The Cinema Hall's Price Decision
Rangghar, a single-screen cinema in Guwahati, has 400 seats and runs two evening shows. At a ticket price of ₹150, average attendance is 300 per show. The manager proposes raising the price to ₹180, arguing that the hall can earn more even if some viewers leave. The owner recalls that when the price was raised from ₹120 to ₹150 two years ago, attendance fell from 360 to 300 per show. A nearby multiplex charges ₹250, and several OTT platforms have recently cut subscription prices. Student discounts currently account for about 20% of tickets. The hall's fixed costs-rent, salaries and projection-are ₹18,000 per show, and variable costs are negligible once the show runs. The manager wants a decision before the next scheduling meeting: raise the price, hold it, or use a targeted discount. The owner also asks whether the earlier price change says anything reliable about demand.
1. Using the earlier price change from ₹120 to ₹150, calculate the price elasticity of demand by the percentage method. Interpret the value and state the main assumption behind your calculation.
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Percentage change in price = (150 − 120)/120 × 100 = 25%. Percentage change in quantity = (300 − 360)/360 × 100 = −16.67%. Price elasticity of demand = −16.67/25 ≈ −0.67, so |Ed| ≈ 0.67. Since |Ed| < 1, demand is inelastic over this range: a 1% price rise reduced attendance by only about 0.67%. Total revenue still rose. The calculation assumes ceteris paribus-that the multiplex, OTT prices, incomes and tastes did not change. That assumption is doubtful because the multiplex and OTT alternatives changed. Using the midpoint formula instead gives (60/330)/(30/135) ≈ 0.82, still inelastic but closer to unit elasticity. The owner should treat 0.67 as a rough, not exact, estimate.
2. Calculate revenue and contribution over fixed costs at ₹150. If elasticity stays at 0.67, estimate attendance and revenue at ₹180. Should the hall raise the price?
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At ₹150: revenue = 300 × 150 = ₹45,000 per show; contribution = 45,000 − 18,000 = ₹27,000. At ₹180, price rises by 20%. With |Ed| = 0.67, quantity falls by about 20% × 0.67 = 13.4%. Attendance ≈ 300 × 0.866 ≈ 260. Revenue ≈ 260 × 180 = ₹46,800; contribution ≈ ₹28,800. To match the old revenue of ₹45,000 at ₹180, attendance must be 45,000/180 = 250. The predicted 260 is only slightly above this break-even, so the gain is thin. If demand is more elastic because of OTT and the multiplex, attendance could fall below 250 and revenue would drop. A cautious decision is to test ₹180 on one show, keep the student discount, and monitor attendance rather than impose a blanket hike.
3. Distinguish between a movement along the demand curve and a shift in demand in this case. How would a fall in OTT subscription prices affect the cinema's demand curve and the pricing decision?
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The change from ₹120 to ₹150 is a movement along the cinema's demand curve caused by a change in its own ticket price. A fall in OTT subscription prices is a change in the price of a substitute, so it shifts the cinema's demand curve to the left: at every ticket price, fewer tickets are demanded. This is a decrease in demand, not a movement along the curve. The old elasticity estimate therefore overstates the attendance the cinema can retain after the OTT price cut. The manager should expect the demand curve to be lower and possibly more elastic, making a price rise riskier. A better response may be targeted discounts for students, bundle deals, or improved screening quality rather than relying on the earlier price experiment.