0. Benefit Estimation Methods
Andrew Foss
Economics 1661 / API-135
Environmental and Resource Economics and Policy
Harvard University
February 27, 2009
Review Section

1. Agenda
Benefits Overview
Travel Cost Method
Random Utility Models
Hedonic Pricing Models
Contingent Valuation

2. Benefits Overview: Concepts
Benefits in environmental economics reflect willingness to pay for improvements in environmental quality
Measures for Environmental “Goods” (e.g., Fish Protection)
Total
Marginal
Total Benefits (TB) =
Total Willingness to Pay (WTP)
Marginal Benefits (MB) =
Marginal Willingness to Pay (MWTP) =
Demand
Measures for Environmental “Bads” (e.g., Fish Kills)
Total
Marginal
Total Damages (TD) =
Total Willingness to Accept (WTA)
Marginal Damages (MD) =
Marginal Willingness to Accept (MWTA)

3. Benefits Overview: Components
Benefit Component Definitions (Two-Way Taxonomy)
Use Value
Non-use Value
Market Use Value
The value to society of using an environmental resource as a market product
Existence Value
The value to society of knowing that an environmental resource exists
Altruism Value
The value to society of knowing other people can use an env. resource
Non-market Use Value
The value to society of using an environmental resource not as a market product
Bequest Value
The value to society of knowing future generations can use an env. resource
Option Value
The value to society of having an option to use an env. resource

4. Benefits Overview: Estimation Methods
Benefit Estimation Methods
Use Value
Non-use Value
Market Use Value
Market quantities and prices
Existence, Altruism, Bequest, and Option Values
Contingent valuation (CV)
Contingent ranking
Choice experiments
Non-market Use Value
Travel cost method, aka Hotelling-Clawson-Knetsch (HCK)
Discrete choice models,
aka random utility models (RUMs)
Hedonic pricing models
Revealed Preference Methods (maybe stated preference methods here as well)
Stated Preference Methods

5. Travel Cost Method: Example Problem
Ruritania is a country with three cities and a beautiful park at its center
Environmental economists in Ruritania have collected the following data on park visitors from the three cities
The environmental economists want to estimate the recreational value (i.e., non-market use value) of the park to the people of Ruritania
Origin
Population
Visitors
Travel Cost 1
100,000
15,000
$12.50 2
750,000
75,000
$25.00 3
1,000,000
50,000
$37.50

6. Travel Cost Method: Example Problem
First calculate the visitation rate for each origin
Origin
Population
Visitors
Visit Rate
Travel Cost 1
100,000
15,000
0.15
$12.50 2
750,000
75,000
0.10
$25.00 3
1,000,000
50,000
0.05
$37.50

7. Travel Cost Method: Example Problem
Next plot the relationship between visitation rate and travel cost and express it in an equation
TC = - (50 / 0.20) * R + 50
= -250 * R + 50 or
R = - (0.20 / 50) * TC + 0.2
= * TC + 0.2

8. Travel Cost Method: Example Problem
Suppose a fee were charged to enter the park
Calculate the relationship between Origin 1’s visitation rate and the hypothetical fee
R = * (TC + Fee) + 0.2
TC1 = 12.5
R1 = * ( Fee) + 0.2
= * Fee or
Fee = -250 * R

9. Travel Cost Method: Example Problem
Calculate the relationship between Origin 2’s visitation rate and the hypothetical fee
R = * (TC + Fee) + 0.2
TC2 = 25
R2 = * (25 + Fee) + 0.2
= * Fee or
Fee = -250 * R2 + 25

10. Travel Cost Method: Example Problem
Calculate the relationship between Origin 3’s visitation rate and the hypothetical fee
R = * (TC + Fee) + 0.2
TC3 = 37.5
R3 = * ( Fee) + 0.2
= * Fee or
Fee = -250 * R

11. Travel Cost Method: Example Problem
Calculate the relationship between all three origins’ visitation rate and the hypothetical fee
R = * (TC + Fee) + 0.2
R1 = * Fee
R2 = * Fee
R3 = * Fee

12. Travel Cost Method: Example Problem
Calculate the relationship between the number of visitors from Origin 1 and the hypothetical fee
If Fee = 0, Q1 = 15,000
(status quo without any fee)
If Fee = 37.5, Q1 = 0
(max fee anyone would pay)
Q1 = - (15,000 / 37.5) * Fee +
15,000
Q1 = -400 * Fee + 15,000

13. Travel Cost Method: Example Problem
Calculate the relationship between the number of visitors from Origin 2 and the hypothetical fee
If Fee = 0, Q2 = 75,000
(status quo without any fee)
If Fee = 25, Q2 = 0
(max fee anyone would pay)
Q2 = - (75,000 / 25) * Fee +
75,000
Q2 = -3,000 * Fee + 75,000

14. Travel Cost Method: Example Problem
Calculate the relationship between the number of visitors from Origin 3 and the hypothetical fee
If Fee = 0, Q3 = 50,000
(status quo without any fee)
If Fee = 12.5, Q3 = 0
(max fee anyone would pay)
Q3 = - (50,000 / 12.5) * Fee +
50,000
Q3 = -4,000 * Fee + 50,000

15. Travel Cost Method: Example Problem
Calculate the relationship between the number of visitors from all three origins and the hypothetical fee
Aggregate curve (from horizontal summation) represents total recreational demand as a function of hypothetic park fee
Q1 = -400 * Fee + 15,000
Q2 = -3,000 * Fee + 75,000
Q3 = -4,000 * Fee + 50,000
Aggregate demand calculated by horizontal summation

16. Travel Cost Method: Example Problem
Calculate recreational value as area under the aggregate curve
The recreational value of the park is $1,531,250
Area A = ½ * ( ) * 5,000
= 31,250
Area B = ½ * ( ) *
(5, ,500)
= 328,125
Area C = ½ * ( ) *
(47, ,000)
= 1,171,875
Total Area = 1,531,250

17. Travel Cost Method: Example Problem
Calculate recreational value as area under each of the origin-specific demand curves (alternative method)
The recreational value of the park is $1,531,250
Area 1 = ½ * 37.5 * 15,000
= 281,250
Area 2 = ½ * 25 * 75,000
= 937,500
Area 3 = ½ * 12.5 * 50,000
= 312,500
Total Area = 1,531,250

18. Travel Cost Method: Example Problem
Calculate the number of visitors and consumer surplus if no fee were charged to enter the park
Total park visitation is 15, , ,000 = 140,000
CS is equal to recreational value: $1,531,250

19. Travel Cost Method: Example Problem
Calculate the number of visitors and consumer surplus if a fee of $20 were charged to enter the park
Total park visitation is 7,000 (Or. 1) + 15,000 (Or. 2) = 22,000
CS is area below demand curve and above price: $98,750
Area A = ½ * ( ) * 5,000
= 31,250
Area D = ½ * ( ) *
(5, ,000)
= 67,500
Total Area = 98,750

20. Random Utility Models (RUMs)
In RUMs for recreational fishing, anglers choose whether to go fishing, how many days to go fishing, where to go fishing, and what to fish for so as to maximize their utility
Frank Lupi, “Case Study of a Travel-Cost Analysis: The Michigan Angling Demand Model,” 2001 (link)

21. Random Utility Models (RUMs)
Increases in recreational fishing benefits due to environmental policies can be estimated from fish values derived from RUMs
Frank Lupi, “Case Study of a Travel-Cost Analysis: The Michigan Angling Demand Model,” 2001 (link)

22. Hedonic Pricing Models
Hedonic pricing models use the attributes of market products, including their environmental attributes, to explain variations in product prices
P = f (x, z, e)
P: price of market product (e.g., house)
x: vector of non-env. product attributes (e.g., lot size, bedrooms)
z: vector of non-env. local attributes (e.g., crime rate)
e: environmental attribute (e.g., local air pollution)
Marginal implicit price of environmental attribute or marginal willingness to pay for environmental attribute:

23. Hedonic Pricing Models
Suppose we wanted to study the variation in housing prices due to proximity to an airport (which generates noise, a negative environmental externality)
Price = β0 + β1*Bedrooms + β2*Bathrooms + β3*Airport + β4*Crime + β5*Scores + β6*Sold error
Price: Sale price of house in dollars
Number of Bedrooms
Number of Bathrooms
Near Airport: Dummy variable equal to 1 if the house is near the airport and 0 otherwise (so coefficient is not a slope in this case)
Crime Rate: Annual number of incidents per 10,000 population
Test Scores: Average test scores at public high school (out of 100)
Sold in 2008: Dummy variable equal to 1 if the house sold this year

24. Hedonic Pricing Models
Results of multiple regression
On average, a house near the airport sells for $49,198 less than a house not near the airport, all else equal
Fictional data

25. Hedonic Pricing Models
Issues and problems
Simultaneity: Prices are determined by both supply and demand, but these models treat supply as exogenous (i.e., unaffected by environmental attributes)
Selection: Individuals differ in their tolerance of negative environmental attributes
Information: Individuals’ perceptions of environmental attributes may differ from measurements
Omitted variable bias: Coefficients are too large or small if an explanatory variable associated with the dependent variable and correlated with other explanatory variables is left out
Scope: Relatively narrow range of applications

26. Contingent Valuation (CV)
CV studies use carefully designed surveys to elicit from individuals the value they place on a change in an environmental amenity or service
To perform CV studies well requires lots of time and money (and even then they may still be inaccurate)
Clear definition of environmental amenity or service and change
Clear identification of geographic scope of non-use value
Focus groups
Pretesting
Sampling
Analysis of results for reliability and validity

27. Contingent Valuation (CV)
Example: Closed-Ended Question
Ronald C. Griffin and James W. Mjelde, “Valuing Water Supply Reliability,” American Journal of Agricultural Economics, Vol. 82, No. 2 (2000), pp

28. Contingent Valuation (CV)
Example: Open-Ended Questions

29. Contingent Valuation (CV)
NOAA Guidelines and Kerala CV Study
Ronald C. Griffin et al., “Contingent Valuation and Actual Behavior: Predicting Connections to New Water Systems in the State of Kerala, India,” World Bank Economic Review, Vol. 9, No. 3 (1995), pp

30. Contingent Valuation (CV)
NOAA Guidelines and Kerala CV Study (continued)

31. Contingent Valuation (CV)
Issues and problems
Information: Respondents may not know much about the environmental amenity or service or the change
Artificiality: Respondents may not give accurate values because the payment or acceptance is hypothetical
Strategy: Respondents may not give accurate values because they want to influence environmental policy
Anchoring: Responses may be sensitive to starting points
Warm glow: Respondents may give accurately high values to make themselves feel good about their environmentalism