Attribute Based Stated Preference Methods by T. Holmes and W. Adamowicz

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Attribute Based Stated Preference Methodsby T.
Holmes and W. Adamowicz

• Carlos Mayen
• Ross Pruitt
• Christiane Schroeter

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Stated Preference

• Historically economists have relied on
  historical data of preference - revealed
  preference
• In many cases there is no historical data, we
  need to rely on what consumers say they will do
  stated preference
• Examples where stated preference data is
  necessary
• Organizations need to estimate demand for new
  products with new features
• Explanatory variables have little variability in
  the market place
• Observational data is time consuming expensive
  to collect
• The product is not traded in the real market
• Examples of SP tool include contingent
  valuation and attribute based SP methods (ABM)

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History of Attribute Based Stated Preference
Methods

• Two Branches
• Conjoint analysis
• Hedonic method (Court, 1939 Griliches, 1961)
• Consumer Demand theory (Lancaster, 1966)
• Discrete Choice Theory
• Random Utility Theory (Thurstone, 1927)
• Integration of Random Utility theory with hedonic
  analysis Conditional Logit model (MacFadden,
  1974)
• Integration of Conjoint Analysis and Discrete
  Choice Theory
• Experimental Designs (Louviere and Woodsworth,
  1983)
• Applications
• Marketing, Environmental Economics,
  Transportation

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General Idea

• A good or service is decomposed into its relevant
  attributes which in turn can have different
  levels price (low, high)
• Consumers are presented with several combinations
  of attribute levels
• Consumers choose, rate or rank such product
  profiles
• Econometric model estimates the utility function
  depending on product attributes
• Model Outputs relative importance of
  attributes, willingness-to-pay, market shares,

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Conducting SP Research

• Define the problem
• What are the objectives?
• Identify and describe choice context, attributes,
  levels, etc.
• Design the experimental apparatus
• Develop the questionnaire
• Collect data
• Estimate the model
• Analyze the results

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Attributes of a Melon Product
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Experimental Design for CE

• Factorial Designs
• Fractional Factorial Designs
• Randomized Designs
• Advantages
• In big experiments it is likely that the entire
  design space will be sampled
• by randomly generated profiles
• All interactions can be estimated
• Application
• Attribute levels assigned randomly to each
  attribute and brand
• 4 brands 4 product alternatives per task with
  inclusion of
• no purchase alternative

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Survey

• Presented to target population through survey
• convenience sample- Meijer
• Four choice tasks presented to each respondent
  in addition to demographic info
• Pictures meant to expedite the response time
• Hypothetical scenario set up through
  introduction of fresh-cut products and attributes

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Conditional Logit Model
The utility that the ith person obtains from
choosing the jth alternative is considered a
linear function of product attributes and a
stochastic term The probability that the ith
respondent chooses the jth alternative from
choice set C is the probability that the utility
for the jth choice is greater than the utility
for all other k choices in the choice
set. Assuming errors are iid with an extreme
value distribution, the probability that the ith
respondent chooses alternative j is
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Coding

• Two types
• Effects coding chosen attribute is coded 1, not
  chosen is coded 0, and the last attribute is
  coded -1
• Dummy variable coding chosen attribute is coded
  1, and not chosen is coded 0

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Coefficient Estimates
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Model Outputs

• Relative Importance of Attributes
• Marginal WTP for changes in attribute levels
• WTP (Coeff. 1 Coeff.2)/ Coeff. Price
• WTP (moderate juice no juice)
• Marginal Effects of attribute levels
• Market shares

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IIA Property

• Conditional logit model based on the following
  assumptions
• Everyone in the population has same preference
  structure
• Ratio of probabilities between any two
  alternatives unaffected by
• other alternatives in choice set
• Relaxing the assumption of common preferences
• Including interaction effects with demographic
  information
• Estimating a Latent class/finite mixture model
• Utilizing a random parameters/mixed logit model
• Relaxing the IIA assumption
• Nested logit model
• Mixed multinomial logit models

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Advantages of ABM

• Useful in behavioral analysis, market research
• Experiment control
• Allows to obtain individual information
• Subjects think about tradeoffs
• Thorough examination of preferences
• Valuation of the product and its attributes
• Potential for combination with revealed
  preference data

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Disadvantages of ABM

• Hypothetical bias
• Cognitively demanding for subjects
• Information provision challenging
• Strategic behavior
• Discrete nature of data problematic
• Cross-sectional/ time-series

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Summary

• ABMs are used to estimate value of attributes in
  goods or services
• As a stated preference method hypothetical bias,
  strategic responses, etc. are still a concern
• Requires good expertise on experimental designs