVC = \frac{TVC}{Q} $$

Using the identity (TC = TFC + TVC), we get:

$$ ATC = \frac{TC}{Q} = \frac{TFC + TVC}{Q} = \frac{TFC}{Q} + \frac{TVC}{Q} = AFC + AVC $$

Shape of average cost curves (as described in the unit):

• AFC curve: Since TFC is constant, as output increases, AFC continuously falls. The AFC curve is a downward-sloping rectangular hyperbola.
• AVC curve: Initially falls due to better utilisation of the fixed factor and increasing returns to the variable input; later rises due to diminishing marginal returns. Hence AVC is typically U-shaped.
• ATC curve: Being the sum of AFC and AVC, ATC is also U-shaped and always lies above the AVC curve. At low levels of output, falling AFC pulls ATC down; at higher output, rising AVC pushes ATC up.

Conceptual diagram (verbal):

• On a graph with output on the horizontal axis and cost on the vertical axis:
  • AFC slopes downward continuously.
  • AVC is U-shaped, with a minimum at some intermediate output.
  • ATC is also U-shaped and lies above AVC; the vertical distance between ATC and AVC narrows as output increases because AFC becomes smaller and smaller.

Managerial implications:

• At low output levels, high AFC (because TFC is spread over very few units) makes ATC high. This is why very small-scale production is often inefficient.
• As output rises, TFC is spread over more units, AFC falls, and ATC falls sharply. There is a range of output where the firm enjoys falling average cost and can be highly competitive in pricing.
• Beyond a certain point, AVC rises steeply due to capacity constraints and diminishing returns, so ATC also rises. Producing beyond the minimum ATC is inefficient.

Thus, the two identities (TC = TFC + TVC) and (ATC = AFC + AVC) are not just algebraic; they describe how total and average costs behave in the short run and guide managers in choosing an efficient scale of operation.

Question 6: Long-run cost functions

Reframed question: Explain the nature and behaviour of long-run cost functions and their relevance for managerial decision-making.

Short run versus long run

In Block 3, the long run is defined as a period sufficiently long for the firm to vary all inputs, including plant size. There are no fixed costs in the long run; all costs are variable. The firm can build a small plant, a medium plant or a large plant and choose whichever is most economical for the desired output.

Long-run total, average and marginal cost functions

• Long-run Total Cost (LTC): The minimum cost of producing each level of output when the firm is free to choose the best combination of all inputs. It is derived from the underlying production function and input prices.
• Long-run Average Cost (LAC): LTC divided by output: $$ LAC(Q) = \frac{LTC(Q)}{Q} $$ It shows the lowest possible cost per unit at which any given output can be produced when the firm is fully flexible.
• Long-run Marginal Cost (LMC): The change in LTC when output is increased by one unit. It indicates how total cost changes at the margin.

The course emphasises that the LAC curve is often seen as an “envelope” of short-run average cost (SAC) curves – for each output level Q, the firm picks the plant size with the lowest SAC, and the locus of these minimum points forms LAC.

Shape of the long-run average cost curve

Empirically and in the text, the LAC curve is usually U-shaped, although sometimes it may have a “L” shape for service sectors. The U-shape reflects three phases:

1. Economies of scale (falling LAC):
   • As the firm increases its scale of operation from very small to medium size, LAC falls because of:
     • Better specialisation and division of labour.
     • More efficient and indivisible machinery.
     • Spreading overheads like R&D and advertising over more units.
     • Bulk buying of inputs and better bargaining power.
2. Minimum efficient scale (MES):
   • At an intermediate output, LAC reaches its lowest point. This is the optimum scale of plant – beyond this scale, further expansion does not reduce average cost and may start increasing it.
3. Diseconomies of scale (rising LAC):
   • When the firm becomes very large, problems of coordination, bureaucracy, delays in decision-making, labour disputes and communication gaps may cause long-run average cost to rise.

Managerial implications of long-run cost functions

From a manager’s perspective, long-run cost functions are crucial for “big” strategic decisions:

• Choice of plant size and capacity: By studying the LAC curve, managers can identify the range of output in which costs are low and stable. They will choose a plant size whose SAC curve is tangent to the LAC near projected demand, so that the firm operates close to minimum LAC.
• Entry, expansion and relocation decisions: When considering entering a new market or building an additional plant, long-run cost data help compare options such as one large plant versus several smaller plants in different locations.
• Make-or-buy decisions and outsourcing: If a supplier can produce at a lower long-run average cost than the firm’s internal cost, outsourcing may be economical; if not, in-house production may be preferred.
• Technology choice: Adopting a new technology may shift the entire LAC curve downward. Managers compare the cost of adopting the technology with the expected reduction in long-run costs.
• Competitive strategy: Firms that operate near the minimum LAC often enjoy a cost leadership advantage and can price aggressively while maintaining profitability.

Thus long-run cost functions summarise how the firm’s costs behave when it has full flexibility to adjust all inputs. They form the backbone of decisions about capacity, technology and long-term competitiveness.

Question 7: Monopoly – characteristics and long-run equilibrium

Reframed question: What are the key characteristics of a monopoly market, and how does a monopoly firm determine its equilibrium price and output in the long run?

Characteristics of monopoly

In a monopoly, one producer supplies the whole market and buyers do not have any close alternative to the product. Because only a single firm serves the market, it holds substantial control over the product’s availability and pricing. The main features are:

• Single seller: One firm constitutes the industry. The firm and the industry are the same from the viewpoint of the product.
• No close substitutes: The monopolist’s product has no close alternative in the market. Consumers have limited options, which gives the firm substantial pricing power.
• Barriers to entry: Entry of new firms is difficult or legally restricted due to patents, control over key raw materials, high capital requirements, government licensing, or natural monopoly conditions.
• Price-maker: The monopolist faces a downward-sloping market demand curve and has the ability to choose the price-output combination. It can raise price by restricting output or reduce price to sell more.
• Firm’s demand curve = market demand curve: Since there is only one firm, the demand curve it faces is the industry demand curve.

Profit-maximising output under monopoly

Block 4 clearly states that a profit-maximising monopoly firm produces at the output level where marginal revenue (MR) equals marginal cost (MC), with MR cutting MC from below.

The conditions can be summarised as:

• Choose output (Q^) such that: $$ MR(Q^) = MC(Q^) $$
• Set the price (P^) from the demand (average revenue) curve at that output.
• At equilibrium, for a typical monopoly:
  • P^* > MC
  • (P^* > AC) (if supernormal profit is being earned)

Long-run equilibrium under monopoly

The graphical explanation in the unit can be described as follows:

• Draw the downward-sloping demand curve (DD) and the corresponding marginal revenue curve (MR), which lies below the demand curve.
• Draw the monopolist’s average cost (AC) and marginal cost (MC) curves.
• The equilibrium output is at point (E) where MC intersects MR from below, giving quantity (OQ).
• The equilibrium price is obtained by extending a vertical line from (Q) to the demand curve and reading off price (OP).
• At this level of output:
  • Average revenue (price) is OP.
  • Average cost is lower, say OC.
  • The monopoly earns supernormal profits equal to (OP − OC) × OQ, represented by a profit rectangle in the diagram in the course.

Why supernormal profits persist even in the long run:

• In perfect competition, supernormal profits attract new firms, which enter until only normal profit remains.
• In monopoly, entry is blocked due to barriers, so new firms cannot enter to compete away the supernormal profits.
• As a result, the monopolist can continue to earn above-normal profit even in the long run, provided the demand and cost conditions remain similar.

Managerial implications and real-world feel

In practice, a monopolist or a firm with strong monopoly power:

• Uses detailed demand information and sometimes trial-and-error to locate the output region where marginal revenue approximately equals marginal cost.
• Chooses a price-output combination that balances short-run profits with long-term considerations such as regulation, public image and potential entry.
• May invest heavily in maintaining entry barriers—patents, brand loyalty, control over key distribution channels, or lobbying for regulatory protection.

Examples in the real world often involve public utilities or firms with strong network effects; while they may not be “pure” monopolies, their behaviour often approximates the monopoly model studied in the unit.

Question 8: Third-degree price discrimination with real-life examples

Reframed question: What is third-degree price discrimination, under what conditions can it be practised, and how does it work in real markets?

Concept of price discrimination

Block 4 introduces price discrimination as a pricing strategy where a firm with monopoly power charges different prices for the same product to different buyers or groups of buyers, not justified by cost differences. Price discrimination cannot occur in a perfectly competitive market because buyers would simply shift to the cheapest seller.

The unit explains three degrees of price discrimination: first-degree, second-degree and third-degree. Our focus here is on the third-degree case.

Third-degree price discrimination – definition

According to the course, in third-degree price discrimination the firm divides the market into two or more identifiable groups of buyers (segments) and charges a different price in each segment for the same product. The difference in prices reflects differences in the price elasticity of demand of each segment.

Typical segmentation bases include:

• Age (students, senior citizens, working adults)
• Location (urban vs. rural, domestic vs. foreign markets)
• Usage category (household vs. industrial/commercial)
• Time of use (peak vs. off-peak hours)

Conditions necessary for third-degree price discrimination

For a firm to successfully practise third-degree price discrimination, the following conditions must hold (as highlighted in the unit):

• Some degree of monopoly power: The firm must have control over price and face a downward-sloping demand curve.
• Segmentation of markets: The firm must be able to classify customers into distinct groups with different elasticities of demand (for example, student ID cards, residential vs. commercial connections).
• Prevention of resale (arbitrage): Customers in the low-price segment should not be able to easily resell the product to high-price segment customers.

Pricing rule under third-degree discrimination

The monopolist equates marginal revenue to marginal cost in each segment and allocates total output across segments accordingly. The general result is:

• The price will be higher in the segment with less elastic demand.
• The price will be lower in the segment with more elastic demand.

In more formal terms, the relation between price, marginal cost and elasticity in each market (i) can be expressed in Lerner-index form:

$$ \frac{P_i – MC}{P_i} = \frac{1}{|e_i|} $$

Where (e_i) is the price elasticity of demand in segment (i). A lower elasticity (less sensitivity to price) allows a higher price-cost margin.

Real-life examples

• Railway and metro fares: Passenger transport often charges lower per-kilometre fares for students and senior citizens, and higher fares for regular business travellers. The student and senior segments typically have more elastic demand (very price-sensitive), so lower prices are charged to keep them travelling, while less elastic segments pay more.
• Electricity tariffs: Electricity boards commonly have lower tariffs for domestic users and higher tariffs for commercial and industrial users for the same electricity units. Domestic demand is often more price-sensitive and politically sensitive; commercial demand can bear higher tariffs.
• Cinema tickets and events: Movie theatres and event organisers charge different ticket prices for morning shows vs. evening shows, weekday vs. weekend screenings, and for different seat categories (balcony, premium, VIP). These time and seating categories effectively represent different market segments with different elasticities.
• Airline pricing: Airlines typically offer discounted tickets to leisure travellers who book early and are flexible, while charging much higher last-minute fares to business travellers who have inelastic demand. Though there are additional elements (yield management, dynamic pricing), this is a clear case of segment-based pricing.

Managerial perspective

From a manager’s point of view, third-degree price discrimination can:

• Increase total profit: By capturing more consumer surplus from less elastic segments while still selling to price-sensitive segments at lower prices.
• Expand total output: Some units that would not have been sold at a single uniform price can now be sold in low-price segments, which may also help achieve economies of scale.
• Support social or policy goals: In utilities and public transport, lower prices for disadvantaged groups (students, senior citizens) are often justified on welfare grounds while higher prices from others keep the provider viable.

However, managers must also consider regulatory constraints and fairness perceptions. If price differences are seen as unfair or discriminatory, the firm may face public backlash or intervention by regulators.

Overall, third-degree price discrimination, is a powerful pricing strategy when applied carefully in markets where segmentation is feasible and resale can be controlled.