1. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Managerial Economics & Business Strategy Chapter 3 Quantitative Demand Analysis

2. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Overview I. Elasticities of Demand n Own Price Elasticity n Elasticity and Total Revenue n Cross-Price Elasticity n Income Elasticity II. Demand Functions n Linear n Log-Linear III. Regression Analysis

3. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Elasticities of Demand How responsive is variable G to a change in variable S + S and G are directly related - S and G are inversely related

4. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Own Price Elasticity of Demand Negative according to the law of demand Elastic: (sensitive) Inelastic: (insensitive) Unitary:

5. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Perfectly Elastic & Inelastic Demand Perfectly Elastic D Price Quantity Perfectly Inelastic D Price Quantity

6. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Own-Price Elasticity and Total Revenue Elastic n Increase in price leads to a decrease in total revenue. Inelastic n Increase in price leads to an increase in total revenue. Unitary n Total revenue is maximized at the point where demand is unitary elastic.

7. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Factors Affecting Price Elasticity n Available Substitutes The more substitutes available for the good, the more elastic the demand. n Time Demand tends to be more inelastic in the short term than in the long term. Time allows consumers to seek out available substitutes. n Expenditure Share Goods that comprise a small share of consumers budgets tend to be more inelastic than goods for which consumers spend a large portion of their incomes.

8. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Cross Price Elasticity of Demand + Substitutes - Complements

9. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Income Elasticity + Normal Good - Inferior Good

10. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Uses of Elasticities Pricing Managing cash flows Impact of changes in competitors prices Impact of economic booms and recessions Impact of advertising campaigns

11. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Example 1: Pricing and Cash Flows According to an FTC Report by Michael Ward, AT&Ts price elasticity of demand for long distance services is -8.64. AT&T needs to boost revenues in order to meet its marketing goals. To accomplish this goal, should AT&T raise or lower its price?

12. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Answer:Reduce the 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.

13. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 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?

14. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Answer Calls would increase by 25.92 percent!

15. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Impact of a change in a competitors price According to an FTC Report by Michael Ward, AT&Ts cross price elasticity of demand for long distance services is 9.06. If MCI and other competitors reduced their prices by 4 percent, what would happen to the demand for AT&T services?

16. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Answer AT&Ts demand would fall by 36.24 percent!

17. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Demand Functions Mathematical representations of demand curves Example: X and Y are substitutes (coefficient of P Y is positive) X is an inferior good (coefficient of M is negative)

18. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Specific Demand Functions Linear Demand Price Elasticity Cross Price Elasticity Income Elasticity

19. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Example of Linear Demand Q d = 10 - 2P Own-Price Elasticity: (-2)P/Q If P=1, Q=8 (since 10 - 2 = 8) Own price elasticity at P=1, Q=8: (-2)(1)/8= - 0.25

20. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Log-Linear Demand

21. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Example of Log-Linear Demand log Q d = 10 - 2 log P Own Price Elasticity: -2

22. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 P Q P Q D D Linear Log Linear

23. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Regression Analysis Used to estimate demand functions Important terminology n Least Squares Regression: Y = a + bX + e n Confidence Intervals n t-statistic n R-square or Coefficient of Determination n F-statistic

24. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Interpreting the Output Estimated demand function: n log Q x = 7.58 - 0.84 logP x n Own price elasticity: -0.84 (inelastic) How good is our estimate? n t-statistics of 5.29 and -2.80 indicate that the estimated coefficients are statistically different from zero n R-square of.17 indicates we explained only 17 percent of the variation n F-statistic significant at the 1 percent level.

25. Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Summary n Elasticities are tools you can use to quantify the impact of changes in prices, income, and advertising on sales and revenues. n Given market or survey data, regression analysis can be used to estimate: Demand functions Elasticities A host of other things, including cost functions n Managers can quantify the impact of changes in prices, income, advertising, etc.