Managerial Economics and Strategy Third Edition

Chapter 1

Introduction

Scarcity at the Bottom of Managerial Problems Economics • Economics is the study of decision-making in the presence of

scarcity.

Managerial Economics

• Managerial economics is the application of economic analysis to managerial decision making.

Managers • Managers make economic decisions by allocating the scarce

resources at their disposal. • They must understand the behavior of consumers, workers,

other managers, and governments to make good decisions.

Learning Objectives 1.1 Managerial Decision Making Describe the major business decisions managers face

1.2 Economic Models Explain how economic models are useful in managerial decision making

1.3 Using Economics Skills in your Career Illustrate how a knowledge of economics can help your career

1.1 Managerial Decision Making (1 of 5) • There are many decisions made by managers.

– A production manager’s objective is normally to achieve a production target at the lowest possible cost. Of course, the manager has to use the existing factory.

– Human resource managers design compensation systems to encourage employees to work hard. Of course, the manager has limited resources and employees are already in the firm.

– A marketing manager must allocate an advertising budget to promote the product most effectively. Of course, the manager has a limited marketing budget.

• The firm’s top manager must coordinate and direct all these activities.

– Could you think on this manager’s constraints?

1.1 Managerial Decision Making (2 of 5) Profit = Revenue − Costs

• The job of the chief executive officer (CEO) is to focus on the bottom line: maximizing profit.

– The CEO is also concerned with how a firm is positioned in a market relative to its rivals. ▪ However, it is critical the CEO focuses on maximizing the firm’s profit rather

than beating a rival.

• Maximizing profit requires coordination. – The CEO orders the production manager to minimize the cost of producing the

particular good or service. – The CEO asks the market research manager to determine how many units can be

sold at any given price, and so forth. – It would be a major coordination failure if the marketing department sets up a

system of pricing and advertising based on selling 8,000 units a year, while the production department managed to produce only 2,000.

1.1 Managerial Decision Making (3 of 5) Trade-Offs • In an environment of scarcity, managers must focus on the trade-offs that directly or

indirectly affect profits. – Evaluating trade-offs often involves marginal reasoning: considering the effect

of a small change. • How to Produce

– To produce a given level of output, a firm must use more of one input if it uses less of another input. Example: Metal and plastic substitute each other in the production of cars. Small increments and reductions of them affect the car’s weight, safety, and cost.

• What Prices to Charge – Consumers buy fewer units of a product when its price rises given their limited

budgets. Example: When a manager can set the price of a product, the manager must consider whether raising the price offsets the loss from selling fewer units.

• Whether to Innovate – There are short-run and long-run profits. Example: Investment in innovation

lowers short-run profit, but may raise the long-run profit.

1.1 Managerial Decision Making (4 of 5) Other Decision Makers

• Consumers purchase products subject to their limited budgets.

• Workers decide on which jobs to take and how much to work given their scarce time and limits on their abilities.

• Rivals may introduce new, superior products or cut the prices of existing products.

• Governments around the world may tax, subsidize, or regulate products.

• Rational Maximizers and Behavioral Economics – To understand how others make economic decisions, most economic

analysis assumes those “others” are maximizers: they do the best they can with their limited resources.

– However, in some contexts, behavioral economics explains those “others” cannot successfully maximize for a variety of psychological reasons.

1.1 Managerial Decision Making (5 of 5) Other Decision Makers

• Most interaction and economic decisions are done in markets. – A market is an exchange mechanism that allows buyers to trade with

sellers. – The primary participants in a market are firms who supply the product and

consumers who buy it. – But, government policies such as taxes also play an important role in the

operation of markets.

Strategy

• A strategy is a battle plan that specifies the actions or moves that the manager will make to maximize the firm’s profit when interacting with a small number of rival firms.

– One tool that is helpful in understanding and developing such strategies is game theory, which we use in several chapters.

1.2 Economic Models (1 of 7) • A model is a description of the relationship between two or more variables.

– Meteorologists use models to predict weather conditions. – Medical researchers use models to describe and predict the effect of

medications on diseases. – Astronomers use models to describe and predict the movement of

comets and meteors. – Economists use economic models to explain how managers and other

decision makers make decisions and to explain the resulting market outcomes.

• Managers use models to consider hypothetical situations—to use a what-if analysis—such as “What would happen if we raised our prices by 10%?” or “Would profit rise if we phased out one of our product lines?”

– Models help managers predict answers to what-if questions and to use those answers to make good decisions.

1.2 Economic Models (2 of 7) Simplifying Assumptions

• The real economic world is too complex to analyze fully. To understand it and be able to make valid predictions, economic models include only the essential issues, leaving aside complications that might disguise those essential elements.

– Economic models can be presented in words, using graphs or mathematics. Regardless of how the model is described, an economic model is a simplification of reality that contains only its most important features.

– Part of the skill in using economic models lies in selecting a model that is appropriate for the task at hand.

• Mini-Case: Income Threshold Model – To explain car purchasing behavior in China, we assume in this model that only

income has an important effect on the decision to buy cars. Other factors are ignored, such as the color of cars.

– If this assumption is correct, we make our analysis of the auto market simpler without losing important details. If the ignored issues are important, our predictions may be inaccurate.

1.2 Economic Models (3 of 7) Testing Theories • Blore’s Razor: When given a choice between two theories,

take the one that is funnier.

1.2 Economic Models (4 of 7) Testing Theories

• Economists test theories by checking whether the theory’s predictions are correct. – One model might argue that prices will go up next quarter. Another, using a

different theory, may contend that prices will fall. Which one is correct? – Use empirical evidence (real facts) to find out which prediction is correct.

• A good model makes clear predictions that are consistent with reality. A good model is one that is a close enough approximation to be useful.

– It is not helpful to have simple models that make incorrect predictions or complex models that make untestable predictions. The skill is to have a model simple enough to make clear predictions but realistic enough to be accurate.

– “If the price of a product rises, the quantity of the product demanded falls” provides a clear prediction.

– “Human behavior depends on tastes, and tastes change randomly at random intervals” is not very useful.

1.2 Economic Models (5 of 7) Positive and Normative Statements • A positive statement concerns what is or what will happen and describes reality.

– A testable hypothesis about matters of fact such as cause-and-effect relationships

– “If we double the amount of sugar in this soft drink, we will significantly increase sales to children.”

– Positive does not mean that we are certain about the truth of our statement; it indicates only that we can test the truth of the statement.

• A normative statement concerns what somebody believes should happen and prescribes a course of action.

– A belief about whether something is good or bad – “The government should tax soft drinks so that people will not consume so much

sugar.” – A normative statement cannot be tested because a value judgment cannot be

refuted by evidence. • Good economists and good managers emphasize positive analysis.

1.2 Economic Models (6 of 7) Positive and Normative Statements

1.2 Economic Models (7 of 7) New Theories

Why does economics continually evolve?

• Because economists always try to improve their understanding of the world around them.

– For instance, traditional theory assumes decision makers always maximize. But, the new theory of behavioral economics studies how psychological biases and cognitive limits can prevent managers and others from optimizing.

• Because economists have to develop new ways to think about disruptive innovations.

– For instance, the internet is a disruptive innovation that has transformed retailing, media, and payment methods allowing two groups of users to interact in these new online markets. The two-sided markets theory has been important to the evolution and governmental policies toward these markets.

1.3 Using Economics Skills in Your Career • This book will help you to develop skills in economic analysis that are

crucial in business decision making. – Assessing financial investment options at financial institutions. – Setting prices or planning other actions based on formal analysis,

such as spreadsheet-based economic modeling. – Using economic skills in a variety of jobs because economic

decisions come up everywhere in business.

• All readers will benefit from familiarity with the application of economics presented in this book.

– Economic skills help to predict the likely outcomes from government actions and other events.

– Economic skills are also relevant for the analysis of personal decisions, such as investment or educational choices.

This work is protected by United States copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning. Dissemination or sale of any part of this work (including on the World Wide Web) will destroy the integrity of the work and is not permitted. The work and materials from it should never be made available to students except by instructors using the accompanying text in their classes. All recipients of this work are expected to abide by these restrictions and to honor the intended pedagogical purposes and the needs of other instructors who rely on these materials.

Managerial Economics and Strategy
Scarcity at the Bottom of Managerial Problems
Learning Objectives
1.1 Managerial Decision Making (1 of 5)
1.1 Managerial Decision Making (2 of 5)
1.1 Managerial Decision Making (3 of 5)
1.1 Managerial Decision Making (4 of 5)
1.1 Managerial Decision Making (5 of 5)
1.2 Economic Models (1 of 7)
1.2 Economic Models (2 of 7)
1.2 Economic Models (3 of 7)
1.2 Economic Models (4 of 7)
1.2 Economic Models (5 of 7)
1.2 Economic Models (6 of 7)
1.2 Economic Models (7 of 7)
1.3 Using Economics Skills in Your Career
Copyright

Managerial Economics and Strategy Third Edition

Chapter 2

Supply and Demand

Managerial Problem Carbon Taxes • What will be the effect of imposing a carbon tax on the price of

gasoline

Solution Approach • Managers use the supply-and-demand model to answer this type of

questions.

Model • The supply-and-demand model provides a good description of many

markets and applies particularly well to markets in which there are many buyers and many sellers.

• In markets where this model is applicable, it allows us to make clear, testable predictions about the effects of new taxes or other shocks on prices and other market outcomes.

Learning Objectives (1 of 2) 2.1 Demand Explain how the quantity of a good or service that consumers want depends on its price and other factors

2.2 Supply Describe how the quantity of a good or service that firms want to sell depends on its price and other factors

2.3 Market Equilibrium Show how the interaction between consumers’ demand and producers’ supply determines the market price and quantity

Learning Objectives (2 of 2) 2.4 Shocks to the Equilibrium Predict how an event that affects consumers or firms changes the market price and quantity

2.5 Effects of Government Interventions Analyze the market effects of government policy using the supply-and-demand model

2.6 When to Use the Supply-and-Demand Model Discuss when to use the supply-and-demand model

2.1 Demand (1 of 7) • Consumers decide whether to buy a particular good or service.

– If they decide to buy, how much is based on its own price and on other factors.

• Own Price – Economists focus most on how a good’s own price affects the quantity

demanded. – To determine how a change in price affects the quantity demanded,

economists ask what happens to quantity when price changes and other factors are held constant.

• Other Factors – The list of other factors usually includes income, price of related goods,

tastes, information, government regulation. – Go to next slide for more detail about these other factors of demand

2.1 Demand (2 of 7) • Other factors of demand include the following:

– Income ▪ When a consumer’s income rises that consumer will often buy more of many

goods. – Price of related goods

▪ Substitute: Different brands of essentially the same good are close substitutes.

▪ Complement: is a good that is used with the good under consideration. – Information

▪ Information about characteristics and the effects of a good has an impact on consumer decisions

– Tastes ▪ Consumers do not purchase goods they dislike. Firms devote significant

resources to trying to change consumer tastes through advertising. – Government Regulations

▪ Governments may ban, restrict, tax, or subsidize goods or services

2.1 Demand (3 of 7) The Demand Curve

• A demand curve shows the quantity demanded at each possible price, holding constant the other factors that influence purchases.

• The quantity demanded is the amount of a good that consumers are willing to buy at a given price, holding constant the other factors that influence purchases.

• Graphical Presentation – In Figure 2.1, the demand curve hits the vertical axis at $12, indicating

that no quantity is demanded when the price is $12 per lb or higher. – The demand curve hits the horizontal quantity axis at 12 million lbs, the

quantity of avocados that consumers would want if the price were zero. – The quantity demanded at a price of $2 per lb is 10 million lbs per year.

Figure 2.1 A Demand Curve

2.1 Demand (4 of 7) Effects of a Price Change on the Quantity Demanded

• The Law of Demand states that consumers demand more of a good if its price is lower or less when its price is higher.

– The law of demand assumes income, the prices of other goods, tastes, and other factors that influence the amount they want to consume are constant.

– The law of demand is an empirical claim—a claim about what actually happens.

– According to the law of demand, demand curves slope downward, as in Figure 2.1.

• The demand curve is a concise summary of the answer to the question: What happens to the quantity demanded as the price changes, when all other factors are held constant?

– Changes in the quantity demanded in response to changes in price are movements along the demand curve.

2.1 Demand (5 of 7) Effects of Other Factors on Demand

• A change in any relevant factor other than the price of the good causes a shift of the demand curve rather than a movement along the demand curve.

• Example and Figure 2.2: – If average family income goes up from $35,000 to $50,000, the

global demand for coffee shifts to the right from 1 2D to D . – The price remains at $2 per pound, but the quantity demanded

increases from 10 to 11.5 million pounds per year. – Verify the same shift of demand would occur if the price of a

substitute of coffee, say tea, goes up.

Figure 2.2 A Shift of the Demand Curve

2.1 Demand (6 of 7) The Demand Function: ,( , )sQ D p p Y=

• Q of coffee demanded is a function of its price p, price of sugar ps and income Y. Other factors are constant.

• Estimated Demand Function: Q = 8.56 − p − 0.3ps + 0.1Y – This specific linear form reflects empirical evidence; p and ps are negative and Y is

positive. The constant term, 8.56, represents all other factors.

• Demand Curve: Q = 12 − p – Straight-line demand curve 1D in Figure 2.1 with ps = 0.20, Y = 35. Notice that

ΔQ =−Δp. So, if Δp = −$2, then 2( 2)Q∆ = − − = million tons per year. • The Law of Demand and Calculus

– The Law of Demand states that the derivative of the demand function with respect to price is negative, d /d 0.Q p <

– The demand function for coffee: Q = 12 − p. So, the derivative of the demand with respect to price: d /d 1.Q p = −

2.1 Demand (7 of 7) Summing Demand Curves

• The overall demand for coffee is composed of the demand of many individual consumers.

• The total quantity demanded at a given price is the sum of the quantity each consumer demands at that price.

• We can generalize this approach to look at the total demand for more than two consumers, or we can apply it to groups of consumers rather than just to individuals.

2.2 Supply (1 of 7) • Firms determine how much of a good to supply on the basis of

the price of that good and on other factors, including the costs of producing the good.

• Own Price – Usually, we expect firms to supply more quantity at a higher

price.

• Other Factors – These other factors usually include costs of production,

technological change, government regulations, and other factors.

– Go to next slide for more detail about these other supply factors.

2.2 Supply (2 of 7) • Costs of Production

– The costs of labor, machinery, fuel, and other costs affect how much of a product firms want to sell.

– As a firm’s cost falls, it is usually willing to supply more, holding price and other factors constant. Conversely, a cost increase will often reduce a firm’s willingness to produce.

• Technological Change – If a technological advance allows a firm to produce its good at lower cost, the

firm supplies more of that good at any given price, holding other factors constant.

• Government Regulations – Government rules and regulations can affect supply directly without working

through costs. – For example, in some parts of the world, retailers may not sell most goods and

services on particular days of religious significance.

2.2 Supply (3 of 7) The Supply Curve

• A supply curve shows the quantity supplied at each possible price, holding constant the other factors that influence firms’ supply decisions.

• The quantity supplied is the amount of a good that firms want to sell at a given price, holding constant other factors that influence firms’ supply decisions, such as costs and government actions.

• Graphical Presentation – In Figure 2.3, the price on the vertical axis is measured in dollars per

physical unit (dollars per lb), and the quantity on the horizontal axis is measured in physical units per time period (millions of tons per year).

– The quantity supplied at a price of $2 per lb is 10 million tons per year and 11 million tons per year when the price is $4.

Figure 2.3 A Supply Curve

2.2 Supply (4 of 7) Effects of Price on Supply

• The supply curve is usually upward sloping. There is no “Law of Supply” stating that the supply curve slopes upward.

• We observe supply curves that are vertical, horizontal, or downward sloping in particular situations. However, supply curves are commonly upward sloping.

• Along an upward-sloping supply curve, a higher price leads to more output being offered for sale, holding other factors constant.

• Changes in Quantity Supplied – An increase in the price of avocados causes a movement along the

supply curve, resulting in more coffee being supplied. – As the price increases, firms supply more. – In Figure 2.3, if the price rises from $2 per lb to $4 per lb, the quantity

supplied rises from 10 to 11 million tons per year.

2.2 Supply (5 of 7) Effects of Other Variables on Supply

• A change in a relevant variable other than the good’s own price causes the entire supply curve to shift rather than a movement along the supply curve.

• Example and Figure 2.4: – When the price of cocoa rises from $3 per lb to $6 per lb, many coffee

farmers switch to producing cocoa. As a consequence, the supply curve for coffee shifts leftward, from 1 2toS S (Figure 2.4).

– That is, firms want to supply less coffee at any given price than before the cocoa price increase. At a price of $2 per lb for coffee, the quantity supplied falls from 10 million lbs on 1S to 9.4 million tons on 2S (after the cocoa price increase).

Figure 2.4 A Shift of a Supply Curve

2.2 Supply (6 of 7) The Supply Function: ( , )cQ S p p= • Q of coffee demanded is a function of its price p and the price of cocoa pc.

Other factors are constant.

• Estimated Supply Function: Q = 9.6 + 0.5p − 0.2pc – This specific linear form reflects empirical evidence; p is positive and pc is

negative. The constant term, 9.6, represents all other factors.

• Supply Curve: Q = 9 + 0.5p – Straight-line supply curve 1S in Figure 2.3 with pc = $3. Notice that

ΔS = 0.5Δp. So, if Δp = $1, then ΔS = 0.5 million tons per year. – Thus, a $1 increase in price causes the quantity supplied to increase by

0.5 million tons per year. – This change in q induced by a change in p is a movement along the

supply curve.

2.2 Supply (7 of 7) Summing Supply Curves

• The total supply curve shows the total quantity produced by all suppliers at each possible price.

• In the coffee case, for example, the overall market quantity supplied at any given price is the sum of the quantity supplied by Brazilian, Vietnamese, Colombian, and other producers in various countries.

2.3 Market Equilibrium (1 of 4) • The D curve shows the q consumers want to buy at various p

• The S curve shows the q firms want to sell at various p – The S and D curves jointly determine the p and q at which a

good or service is bought and sold. – The market is in equilibrium when all market participants

are able to buy or sell as much as they want (no participant wants to change its behavior).

– The p at which consumers can buy as much as they want and sellers can sell as much as they want is an equilibrium price.

– The resulting q is the equilibrium quantity because the quantity demanded equals the quantity supplied.

2.3 Market Equilibrium (2 of 4) Using a Graph to Determine the Equilibrium

• In a graph, the market equilibrium is the point at which the demand and supply curves cross each other. This point gives the q and p of equilibrium.

• Graphical Presentation – Figure 2.5 shows the supply curve, S, and demand curve, D,

for coffee. – The D and S curves intersect at point e, the market

equilibrium. – The equilibrium price is $2 per lb, and the equilibrium quantity

is 10 million tons per year, which is the quantity firms want to sell and the quantity consumers want to buy.

Figure 2.5 Market Equilibrium

2.3 Market Equilibrium (3 of 4) Using Math to Determine the Equilibrium

• D and S Curves: Qd = 12 − p and Qs = 9 + 0.5p – We want to find the p at which Qd = Qs = Q, the equilibrium

quantity. In equilibrium, it must be that Qs = Qd. • In Equilibrium Qd = Qs: 12 − p = 9 + 0.5p

– We use algebra to find the equilibrium price: 3 = 1.5p, so p = $2. We can determine the equilibrium q by substituting this p into either Qd or Qs.

• Using the D Curve: Q = 12 − 2 = 10 – We find that the equilibrium quantity is 10 million tons per year.

We can obtain the same result if we use the S curve.

2.3 Market Equilibrium (4 of 4) Forces That Drive the Market to Equilibrium

Excess Demand • Figure 2.5 shows the supply curve, S, and demand curve, D, for coffee. • If the price of coffee were $1, firms are willing to supply 9.5 million tons per year but

consumers demand 11 million tons. The market is in disequilibrium, and there is excess demand…but not for long.

• Frustrated consumers may offer to pay suppliers more than $1 per lb and some suppliers might raise their prices. Such actions cause the market price to rise until it reaches the equilibrium price, $2 (excess D eliminated).

Excess Supply • If instead the price were $3, firms are willing to supply 10.5 million tons per year but

consumers demand 9 million tons. The market is in disequilibrium again, and there is excess supply…but not for long.

• To avoid unsold coffee to stale, firms lower the price to attract additional customers. The price falls until it reaches the equilibrium level, $2 (excess S eliminated and no more pressure to lower the price further).

2.4 Shocks to the Equilibrium (1 of 5) • The D curve shows the q consumers want to buy at

various p

• The S curve shows the q firms want to sell at various p – The equilibrium changes only if a shock occurs that

shifts the D curve or the S curve. – These curves shift if one of the variables we were

holding constant changes. – If tastes, income, government policies, or costs of

production change, the D curve or the S curve or both may shift, and the equilibrium changes.

2.4 Shocks to the Equilibrium (2 of 5) Effects of a Shift in the Demand Curve

• Suppose that the average annual income in developed countries increases by $15,000 from $35,000 to $50,000, so consumers can buy more coffee at any given price. As a result, the demand curve for coffee shifts to the right from 1 2to D D in Figure 2.6, panel (a).

– At the original equilibrium, e1, price is $2, and there is excess demand of 1.5 million lbs per month. Market pressures drive the price up until it reaches $3 at the new equilibrium, e2.

– Here the increase in income causes a shift of the demand curve, which in turn causes a movement along the supply curve from e1 to e2.

Figure 2.6 Equilibrium Effects of a Shift of a Demand or Supply Curve

2.4 Shocks to the Equilibrium (3 of 5) Effects of a Shift in the Supply Curve

2.4 Shocks to the Equilibrium (4 of 5) Effects of a Shift in the Supply Curve

• Assuming that income remains at its $35,000 original level, an increase in the price of cocoa from $3 to $6 per lb causes some coffee producers to switch to cocoa production. So there are fewer suppliers and less coffee at every price. The supply curve for coffee shifts to the left from 1 2to ,S S in Figure 2.6, panel (b).

– At the original equilibrium, e1, price is $2 per lb, and there is excess demand of 0.6 million tons per year. Market pressures drive the price up until it reaches $2.40 at the new equilibrium, e2.

– Here, a shift of the supply curve results in a movement along the demand curve.

2.4 Shocks to the Equilibrium (5 of 5) Managerial Implication: Taking Advantage of Future Shocks

• Managers can use the supply-and-demand model to anticipate how shocks to supply or demand will affect future business conditions and can take advantage of that knowledge.

– Mars is one of the world’s largest chocolate producers and managers use a supply-and-demand model to make cocoa buying decisions.

– If they expect prices to increase substantially, they immediately buy a great deal of cocoa at relatively low prices directly from suppliers in Africa.

– Alternatively, if they expect prices to fall, they may hold off buying now and then buy later at a lower price from organized markets such as ICE Futures U.S. or the London International Financial Futures and Options Exchange.

2.5 Effects of Government Interventions (1 of 6) Policies that Shift Curves

• Limits on Who can Buy – For example, governments usually forbid selling alcohol to young

people. This decreases the quantity demanded at each price and thereby shifts the demand curve to the left.

• Restriction of Imports – The effect of this governmental restriction is to decrease the

quantity supplied of imported goods at each price and shifts the importing country’s supply curve to the left.

• Start buying a good – The effect of governments starting to buy goods is to increase the

quantity demanded at each price for the good and shifts the demand curve to the right.

2.5 Effects of Government Interventions (2 of 6) Price Controls—Price Ceilings

• When the government sets a price ceiling at p and the unregulated equilibrium price is above it, the price that is actually observed in the market is the price ceiling.

– Price ceilings have no effect if they are set above the equilibrium price that would be observed in the absence of the price controls.

– In Figure 2.7, the new equilibrium gasoline price would be p2 but a price ceiling of p1 is imposed, then the ceiling price of p1 is charged.

• With a binding price ceiling, the supply-and-demand model predicts an equilibrium with a shortage: a persistent excess demand.

– The new equilibrium with a shortage in Figure 2.7 occurs with a quantity Qs and price p1 (the excess demand is Qs−Q1). If the price ceiling were removed, the new equilibrium would be e2.

– Deacon and Sonstelie (1989) found that for every dollar consumers saved during the 1980 gasoline price controls, they lost $1.16 in waiting time and other factors.

Figure 2.7 A Price Ceiling on Gasoline

2.5 Effects of Government Interventions (3 of 6) Price Controls—Price Floors

• When the government sets a price floor below the unregulated equilibrium price, the price that is actually observed in the market is the price floor.

– A minimum wage law forbids employers from paying less than the minimum wage, w.

• With a binding price floor, the supply-and-demand model predicts an equilibrium with a persistent excess supply.

– The minimum wage prevents market forces from eliminating this excess supply, so it leads to an equilibrium with unemployment.

– The new equilibrium with unemployment in Figure 2.8 occurs with a quantity Ld and wage w (the excess supply is Ls−Ld). If the price ceiling were removed, the new equilibrium would be e2

Figure 2.8 The Minimum Wage: A Price Floor

2.5 Effects of Government Interventions (4 of 6) Why Supply Need Not Equal Demand

• The theory says that the price and quantity in a market are determined by the intersection of the supply curve and the demand curve and the market clears if the government does not intervene.

• However, the theory also tells us that government intervention can prevent market clearing.

– The price ceiling and price floor examples show that the quantity supplied does not necessarily equal the quantity demanded in a supply-and-demand model.

– The quantity that sellers want to sell and the quantity that buyers want to buy at a given price need not equal the actual quantity that is bought and sold.

2.5 Effects of Government Interventions (5 of 6) Sales Taxes Equilibrium Effects of a Specific Tax

• The specific sales tax causes the equilibrium price consumers pay to rise, the equilibrium quantity that firms receive to fall, and the equilibrium quantity to fall (p2, Q2, and T in Figure 2.9)

• Although the consumers and producers are worse off because of the tax, the government acquires new tax revenue, $27.84 billion in Figure 2.9.

Pass-Through

• Common Confusion: Businesses pass-through any sales tax to consumers, so that the price that consumers pay increases by the amount of the tax.

• This belief is not accurate in general. Full pass-through can occur, but partial pass- through is more common.

– In Figure 2.9, after a $2.40 specific tax is imposed on firms, the price consumers pay rises from $7.20 to $8.00. So, firms pass-through $0.80 to consumers and absorb $1.60 of the tax.

• The degree of the pass-through depends on the S and D shapes.

Figure 2.9 Effect of a $2.40 Specific Tax on Corn Collected from Producers

2.5 Effects of Government Interventions (6 of 6) Managerial Implication: Cost Pass-Through

• Managers should use pass-through analysis to predict the effect on their price and quantity from not just a new tax but from any per unit increase in costs.

– Suppose that the cost of producing corn rises $2.40 per bushel because of an increase in the cost of labor or other factors of production, rather than because of a tax.

– Then, the same analysis as in Figure 2.9 would apply, so a manager would know that only 80¢ of this cost increase could be passed through to consumers.

2.6 When to Use the Supply-And- Demand Model (1 of 2) • The Supply-and-Demand (S-D) model can help us to understand and predict real-world

events in many markets. Like a map, it need not be perfect to be useful.

• The model is useful if the market to be analyzed is “competitive enough.”

• It is reliable in markets, such as those for agriculture, financial products, labor, construction, many services, real estate, wholesale trade, and retail trade.

• The S-D model is accurate for perfectly competitive markets. – It is precisely accurate in perfectly competitive markets, which are markets in

which all firms and consumers are price takers (no market participant can affect the market price).

– See next slide for characteristics of perfectly competitive markets.

• The S-D model is not accurate for noncompetitive markets.

• In markets with firms that are price setters, the market price is usually higher than that predicted by the S-D model.

– Monopoly and oligopoly markets have few sellers that are price setters. These markets need a different model.

2.6 When to Use the Supply-And- Demand Model (2 of 2) • Five characteristics of a perfect competitive market:

– Many buyers and sellers, all relatively small with respect to the size of the market.

– Consumers believe all firms produce identical products, so they only care about price.

– All market participants have full information about price and product characteristics, so no participant can take advantage of each other.

– Transaction costs (expenses over and above the price) are negligible.

– Firms can easily enter and exit the market over time, so competition is very high.

Managerial Solution Carbon Taxes

• What will be the effect of imposing a carbon tax on the price of gasoline?

Solution

• The degree to which a tax is passed through to consumers depends on the shapes of the demand-and-supply curves. Typically, short-run supply and demand curves differ from the long-run curves.

• In the long-run, the supply curve is upward sloping, as in our typical figure. However, the U.S. short-run supply curve of gasoline is very close to vertical.

• From empirical studies, we know that the U.S. federal gasoline specific tax of t = 18.4¢ per gallon is shared roughly equally between gasoline companies and consumers in the long run. However, based on what we learned, we expect that most of the tax will fall on firms that sell gasoline in the short run.

• Manufacturing and other firms that ship goods are consumers of gasoline. They can expect to absorb relatively little of a carbon tax when it is first imposed, but half of the tax in the long run.

Managerial Economics and Strategy
Managerial Problem
Learning Objectives (1 of 2)
Learning Objectives (2 of 2)
2.1 Demand (1 of 7)
2.1 Demand (2 of 7)
2.1 Demand (3 of 7)
Figure 2.1 A Demand Curve
2.1 Demand (4 of 7)
2.1 Demand (5 of 7)
Figure 2.2 A Shift of the Demand Curve
2.1 Demand (6 of 7)
2.1 Demand (7 of 7)
2.2 Supply (1 of 7)
2.2 Supply (2 of 7)
2.2 Supply (3 of 7)
Figure 2.3 A Supply Curve
2.2 Supply (4 of 7)
2.2 Supply (5 of 7)
Figure 2.4 A Shift of a Supply Curve
2.2 Supply (6 of 7)
2.2 Supply (7 of 7)
2.3 Market Equilibrium (1 of 4)
2.3 Market Equilibrium (2 of 4)
Figure 2.5 Market Equilibrium
2.3 Market Equilibrium (3 of 4)
2.3 Market Equilibrium (4 of 4)
2.4 Shocks to the Equilibrium (1 of 5)
2.4 Shocks to the Equilibrium (2 of 5)
Figure 2.6 Equilibrium Effects of a Shift of a Demand or Supply Curve
2.4 Shocks to the Equilibrium (3 of 5)
2.4 Shocks to the Equilibrium (4 of 5)
2.4 Shocks to the Equilibrium (5 of 5)
2.5 Effects of Government Interventions (1 of 6)
2.5 Effects of Government Interventions (2 of 6)
Figure 2.7 A Price Ceiling on Gasoline
2.5 Effects of Government Interventions (3 of 6)
Figure 2.8 The Minimum Wage: A Price Floor
2.5 Effects of Government Interventions (4 of 6)
2.5 Effects of Government Interventions (5 of 6)
Figure 2.9 Effect of a $2.40 Specific Tax on Corn Collected from Producers
2.5 Effects of Government Interventions (6 of 6)
2.6 When to Use the Supply-And-Demand Model (1 of 2)
2.6 When to Use the Supply-And-Demand Model (2 of 2)
Managerial Solution

Chapter 3

Empirical Methods for Demand Analysis

Managerial Problem Estimating the Effect of an iTunes Price Change • How can managers use the data to estimate the demand curve facing iTunes? How can

managers determine if a price increase is likely to raise revenue, even though the quantity demanded will fall?

Solution Approach • Managers can use empirical methods to analyze economic relationships that affect a

firm’s demand.

Empirical Methods • Elasticity measures the responsiveness of one variable, such as quantity demanded, to

a change in another variable, such as price. • Regression analysis is a method to estimate a relationship between a dependent

variable (quantity demanded) and explanatory variables (price and income). It requires identifying the properties and statistical significance of estimated coefficients, as well as model identification.

• Forecasting is the use of regression analysis to predict future values of important variables as sales or revenue.

Learning Objectives (1 of 2) 3.1 Elasticity Calculate elasticities and apply them to managerial problems

Regression Analysis 3.2 Use regression analysis to estimate business relationships

3.3 Properties and Statistical Significance of Estimated Coefficients Determine the confidence we can place in a regression analysis

Learning Objectives (2 of 2) 3.4 Regression Specification Explain how to choose an appropriate regression specification

3.5 Forecasting Forecast important business variables using regression analysis

3.1 Elasticity (1 of 9) The Price Elasticity of Demand

• The price elasticity of demand (or elasticity of demand or demand elasticity) is the percentage change in quantity demanded, Q, divided by the percentage change in price, p.

percentage change in quantity demanded / . (3.1)

/percentage change in price

Q Q p p

∆ ε= =

∆

Arc Elasticity: / . /

Q Q p p

∆ ε=

∆

• It is an elasticity that uses the average quantity, ,Q and average price, p

as the denominators for percentage calculations.

• In the formula ( / )Q Q∆ is the percentage change in quantity demanded and ( / )p p∆

is the percentage change in price.

• Arc elasticity is based on a discrete change between two distinct price-quantity combinations on a demand curve.

3.1 Elasticity (2 of 9) Managerial Implication:

Changing Prices to Calculate an Arc Elasticity

• One of the easiest and most straightforward ways for a manager to determine the elasticity of demand for a firm’s product is to conduct an experiment.

– If the firm is a price setter and can vary the price of its product, the manager can change the price and observe how the quantity sold varies.

– Armed with two observations—the quantity sold at the original price and the quantity sold at the new price—the manager can calculate an arc elasticity.

– Depending on the size of the calculated elasticity, the manager may continue to sell at the new price or revert to the original price.

– It is often possible to obtain useful information from an experiment in a few markets or even just one small submarket—in one country, in one city, or even in one supermarket.

3.1 Elasticity (3 of 9) Point Elasticity: ( ) ( )/ /Q p p Qε= ∆ ∆

• Point elasticity measures the effect of a small change in price on the quantity demanded.

• In the formula, we are evaluating the elasticity at the point (Q, p) and /Q p∆ ∆ is the ratio of the change in quantity to the change in price.

• Point elasticity is useful when the entire demand information is available.

Point Elasticity with Calculus: ( )( )/ /Q p p Q∂ ∂ε= • To use calculus, the change in price becomes very small.

• 0,p∆ → the ratio /Q p∆ ∆ converges to the derivative / .Q p∂ ∂

3.1 Elasticity (4 of 9) Elasticity Along the Demand Curve

• The elasticity of demand is different at every point along a downward-sloping linear demand curve.

• However, horizontal and vertical demand curves, which are extreme cases of a linear demand curve, have the same elasticity at every point.

3.1 Elasticity (5 of 9) Downward-Sloping Linear Demand Curves

• If the shape of the linear demand curve is downward sloping, elasticity varies along the demand curve.

– The elasticity of demand is a more negative number the higher the price and hence the smaller the quantity.

• In Figure 3.1, the higher the price, the more elastic the demand curve. A 1% increase in price causes a larger percentage fall in quantity near the top of the demand curve than near the bottom.

– The coffee demand curve is perfectly elastic ( )ε < −∞ where the demand curve hits the vertical axis at $12 per lb.

– It is elastic (ε < −1) for high prices below $12 and above $6 per lb. – It has unitary elasticity (ε = −1) at the midpoint. – It is inelastic (ε = 0) for low prices below $6 and above $0 per lb. – It is perfectly inelastic (ε = 0) where the demand curve hits the horizontal axis at $0

per lb.

Figure 3.1 The Elasticity of Demand Varies Along the Linear Coffee Demand Curve

3.1 Elasticity (6 of 9) Horizontal Demand Curves: ε = −∞ at every point

• If the price increases even slightly, demand falls to zero. In Figure 3.2, panel a, the demand is horizontal at p*.

• The demand curve is perfectly elastic: a small increase in price causes an infinite drop in quantity.

• Why would a good’s demand curve be horizontal? One reason is that consumers view this good as identical to another good and do not care which one they buy.

Vertical Demand Curves: ε = 0 at every point

• If the price goes up, the quantity demanded is unchanged, so ∆Q=0. In Figure 3.2, panel b, the demand is vertical at Q*.

• The demand curve is perfectly inelastic.

• A demand curve is vertical for essential goods—goods that people feel they must have and will pay anything to get it.

Figure 3.2 Vertical and Horizontal Demand Curves

3.1 Elasticity (7 of 9) Other Types of Demand Elasticities

• Income Elasticity of Demand, ( / ) ( / )Q Y Y Q∆ ∆

– Income elasticity is the percentage change in the quantity demanded divided by the percentage change in income Y.

– Normal goods have positive income elasticity, such as coffee.

– Inferior goods have negative income elasticity, such as instant soup.

• Cross-Price Elasticity of Demand, o o( / ) ( / )Q p p Q∆ ∆

– Cross-price elasticity is the percentage change in the quantity demanded divided by the percentage change in the price of another good, po

– Complement goods have negative cross-price elasticity, such as cream and coffee.

– Substitute goods have positive cross-price elasticity, such as cotton and wool.

3.1 Elasticity (8 of 9) Demand Elasticities over Time

• The shape of a demand curve depends on the time period under consideration. – It is easy to substitute between products in the long run but not in the short run. – Liddle (2012) estimated gasoline demand elasticities across many countries and

found that the short-run elasticity was −0.16, and the long-run elasticity was −0.43.

Other Elasticities

• The relationship between any two related variables can be summarized by an elasticity. A manager might be interested in:

– The price elasticity of supply—percentage increase in quantity supplied arising from a 1% increase in price.

– Or, the elasticity of cost with respect to output—percentage increase in cost arising from a 1% increase in output.

– Or, during labor negotiations, the elasticity of output with respect to labor—the percentage increase in output arising from a 1% increase in labor input, holding other inputs constant.

3.1 Elasticity (9 of 9) Estimating Demand Elasticities

• Managers use price, income, and cross-price elasticities to set prices.

• To calculate an arc elasticity, managers use data from before and after the price change.

– By comparing quantities just before and just after a price change, managers can be reasonably sure that other variables, such as income, have not changed appreciably.

• A manager might want an estimate of the demand elasticity before actually making a price change to avoid a potentially expensive mistake.

– A manager may fear a reaction by a rival firm in response to a pricing experiment, so they would like to have demand elasticity in advance.

– A manager would like to know the effect on demand of many possible price changes rather than focusing on just one price change.

• However, managers might need an estimate of the entire demand curve to have demand elasticities before making any real price change. The tool needed is regression analysis.

3.2 Regression Analysis (1 of 6) • A regression analysis is a statistical technique used to estimate the relationship

between a dependent variable and explanatory variables. A Demand Function Example • Demand Function: Q = a + bp + e

– Quantity is a function of price; Q to the left is the dependent variable; p to the right is the explanatory variable; e is the random error (unpredictable and unobservable effects on dependent variable).

– It is a linear demand and the estimated sign of b must be negative – If a manager surveys customers about how many units they will buy at various

prices, he is using data to estimate the demand function. • Inverse Demand Function: p = g + hQ + e

– Price is a function of quantity; p to the left is the dependent variable; Q to the right is the explanatory variable; e is the random error.

– Based on the previous demand function, so / 0 and 1 / 0g a b h b= − > = < and has a specific linear form. The sign of h must be negative

– If a manager surveys how much customers were willing to pay for various units of a product or service, he would estimate the inverse demand equation.

3.2 Regression Analysis (2 of 6) Regression Analysis Using Microsoft Excel • Portland Fish Inverse Demand Function: p = g + hQ + e

– g and h are true coefficients. • Inverse Demand Function Estimation: ˆˆ ˆp g hQ= +

– The OLS regression provides estimates of these coefficients, ˆˆ and ,g h which we can use to predict the expected price, ˆ,p for a given quantity. It is assumed e=0.

– Use Microsoft Excel Trendline option for scatterplots to estimate ˆˆ andg h, using OLS and get the respective graph and function (steps next).

– The estimated inverse demand curve is ˆ 1.96 0.15p Q= − – The estimated change in price needed to induce buyers to purchase one more

unit (1,000 libras) is ˆ $0.1 ¢5 15 .h =− =− Ordinary Least Squares (OLS) Regression • OLS is the most common regression method. It fits the line to minimize the sum of the

squared residuals, as shown in Figure 3.4

Figure 3.4 An Estimated Demand Curve for Cod at the Portland Fish Exchange

3.2 Regression Analysis (3 of 6) Microsoft Excel Trendline Option for Scatterplots

• Steps Inverse Demand Function Estimation: ˆˆ ˆp g hQ= +

1. Enter the quantity data in column A and the price data in column B 2. Select the data, click on the Insert tab, and select the “Insert Scatter (X, Y) or

Bubble Chart” option in the Chart area of the toolbar. A menu of scatterplot types will appear (Excel Screenshots, panel a)

3. Click “Scatter.” A chart appears in the spreadsheet. 4. Click on the plus sign to obtain the Chart Elements menu. 5. Place the cursor over the Trendline option and click on the arrow beside it to show

an additional menu. Click on More Options. A Format Trendline dialog box opens to the right.

6. Select the options “Linear,” “Display Equation on chart,” and “Display R-squared value on chart” (Excel Screenshots, panel b).

7. The estimated regression line appears in the diagram. By default, Excel refers to the variable on the vertical axis as y (which is our p) and the variable on the horizontal axis as x (which is our Q).

Microsoft Excel Screenshots (Windows Version 2016) (1 of 2) a) Scatter Options b) Trendline Estimation

3.2 Regression Analysis (4 of 6) Multivariate Regression: p = g + hQ + i3Y + e

• Multivariate Regression is a regression with two or more explanatory variables.

• The inverse demand function above incorporates both quantity Q and income Y as explanatory variables.

• g, h, and i are coefficients to be estimated, and e is a random error.

Corresponding Estimated Regression: ˆ ˆˆ ˆ=g+hQ+ Ip Y

• ˆĝ,h,andî are the estimated coefficients and p̂ is the predicted value of p for any given levels of Q and Y.

• The objective of an OLS multivariate regression is to fit the data so that the sum of squared residuals is as small as possible.

• A multivariate regression is able to isolate the effects of each explanatory variable holding the other explanatory variables constant.

3.2 Regression Analysis (5 of 6) Goodness of Fit and the 2R Statistic • The 2R (R-squared) statistic is a measure of the goodness of fit of the regression

line to the data.

– The 2R statistic is the share of the dependent variable’s variation that is explained by the regression.

– The 2R statistic must lie between 0 and 1. – 1 indicates that 100% of the variation in the dependent variable is explained by

the regression. • Figure 3.5 shows two apple pie demand regressions for two different cities.

– Data points in panel a are close to the linear estimated demand, while they are more widely scattered in panel b.

– 2 0.98R = in panel a and 2 0.54R = in panel b.

– Mai, the bakery owner, is more confident that she can predict the effect of a price change in the first town (panel a) than in the second (panel b).

Figure 3.5 Two Estimated Apple Pie Demand Curves with Different R-squared Statistics2R

3.2 Regression Analysis (6 of 6) Managerial Implication: Focus Groups

• Managers interested in estimating market demand curves often can obtain data from published sources, as in our Portland Fish Exchange example.

• However, if managers want to estimate the demand function for their own individual firm, they must collect information about how many units customers would demand at various prices.

– They can hire a specialized marketing firm to recruit and question a focus group (a number of actual or potential consumers).

– Alternatively, the marketing firm might conduct an online or written survey of potential customers designed to elicit similar information.

– Managers should use a focus group if it’s the least costly method of learning about the demand curve they face.

3.3 Properties and Statistical Significance of Estimated Coefficients • There are key questions when estimating coefficients:

– How close are the estimated coefficients of the demand equation to

the true values, for instance â respect to the true value a?

– How are the estimates based on a sample reflecting the true values of the entire population?

– Are the sample estimates on target? Repeated Samples

• The intuition underlying statistical measures of confidence and significance is based on repeated samples.

– We trust the regression results if the estimated coefficients were the same or very close for regressions performed with repeated samples.

– However, it is costly, difficult, or impossible to gather repeated samples to assess the reliability of regression estimates.

– So, we focus on the properties of both estimating methods and estimated coefficients.

3.3 Properties and Significance of Coefficients (1 of 5) Desirable Properties for Estimated Coefficients

• Ordinary Least Squares estimation method (OLS) is an unbiased estimation method under mild conditions.

– It produces an estimated coefficient, ,â that equals the true coefficient, a, on average.

• OLS is a consistent estimation method. – OLS produces consistent estimates that vary less than other relevant unbiased

estimation methods under a wide range of conditions.

• The smaller the standard error of an estimated coefficient, the smaller the expected variation in the estimates obtained from different samples.

– Each estimated coefficient has a standard error. – We use the standard error to evaluate the significance of estimated coefficients. – We prefer a small standard error.

3.3 Properties and Significance of Coefficients (2 of 5) A Focus Group Example • Estimate a linear D curve for Toyota Camry cars, Q = a + bp + e

– A focus group of 50 potential buyers are asked about their willingness to buy Camry’s at prices from $5,000 to $40,000.

– We use OLS using Microsoft Excel’s Regression tool in the Data tab (steps in next slide).

• The estimated D curve is = 53.857 − 1.438p – The estimate of b is −1.438, and its estimated standard error is 0.090. – The estimate for a is 53.857, and its estimated standard error is 2.260.

– 2R is 0.977. This high 2R indicates that the regression line explains almost

all the variation in the observed quantity.

• Use the estimated D to estimate the q for any p – If the price is 27 ($27, 000), we expect the focus group consumers to buy 15 cars

[53.857 (1.438 * 27) 15.031].− =

– If this focus group represented a large group, perhaps a thousand times larger, the quantity demanded estimate would be 15,031 cars.

3.3 Properties and Significance of Coefficients (3 of 5) Using Microsoft Excel’s Regression Tool

• Steps Linear Demand Function Estimation: Q = a + bp + e

1. Verify you have Data Analysis under Data. If not, install Analysis ToolPak: go to Files Options Add-ins=> => (Screenshots, panel a)

2. Enter your data, click on the Data tab, then on the Data Analysis icon. The Data Analysis dialog box displays. Select “Regression” and click OK (Screenshots, panel b)

3. In the Regression dialog, fill in the Input Y Range field (dependent variable), the Input X Range field (explanatory variable) and enter A22 in the box for the Output Range button (Screenshots, panel c). Then, click OK.

4. Excel displays results starting in cell A22 with a Summary Output. The regression is Q = 53.857 − 1.438p. If the price is 27, we expect consumers to buy Q = 53.857 − 1.438p = 15.03 Camrys.

5. The 2 0.977,R = very high. Excel also displays standard errors and confidence

intervals.

Microsoft Excel Screenshots (Windows Version 2016) (2 of 2) a) Analysis ToolPak

b) Data Analysis Box

c) Regression Box

3.3 Properties and Significance of Coefficients (4 of 5) Confidence Intervals

• A confidence interval provides a range of likely values for the true value of a coefficient, centered on the estimated coefficient.

– A 95% confidence interval is a range of coefficient values such that there is a 95% probability that the true value of the coefficient lies in the specified interval.

• Simple Rule for Confidence Intervals – In regressions with large sample sizes, the 95% confidence interval is

approximately the estimated coefficient minus/plus twice its estimated standard error.

– With smaller sample sizes, the confidence interval is larger and its calculation needs a t-statistic distribution table.

• If the confidence interval is small, then we are reasonably sure that the true parameter lies close to the estimated coefficient. Having a larger data set tends to increase our confidence in our results.

3.3 Properties and Significance of Coefficients (5 of 5) Hypothesis Testing and Statistical Significance • Null Hypothesis Problem

– Suppose a firm’s manager runs a regression where the demand for the firm’s product is a function of the product’s price and the prices charged by several possible rivals.

– If the true coefficient on a rival’s price is 0, the manager can ignore that firm when making decisions.

– Thus, the manager wants to formally test the null hypothesis that the rival’s coefficient is 0.

• Testing Approach Using the t-statistic – The t-statistic equals the estimated coefficient divided by its estimated standard

error. That is, the t-statistic measures whether the estimated coefficient is large relative to the standard error.

– In a large sample, if the t-statistic > 2, we reject the null hypothesis that the proposed explanatory variable has no effect at the 5% significance level or 95% confidence level.

– Most analysts would just say the explanatory variable is statistically significant.

3.4 Regression Specification (1 of 6) • Regression specification is the first step in a regression analysis.

– It includes the choice of the dependent variable, the explanatory variables, and the functional relationship between them (linear, quadratic, or exponential).

Selecting Explanatory Variables

• A regression analysis is valid only if the regression equation is correctly specified. – It should include all the observable variables that are likely to have a meaningful

effect on the dependent variable. – It must closely approximate the true functional form. – The underlying assumptions about the error term should be correct. – We use our understanding of causal relationships, including those that derive from

economic theory, to select explanatory variables.

• See an application in the next slide.

3.4 Regression Specification (2 of 6) Selecting Variables, Mini Case: Determinants of CEO Compensation

• Y = a + bA + cL + dS + fX + e – The dependent variable, Y, is CEO compensation in 000 of dollars. – Explanatory variables are assets A, number of workers L, average return on stocks

S, and CEO’s experience X.

• OLS regression is ˆ 6, 787 11.4 14.0 35.1 79.9Y A L S X= + + + + – t-statistics for the coefficients of A, L, S, and X are 10.1, 8.77, 5.25, and 3.40,

respectively. – Based on these t-statistics, all four variables are “statistically significant.”

• Although these variables are statistically significantly different than zero, not all of them are economically significant.

– For instance, S is statistically significant but its effect on CEO’s compensation is very small: one percentage point increase of shareholder return would add $35,000 per year to the CEO’s wage.

– So, S is statistically significant but economically not very important.

3.4 Regression Specification (3 of 6) Correlation and Causation is a Common Confusion

3.4 Regression Specification (4 of 6) Correlation and Causation—Common Confusion • Two variables are correlated if they move together. However, correlation does not

necessarily imply causation. – The q demanded and p are negatively correlated: p goes up, q goes down. This

correlation is causal, changes in p directly affect q. – Sales of gasoline and the incidence of sunburn are positively correlated, but one

doesn’t cause the other. – Thus, it is critical that we do not include explanatory variables that have only a

spurious relationship to the dependent variable in a regression equation. In estimating gasoline demand, we would include price, income, sunshine hours, but never sunburn incidence.

Omitted Variables • These are variables not included in the regression specification because of lack of

information. So, there is not too much a manager can do. • However, if one or more key explanatory variables are missing, then the resulting

coefficient estimates and hypothesis tests may be unreliable.

• A low 2R may signal the presence of omitted variables, but theory and logic must determine what key variables are missing.

3.4 Regression Specification (5 of 6) Functional Form

• We cannot assume that demand curves or other economic relationships are always linear.

– Choosing the correct functional form may be difficult. – One useful step, especially if there is only one explanatory variable, is to plot the

data and the estimated regression line for each functional form under consideration.

• Graphical Presentation in Figure 3.6 – Panel a shows a linear regression line of the form Q = a + bA + e

– Panel b shows a quadratic regression curve of the form 2Q a bA cA e= + + +

– Linear form: 2 0.85.R = Quadratic form: 2 0.99R = – The quadratic regression in panel b fits better than the linear regression in panel a.

Figure 3.6 The Effect of Advertising on Demand

3.4 Regression Specification (6 of 6) Managerial Implication: Experiments

• Many firms use controlled experiments. – For example, a firm can vary its price and observe how consumers react.

Unfortunately, the firm cannot control other variables that affect consumer reactions.

• So, firms often use regressions to hold constant some variables that they could not control explicitly and to analyze their results.

– Harrah’s Entertainment relies its marketing on randomized tests of various hypotheses (compares answers from test and control groups).

– Google shows on its website how a firm can run randomized experiments on the effectiveness of advertising while controlling for geographic or other differences.

• Managers can benefit from running experiments, particularly if they can make use of low-cost internet experiments, as Amazon, Facebook, Netflix, Google, and others do.

3.5 Forecasting (1 of 4) Extrapolation

• Extrapolation seeks to forecast a variable as a function of time.

• Extrapolation starts with a series of observations called time series.

• The time series is smoothed in some way to reveal the underlying pattern, and this pattern is then extrapolated into the future.

• Two linear smoothing techniques are trend line and seasonal variation.

• Not all time trends are linear.

Trends

• Trend line: R = a + bt + e, where t is time – If this is the trend for Nike’s Revenue, a and b are the coefficients to be estimated.

• The estimated trend line is R = 4.189 + 0.134t, with statistically significant coefficients.

– Nike could forecast its sales in the summer quarter of 2020, which is quarter 47, as 4.189 (0.134 47) $10.502 billion+ × = (Figure 3.7).

Figure 3.7 Nike’s Quarterly Revenue: 2009–2018

3.5 Forecasting (2 of 4) Seasonal Variation

• It appears the demand for Nike products varies seasonally. – In Figure 3.7, there is a pattern around the trend line: revenue in every summer

quarter and most spring quarters is above the trend line while revenue in every fall quarter and most winter quarters is below the trend.

• Seasonal variation model: R = a + bt + c1W + c2S + c3M + e – Nike’s revenue data shows a quarterly trend that is captured with seasonal

dummy variables, W, S, and M.

• The new estimated trend is R = 3.847 + 0.135t + 0.179W + 0.452S + 0.675M, with all coefficients statistically significant.

– The forecast value for the summer quarter of 2020 is 3.847 (0.135 47) (0.179 0) (0.452 0) 0.675 1 $1( 0.84billion.)+ × + × + × + × =

• This adjusted forecast is $340 million more than our previous forecast that ignored seasonal effects.

3.5 Forecasting (3 of 4) Nonlinear Trends

• Not all time trends are linear. In particular, the revenue growth of new products is often nonlinear.

– After a new product first reaches the market, its market share often grows slowly, as consumers need some time to become familiar with the product.

– At some point, a successful product takes off and sales grow very rapidly.

– Then, when the product eventually approaches market saturation, sales grow slowly in line with underlying population or real income growth.

– Ultimately, if other products displace this product, its sales will fall sharply.

3.5 Forecasting (4 of 4) Theory-Based Econometric Forecasting

• We estimated Nike’s revenue with time trend and dummy seasonal variables. However, revenue is determined in large part by the consumers’ demand curve, and the demand is affected by variables such as income, population, and advertising. Extrapolation (pure time series analysis) ignored these structural (causal) variables.

• Theory-based econometric forecasting methods incorporate both extrapolation and estimation of causal or explanatory economic relationships.

• We use these estimates to make conditional forecasts, where our forecast is based on specified values for the explanatory variables.

Managerial Solution Estimating the Effect of an iTunes Price Change

• How could Apple use a focus group to estimate the demand curve for iTunes to determine if raising its price would raise or lower its revenue?

Solution • To generate data, authors asked a focus group of 20 Canadian college students how

many songs they would downloaded from iTunes at various prices, assuming income and other prices constant.

– The estimated linear demand curve is Q = 1024 − 413p. – The t-statistic = −12.6, so this coefficient for price is significantly different from

zero. The 2 0.96,R = so the regression line fits the data closely.

– Apple’s manager could use such an estimated demand curve to determine how

revenue, ( ,)R p Q= × varies with price. At p = 99¢, 615 songs were downloaded,

so R1 = $609. When p = $1.24, the number of songs drop to 512, R2 = $635. Revenue increased by $26.

– If the general population has similar tastes to the focus group, then Apple’s revenue would increase if it raised its price to $1.24 per song.

Table 3.1 Data Used to Estimate the Cod Demand Curve at the Portland Fish Exchange

Price, dollars per pound Quantity, thousand pounds per day 1.90 1.5 1.35 2.2 1.25 4.4 1.20 5.9 0.95 6.5 0.85 7.0 0.73 8.8 0.25 10.1

Figure 3.3 Observed Price-Quantity Data Points for the Portland Fish Exchange

Table 3.2 Regressions of Quantity on Advertising Linear Specification

Blank Coefficient Standard Error t-Statistic

Constant 5.43 0.54 10.05*

Adverting, A 0.53 0.06 8.47*

Advertising, a squared Blank Blank Blank2A

Quadratic Specification

Coefficient Standard Error t-Statistic

3.95 0.30 13.18*

1.20 0.10 12.18*

−0.04 0.01 −7.05*

*indicates that we can reject the null hypothesis that the coefficient is zero at the 5% significance level.

Figure 3.8 iTunes Focus Group Demand and Revenue Curves

Managerial Economics and Strategy
Managerial Problem
Learning Objectives (1 of 2)
Learning Objectives (2 of 2)
3.1 Elasticity (1 of 9)
3.1 Elasticity (2 of 9)
3.1 Elasticity (3 of 9)
3.1 Elasticity (4 of 9)
3.1 Elasticity (5 of 9)
Figure 3.1 The Elasticity of Demand Varies Along the Linear Coffee Demand Curve
3.1 Elasticity (6 of 9)
Figure 3.2 Vertical and Horizontal Demand Curves
3.1 Elasticity (7 of 9)
3.1 Elasticity (8 of 9)
3.1 Elasticity (9 of 9)
3.2 Regression Analysis (1 of 6)
3.2 Regression Analysis (2 of 6)
Figure 3.4 An Estimated Demand Curve for Cod at the Portland Fish Exchange
3.2 Regression Analysis (3 of 6)
Microsoft Excel Screenshots (Windows Version 2016) (1 of 2)
3.2 Regression Analysis (4 of 6)
3.2 Regression Analysis (5 of 6)
Figure 3.5 Two Estimated Apple Pie Demand Curves with Different R-squared Statistics
3.2 Regression Analysis (6 of 6)
3.3 Properties and Statistical Significance of Estimated Coefficients
3.3 Properties and Significance of Coefficients (1 of 5)
3.3 Properties and Significance of Coefficients (2 of 5)
3.3 Properties and Significance of Coefficients (3 of 5)
Microsoft Excel Screenshots (Windows Version 2016) (2 of 2)
3.3 Properties and Significance of Coefficients (4 of 5)
3.3 Properties and Significance of Coefficients (5 of 5)
3.4 Regression Specification (1 of 6)
3.4 Regression Specification (2 of 6)
3.4 Regression Specification (3 of 6)
3.4 Regression Specification (4 of 6)
3.4 Regression Specification (5 of 6)
Figure 3.6 The Effect of Advertising on Demand
3.4 Regression Specification (6 of 6)
3.5 Forecasting (1 of 4)
Figure 3.7 Nike’s Quarterly Revenue: 2009–2018
3.5 Forecasting (2 of 4)
3.5 Forecasting (3 of 4)
3.5 Forecasting (4 of 4)
Managerial Solution
Table 3.1 Data Used to Estimate the Cod Demand Curve at the Portland Fish Exchange
Figure 3.3 Observed Price-Quantity Data Points for the Portland Fish Exchange
Table 3.2 Regressions of Quantity on Advertising
Figure 3.8 iTunes Focus Group Demand and Revenue Curves

Chapter 1 Discussion- Mini Case: Using an Income Threshold Model in China

After reading the above Mini Case on p. 4, answer the following questions.

1. Explain the Income Threshold Model concept.

2. Speculate the relevance of the Income Threshold Model for making Managerial decisions.

3. How might business managers use the concept in making resource allocation decisions?

Discussion Rubrics: Critical thinking, logical argument and/or communication skills

Chapter 2 Discussion – Mini Case: Venezuelan Price ceilings and Shortages

After reading the above Mini Case on p. 30, answer the following questions

1. Explain the price ceiling.

2. What is the impact of price ceiling on the welfare of consumers and producers?

3. How would you mitigate the unintended consequence of price ceiling?

Discussion Rubrics: Critical thinking, logical argument and/or communication skills

Chapter 3 Discussion: Mini Case 3-Anti-Smoking Policies May Reduce Drunk Driving

After reading the above Mini Case on p. 53-54, answer the following questions.

1. Define the cross-price elasticity of demand for alcohol consumption with respect to cigarette prices.

2. What is the impact of an increase in the price of cigarettes on the consumption alcohol?

3. How effective are the policies of raising taxes on cigarettes and banning smoking in public places on the consumption of these goods?

Discussion Rubrics: Critical thinking, logical argument and/or communication skills

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Chapter 1 Discussion- Mini Case: Using an Income Threshold Model in China

Chapter 2 Discussion – Mini Case: Venezuelan Price ceilings and Shortages

Chapter 3 Discussion: Mini Case 3-Anti-Smoking Policies May Reduce Drunk Driving

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