Wk8 DQ - Managerial Eonomics
voyage
Baye_9e_Chapter_031.pptxQuantitative Demand Analysis
© 2017 by McGraw-Hill Education. All Rights Reserved. Authorized only for instructor use in the classroom. No reproduction or distribution without the prior written consent of McGraw-Hill Education.
Chapter 3
Learning Objectives
Apply various elasticities of demand as a quantitative tool to forecast changes in revenues, prices, and/or units sold.
Illustrate the relationship between the elasticity of demand and total revenues.
Discuss three factors that influence whether the demand for a given product is relatively elastic or inelastic.
Explain the relationship between marginal revenue and the own price elasticity of demand.
Show how to determine elasticities from linear and log-linear demand functions.
Explain how regression analysis may be used to estimate demand functions, and how to interpret and use the output of a regression.
© 2017 by McGraw-Hill Education. All Rights Reserved.
2
The Elasticity Concept
Elasticity
A measure of the responsiveness of one variable to changes in another variable; the percentage change in one variable that arises due to a given percentage change in another variable.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-3
The Elasticity Concept
3
The Elasticity Concept
The elasticity between two variables, and , is mathematically expressed as:
When a functional relationship exists, like , the elasticity is:
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-4
The Elasticity Concept
4
Measurement Aspects of Elasticity
Important aspects of the elasticity:
Sign of the relationship:
Positive
Negative
Absolute value of elasticity magnitude relative to unity:
is highly responsive to changes in .
is slightly responsive to changes in .
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-5
The Elasticity Concept
5
Own Price Elasticity of Demand
Own price elasticity of demand
Measures the responsiveness of a percentage change in the quantity demanded of good X to a percentage change in its price.
Sign: negative by law of demand.
Magnitude of absolute value relative to unity:
: Elastic.
: Inelastic.
: Unitary elastic.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-6
Own Price Elasticity of Demand
6
Linear Demand, Elasticity, and Revenue
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-7
Quantity
Price
Demand
$40
0
$20
$10
20
30
$5
40
$15
$30
$25
$35
10
50
60
70
80
Linear Inverse Demand:
Demand:
Revenue = $
Elasticity:
Conclusion: Demand is elastic.
Revenue = $
Elasticity:
Conclusion: Demand is unitary elastic.
Revenue = $
Elasticity:
Conclusion: Demand is inelastic.
Observation: Elasticity varies along a linear (inverse) demand curve
Own Price Elasticity of Demand
7
Total Revenue Test
When demand is elastic:
A price increase (decrease) leads to a decrease (increase) in total revenue.
When demand is inelastic:
A price increase (decrease) leads to an increase (decrease) in total revenue.
When demand is unitary elastic:
Total revenue is maximized.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-8
Own Price Elasticity of Demand
8
Perfectly Elastic and Inelastic Demand
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-9
Quantity
Demand
Price
Perfectly Inelastic
Demand
Perfectly
elastic
Own Price Elasticity of Demand
9
Factors Affecting the Own Price Elasticity
Three factors can impact the own price elasticity of demand:
Availability of consumption substitutes
Time/duration of purchase horizon
Expenditure share of consumers’ budgets
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-10
Own Price Elasticity of Demand
10
Marginal Revenue and the Own Price Elasticity of Demand
The marginal revenue can be derived from a market demand curve.
Marginal revenue measures the additional revenue due to a change in output.
This link relates marginal revenue to the own price elasticity of demand as follows:
When then, .
When then, .
When then, .
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-11
Own Price Elasticity of Demand
11
Demand and Marginal Revenue
© 2017 by McGraw-Hill Education. All Rights Reserved.
2-12
Quantity
0
MR
3
Price
6
Elastic
Demand
Own Price Elasticity of Demand
1
6
Inelastic
Unitary
Marginal Revenue (MR)
Cross-Price Elasticity
Cross-price elasticity
Measures responsiveness of a percent change in demand for good X due to a percent change in the price of good Y.
If , then and are substitutes.
If , then and are complements.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-13
Cross-Price Elasticity
13
Cross-Price Elasticity in Action
Suppose it is estimated that the cross-price elasticity of demand between clothing and food is -0.18. If the price of food is projected to increase by 10 percent, by how much will demand for clothing change?
That is, demand for clothing is expected to decline by 1.8 percent when the price of food increases 10 percent.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-14
Cross-Price Elasticity
14
Cross-Price Elasticity
Cross-price elasticity is important for firms selling multiple products.
Price changes for one product impact demand for other products.
Assessing the overall change in revenue from a price change for one good when a firm sells two goods is:
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-15
Cross-Price Elasticity
15
Cross-Price Elasticity in Action
Suppose a restaurant earns $4,000 per week in revenues from hamburger sales (X) and $2,000 per week from soda sales (Y).
If the own price elasticity for burgers is and the cross-price elasticity of demand between sodas and hamburgers is , what would happen to the firm’s total revenues if it reduced the price of hamburgers by 1 percent?
That is, lowering the price of hamburgers 1 percent increases total revenue by $100.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-16
Cross-Price Elasticity
16
Income Elasticity
Income elasticity
Measures responsiveness of a percent change in demand for good X due to a percent change in income.
If , then is a normal good.
If , then is an inferior good.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-17
Income Elasticity
17
Income Elasticity in Action
Suppose that the income elasticity of demand for transportation is estimated to be 1.80. If income is projected to decrease by 15 percent,
what is the impact on the demand for transportation?
Demand for transportation will decline by 27 percent.
is transportation a normal or inferior good?
Since demand decreases as income declines, transportation is a normal good.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-18
Income Elasticity
18
Other Elasticities
Own advertising elasticity of demand for good X is the ratio of the percentage change in the consumption of X to the percentage change in advertising spent on X.
Cross-advertising elasticity between goods X and Y would measure the percentage change in the consumption of X that results from a 1 percent change in advertising toward Y.
© 2017 by McGraw-Hill Education. All Rights Reserved.
2-19
Other Elasticities
Elasticities for Linear Demand Functions
From a linear demand function, we can easily compute various elasticities.
Given a linear demand function:
Own price elasticity: .
Cross price elasticity: .
Income elasticity: .
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-20
Obtaining Elasticities From Demand Functions
20
Elasticities for Linear Demand Functions In Action
The daily demand for Invigorated PED shoes is estimated to be:
Suppose good X sells at $25 a pair, good Y sells at $35, the company utilizes 50 units of advertising, and average consumer income is $20,000. Calculate the own price, cross-price and income elasticities of demand.
units.
Own price elasticity: .
Cross-price elasticity: .
Income elasticity: .
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-21
Obtaining Elasticities From Demand Functions
21
Elasticities for Nonlinear Demand Functions
One non-linear demand function is the log-linear demand function:
Own price elasticity: .
Cross price elasticity: .
Income elasticity: .
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-22
Obtaining Elasticities From Demand Functions
22
Elasticities for Nonlinear Demand Functions In Action
An analyst for a major apparel company estimates that the demand for its raincoats is given by
where denotes the daily amount of rainfall and the level of advertising on good Y. What would be the impact on demand of a 10 percent increase in the daily amount of rainfall?
. So, .
A 10 percent increase in rainfall will lead to a 30 percent increase in the demand for raincoats.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-23
Obtaining Elasticities From Demand Functions
23
Regression Analysis
How does one obtain information on the demand function?
Published studies
Hire consultant
Statistical technique called regression analysis using data on quantity, price, income and other important variables.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-24
Regression Analysis
24
Regression Line and Least Squares Regression
True (or population) regression model
unknown population intercept parameter.
unknown population slope parameter.
random error term with mean zero and standard deviation .
Least squares regression line
least squares estimate of the unknown parameter .
least squares estimate of the unknown parameter.
The parameter estimates and , represent the values of and that result in the smallest sum of squared errors between a line and the actual data.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-25
Regression Analysis
25
Excel and Least Squares Estimates
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-26
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.87 | |||||
| R Square | 0.75 | |||||
| Adjusted R Square | 0.72 | |||||
| Standard Error | 112.22 | |||||
| Observations | 10.00 | |||||
| ANOVA | ||||||
| Df | SS | MS | F | Significance F | ||
| Regression | 1 | 301470.89 | 301470.89 | 23.94 | 0.0012 | |
| Residual | 8 | 100751.61 | 12593.95 | |||
| Total | 9 | 402222.50 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 1631.47 | 243.97 | 6.69 | 0.0002 | 1068.87 | 2194.07 |
| Price | -2.60 | 0.53 | -4.89 | 0.0012 | -3.82 | -1.37 |
Estimated Demand:
Regression Analysis
26
Evaluating Statistical Significance
Standard error
Measure of how much each estimated estimate varies in regressions based on the same true demand model using different data.
95 Percent Confidence interval rule of thumb
t-statistics rule of thumb
When , we are 95 percent confident the true parameter is in the regression is not zero.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-27
Regression Analysis
27
Excel and Least Squares Estimates
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-28
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.87 | |||||
| R Square | 0.75 | |||||
| Adjusted R Square | 0.72 | |||||
| Standard Error | 112.22 | |||||
| Observations | 10.00 | |||||
| ANOVA | ||||||
| Df | SS | MS | F | Significance F | ||
| Regression | 1 | 301470.89 | 301470.89 | 23.94 | 0.0012 | |
| Residual | 8 | 100751.61 | 12593.95 | |||
| Total | 9 | 402222.50 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 1631.47 | 243.97 | 6.69 | 0.0002 | 1068.87 | 2194.07 |
| Price | -2.60 | 0.53 | -4.89 | 0.0012 | -3.82 | -1.37 |
Regression Analysis
, the intercept is different
from zero.
, the intercept is different
from zero.
28
Evaluating the Overall Fit of the Regression Line
R-Square
Also called the coefficient of determination.
Fraction of the total variation in the dependent variable that is explained by the regression.
Ranges between 0 and 1.
Values closer to 1 indicate “better” fit.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-29
Regression Analysis
29
Evaluating the Overall Fit of the Regression Line
Adjusted R-Square
A version of the R-square that penalize researchers for having few degrees of freedom.
is total observations.
is the number of estimated coefficients.
is the degrees of freedom for the regression.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-30
Regression Analysis
30
Evaluating the Overall Fit of the Regression Line
The F- Statistic
A measure of the total variation explained by the regression relative to the total unexplained variation.
The greater the F-statistic, the better the overall regression fit.
Equivalently, the P-value is another measure of the F-statistic.
Lower P-values are associated with better overall regression fit.
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-31
Regression Analysis
31
Excel and Least Squares Estimates
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-32
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.87 | |||||
| R Square | 0.75 | |||||
| Adjusted R Square | 0.72 | |||||
| Standard Error | 112.22 | |||||
| Observations | 10.00 | |||||
| ANOVA | ||||||
| Df | SS | MS | F | Significance F | ||
| Regression | 1 | 301470.89 | 301470.89 | 23.94 | 0.0012 | |
| Residual | 8 | 100751.61 | 12593.95 | |||
| Total | 9 | 402222.50 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 1631.47 | 243.97 | 6.69 | 0.0002 | 1068.87 | 2194.07 |
| Price | -2.60 | 0.53 | -4.89 | 0.0012 | -3.82 | -1.37 |
Regression Analysis
32
Regression for Nonlinear Functions and Multiple Regression
Regression techniques can also be applied to the following settings:
Nonlinear functional relationships:
Nonlinear regression example:
Functional relationships with multiple variables:
Multiple regression example:
or
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-33
Regression Analysis
33
Excel and Least Squares Estimates
© 2017 by McGraw-Hill Education. All Rights Reserved.
3-34
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.89 | |||||
| R Square | 0.79 | |||||
| Adjusted R Square | 0.69 | |||||
| Standard Error | 9.18 | |||||
| Observations | 10.00 | |||||
| ANOVA | ||||||
| Df | SS | MS | F | Significance F | ||
| Regression | 3 | 1920.99 | 640.33 | 7.59 | 0.182 | |
| Residual | 6 | 505.91 | 84.32 | |||
| Total | 9 | 2426.90 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 135.15 | 20.65 | 6.54 | 0.0006 | 84.61 | 185.68 |
| Price | -0.14 | 0.06 | -2.41 | 0.0500 | -0.29 | 0.00 |
| Advertising | 0.54 | 0.64 | 0.85 | 0.4296 | -1.02 | 2.09 |
| Distance | -5.78 | 1.26 | -4.61 | 0.0037 | -8.86 | -2.71 |
Regression Analysis
34
