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ECO518Unit2Lecture.pdf
ECO 518
Unit 2 Lecture This lecture will function as a guide to assist you in studying. It contains questions that you
should be able to answer after you read the chapters. As you read the text book think about the
questions posed below. The answers comprise the main topics and concepts that you should
know and understand after you have finished your readings. There are no quiz or test activities
associated with this lecture content.
Unit Learning Outcomes
Unit 2
ULO 1. Illustrate how the concepts of supply and demand can be used to analyze market
conditions in which management decisions about price and allocations of resources
must be made
ULO 2. Apply the concepts of price elasticity, cross-elasticity, and income elasticity
ULO 3. Specify the components of a regression model to estimate a demand equation and
interpret the regression results (i.e., explain the quantitative impact that changes in the
determinants have on the quantity demanded).
CHAPTER THREE
Supply and Demand
KEY LEARNING POINTS The chapter discusses the elements of supply and demand by introducing the law of demand
and of supply, how price serves a short-run rationing function and a long-run guiding function in
the market place. In addition, this chapter will discuss techniques involving the comparison of
equilibrium points before and after changes in the market have occurred, as a standard way of
analyzing problems.
Market Demand The authors provided a good summary of Demand and Market Demand with an illustration of
market demand for pizza.
Demand for a good or service is defined as quantities that people are ready (willing and able) to
buy at various prices within some given time period.
Other factors besides price are held constant
Market demand is the sum of all the individual demands
Example: Demand for Pizza
The inverse relationship between price and the quantity demanded of a good or service is called
the Law of Demand. Below you will find the example provided.
Figure 3.1 Market Demand Curve for Pizza
Changes in price result in changes in the quantity demanded. This is shown as movement
along the demand curve.
Changes in non-price factors result in changes in demand. This is shown as a shift in the
demand curve.
Nonprice determinants of demand:
• Tastes and preferences income
• Prices of related products
• Future expectations
• Number of buyers
Ask yourself: Suppose that the demand for oranges increase. What you think the long -run
effects of the guiding function of price would be?
Ask yourself: What is the distinction between the "long run" and the "short run"?
Market Supply The supply of a good or service is defined as quantities that people are ready to sell at various
prices within some given time period.
Other factors besides price held constant
Changes in price result in changes in the quantity supplied shown as movement along the
supply curve.
Changes in non-price determinants result in changes in supply shown as a shift in the supply
curve
Nonprice determinants of supply:
• Costs and technology
• Prices of other goods or services offered by the seller
• Future expectations
• Number of sellers
• Weather conditions
Market Equilibrium Equilibrium price: the price that equates the quantity demanded with the quantity supplied.
Equilibrium quantity: the amount that people are willing to buy and sellers are willing to offer at
the equilibrium price level.
Shortage: a market situation in which the quantity demanded exceeds the quantity supplied
shortage occurs at a price below the equilibrium level.
Surplus: a market situation in which the quantity supplied exceeds the quantity demanded
surplus occurs at a price above the equilibrium level.
Figure 3.4 Supply and Demand Curves for Pizza, Indicating Market Equilibrium
Ask yourself: What are the factors that will cause the supply to increase for decrease on the
following products:
• Crude oil
• Beef
• Computer memory chip
Comparative Statics Analysis Comparative statics is a form of sensitivity (or what-if) analysis:
• Commonly used method in economic analysis
• Process of comparative statics analysis:
• State all the assumptions needed to construct the model
• Begin by assuming that the model is in equilibrium
• Introduce a change in the model, so a condition of disequilibrium is created
• Find the new point of equilibrium
• Compare the new equilibrium point with the original one
Step 1
Assume all factors except the price of pizza are constant
Buyers’ demand and sellers’ supply are represented by lines shown
Step 2
Begin the analysis in equilibrium as shown by Q1 and P1
Step 3
Assume that a new study shows pizza to be the most nutritious of all fast foods
Consumers increase their demand for pizza as a result
Figure 3.4 Supply and Demand Curves for Pizza, Indicating Market Equilibrium
Figure 3.5 Increases in Demand for Pizza and Resulting Impact on Market Equilibrium
Step 4
The shift in demand results in a new equilibrium price (P2) and a new equilibrium quantity (Q2)
Step 5
Comparing the new equilibrium point with the original one, we see that both equilibrium price
and quantity have increased
The short run is the period of time in which:
• Sellers already in the market respond to a change in equilibrium price by adjusting
variable inputs
• Buyers already in the market respond to changes in equilibrium price by adjusting the
quantity demanded for the good or service
Short run changes show the rationing function of price
• The rationing function of price is the change in market price to eliminate the imbalance
between quantities supplied and demanded is the change in market price to eliminate
the imbalance between quantities supplied and demanded
Short-run Analysis An increase in demand causes equilibrium price and quantity to rise:
Figure 3.6 Changes in Supply and Demand and Their Short-Run Impact on Market Equilibrium
(the Rationing Function of Price)
A decrease in demand causes equilibrium price and quantity to fall:
Figure 3.6 Changes in Supply and Demand and Their Short-Run Impact on Market Equilibrium
(the Rationing Function of Price)
An increase in supply causes equilibrium price to fall and equilibrium quantity to rise:
Figure 3.6 Changes in Supply and Demand and Their Short-Run Impact on Market Equilibrium
(the Rationing Function of Price)
A decrease in supply causes equilibrium price to rise and equilibrium quantity to fall:
Figure 3.6 Changes in Supply and Demand and Their Short-Run Impact on Market Equilibrium
(the Rationing Function of Price)
Long-run Analysis The long run is the period of time in which:
• New sellers may enter a market
• Existing sellers may exit from a market
• Existing sellers may adjust fixed factors of production
• Buyers may react to a change in equilibrium price by changing their tastes and
preferences
• Long run changes show the allocating function of price
The guiding or allocating function of price is the movement of resources into or out of
markets in response to a change in the equilibrium price.
Initial change: decrease in demand from D1 to D2:
Figure 3.7 Short-Run and long-Run Changes in Supply (in Response to an Initial Change in
Demand)
Result: reduction in equilibrium price and quantity (to P2,Q2)
Follow-on adjustment:
Movement of resources out of the market
Leftward shift in the supply curve to S2
equilibrium price and quantity (to P3,Q3)
Figure 3.7 Short-Run and long-Run Changes in Supply (in Response to an Initial Change in
Demand)
Initial change: increase in demand from D1 to D2
Result: increase in equilibrium price and quantity (to P2,Q2)
Follow-on adjustment:
Movement of resources into the market
Rightward shift in the supply curve to S2
equilibrium price and quantity (to P3,Q3)
Supply, Demand, and Price: The Managerial Challenge In the extreme case, the forces of supply and demand are the sole determinants of the market
price, not any single firm this type of market is ‘perfect competition’
In many cases, individual firms can exert market power over price because of their: Dominant
size ability to differentiate their product through advertising, brand name, features, or services
Example: coffee
‘Buy low, sell high’
2000: overproduction led to price falls
2004: prices moved up again
Starbucks effects
Ask yourself: List the major non-price determinants of supply
Ask yourself: Suppose that macroeconomic forecasters predict that the economy will be
expanding in the near future. How might managers use this information?
Global application: The Market for Cobalt Review the Market for Cobalt and consider the following main points:
• Rare metal
• Produced as a by-product
• Strategic item
• Prices rising
CHAPTER FOUR
Demand Elasticity
KEY LEARNING POINTS Chapter 4 discusses the question of how sensitive the change in quantity demanded is to a
change in price. The measurement of this sensitivity in percentage terms is called the price
elasticity of demand.
The Economic Concept of Elasticity Elasticity: the percentage change in one variable relative to a percentage change in another.
Bin changepercent
Ain changepercent Elasticity oft Coefficien
Price Elasticity of Demand Price elasticity of demand: the percentage change in quantity demanded caused by a 1
percent change in price.
Price %
Quantity % E
p
Measurement of Price Elasticity
Arc elasticity: elasticity which is measured over a discrete interval of a curve
Ep = coefficient of arc price elasticity
Q1 = original quantity demanded
Q2 = new quantity demanded
P1 = original price
P2 = new price
Examples: some real world elasticities
a. White pan bread:-0.69
b. Cigarettes: short run -0.4, long run -0.6
c. Wine imports: -0.15
d. Crude oil: -0.06
e. Internet services: -0.6/-0.7
Ask yourself: What do you think the Price elasticity of Beer is?
Point elasticity: elasticity measured at a given point of a demand (or a supply) curve:
The point elasticity of a linear demand function can be expressed as:
Ask yourself: Do you know the definition of Elasticity as it applies to economics?
Elasticity varies along a linear demand curve
1
1
ε P
PdQ x
dP Q =
1
1
Q
P
P
Q
p
Figure 4.5 The Elasticity-Demand Relationship
Some demand curves have constant elasticity
such a curve has a nonlinear equation:
Q = aP-b
where –b is the elasticity coefficient
Categories of elasticity
• Relative elasticity of demand: Ep > 1
• Relative inelasticity of demand: 0 < Ep < 1
• Unitary elasticity of demand: Ep = 1
• Perfect elasticity: Ep = ∞
• Perfect inelasticity: Ep = 0
Factors affecting demand elasticity
• Ease of substitution
• Proportion of total expenditures
• Durability of product
o Possibility of postponing purchase
o Possibility of repair
o Used product market
o Length of time period
Derived demand: the demand for products or factors that are not directly consumed, but go into
the production of another (final) product.
The demand for such a product or factor exists because there is demand for the final product
The derived demand curve will be more inelastic:
o The more essential is the component
o The more inelastic is the demand curve for the final product
o The smaller is the fraction of total cost going to this component
o The more inelastic is the supply curve of cooperating factors
A long-run demand curve will generally be more elastic than a short-run curve
As the time period lengthens consumers find ways to adjust to the price change, via substitution
or shifting consumption (Figure 4.4).
Figure 4.4 Short-Run versus Long-Run Elasticity
The relationship between price and revenue depends on elasticity
Why? By itself, a price fall will reduce receipts … BUT because the demand curve is downward
sloping, the drop in price will also increase quantity demanded
Q: which effect will be stronger?
The relationship between price and revenue depends on elasticity
Why? By itself, a price fall will reduce receipts … BUT because the demand curve
is downward sloping, the drop in price will also increase quantity demanded
Q: which effect will be stronger?
Figure 4.5 The Elasticity – Demand Relationship
Figure 4.6 The Effect of Elasticity on Total Revenue
Marginal revenue: the change in total revenue resulting from changing quantity by one unit
Ask yourself: Can you explain the difference between point elasticity and arc elasticity?
Marginal revenue curve is twice as steep as the demand curve
Figure 4.7 The Relationship between Demand and marginal Revenue
Quantity MR
Revenue Total
At the point where marginal revenue crosses the X-axis, the demand curve is unitary elastic and
total revenue reaches a maximum:
Elasticity of Supply
When the supply curve is more elastic, the effect of a change in demand will be greater on
quantity than on the price of the product
When the supply curve is less elastic, a change in demand will have a greater effect on price
than on quantity
Examples: some real world elasticities
1. coffee: short run -0.2, long run -0.33
2. kitchen and household appliances:
3. -0.63
4. meals at restaurants: -2.27
5. airline travel in U.S.: -1.98
6. beer: -0.84, Wine: -0.55
Cross-Elasticity of Demand Cross-elasticity of demand: the percentage change in quantity consumed of one product as a
result of a 1 percent change in the price of a related product.
Arc cross-elasticity:
Point cross-elasticity:
o The sign of cross-elasticity for substitutes is positive
o The sign of cross-elasticity for complements is negative
o Two products are considered good substitutes or complements when the coefficient is
larger than 0.5 (in ab. value)
Income Elasticity
Income elasticity of demand: the percentage change in quantity demanded caused by a 1
percent change in income
Y is shorthand for income
Arc income elasticity:
2/)(2/)( 21
12
21
12
BB
BB
AA
AA
PP
PP
QQ EX
B
A x
P
Q E
%
%
2/)(2/)( 21
12
21
12
YY
YY
QQ EY
B
B
A
A X
P
P
Q
Q E
Categories of income elasticity
• superior goods: EY > 1
• normal goods: 0 ≤ EY ≤ 1
• inferior goods: EY < 0
Other Demand Elasticities Examples: elasticity is encountered every time a change in some variable affects demand;
• advertising expenditure
• interest rates
• population size
Elasticity of Price Price elasticity of supply: the percentage change in quantity supplied as a result of a 1
percent change in price
The coefficient of supply elasticity is a normally a positive number
Elasticity of Supply Arc elasticity of supply
When the supply curve is more elastic, the effect of a change in demand will be greater on
quantity than on the price of the product.
When the supply curve is less elastic, a change in demand will have a greater effect on price
than on quantity.
Global Application: Price Elasticities in Asia Review the below main points:
• Imports almost always price inelastic
• If exports price inelastic, export earnings will rise as prices rise
Price %
SuppliedQuantity % E
S
2/)(2/)( 21
12
21
12
PP
PP
QQ Es
• If exports price elastic, export earnings will rise with world incomes
CHAPTER FIVE
Demand Estimation and Forecasting
KEY LEARNING POINTS Chapter 5 presents two important statistical approaches to estimating and forecasting the
demand for a product. This chapter gives an overview of how the techniques of analysis are
used in various types of studies.
Data Collection Data for studies pertaining to countries, regions, or industries are readily available.
Data for analysis of specific product categories may be more difficult to obtain. To obtain them
you could do the following:
• Buy from data providers (e.g. ACNielsen, IRI)
• Perform a consumer survey
• Focus groups
• Technology: point-of-sale, bar codes
• Regression analysis
Regression Analysis Regression analysis: a procedure commonly used by economists to estimate consumer
demand with available data
Two types of regression:
• Cross-sectional: analyze several variables for a single period of time
• Time series data: analyze a single variable over multiple periods of time Regression equation: linear, additive eg: Y = a + b1X1 + b2X2 + b3X3 + b4X4
Y: dependent variable a: constant value, y-intercept Xn: independent variables, used to explain Y bn: regression coefficients (measure impact of independent variables)
Interpreting the regression results:
Coefficients:
• Negative coefficient shows that as the independent variable (Xn) changes, the variable (Y) changes in the opposite direction
• Positive coefficient shows that as the independent variable (Xn) changes, the dependent variable (Y) changes in the same direction
• Magnitude of regression coefficients is a measure of elasticity of each variable Statistical evaluation of regression results:
t-test: test of statistical significance of each estimated coefficient
b = estimated coefficient SEb = standard error of estimated coefficient
Statistical evaluation of regression results:
‘rule of 2’: if absolute value of t is greater than 2, estimated coefficient is significant at the 5% level
if coefficient passes t-test, the variable has a true impact on demand R2 (coefficient of determination): percentage of variation in the variable (Y) accounted for by variation in all explanatory variables (Xn) R2 value ranges from 0.0 to 1.0 the closer to 1.0, the greater the explanatory power of the regression F-test: measures statistical significance of the entire regression as a whole (not each coefficient)
Regression Results Steps for analyzing regression results
• Check coefficient signs and magnitudes
• Compute implied elasticities
• Determine statistical significance Example: estimating demand for pizza
Demand for pizza affected by
1. price of pizza
2. price of complement (soda) managers can expect price decreases to lead to lower
revenue tuition and location are not significant
Identification problem: the estimation of demand may produce biased results due to
simultaneous shifting of supply and demand curves
Solution: use advanced correction techniques, such as two-stage least squares and indirect
least squares
Multicollinearity problem (p. 137): two or more independent variables are highly correlated,
thus it is difficult to separate the effect each has on the dependent variable
Solution: a standard remedy is to drop one of the closely related independent variables from the
regression
Autocorrelation problem (p. 137): also known as serial correlation, occurs when the
dependent variable relates to the Y variable according to a certain pattern
b̂ SE
b̂ t
Note: possible causes include omitted variables, or non-linearity; Durbin-Watson statistic is used
to identify autocorrelation
Solution: to correct autocorrelation consider transforming the data into a different order of
magnitude or introducing leading or lagging data
Ask yourself: Can you describe Regression Analysis?
Forecasting Examples: common subjects of business forecasts:
• Gross domestic product (GDP)
• Components of GDP o eg consumption expenditure, producer durable equipment expenditure,
residential construction
• Industry forecasts o eg sales of products across an industry
• Sales of a specific product
A good forecast should:
• Be consistent with other parts of the business
• Be based on knowledge of the relevant past
• Consider the economic and political environment as well as changes be timely
Forecasting Techniques Factors in choosing the right forecasting technique:
Item to be forecast
Interaction of the situation with the forecasting methodology
Amount of historical data available
Time allowed to prepare forecast
Approaches to forecasting
• Qualitative forecasting is based on judgments expressed by individuals or group
• Quantitative forecasting utilizes significant amounts of data and equations
• Naïve forecasting projects past data without explaining future trends
• Causal (or explanatory) forecasting attempts to explain the functional relationships
between the dependent variable and the independent variables
Six forecasting techniques
• Expert opinion
• Opinion polls and market research
• Surveys of spending plans
• Economic indicators
• Projections
• Econometric models
Expert opinion techniques
• Jury of executive opinion: forecasts generated by a group of corporate executives
assembled together Drawback: persons with strong personalities may exercise
disproportionate influence
• The Delphi method: a form of expert opinion forecasting that uses a series of questions
and answers to obtain a consensus forecast, where experts do not meet
Opinion polls: sample populations are surveyed to determine consumption trends
• May identify changes in trends
• Choice of sample is important
• Questions must be simple and clear
Market research: is closely related to opinion polling and will indicate not only why the
consumer is (or is not) buying, but also
• Who the consumer is
• How he or she is using the product
• Characteristics the consumer thinks are most important in the purchasing decision
Surveys of spending plans: yields information about ‘macro-type’ data relating to the
economy, especially:
• Consumer intentions
o Examples: Survey of Consumers (University of Michigan); Consumer
Confidence Survey (Conference Board)
• Inventories and sales expectations
Economic indicators: a barometric method of forecasting designed to alert business to
changes in conditions
• Leading, coincident, and lagging indicators
• Composite index: one indicator alone may not be very reliable, but a mix of leading
indicators may be effective
Leading indicators predict future economic activity
• average hours, manufacturing
• initial claims for unemployment insurance
• manufacturers’ new orders for consumer goods and materials
• vendor performance, slower deliveries diffusion index
• manufacturers’ new orders, nondefense capital goods
• building permits, new private housing units
• stock prices, 500 common stocks
• money supply, M2
• interest rate spread, 10-year Treasury bonds minus federal funds
• index of consumer expectations
Coincident indicators identify trends in current economic activity
• employees on nonagricultural payrolls
• personal income less transfer payments
• industrial production
• manufacturing and trade sales
Lagging indicators confirm swings in past economic activity
• average duration of unemployment, weeks
• ratio, manufacturing and trade inventories to sales
• change in labor cost per unit of output, manufacturing (%)
• average prime rate charged by banks
• commercial and industrial loans outstanding
• ratio, consumer installment credit outstanding to personal income
• change in consumer price index for services
Economic indicators: drawbacks
• leading indicator index has forecast a recession when none ensued
• a change in the index does not indicate the precise size of the decline or increase
• the data are subject to revision in the ensuing months
Trend projections: a form of naïve forecasting that projects trends from past data without
taking into consideration reasons for the change
• compound growth rate
• visual time series projections
• least squares time series projection
Compound growth rate: forecasting by projecting the average growth rate of the past into the
future
• provides a relatively simple and timely forecast
• appropriate when the variable to be predicted increases at a constant %
General compound growth rate formula:
E = B(1+i)n
E = final value n = years in the series
B = beginning value i = constant growth rate
Visual time series projections: plotting observations on a graph and viewing the shape of the
data and any trends
Time series analysis: a naïve method of forecasting from past data by using least squares
statistical methods to identify trends, cycles, seasonality and irregular movements
Time series analysis:
Advantages:
• easy to calculate
• does not require much judgment or analytical skill
• describes the best possible fit for past data
• usually reasonably reliable in the short run
Time series data can be represented as:
Yt = f(Tt, Ct, St, Rt)
Yt = actual value of the data at time t Tt = trend component at t Ct = cyclical component at t St = seasonal component at t Rt = random component at t
Time series components: seasonality
• need to identify and remove seasonal factors, using moving averages to isolate those factors
• remove seasonality by dividing data by seasonal factor
• Time series components: trend
• to remove trend line, use least squares method
• possible best-fit line styles: straight Line: Y = a + b(t) exponential Line: Y = abt quadratic Line: Y = a + b(t) + c(t)2
choose one with best R2
Time series components: cycle, noise
• isolate cycle by smoothing with a moving average
• random factors cannot be predicted and should be ignored
Smoothing techniques (p. 158)
• moving average
• exponential smoothing
work best when:
• no strong trend in series
• infrequent changes in direction of series
• fluctuations are random rather than seasonal or cyclical
Moving average: average of actual past results used to forecast one period ahead
Et+1 = (Xt + Xt-1 + … + Xt-N+1)/N Et+1 = forecast for next period Xt, Xt-1 = actual values at their respective times N = number of observations included in average
Exponential smoothing: allows for decreasing importance of information in the more distant
past, through geometric progression
Et+1 = w·Xt + (1-w) · Et
w = weight assigned to an actual observation at period t
Econometric models (p. 161): causal or explanatory models of forecasting
• regression analysis
• multiple equation systems o endogenous variables: dependent variables that may influence other dependent
variables o exogenous variables: from outside the system, truly independent variables
Example: econometric model Suits (1958) forecast demand for new automobiles ∆R = a0 + a1 ∆Y + a2 ∆P/M + a3 ∆S + a4 ∆X R = retail sales Y = real disposable income P = real retail price of cars M = average credit terms S = existing stock X = dummy variable
Global Application: Forecasting Exchange Rates Review Forecasting Exchange Rates and consider the main points listed below:
• GDP
• interest rates
• inflation rates
• balance of payments
