Lecture 17

Valuing the Environment: Methods

Byeong-Hak Choe
bchoe@geneseo.edu

SUNY Geneseo

October 16, 2024

Valuing the Envrionment: Methods

Revealed Preference Methods

Overview

  • Observable Methods: Analyze actual behavior and expenditures in real-world scenarios.

  • Indirect Methods: Infer values of non-market goods and services by observing related market behaviors.

  • Advantages: Based on actual choices, providing credible and robust estimates.

  • Limitations: Cannot capture non-use values, may underestimate values for those with lower costs.

  • Main Methods Discussed

    1. Travel-Cost Method (TCM)
    2. Hedonic Property Value Models
    3. Hedonic Wage Models

Revealed Preference Methods

Understanding the Need for Revealed Preference Methods

  • Example: A sport fishery is threatened by pollution, leading to a reduction in sportfishing opportunities.

  • Challenge: Determining the economic loss when access to the fishery is free and there’s no direct market price.

  • Key Question

    • How can we assign an economic value to the loss of a recreational resource that is not priced in the market?

Revealed Preference Methods

Travel-Cost Method (TCM) Overview

  • Concept: Travel Expenditure as a Proxy for Value.
    • Assumes that the time and money visitors spend traveling to a site reflect the site’s recreational value.
    • Demand Curve Construction: By analyzing how visitation rates vary with travel costs, a demand curve for the site is developed.
  • Applications of TCM
    • Recreational Resources: Valuing parks, lakes, wildlife reserves, and other natural attractions.
    • Policy Evaluation: Assessing the benefits of maintaining or improving recreational sites.

First Variant of TCM

Travel-Cost Demand Function

  • Demand Function: Relationship between travel costs and visitation rates.
  • Steps:
    1. Collect data on visits and travel costs.
    2. Estimate the demand function using regression analysis.
    3. Calculate consumer surplus.

Travel-Cost Demand Function

Example: National Park Visitation

Step 1. Data Collection

Origin Number of Visits Average Travel Cost (USD)
Region A 150 20
Region B 120 40
Region C 80 60
Region D 40 80
Region E 10 100

Travel-Cost Demand Function

Example: National Park Visitation

Step 2: Estimate the Demand Function

  • Perform linear regression to estimate the relationship between visits and travel costs.

  • Formula: \[ Q = \alpha + \beta P \]

  • Where:

    • \(Q\): Number of visits.
    • \(P\): Travel cost.
    • \(\alpha\), \(\beta\): Regression coefficients.

Travel-Cost Demand Function

Example: National Park Visitation

Step 2: Estimate the Demand Function

Travel-Cost Demand Function

Example: National Park Visitation

Step 3: Example Regression Result

Demand Function:

\[ Q = 188 - 1.8P \]

  • Interpretation:
    • \(\beta = -1.8\) → Each $1 increase in travel cost reduces visits by 1.8 visitors.
    • \(\alpha = 188\) → Hypothetical number of visitors if travel cost were zero.

Travel-Cost Demand Function

Example: National Park Visitation

Step 4: Predict Visitation

  • Example: If the travel cost is $60, the predicted number of visits is: \[ \begin{align} Q &= 188 - 1.8(60) \\ &= 80 \text{ visits} \end{align} \]

Travel-Cost Demand Function

Example: National Park Visitation

Step 5: Estimate Consumer Surplus

  • Consumer Surplus: Total recreational value derived from the site.
    • Area under the demand curve but above the travel cost represents the consumer surplus.
    • If travel cost is $60, consumer surplus will be:

Second Variant of TCM

Random Utility Model (RUM)

  • Purpose: Model the choice behavior of individuals when selecting among multiple recreational sites.

  • In the RUM, a person choosing a particular site takes into consideration site characteristics and its price (trip cost).

  • Characteristics affecting the site choice include ease of access and environmental quality.

  • Each site results in a unique level of utility and a person is assumed to choose the site giving the highest level of utility to that person.

  • Welfare losses from an event such as an oil spill can then be measured by the resulting change in utility should the person have to choose an alternate, less desirable site.

Challenges in TCM

  • Proximity Paradox
    • Those who live closest to the site may actually visit frequently and have low travel costs.
    • This would potentially be underestimating their true valuation.
  • Opportunity Cost of Time
    • Time spent traveling could have been used for other valuable activities.
    • Approaches: Wage Rate Method, Shadow Price of Time (Implicit value that individuals place on their time).

Using the TCM to Estimate Recreational Value

Beaches in Minorca, Spain

  • Minorca, Spain is a popular tourist destination, with its population doubling in summer due to tourism, mainly drawn by its beaches.
  • Researchers estimated the economic value of Minorca’s beaches using the TCM, with a hypothetical oil spill causing beach closures.

Using the TCM to Estimate Recreational Value

Beaches in Minorca, Spain

  • A RUM based on survey data from 573 individual surveys conducted at 51 beaches in 2008 was used for the analysis.
  • Surveys gathered travel details (origin, transportation, group size, demographics) and attitudes toward beach attributes (urbanization, sand type, cleanliness, crowding, amenities, environmental quality).
  • Positive characteristics for beach utility included north-facing, presence of lifeguards, toilets, drink vendors, thin sand, nudism, warm water, and environmental quality.
  • Negative characteristics included urban setting, crowding, algae, calm water, and non-northern orientation.
  • WTP to avoid beach closures ranges from 0.24 euros per day per person for west coast beaches (resulting in a 6,000-euro daily loss) to 1.73 euros for northern beaches (43,250-euro daily loss).

Using the TCM to Estimate Recreational Value

Florida Beaches

  • Survey 826 tourists:
    • Days spent at the beach and expenses incurred to use the beach, meals, travel and access fees, initial travel costs in and out of the state, length of stay, age, income, and other control data.
  • Hypothesis:
    • Holding all the other factors constant, lower beach expenses \(\rightarrow\) greater number of days at the beach
  • Results:
    • A 10% increase in “price” lead to a 1.5% decrease in time on the beach.
    • Average CS per day of $38.
    • Over 2 million tourists: $2.37 billion per year.