Mapping the WTP Distribution from Individual Level Parameter Estimates - PowerPoint PPT Presentation

Mapping the wtp distribution from individual level parameter estimates
Download
1 / 12

  • 56 Views
  • Uploaded on
  • Presentation posted in: General

Mapping the WTP Distribution from Individual Level Parameter Estimates. Matthew W. Winden University of Wisconsin - Whitewater WEA Conference – November 2012. Motivation.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

Download Presentation

Mapping the WTP Distribution from Individual Level Parameter Estimates

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Mapping the wtp distribution from individual level parameter estimates

Mapping the WTP Distribution from Individual Level Parameter Estimates

Matthew W. Winden

University of Wisconsin - Whitewater

WEA Conference – November 2012


Motivation

Motivation

  • Heterogeneity exists in respondents’ preferences, WTP, and error variances within the population (Lanscar and Louviere 2008)

  • Traditional Models Used in Non-Market Valuation Impose Distributional Assumptions About Preference Heterogeneity in the Population (Train 2009, Revelt and Train 1999)

    • Top-Down Modeling (Mixed Logit, Latent Class Logit)

  • Misspecification May Lead to Bias in Parameter, Marginal Price (MP), and Willingness-To-Pay (WTP) Estimates

    • Leads to inefficient policy analysis and recommendations

Matthew Winden, UW - Whitewater


Previous work

Previous Work

  • Louviere et al. (2008) estimate individual level parameters using conditional logit estimator (no welfare analysis)

    • Convergence issue 1: Collinearityof attributes

    • Convergence issue 2: Perfect Predictability

    • Cognitive Burden (Number of Questions/Attributes)

  • Louviere et al. (2010)

    • Best-Worst Scaling As Solution

  • Individual Models = “Bottom-Up Modeling Approach”

Matthew Winden, UW - Whitewater


Top down versus bottom up

Top-Down Versus Bottom-Up

“Top-Down” “Bottom-Up”

Assume

(, ) EstimateDerive

DeriveEstimate

Matthew Winden, UW - Whitewater


Contributions

Contributions

  • Objective 1: Use Monte-Carlo Simulation to Provide Evidence of the Validity of Individual Level Estimation Techniques

  • Objective 2a: Estimate Traditional and Individual Level Models on a Stated Preference Dataset

    • Eliminates Collinearity as a Convergence Problem

  • Objective 2b: Estimate Traditional and Individual Level Models on a Revealed Preference Dataset

  • Objective 3: Use Individual Level Estimates to Demonstrate Potential Bias Resulting from Distributional Assumptions in Traditional Models

Matthew Winden, UW - Whitewater


Traditional mixed logit

Traditional Mixed Logit

P(j|vi) = exp(Uji)/Σexp(Uji)

Utility of choice j for respondent i:

= αji + Βj+ ΦjZji + ΘjiWji

where:

αji= alternative-specific constant

Βj= vector of fixed coefficients

Χi= fixed individual characteristics

Φj= vector of fixed coefficients

Θj= vector of varying coefficients

Zji& Wji = choice-varying attributes of choices

Matthew Winden, UW - Whitewater


Individual level simulation estimation strategy

Individual Level Simulation & Estimation Strategy

  • 3 Datasets (A, B, C)

    • Known parameter, attribute, and error distributions

    • 100 respondents, 100 choice scenarios

    • Face 3 attributes (X1 & X2 - Uniform, X3 – Zero, Status Quo)

    • Face 3 alternatives (Respondent Specific Error Term to Each Alternative)

    • Have 3 individual specific betas for each of the three attributes

  • Simulation A

    • Beta 1 = Normal, Beta 2 = Normal, Beta 3 = Normal

  • Simulation B

    • Beta 1 = Normal, Beta 2 = Normal, Beta 3 = Uniform

  • Simulation C

    • Beta 1 = Normal, Beta 2 = Normal, Beta 3 = Exponential

Matthew Winden, UW - Whitewater


Mapping the wtp distribution from individual level parameter estimates

  • Individual Level Model Simulation

  • Results:

    • LL for Individual Level Models Indicates Better Fit than Correctly Specified Mixed Logits

    • Comparing True X3βValues, the Individual Level Model Performs Well Under All Distributional Specifications for the X3 Attribute

Matthew Winden, UW - Whitewater


Mapping the wtp distribution from individual level parameter estimates

  • Traditional and Individual Model Comparisons

  • Results:

    Table 34: Willingness-To-Pay Estimates ($/Gal)

Matthew Winden, UW - Whitewater


Conclusions so far

Conclusions? (So-Far)

  • Result 1: Validity of Individual Estimation Demonstrated through Simulation  Kind Of...

  • Result 2: Individual Level Model Distributions, MPs, & WTPs Differ Significantly from Outcomes Using Traditional Models

    • Role of Including or Excluding Individuals with Statistically Significant (but possibly Lexicographic) Preferences on Estimates

    • Role of Including or Excluding Individuals with Statistically Insignificant values (Round to Zero?)

  • Result 3: Without knowing underlying distribution, may inadvertently choose incorrect mixing distribution based on LL

Matthew Winden, UW - Whitewater


Extensions

Extensions

  • E1: True (Full) Monte-Carlo Simulation For Individual Level Specifcations

    • Vary Over Number Respondents, Number Choice Occasions, Number Attributes, Types of Distributions

  • E2: Comparison using Revealed Preference Dataset (Beach)

    • Introduced Potential Collinearity as a Convergence Issue

    • More Realistic Situation Under Which Heterogenity May Matter

  • E3: Develop Appropriate Significance Tests for Individual Level Models

  • E4: Scale Issues in Aggregation of Individual Respondents

Matthew Winden, UW - Whitewater


Thank you all for your time and attention

Thank You All For Your Time and Attention


  • Login