1 / 30

Managerial Economics & Business Strategy

Managerial Economics & Business Strategy. Chapter 3 Quantitative Demand Analysis. Overview. I. The Elasticity Concept Own Price Elasticity Elasticity and Total Revenue Cross-Price Elasticity Income Elasticity II. Demand Functions Linear Log-Linear III. Regression Analysis.

torie
Télécharger la présentation

Managerial Economics & Business Strategy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Managerial Economics & Business Strategy Chapter 3 Quantitative Demand Analysis

  2. Overview I. The Elasticity Concept • Own Price Elasticity • Elasticity and Total Revenue • Cross-Price Elasticity • Income Elasticity II. Demand Functions • Linear • Log-Linear III. Regression Analysis

  3. Own Price Elasticity of Demand • Negative according to the “law of demand” Elastic: Inelastic: Unitary:

  4. Perfectly Elastic & Inelastic Demand Price Price D D Quantity Quantity Perfectly Elastic Perfectly Inelastic

  5. Own-Price Elasticity and Total Revenue • Elastic: Increase (a decrease) in price leads to a decrease (an increase) in total revenue. • E.G., %  in P leads to a larger %  in Qd TR  • Inelastic: Increase (a decrease) in price leads to an increase (a decrease) in total revenue. • E.G., %  in P leads to a smaller %  in Qd TR  • Unitary: Total revenue is maximized at the point where demand is unitary elastic. • E.G., %  in P leads to a same %  in Qd TR remains unchanged and is maximized.

  6. Therefore, Linear Demand & Elasticity • Suppose you have the following demand function: Inverse Demand

  7. Price 10 = 3 Elastic 8 Unit Elastic 6 = 2/3 5 Inelastic 4 = 1/4 2 D 1 2 3 4 5 2.5 Quantity Linear Demand & Elasticity

  8. Demand, Market Elasticity, TR and MR Using the demand function, find TR(Q) & MR. TR=PQ, plug-in inverse demand function for P TR(Q)=10Q 2Q2 Note: MR looks like inverse demand (P = 10 – 2Q), but has twice the slope, which means MR < P. Why?

  9. 10 Elastic: P , Qd , and TR  Unit Elastic: TR is maximized Price, MR 5 Inelastic: P , Qd , and TR D MR Quantity 2.5 5 When MR = 0 (i.e., slope of TR function is zero), TR is maximized Total Revenue 12.5 Quantity

  10. Factors Affecting Own Price Elasticity • Available Substitutes • The more substitutes available for the good, the more elastic the demand. • Firm demand curve will be more elastic than the market demand curve • Time • Demand tends to be more inelastic in the short term than in the long term. • Time allows consumers to seek out available substitutes. • Expenditure Share • Goods that comprise a small share of consumer’s budgets tend to be more inelastic than goods for which consumers spend a large portion of their incomes.

  11. Cross Price Elasticity of Demand + Substitutes - Complements

  12. Cross-Price Elasticity of Demand When a firm’s revenues are derived from the sale of two goods, X and Y We can calculate the change in revenues when the price of good X changes as

  13. Income Elasticity + Normal Good - Inferior Good

  14. Example 1: Pricing and Cash Flows • According to an FTC Report by Michael Ward, AT&T’s own price elasticity of demand for long distance services is -8.64. • AT&T needs to boost revenues in order to meet it’s marketing goals. • To accomplish this goal, should AT&T raise or lower it’s price?

  15. Answer: Lower price! • Since demand is elastic, a reduction in price will increase quantity demanded by a greater percentage than the price decline, resulting in more revenues for AT&T.

  16. Example 2: Quantifying the Change • If AT&T lowered price by 3 percent, what would happen to the volume of long distance telephone calls routed through AT&T?

  17. Answer • Calls would increase by 25.92 percent!

  18. Example 3: Impact of a change in a competitor’s price • According to an FTC Report by Michael Ward, AT&T’s cross price elasticity of demand for long distance services is 9.06. • If competitors reduced their prices by 4 percent, what would happen to the demand for AT&T services?

  19. Answer • AT&T’s demand would fall by 36.24 percent!

  20. Information Found in Demand Functions • Example: • X and Y are substitutes (coefficient of PY is positive) • X is an inferior good (coefficient of M is negative)

  21. Income Elasticity Own Price Elasticity Cross Price Elasticity Calculating Elasticities from Linear Demand Functions • Linear Demand

  22. Example of Linear Demand • Given: PX=$40, PY=$30, M=$48,000 • QXd = 100 - 2PX + 4PY + ¼ M • Find Q given the above data. • Calculate Own-Price Elasticity. • Calculate Cross-Price Elasticity. • Calculate Income Elasticity.

  23. constant elasticities Log-Linear Demand

  24. P Q P D D Q Log Linear Linear

  25. Example of Log-Linear Demand • ln Qd = 10 - 2 ln P • Own Price Elasticity: -2 • If price falls by 20%, by what percentage will Qd change?

  26. Regression Analysis • Used to estimate demand functions • Important terminology (MBA 6041 and covered in the Baye Managerial textbook). • Least Squares Regression: Y = a + bX + e • Confidence Intervals • t-statistic • R-square • F-statistic

  27. An Example • Go out and collect data on price and quantity • Cautionary note about identification. P S0 S1 $8 $6 D0 D improperly identified D1 Q 100 250 • Use a spreadsheet or statistical package (e.g., Minitab) to estimate demand:

  28. Summary Output

  29. Interpreting the Output • Estimated demand function: • ln Qx = 7.58 - 0.84 lnPx • Own price elasticity: -0.84 (inelastic) • How good is our estimate? • t-statistics of 5.29 and -2.80 indicate that the estimated coefficients are statistically different from zero • R-square of .17 indicates we explained only 17 percent of the variation • F-statistic significant at the 1 percent level tells us that only 1% chance that estimated regression model fits the data purely by accident.

  30. Summary • Elasticities are tools you can use to quantify the impact of changes in prices, income, and advertising on sales and revenues. • Given market or survey data, regression analysis can be used to estimate: • Demand functions • Elasticities • A host of other things, including cost functions • Managers can quantify the impact of changes in prices, income, advertising, etc.

More Related