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THE ECONOMICS OF MANAGERIAL DECISIONS

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THE ECONOMICS OF MANAGERIAL DECISIONS

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THE ECONOMICS OF MANAGERIAL DECISIONS

ROGER D. BLAIR University of Florida

MARK RUSH University of Florida

New York, NY

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Ayan
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For Chau, our kids and our grandkids Roger D. Blair

For Sue’s memory and our kids Mark B. Rush

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Roger D. Blair is the Walter J. Matherly Professor and chair of economics at the University of Florida. He has been a visiting professor at the University of Hawaii and the University of California–Berkeley as well as Visiting Scholar in Residence, Center for the Study of American Business, Washington University. Professor Blair’s research centers on antitrust economics and policy. He has published 10 books and 200 journal articles. He has also served as an antitrust consultant to numerous corpo- rations, including Intel, Anheuser-Busch, TracFone, Blue Cross–Blue Shield, Waste Management, Astellas Pharma, and many others.

Mark Rush is a professor of economics at the University of Florida. Prior to teach- ing at Florida, he was an assistant professor of economics at the University of Pittsburgh. He has spent eight months at the Kansas City Federal Reserve Bank as a Visiting Scholar. Professor Rush has taught MBA classes for many years and has won teaching awards for his classes. He has published in numerous professional journals, including the Journal of Political Economy; the Journal of Monetary Economics; the Journal of Money, Credit, and Banking; the Journal of International Money and Finance; and the Journal of Labor Economics.

ABOUT THE AUTHORS

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PART 1 ECONOMIC FOUNDATIONS

1 Managerial Economics and Decision Making 1 2 Demand and Supply 33 3 Measuring and Using Demand 86

PART 2 MARKET STRUCTURE AND MANAGERIAL DECISIONS

4 Production and Costs 138 5 Perfect Competition 186 6 Monopoly and Monopolistic Competition 227 7 Cartels and Oligopoly 274 8 Game Theory and Oligopoly 318 9 A Manager’s Guide to Antitrust Policy 371

PART 3 MANAGERIAL DECISIONS

10 Advanced Pricing Decisions 414 11 Decisions About Vertical Integration

and Distribution 465

12 Decisions About Production, Products, and Location 499

13 Marketing Decisions: Advertising and Promotion 541 14 Business Decisions Under Uncertainty 587 15 Managerial Decisions About Information 635 16 Using Present Value to Make Multiperiod

Managerial Decisions 677

Content on the Web:

Appendix: The Business Plan Chapter: Franchising Decisions

BRIEF CONTENTS

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viii

CONTENTS

1 Managerial Economics and Decision Making 1 Managers at Sears Holdings Use Opportunity Cost to Make Tough Decisions 1

Introduction 1

1.1 Managerial Economics and Your Career 2

1.2 Firms and Their Organizational Structure 3 Definition of a Firm 3 The Legal Organization of Firms 3

1.3 Profit, Accounting Cost, and Opportunity Cost 6 Goal: Profit Maximization 6 Total Revenue 7 Accounting Cost and Opportunity Cost 8

DECISION SNAPSHOT Sunk Costs in the Stock Market 11

DECISION SNAPSHOT Opportunity Cost at Singing the Blues Blueberry Farm 13

Comparing Accounting Cost and Opportunity Cost 15 Using Opportunity Cost to Make Decisions 17

SOLVED PROBLEM Resting Energy’s Opportunity Cost 17

1.4 Marginal Analysis 18 The Marginal Analysis Rule 18 Using Marginal Analysis 19

SOLVED PROBLEM How to Respond Profitably to Changes in Marginal Cost 20

Revisiting How Managers at Sears Holdings Used Opportunity Cost to Make Tough Decisions 21

Summary: The Bottom Line 22

Key Terms and Concepts 23

Questions and Problems 23

MyLab Economics Auto-Graded Excel Projects 25

APPENDIX The Calculus of Marginal Analysis 28 A. Review of Mathematical Results 28 B. Marginal Benefit and Marginal Cost 29 C. Maximizing Total Surplus 29 D. Maximizing Total Surplus: Example 30

Calculus Questions and Problems 31

PART 1 ECONOMIC FOUNDATIONS

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Contents ix

2 Demand and Supply 33 Managers at Red Lobster Cope with Early Mortality Syndrome 33

Introduction 33 2.1 Demand 34

Law of Demand 34 Demand Curve 35 Factors That Change Demand 37

DECISION SNAPSHOT Demand for the Cadillac Escalade 41

Changes in Demand: Demand Function 41

SOLVED PROBLEM Demand for Lobster Dinners 43

2.2 Supply 44 Law of Supply 44 Supply Curve 44 Factors That Change Supply 46 Changes in Supply: Supply Function 49

SOLVED PROBLEM The Supply of Gasoline-Powered Cars and the Price of Hybrid Cars 50

2.3 Market Equilibrium 51 Equilibrium Price and Equilibrium Quantity 51 Demand and Supply Functions: Equilibrium 53

SOLVED PROBLEM Equilibrium Price and Quantity of Plush Toys 54

2.4 Competition and Society 54 Total Surplus 54 Consumer Surplus 58 Producer Surplus 59

SOLVED PROBLEM Total Surplus, Consumer Surplus, and Producer Surplus in the Webcam Market 60

2.5 Changes in Market Equilibrium 61 Use of the Demand and Supply Model When One Curve Shifts: Demand 61 Use of the Demand and Supply Model When One Curve Shifts: Supply 63 Use of the Demand and Supply Model When Both Curves Shift 64 Demand and Supply Functions: Changes in Market Equilibrium 68

SOLVED PROBLEM Demand and Supply for Tablets Both Change 70

2.6 Price Controls 70 Price Ceiling 70 Price Floor 72

SOLVED PROBLEM The Effectiveness of a Minimum Wage 74

2.7 Using the Demand and Supply Model 75 Predicting Your Costs 75 Predicting Your Price 76

Revisiting How Managers at Red Lobster Coped with Early Mortality Syndrome 78

Summary: The Bottom Line 78

Key Terms and Concepts 79

Questions and Problems 80

MyLab Economics Auto-Graded Excel Projects 83

MANAGERIAL APPLICATION

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x Contents

3 Measuring and Using Demand 86 Managers at the Gates Foundation Decide to Subsidize Antimalarial Drugs 86

Introduction 87

3.1 Regression: Estimating Demand 87 The Basics of Regression Analysis 88 Regression Analysis 89 Regression Results: Estimated Coefficients and Estimated Demand Curve 92

SOLVED PROBLEM Regression Analysis at Your Steak Chain 94

3.2 Interpreting the Results of Regression Analysis 94 Estimated Coefficients 94 Fit of the Regression 99

SOLVED PROBLEM Confidence Intervals and Predictions for the Demand for Doors 100

3.3 Limitations of Regression Analysis 101 Specification of the Regression Equation 101 Functional Form of the Regression Equation 102

SOLVED PROBLEM Which Regression to Use? 104

3.4 Elasticity 105 The Price Elasticity of Demand 105

DECISION SNAPSHOT Advertising and the Price Elasticity of Demand 117

Income Elasticity and Cross-Price Elasticity of Demand 117

SOLVED PROBLEM The Price Elasticity of Demand for a Touch-Screen Smartphone 119

3.5 Regression Analysis and Elasticity 120 Using Regression Analysis 120 Using the Price Elasticity of Demand 122 Using the Income Elasticity of Demand Through the Business Cycle 122

Revisiting How Managers at the Gates Foundation Decided to Subsidize Antimalarial Drugs 123

Summary: The Bottom Line 123

Key Terms and Concepts 124

Questions and Problems 124

MyLab Economics Auto-Graded Excel Projects 128

CASE STUDY Decision Making Using Regression 130

APPENDIX The Calculus of Elasticity 133 A. Price Elasticity of Demand for a Linear and a Log-Linear Demand Function 133 B. Total Revenue Test 134 C. Income Elasticity of Demand and Cross-Price Elasticity of Demand 135

Calculus Questions and Problems 136

MANAGERIAL APPLICATION

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Contents xi

4 Production and Costs 138 Pizza Hut Managers Learn That Size Matters 138

Introduction 138 4.1 Production 139

Production Function 139 Short-Run Production Function 141 Long-Run Production Function 145

SOLVED PROBLEM Marginal Product of Labor at a Bicycle Courier Service 147

4.2 Cost Minimization 147 Cost-Minimization Rule 148 Generalizing the Cost-Minimization Rule 149

SOLVED PROBLEM Cost Minimization at a Construction Firm 150

4.3 Short-Run Cost 150 Fixed Cost, Variable Cost, and Total Cost 151 Average Fixed Cost, Average Variable Cost, and Average Total Cost 152 Marginal Cost 153

DECISION SNAPSHOT Input Price Changes and Changes in the Marginal Cost of an Eiffel Tower Tour 154

Competitive Return 156 Shifts in Cost Curves 157

DECISION SNAPSHOT Changes in Input Prices and Cost Changes at Shagang Group 159

SOLVED PROBLEM Calculating Different Costs at a Caribbean Restaurant 161

4.4 Long-Run Cost 162 Long-Run Average Cost 162 Economies of Scale, Constant Returns to Scale, and Diseconomies of Scale 166

SOLVED PROBLEM Long-Run Average Cost 169

4.5 Using Production and Cost Theory 170 Effects of a Change in the Price of an Input 170 Economies and Diseconomies of Scale 171

Revisiting How Pizza Hut Managers Learned That Size Matters 173

Summary: The Bottom Line 174

Key Terms and Concepts 174

Questions and Problems 175

MyLab Economics Auto-Graded Excel Projects 178

APPENDIX The Calculus of Cost 179 A. Marginal Product 179 B. Cost Minimization 180 C. Marginal Cost and the Marginal/Average Relationship 183

Calculus Questions and Problems 184

MANAGERIAL APPLICATION

PART 2 MARKET STRUCTURE AND MANAGERIAL DECISIONS

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xii Contents

5 Perfect Competition 186 Burger King Managers Decide to Let Chickens Have It Their Way 186

Introduction 186

5.1 Characteristics of Competitive Markets 187 Defining Characteristics of Perfect Competition 188 Perfectly Competitive Markets 189

SOLVED PROBLEM The Markets for Fencing and Cell Phones 190

5.2 Short-Run Profit Maximization in Competitive Markets 191 Marginal Analysis 191 Using Marginal Analysis to Maximize Profit 194

DECISION SNAPSHOT Marginal Analysis at the American Cancer Society 196

Changes in Costs 196 Amount of Profit 197 Shutting Down 201

DECISION SNAPSHOT Lundberg Family Farms Responds to a Fall in the Price of Rice 203

The Firm’s Short-Run Supply Curve 204

DECISION SNAPSHOT A Particleboard Firm Responds to a Fall in the Price of an Input 205

The Short-Run Market Supply Curve 206

SOLVED PROBLEM Amount of Profit and Shutting Down at a Plywood Producer 207

5.3 Long-Run Profit Maximization in Competitive Markets 208 Long-Run Effects of an Increase in Market Demand 208 Change in Technology 212

SOLVED PROBLEM The Long Run at a Plywood Producer 214

5.4 Perfect Competition 215 Applying Marginal Analysis 215 Optimal Long-Run Adjustments 215

Revisiting How Burger King Managers Decided to Let Chickens Have It Their Way 217

Summary: The Bottom Line 218

Key Terms and Concepts 218

Questions and Problems 219

MyLab Economics Auto-Graded Excel Projects 222

APPENDIX The Calculus of Profit Maximization for Perfectly Competitive Firms 224 A. Marginal Revenue 224 B. Maximizing Profit 224 C. Maximizing Profit: Example 224

Calculus Questions and Problems 226

MANAGERIAL APPLICATION

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6 Monopoly and Monopolistic Competition 227 Premature Rejoicing by the Managers at KV Pharmaceutical 227

Introduction 228

6.1 A Monopoly Market 228 Defining Characteristics of a Monopoly Market 228 Demand and Marginal Revenue for a Monopoly 229

DECISION SNAPSHOT Is Delta Airlines a Monopoly? 229

SOLVED PROBLEM The Relationship Among the Price Elasticity of Demand, Marginal Revenue, and Price 233

6.2 Monopoly Profit Maximization 234 Profit Maximization for a Monopoly 234

DECISION SNAPSHOT Profit-Maximizing Range of Prices for Tires 237

Comparing Perfect Competition and Monopoly 239 Barriers to Entry 241

SOLVED PROBLEM Merck’s Profit-Maximizing Price, Quantity, and Economic Profit 247

6.3 Dominant Firm 247 Dominant Firm’s Profit Maximization 248

DECISION SNAPSHOT How a Technology Firm Responds to Changes in the Competitive Fringe 251

SOLVED PROBLEM The Demand for Shoes at a Dominant Firm 252

6.4 Monopolistic Competition 252 Defining Characteristics of Monopolistic Competition 253 Short-Run Profit Maximization for a Monopolistically Competitive Firm 253 Long-Run Equilibrium for a Monopolistically Competitive Firm 255

SOLVED PROBLEM J-Phone’s Camera Phone 256

6.5 The Monopoly, Dominant Firm, and Monopolistic Competition Models 257 Using the Models in Managerial Decision Making 257 Applying the Monopolistic Competition Model 259

Revisiting Premature Rejoicing by the Managers at KV Pharmaceutical 261

Summary: The Bottom Line 261

Key Terms and Concepts 262

Questions and Problems 262

MyLab Economics Auto-Graded Excel Projects 268

APPENDIX The Calculus of Profit Maximization for Firms with Market Power 269 A. Marginal Revenue Curve 269 B. Elasticity, Price, and Marginal Revenue 269 C. Maximizing Profit 270 D. Maximizing Profit: Example 271

Calculus Questions and Problems 272

MANAGERIAL APPLICATION

Contents xiii

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xiv Contents

7 Cartels and Oligopoly 274 Managers at Major Publishers Read the e-Writing on the e-Wall 274

Introduction 274

7.1 Cartels 275 Cartel Profit Maximization 276 Instability of a Cartel 277

SOLVED PROBLEM Potential Profit from a Cellular Telephone Cartel 280

7.2 Tacit Collusion 280 Price Visibility 281

DECISION SNAPSHOT A Contract in the Market for Propane 282

Preannouncements 283 Precommitments 283 Price Leadership 284

SOLVED PROBLEM Price Leadership in the Market for Insulin 284

7.3 Four Types of Oligopolies 285 Cournot Oligopoly 285

DECISION SNAPSHOT South Africa’s Impala Platinum as a Cournot Oligopolist 293

Chamberlin Oligopoly 294 Stackelberg Oligopoly 296 Bertrand Oligopoly 297 Comparing Oligopoly Models 298

SOLVED PROBLEM Coca-Cola Reacts to PepsiCo 299

7.4 Cartels and Oligopoly 300 Using Cartel Theory and Tacit Collusion for Managerial Decision Making 301 Using Types of Oligopolies for Managerial Decision Making 301

Revisiting How Managers at Major Publishers Read the e-Writing on the e-Wall 302

Summary: The Bottom Line 303

Key Terms and Concepts 303

Questions and Problems 304

MyLab Economics Auto-Graded Excel Projects 307

APPENDIX The Calculus of Oligopoly 309 A. Cournot Oligopoly 309 B. Stackelberg Oligopoly 315

Calculus Questions and Problems 316

MANAGERIAL APPLICATION

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8 Game Theory and Oligopoly 318 Managers at Pfizer Welcome a Competitor in the Market for Lipitor 318

Introduction 318

8.1 Basic Game Theory and Games 319 Elements of a Game 320 A Sample Game 320 Nash Equilibrium 322 A Dilemma 323

DECISION SNAPSHOT An Advertising Game 324

Repeated Games 325

DECISION SNAPSHOT TragoCo and Boca-Cola Play a Repeated Game 327

Dominated Strategies 330

SOLVED PROBLEM Games Between Two Smartphone Producers 332

8.2 Advanced Games 334 Multiple Nash Equilibria 334 Mixed-Strategy Nash Equilibrium 337

SOLVED PROBLEM Custom’s Flower of the Day 343

8.3 Sequential Games 344 An Entry Game 344

DECISION SNAPSHOT Game Tree Between Disney and Warner Brothers 347

Commitment and Credibility 348

SOLVED PROBLEM A Pharmaceutical Company Uses Game Theory to Make an Offer 352

8.4 Game Theory 354 Using Basic Games for Managerial Decision Making 354 Using Advanced Games for Managerial Decision Making 356 Using Sequential Games for Managerial Decision Making 357

SOLVED PROBLEM Is a Threat Credible? 359

Revisiting How Managers at Pfizer Welcomed a Competitor in the Market for Lipitor 360

Summary: The Bottom Line 361

Key Terms and Concepts 362

Questions and Problems 362

MyLab Economics Auto-Graded Excel Projects 368

MANAGERIAL APPLICATION

Contents xv

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xvi Contents

9 A Manager’s Guide to Antitrust Policy 371 The Managers of Sea Star Line Walk the Plank 371

Introduction 372

9.1 Overview of U.S. Antitrust Policy 372 The Monopoly Problem 372 The Sherman Act, 1890 374 The Clayton Act, 1914 374 The Federal Trade Commission Act, 1914 375 Sanctions for Antitrust Violations 375 Recent Antitrust Cases 377

SOLVED PROBLEM A Perfectly Competitive Market Versus a Monopoly Market 378

9.2 The Sherman Act 379 Sherman Act Section 1: Restraint of Trade 379 Sherman Act Section 2: Monopolization and Attempt to Monopolize 383

SOLVED PROBLEM Going, Going, Gone: Price Fixing in the Market for Fine Art 387

9.3 The Clayton Act 388 Clayton Act Section 2: Price Discrimination 388 Clayton Act Section 3: Conditional Sales 388 Clayton Act Section 7: Mergers 391

SOLVED PROBLEM The Business Practices Covered in the Clayton Act 392

9.4 U.S. Merger Policy 392 Economic Effects of Horizontal Mergers 393 Antitrust Merger Policy 394

DECISION SNAPSHOT The XM/Sirius Satellite Radio Merger 396

SOLVED PROBLEM Mergers in the Office-Supply Market 397

9.5 International Competition Laws 398 European Union Laws 398 Chinese Laws 400 Worldwide Competition Laws 401

SOLVED PROBLEM Gazprom Gas Prices Create Indigestion in the European Union 402

9.6 Antitrust Policy 402 Using the Sherman Act and the Clayton Act 402 Using International Competition Laws 403 Antitrust Advice for Managers 403

Revisiting How the Managers of Sea Star Line Walked the Plank 404

Summary: The Bottom Line 405

Key Terms and Concepts 405

Questions and Problems 406

MyLab Economics Auto-Graded Excel Projects 410

CASE STUDY Student Athletes and the NCAA 412

MANAGERIAL APPLICATION

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10 Advanced Pricing Decisions 414 Managers at the Turtle Bay Resort Think Kama’aina Pricing Is Par for the Course 414

Introduction 414

10.1 Price Discrimination 416 First-Degree Price Discrimination 416 Second-Degree Price Discrimination 418 Third-Degree Price Discrimination 419

DECISION SNAPSHOT American Airlines Identifies a Customer Type 425

SOLVED PROBLEM Price Discrimination at Warner Brothers: That’s All, Folks! 426

10.2 Peak-Load Pricing 427 Long-Run Capacity Decision 428 Short-Run Pricing and Quantity Decisions 429

DECISION SNAPSHOT Peak-Load Pricing by the Minneapolis–St. Paul Metropolitan Airport 432

SOLVED PROBLEM Peak-Load Pricing 433

10.3 Nonlinear Pricing 434 Two-Part Pricing 434 All-or-Nothing Offers 440

DECISION SNAPSHOT Nonlinear Pricing at the 55 Bar 443

Commodity Bundling 443

SOLVED PROBLEM Movie Magic 446

10.4 Using Advanced Pricing Decisions 447 Managerial Use of Price Discrimination 447 Managerial Use of Peak-Load Pricing 448 Managerial Use of Nonlinear Pricing 449

Revisiting How the Managers at Turtle Bay Resort Came to Think Kama’aina Pricing Is Par for the Course 450

Summary: The Bottom Line 451

Key Terms and Concepts 451

Questions and Problems 451

MyLab Economics Auto-Graded Excel Projects 456

APPENDIX The Calculus of Advanced Pricing Decisions 458 A. Third-Degree Price Discrimination 458 B. Two-Part Pricing 459

Calculus Questions and Problems 463

MANAGERIAL APPLICATION

PART 3 MANAGERIAL DECISIONS

Contents xvii

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xviii Contents

12 Decisions About Production, Products, and Location 499 Managers at Freeport-McMoRan Dig Deep to Make a Decision 499

Introduction 500

12.1 Joint Production 500 Fixed Proportions 501 Variable Proportions 502

SOLVED PROBLEM A Refinery Responds to an Increase in the Profit from Gasoline 506

11 Decisions About Vertical Integration and Distribution 465 Why Would Walgreens Boots Alliance Purchase Wholesaler AmerisourceBergen? 465

Introduction 465

11.1 The Basics of Vertical Integration 467 Markets Versus Vertical Integration 467 Types of Vertical Integration 468 Transfer Prices and Taxes 469

SOLVED PROBLEM Vertical Integration 470

11.2 The Economics of Vertical Integration 471 Synergies 471 Costs of Using a Market: Transaction Costs, the Holdup Problem, and Technological Interdependencies 471

DECISION SNAPSHOT PepsiCo Reduces Transaction Costs 473

Costs of Using Vertical Integration 476

DECISION SNAPSHOT Pilgrim’s Pride and the Limits of Vertical Integration 477

SOLVED PROBLEM IBM Avoids a Holdup Problem 478

11.3 Vertical Integration and Market Structure 478 Vertical Integration with Competitive Distributors 479 Vertical Integration with a Monopoly Distributor 483

SOLVED PROBLEM Price and Quantity with Competitive Distributors and a Monopoly Distributor 488

11.4 Vertical Integration and Distribution 489 Using the Economics of Vertical Integration for Managerial Decision Making 489 Using Vertical Integration and Market Structure for Managerial Decision Making Within a Firm 490

Revisiting Why Walgreens Boots Alliance Would Purchase Wholesaler AmerisourceBergen 490

Summary: The Bottom Line 491

Key Terms and Concepts 492

Questions and Problems 492

MyLab Economics Auto-Graded Excel Projects 496

MANAGERIAL APPLICATION

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13 Marketing Decisions: Advertising and Promotion 541 Heads Up for Advertising Decisions at Riddell 541

Introduction 541

13.1 Profit-Maximizing Advertising by a Firm 542 Advertising and Profit Maximization 543 Choosing Advertising Media 547

Contents xix

12.2 The Multi-Plant Firm 506 Marginal Cost for a Multi-Plant Firm 507 Profit Maximization for a Multi-Plant Firm 508

SOLVED PROBLEM Can Producing Too Many Cookies Hurt Your Firm’s Profit? 510

12.3 Location Decisions 511 Changes in Costs from Adding Plants 511 The Effect of Transportation Costs on Location Decisions 513

DECISION SNAPSHOT Quaker Oats’ Location Decision 514

DECISION SNAPSHOT Walgreens and CVS Compete for Your Drug Prescription 515

The Effect of Geographic Variation in Input Prices on Location Decisions 516

SOLVED PROBLEM A Department Store Pays for Transportation 518

12.4 Decisions About Product Quality 518 SOLVED PROBLEM Flower Quality 520

12.5 Optimal Inventories 521 Economic Order Quantity Model 521 General Optimal Inventory Decisions 523

SOLVED PROBLEM How a Decrease in Demand Affects the Economic Order Quantity 524

12.6 Production, Products, and Location 525 Joint Production of an Input 525 Transportation Costs, Plant Size, and Location 526

Revisiting How Managers at Freeport-McMoRan Had to Dig Deep to Make a Decision 528

Summary: The Bottom Line 528

Key Terms and Concepts 529

Questions and Problems 529

MyLab Economics Auto-Graded Excel Projects 534

APPENDIX The Calculus of Multi-Plant Profit-Maximization and Inventory Decisions 536 A. Production Decisions at a Multi-Plant Firm 536 B. Economic Order Quantity Inventory Model 537

Calculus Questions and Problems 539

MANAGERIAL APPLICATION

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xx Contents

DECISION SNAPSHOT PepsiCo Allocates Its Advertising Dollars 548

SOLVED PROBLEM Marginal Benefit from Automobile Advertising 549

13.2 Optimal Advertising by an Industry 550 Industry-Wide Advertising as a Public Good 550 Challenges of Industry-Wide Advertising 551

SOLVED PROBLEM The National Football League’s Advertising Problem 554

13.3 False Advertising 554 When Can False Advertising Be Successful? 555 What Are the Penalties for False Advertising? 557

SOLVED PROBLEM Advertising for Skechers Shape-Ups Gets the Boot 558

13.4 Resale Price Maintenance and Product Promotion 558 The Effect of Resale Price Maintenance 559 Profit Maximization with Resale Price Maintenance 560 Resale Price Maintenance and Antitrust Policy 561

DECISION SNAPSHOT Amazon.com Markets Its Kindle 562

SOLVED PROBLEM Profit-Maximizing Resale Price Maintenance for Designer Shoes 563

13.5 International Marketing: Entry and Corruption Laws 564 Entering a Foreign Market 564 U.S. Anticorruption Law: The Foreign Corrupt Practices Act 566

DECISION SNAPSHOT JPMorgan “Sons and Daughters” Program 569

U.K. Bribery Act 569

SOLVED PROBLEM Legal or Illegal? 570

13.6 Marketing and Promotional Decisions 571 Industry-Wide Advertising 571 Resale Price Maintenance 571 Foreign Marketing Issues 573

Revisiting Heads Up for Advertising Decisions at Riddell 573

Summary: The Bottom Line 575

Key Terms and Concepts 576

Questions and Problems 576

MyLab Economics Auto-Graded Excel Projects 580

APPENDIX The Calculus of Advertising 582 A. Profit-Maximizing Amount of Advertising with a Single Advertising Medium 582 B. Profit-Maximizing Amount of Advertising with Two or More Advertising Media 584

Calculus Questions and Problems 585

MANAGERIAL APPLICATION

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Contents xxi

14 Business Decisions Under Uncertainty 587 Embezzlement Makes Managers at a Nonprofit See Red 587

Introduction 587

14.1 Basics of Probability 588 Relative Frequency 588

DECISION SNAPSHOT Probability of Success at a New Branch 589

Expected Value 590 Subjective Probability 591

SOLVED PROBLEM Expected Customers at a Car Dealership 592

14.2 Profit Maximization with Random Demand and Random Cost 593 Expected Profit Maximization with Random Demand 593 Expected Profit Maximization with Random Cost 596 Expected Profit Maximization with Random Demand and Random Cost 598

SOLVED PROBLEM Profit Maximization for a Vineyard 599

14.3 Optimal Inventories with Random Demand 600 The Inventory Problem 600 Profit-Maximizing Inventory 601

SOLVED PROBLEM Profit-Maximizing Inventory of Pastry Rings 603

14.4 Minimizing the Cost of Random Adverse Events 604 Minimizing the Cost of Undesirable Outcomes 604 Expected Marginal Benefit from Avoiding Undesirable Outcomes 604 Marginal Cost of Avoiding Undesirable Outcomes 606 Optimal Accident Avoidance 607

DECISION SNAPSHOT Patent Search at a Pharmaceutical Firm 608

The Role of Marginal Analysis in Minimizing the Cost of Accidents 611

SOLVED PROBLEM Safety at an Energy Firm 611

14.5 The Business Decision to Settle Litigation 612 Basic Economic Model of Settlements: Parties with Similar Assessments 612

DECISION SNAPSHOT Actavis Versus Solvay Pharmaceuticals 614

Parties with Different Assessments 615

SOLVED PROBLEM To Settle or Not To Settle, That Is the Question 616

14.6 Risk Aversion 616 Insurance 617 Risk Aversion and Diversification 617 Risk Aversion and Litigation 618

SOLVED PROBLEM Merck Takes Advantage of Risk Aversion 618

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xxii Contents

15 Managerial Decisions About Information 635 Auctions Float the Navy’s Boat 635

Introduction 635

15.1 Intellectual Property 636 Patents and Trade Secrets 637 Copyrights 639 Trademarks 640

SOLVED PROBLEM Patent Infringement 641

15.2 Value of Forecasts 642 Random Demand Model 642 Factors Affecting the Value of Forecasts 644

SOLVED PROBLEM Value of a Forecast 648

15.3 Auctions 650 Types of Auctions 650 Bidding Strategy 651

DECISION SNAPSHOT Strategy in an English Auction of a U.S. Silver Dollar 655

Expected Revenue 656

SOLVED PROBLEM The San Francisco Giants Strike Out 658

15.4 Asymmetric Information 658 Adverse Selection 659 Moral Hazard 663

SOLVED PROBLEM Adverse Selection and Insurance Companies 665

15.5 Decisions about Information 666 Value of Forecasts for Different Time Periods 666 Managing the Winner’s Curse When Selling a Product 667 Incentives and the Principal–Agent Problem 667

Revisiting How Auctions Float the Navy’s Boat 669

Summary: The Bottom Line 669

Key Terms and Concepts 670

Questions and Problems 671

MyLab Economics Auto-Graded Excel Projects 674

MANAGERIAL APPLICATION

14.7 Making Business Decisions Under Uncertainty 619 Maximizing Profit with Random Demand and Random Cost 619 Optimal Inventories with Uncertainty About Demand 620 Making Business Decisions to Settle Litigation 622

Revisiting How Embezzlement Made Managers at a Nonprofit See Red 622

Summary: The Bottom Line 623

Key Terms and Concepts 624

Questions and Problems 624

MyLab Economics Auto-Graded Excel Projects 630

CASE STUDY Decision Making with Final Offer Arbitration 632

MANAGERIAL APPLICATION

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16 Using Present Value to Make Multiperiod Managerial Decisions 677 Why Did Ziosk’s Managers Give Their Tablets to Chili’s for Free? 677

Introduction 677

16.1 Fundamentals of Present Value 678 Calculating Future Values 679 Calculating Present Values 680 Valuing a Stream of Future Payments 683 Future and Present Value Formulas 688

SOLVED PROBLEM Choosing a Loan Repayment Schedule 688

16.2 Evaluating Investment Options 689 Net Present Value and the Net Present Value Rule 689 Extensions to the Net Present Value Rule 692

DECISION SNAPSHOT Salvage Value at a Car Rental Firm 693

DECISION SNAPSHOT Depreciation Allowance: Should a Tax Firm Take It Now or Later? 697

Selection of the Discount Rate 698 Risk and the Net Present Value Rule 698

SOLVED PROBLEM Investment Decision for an Electric Car Maker 700

16.3 Make-or-Buy Decisions 701 Make-or-Buy Basics 701 Make-or-Buy Net Present Value Calculations 703

SOLVED PROBLEM A Make-or-Buy Decision with Learning by Doing 704

16.4 Present Value and Net Present Value 704 Valuing Financial Assets 704 Using the Net Present Value Rule in the Real World 705 The Effect of Tax Shields on Net Present Value 706

Revisiting Why Ziosk’s Managers Gave Their Tablets to Chili’s for Free 707

Summary: The Bottom Line 708

Key Terms and Concepts 709

Questions and Problems 709

MyLab Economics Auto-Graded Excel Projects 712

CASE STUDY Analyzing Predatory Pricing as an Investment 715

Answer Key to Chapters 717

Answer Key to Calculus Appendices 756

Index 765

MANAGERIAL APPLICATION

Contents xxiii

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xxiv Contents

Content on the Web

The following content is available on www.pearson.com/mylab/economics

Web Appendix: The Business Plan

A. Dehydrated Business Plan

B. Funding Business Plan Executive Summary Market and Customer Analysis Company Description, Product Description, and Competitor Analysis Marketing and Pricing Strategies

DECISION SNAPSHOT Gilead Sciences Needs a Price

Operations Plan Development Plan Team Critical Risks Offering Financial Plan

Key Terms and Concepts

Questions and Problems

Web Chapter: Franchising Decisions

Quiznos Sandwiches Finds Its Stores Under Water

Introduction

WC.1 Franchising Franchising Issues Monopoly Benchmark Input Purchase Requirements Sales Revenue Royalties Resale Price Controls and Sales Quotas

WORKED PROBLEM Subway Uses an Input Purchase Requirement

WC.2 Managerial Application: Franchising Theory Managerial Use of Lump-Sum Franchise Fees Managerial Use of Sales Revenue Royalties Managerial Use of Resale Price Controls and Sales Quotas Summary

Revisiting How Quiznos Sandwiches Found Its Stores Under Water

Summary: The Bottom Line

Key Terms and Concepts

Questions and Problems

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http://www.pearson.com/mylab/economics
PREFACE

Solving Teaching and Learning Challenges Students who enroll in the managerial economics course are typically not economics majors. They take the course with the goal of building skills that will help them be- come better managers in a variety of business settings, including small and large firms, nonprofit organizations, and public service. In teaching our classes, we often skipped theoretical, abstract coverage in existing books—such as indifference curves, isoquants, the Cobb–Douglas production function, the Rothschild Index, and the Lerner Index—because these topics are not useful to students pursuing careers in management. Based on our teaching experiences and feedback from many reviewers and class testers, we have omitted this sort of theoretical, abstract coverage from our book.

Our decision to omit these topics does not mean that we shortchange economic theory. On the contrary, our book and a wide range of media assets show students how economic theory and concepts—including opportunity cost, marginal analysis, and profit maximization—can provide important insights into real-world manage- rial challenges such as how to price a product, how many workers to hire, whether to expand production, and how much to spend on advertising. Applications and extensions of the core theory abound. Some of the topics include bundled pricing, vertical integration, resale price maintenance, industry-wide advertising, settle- ment of legal disputes, present value and investment decisions, auctions and opti- mal bidding, and optimal patent search. We focus on how to think critically and make decisions in real-world business situations—in other words, how to apply economic theory.

MyLab Economics MyLab Economics is an online homework, tutorial, and assessment program that delivers technology-enhanced learning in tandem with printed textbooks and etexts. It improves results by helping students quickly grasp concepts and by providing educators with a robust set of tools to easily gauge and address the performance of individuals and classrooms.

The Study Plan provides personalized recommendations for each student, based on his or her ability to master the learning objectives in your course. This allows stu- dents to focus their study time by pinpointing the precise areas they need to review, and allowing them to use customized practice and learning aids—such as videos, eText, tutorials, and more—to keep them on track.

First-in-class content is delivered digitally to help every student master criti- cal course concepts. MyLab Economics includes Mini Sims, Auto-Graded Excel Projects, and Digital Interactives to not only help students understand important economic concepts, but also help them learn how to apply these concepts in a variety of ways so they can see how they can use economics long after the last day of class.

MyLab Economics allows for easy and flexible assignment creation, so instructors can assign a variety of assignments tailored to meet their specific course needs.

Visit www.pearson.com/mylab/economics for more information on Mini Sims, Auto-Graded Excel Projects, Digital Interactives, our LMS integration options, and course management options for any course of any size.

xxv

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http://www.pearson.com/mylab/economics
Chapter Features The following key features and media assets demonstrate how The Economics of Managerial Decisions keeps the spotlight on the student as a future manager.

Real-world chapter openers and closers: Each chapter begins with a real-world example that piques student interest and poses a managerial decision-making ques- tion. We revisit this question and apply the chapter content to provide an answer at the end. Because students pursue careers in various fields, the chapter openers pres- ent challenges faced by a number of different types of organizations, including large and small profit-seeking firms, government organizations, nongovernmental organi- zations, and nonprofits.

xxvi Preface

C H

A P

T E

R

3 Measuring and Using Demand Learning Objectives After studying this chapter, you will be able to

3.1 Explain the basics of regression analysis. 3.2 Interpret the results from a regression. 3.3 Describe the limitations of regression analysis and how they affect its use by managers. 3.4 Discuss different elasticity measures and their use. 3.5 Use regression analysis and the different elasticity measures to make better managerial

decisions.

Managers at the Gates Foundation Decide to Subsidize Antimalarial Drugs

The Bill and Melinda Gates Foundation (Gates Foundation) is the world’s largest philanthropic organization, with a trust endowment of nearly $40 billion. The foundation provides grants for education, medical research, and vac- cinations around the world. As of 2015, the foundation had made total grants of $37 billion. The goal of the Gates Foundation is not maximizing profit. Instead, its goal is to save lives and improve health in developing countries.

In 2010, the Global Fund to Fight AIDS, Tuberculosis and Malaria presented proposals to the Gates Foundation to subsidize antimalarial drugs in Kenya and other nations of sub-Saharan Africa. Although the Gates Foundation pro- vides nearly $4 billion in grants per year, there are more than $4 billion worth of competing uses for its resources. Consequently, before the managers accepted these proposals, they needed to determine their expected impact: How many people would these projects save compared to alternative uses of the funds? The managers

realized that lives hinged on their decision, so they wanted to be certain that they were getting the most value for their money.

The proposed subsidy programs would lower the price patients pay for the drugs. As you learned in Chapter 2, according to the law of demand, a decrease in the price of a product increases the quantity demanded. Antimalarial drugs are no exception; if their price falls, more patients will buy them. To make the proper decision about the proposals, however, the foundation’s manag- ers needed a more quantitative estimate: Precisely how many additional patients would buy the drugs when their prices were lower?

This chapter explains how to answer this and other questions that require quantitative answers. At the end of the chapter, you will learn how the Gates Foundation’s managers could forecast the number of patients they would help by subsidizing the drugs.

Sources: Karl Mathiesen, “What Is the Bill and Melinda Gates Foundation?” The Guardian. March 16, 2015; Gavin Yamey, Marco Schaferhoff, and Dominic Montagu, “Piloting the Affordable Medicines Facility-Malaria: What Will Success Look Like?” Bulletin of the World Health Organization, February 3, 2012, http://www.who .int/bulletin/volumes/90/6/11-091199/en; Erinstar, “Availability of Subsidized Malaria Drugs in Kenya,” Social and Behavioral Foundations of Primary Health Care Policy Advocacy, March 11, 2012, https://sbfphc.wordpress .com/2012/03/11/availability-of-subsidized-malaria-drugs-in-kenya-18-2.

86

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Revisiting How Managers at the Gates Foundation Decided to Subsidize Antimalarial Drugs

As noted at the beginning of the chapter, the manag-ers at the Bill and Melinda Gates Foundation want to use their funds in the best way possible. Because wast- ing their resources means that people could die unneces- sarily, managers at the foundation want to fund the most cost-effective programs. To achieve that goal, they must determine the quantitative impact of the proposals pre- sented to them.

In the case of the proposals to subsidize antimalarial drugs in Kenya and other nations, the managers were unlikely to have an estimated demand curve for the drugs in these countries because of data limitations. Instead, they proba- bly relied on estimates of the price elasticity of demand to determine the increase in the quantity of drugs demanded.

The subsidy programs lowered the price of these drugs between 29 percent and 78 percent (the fall in price differed from nation to nation and from drug to drug). Overall, the average decrease in price was roughly 50 percent. Because there are few substitutes, the demand for pharmaceutical drugs is price inelastic. The price elas- ticity of demand for pharmaceutical drugs for low-income Danish consumers is estimated to be 0.31. Denmark and

Kenya differ in an important respect: Low-income consum- ers in Kenya have much lower incomes than their coun- terparts in Denmark. Consequently, the expenditure on drugs in Kenya is a much larger fraction of consumers’ income, which means that the price elasticity of demand for drugs in Kenya is larger than in Denmark. If the man- agers at the Bill and Melinda Gates Foundation estimated that the price elasticity of demand for drugs in Kenya was about twice that in Denmark-—say, 0.60-—they could then predict that lowering the price of the drugs by 50 percent would increase the quantity demanded by 50 percent * 0.60 = 30 percent.

The Gates Foundation funded the proposals to sub- sidize antimalarial drugs. The actual outcome was that the quantity of the drugs demanded in the different na- tions increased by 20 to 40 percent. The quantitative estimate was right in line with what occurred. Using the price elasticity of demand to estimate the impact of the drug subsidy proposals allowed the managers at the foundation to compare them to competing proposals and to make decisions that saved the maximum number of lives.

Summary: The Bottom Line 3.1 Regression: Estimating Demand • Regression analysis is a statistical tool used to estimate

the relationships between two or more variables. • Regression analysis assumes that the function to be es-

timated has a random element. The estimated coeffi- cients minimize the sum of the squared residuals between the actual values of the dependent variable and the values predicted by the regression.

3.2 Interpreting the Results of Regression Analysis

• The coefficients estimated by a regression change when the data change. The statistical programs used in regression analysis calculate confidence intervals for each estimated coefficient. For the 95 percent confi- dence interval, the value of the true coefficient falls within the interval 95 percent of the time.

• The P-value indicates whether an estimated coefficient is statistically significantly different from zero. If the P- value is 5 percent (0.05) or less, then you can be 95 percent confident that the true coefficient is not equal to zero.

• The R2 statistic, which measures the overall fit of the regression, varies between 100 percent (the predicted values capture all the variation in the actual dependent variable) and 0 (the predicted values capture none of the variation in the actual dependent variable).

3.3 Limitations of Regression Analysis • Managers should examine regressions reported to

them to be certain that all the relevant variables are included.

• Managers should determine whether a regression’s functional form (curve or straight line) is the best fit for the data.

3.4 Elasticity • The price elasticity of demand measures how strongly

the quantity demanded responds to a change in the price of a product. It equals the absolute value of the percentage change in the quantity demanded divided by the percentage change in the price.

Summary: The Bottom Line 123

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120 CHAPTER 3 Measuring and Using Demand

3.5 Regression Analysis and Elasticity Learning Objective 3.5 Use regression analysis and the different elasticity measures to make better managerial decisions.

Regression analysis and the different elasticity measures are important to managers because they help quantify decision making. As a manager, you will face situations in which you need to know the exact amount of a change in the price of an input, the precise change in your cost when you change your production, or the actual decrease in quantity demanded when you raise the price of your product. Regression analysis and the application of the different elasticity measures can help you answer these and many other important questions.

Using Regression Analysis Using the results from regression analysis is an essential task in many managerial positions. Analysts can use regression analysis for much more than estimating a demand curve. For example, you can use it to estimate how your costs change when production changes. We explain this important concept, called marginal cost, in Chapter 4 and use it in all future chapters. Large companies with demand that depends significantly on a specific influence often use regression analysis to forecast changes in such factors as personal income (important to automobile manufacturers such as General Motors and Honda) or new home sales (important to home improve- ment stores such as Home Depot and Lowe’s).

The ultimate goal of regression analysis is to help you make better decisions. For example, as a manager at the high-end steak restaurant chain, you can use an esti- mated demand function to help you make both immediate decisions about the price to set and long-term decisions about whether to open a new location. Suppose that an analyst for your firm has used regression to determine that the nightly demand for your chain’s steak dinners depends on the following factors:

1. The price of the dinners, measured as dollars per dinner 2. The average income of residents living within the city, measured as dollars per

person 3. The unemployment rate within the city, measured as the percentage unemploy-

ment rate 4. The population within 30 miles of the restaurant

Suppose that Table 3.4 includes the estimated coefficients and their standard er- rors, t-statistics, and P-values.4 The R2 of the regression is 0.72, so the regression pre- dicts the data reasonably well. In the table, the t-statistics for all five coefficients are greater than 1.96, and accordingly all five P-values are less than 5 percent (0.05). Therefore, you are confident that all the variables included in the regression affect the demand for steak dinners. The coefficient for the price variable, −12.9, shows that a $1 increase in the price of a dinner decreases the quantity demanded by -12.9 * $1, or 12.9 dinners per night. Similarly, the coefficient for the average income variable, 0.0073, shows that a $1,000 increase in average income increases the demand by

MANAGERIAL APPLICATION

4 Often regression results are written with the standard errors in parentheses below the estimated coefficients: Qd = 139.2 - 112.9 * PRICE2 + 10.0073 * INCOME2 - 110.0 * UNEMPLOYMENT2+ 10.0005 * POPULATION2 (11.9) (1.8) (0.0012) (3.2) (0.0002)

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Preface xxvii

NEW! Mini Sims: The Managerial Applications are accompanied by Mini Sims that are located in MyLab Economics. Written by David Switzer of St. Cloud State University and Casey DiRienzo of Elon University, these Mini Sims are designed to build students’ critical-thinking and decision-making skills through an engaging, active learning experi- ence. Each Mini Sim requires students to make a series of decisions based on a business scenario, which helps them move from memorization to understanding and application. These also allow students to experience how different functional areas of a business interact and how each employee’s decisions affect the organization.

Managerial Applications: Fifteen of the sixteen chapters include a major numbered section devoted to managerial applications of the chapter content.

3.5 Managerial Application: Regression Analysis and Elasticity 121

0.0073 * 1,000, or 7.3 dinners per night. The coefficient for the unemployment rate variable, −10.0, shows that a one percentage point increase in the unemployment rate decreases the demand by -10.0 * 1, or 10 dinners per night. And the coefficient for the population variable, 0.0005, shows that a 1,000-person increase in population increases the demand by 0.0005 * 1,000, or 0.5 dinners per night.

Short-Run Decisions Using Regression Analysis Although a more detailed explanation of how managers determine price must wait until Chapter 6, intuitively it is clear that demand must play a role. The estimated demand function can help determine what price to charge in different cities because you can use it to estimate the nightly quantity of dinners your customers will demand in those cities. Suppose that one of the restaurants is located in a city of 900,000 people, in which aver- age income is $66,300 and the unemployment rate is 5.9 percent. If you set a price of $60 per dinner, you can predict that the nightly demand for steak dinners equals

Qd = 139.2 - 112.9 * $602 + 10.0073 * $66,3002 - 110.0 * 5.92 + 10.0005 * 900,0002 or 240 dinners per night. You can now calculate consumer response to a change in the price. For example, if you raise the price by $1, then the quantity of dinners de- manded decreases by 12.9 per night, to approximately 227 dinners.

Long-Run Decisions Using Regression Analysis You can also use the estimated demand function to forecast the demand for your product. Such forecasts can help you make better decisions. For example, you and the other executives at your steak chain might be deciding whether to open a restaurant in a city of 750,000 residents, with average income of $60,000 and an unemployment rate of 6.0 percent. Using the estimated demand function in Table 3.4 and a price of $60 per dinner, you predict demand of about 118 meals per night. Suppose this quan- tity of sales is too small to be profitable, but you expect rapid growth for the city: In three years, you forecast the city’s population will rise to 950,000, average income will increase to $70,000, and the unemployment rate will fall to 5.8 percent. Three years from now, if you set a price of $60 per dinner, you forecast the demand will be 293 dinners per night. This quantity of dinners provides support for a plan to open a restaurant in three years. You might start looking for a good location!

Other companies can use an estimated demand function to forecast their future input needs. General Motors, for example, can use an estimated demand function for their automobiles to forecast the quantity of steel it expects to need for next year’s production. This information can help its managers make better decisions about the contracts they will negotiate with their suppliers.

Table 3.4 Estimated Demand Function for Steak Dinners The table shows the results of a regression of the demand for meals at an upscale steak restaurant, with the estimated coefficients for the price, average income in the city in which the restaurant is located, unemployment rate in the city, and population of the city.

Coefficient Standard Error t Stat P-value Lower 95% Upper 95%

Constant 139.2 11.9 11.7 0.00 117.3 163.1

Price of dinner −12.9 1.8 7.2 0.00 −9.4 −16.4

Average income 0.0073 0.0012 6.1 0.00 4.9 9.7

Unemployment rate −10.0 3.1 3.1 0.00 −3.9 −16.5

Population 0.0005 0.0002 2.5 0.02 0.0001 0.0009

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Solved Problems: This section-ending feature guides students step by step in solving a managerial problem, set in the context of a situation managers may encounter.

Decision Snapshots: This feature places readers in the role of managers facing a decision in a range of indus- tries, including large and small for-profit firms, public service organizations, and nonprofits. An answer is in- cluded so students can con- firm the decision they have made.

Integrated examples: We consistently present economic concepts in the context of business scenarios from a range of industries. For example:

• Chapter 4, “Production and Costs,” uses dinners at a restaurant to present the concepts of production and costs.

• Chapter 13, “Marketing Decisions: Advertising and Promotion,” includes exam- ples of advertising by a private company as well as by an entire industry.

• Chapter 14, “Business Decisions Under Uncertainty,” discusses the effect of uncertainty on business decisions using examples including Starbucks and Samsung.

xxviii Preface

104 CHAPTER 3 Measuring and Using Demand

SOLVED PROBLEM Which Regression to Use?

Your research department gives you the following two estimated demand curves. The estimated demand curve to the left is log-linear, and the estimated demand curve to the right is linear.

Price (dollars per dinner)

Quantity (dinners per day)

$75

$45

$50

1,100

D

1,000900800700600500

$55

$60

$65

$70

0

Price (dollars per dinner)

Quantity (dinners per day)

$75

$45

$50

1,1001,000900800700600500

$55

$60

$65

$70

0

D

a. Which regression do you think has the highest R2—the one with the log-linear speci- fication or the one with the linear specification? Explain your answer.

b. Are the predicted quantities from one demand curve always closer to the actual quantities than the predicted quantities from the other demand curve?

c. Which estimated demand curve would you use to make your decisions? Why?

Answer

a. The log-linear specification is closer to more of the data points than the linear speci- fication. So the R2 of the log-linear specification exceeds that of the linear specification.

b. Even though the predicted quantities from the log-linear specification are closer to most of the actual quantities, there are a few predicted quantities that are closer when using the linear specification. In particular, for prices of $67 and $64, the pre- dicted quantities from the linear specification are closer to the actual quantities than the predictions from the log-linear specification.

c. As a manager, you want to base your decisions on the most accurate information possible. The log-linear specification has the higher R2, which means that it does a better job of capturing the variation in the actual quantities than does the linear specification. Consequently, you should use the log-linear specification as the basis for your decisions.

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3.4 Elasticity 117

maximizes Pfizer’s total revenue because that will maximize your royalty and profit. Knowing the price elasticity of demand for your drug is important to you. For example, if your drug is the only one to treat an illness, Pfizer has a monopoly. In other words, it is the only seller in the market. You will learn in Chapter 6 that because Pfizer has a monopoly, its profit-maximizing price for the product will fall in the elastic range of the demand. Accepting this result, you can see that when you license your drug to Pfizer, you need to push Pfizer to cut the price from what it wants to set because the total revenue test shows that when demand is elastic, a decrease in the price increases total revenue. If Pfizer’s total revenue increases, the royalty revenue your company receives will get a boost as well. Of course, Pfizer will resist lowering the price, but because you know that the demand for the drug is elastic, your biotech company will keep pressuring Pfizer.

Your marketing department estimates that at the current price and quantity, your firm’s product has a price elasticity of demand of 1.1. You run an advertising cam- paign that changes the demand, so that at the current price and quantity the elas- ticity falls to 0.8. In response to this change, would you raise the price, lower it, or keep it the same? Explain your answer.

Answer You should raise your price. Before the advertising campaign, the demand for your product was elastic, so according to the total revenue test, a price hike would lower your firm’s total revenue. After the campaign, the demand became inelastic. You now will be able to increase your firm’s profit by raising the price. Because the demand is inelastic, a price hike raises your firm’s total revenue. A price hike also decreases the quantity demanded, so your firm produces less, which decreases your costs. Raising revenue and lowering cost unambiguously boost your firm’s profit!

DECISION SNAPSHOT

Advertising and the Price Elasticity of Demand

Income Elasticity and Cross-Price Elasticity of Demand So far, you have learned about only one type of elasticity, the price elasticity of demand. Although this is the most important elasticity, there are two others to keep in mind: the income elasticity of demand and the cross-price elasticity of demand. You are unlikely to use either of these two measures often, but under- standing the different types of elasticity will help you avoid confusing them. In addition, learning about income elasticity and cross-price elasticity will help rein- force your understanding of the price elasticity of demand because all elasticities have four points in common: (1) changes are expressed as percentages, (2) fractions are used, (3) the factor driving the change is in the denominator, and (4) the factor responding to the change is in the numerator. (The Appendix at the end of this chapter presents a calculus treatment of these elasticities.)

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124 CHAPTER 3 Measuring and Using Demand

• If the price elasticity of demand exceeds 1.0, consum- ers respond strongly to a change in price, and demand is elastic. If the price elasticity of demand equals 1.0, demand is unit elastic. If the price elasticity of demand is less than 1.0, consumers respond weakly to a change in price, and demand is inelastic.

• The more substitutes available for the product and the larger the fraction of the consumer’s budget spent on the product, the larger the price elasticity of demand.

• The income elasticity of demand equals the percentage change in the quantity demanded divided by the per- centage change in income. It is positive for normal goods and negative for inferior goods.

• The cross-price elasticity of demand equals the per- centage change in the quantity demanded of one good

divided by the percentage change in the price of a re- lated good. It is positive for products that are substi- tutes and negative for those that are complements.

3.5 Managerial Application: Regression Analysis and Elasticity

• Regression analysis can estimate a firm’s demand function and other important relationships. You can use the estimated functions to make forecasts and pre- dictions that improve your decisions.

• When there are not enough data to estimate a demand function, you can use the price elasticity of demand, the income elasticity of demand, and/or the cross- price elasticity of demand to estimate or forecast the effect of changes in market factors.

Key Terms and Concepts Confidence interval

Critical value

Cross-price elasticity of demand

Elastic demand

Elasticity

Income elasticity of demand

Inelastic demand

Perfectly elastic demand

Perfectly inelastic demand

Price elasticity of demand

P-value

Regression analysis

R2 statistic

Significance level

t-statistic

Unit-elastic demand

Questions and Problems All exercises are available on MyEconLab; solutions to even-numbered Questions and Problems appear in the back of this book.

3.1 Regression: Estimating Demand Learning Objective 3.1 Explain the basics of regression analysis.

1.1 In the context of regression analysis, explain the meaning of the terms dependent variable, indepen- dent variable, explanatory variable, univariate equa- tion, and multivariate equation.

1.2 Why does regression analysis presume the pres- ence of a random error term?

1.3 Explain why minimizing the sum of the squared residuals is a reasonable objective for regression analysis.

3.2 Interpreting the Results of Regression Analysis Learning Objective 3.2 Interpret the results from a regression.

2.1 Your marketing research department provides the following estimated demand function for your

product: Qd = 500.6 - 11.4P + 0.5INCOME, where P is the price of your product and INCOME is average income. a. Is your product a normal good or an inferior

good? Explain your answer. b. The standard error for the price coefficient

is 2.0. What is its t-statistic? What can you conclude about the coefficient’s statistical significance?

c. The standard error for the income coeffi- cient is 0.3. What is its t-statistic? What can you conclude about the coefficient’s statisti- cal significance?

2.2 What does the R2 statistic measure? Why is it important?

2.3 The estimated coefficient for a variable in a regression is 3.5, with a P-value of 0.12. Given these two values, what conclusions can you make about the estimated coefficient?