Choice Modeling Externalities:A Conjoint Analysis of Transportation Fuel

Preferences

Matthew Winden and T.C. Haab, Ph.D.

Agricultural, Environmental, and Development Economics

The Ohio State University

Outline

• Motivation

• Methodology

• Results

• Conclusions

Motivation

• Transportation Fuel Consumption Creates Large Externalities

• Market Pricing Mechanism Has Failed-Public Goods Nature of Externalities

• Government Correction Has Failed-Regressive Nature of Price Correction-Lack of Political Will Power

Motivation

• Correct price is necessary to achieve efficiency

So,• What are the optimal levels (costs) of

externalities to society?

• Knowing allows internalization (MSC=MPC)

Motivation

• Are externality types valued differently?

• Impacts on:(1) Human Health RiskVs(2) Natural Resource DepletionVs(3) Environmental Damage

Motivation

Attribute Examples of Attribute ComponentsEnv. Damage: Fish and Animal Populations

Levels of Air and Water Pollution

Nat. Res. Use: Extraction Rates and Stocks for Ores, Minerals, Oil, Natural Gas

Hum. Health Risk: Incidence Rate of Asthma & Cancers

Motivation

• Goals:

1.) Establish Willingness-To-Pay estimates for reductions in damages

2.) Establish Marginal Price estimates for externality classes

Methodology: Conjoint Analysis

• Estimates the structure of preferences

• Specify attributes & bundle into alternatives

• Respondent chooses preferred alternative

• Resultant choices allow for statistical inference

Methodology: Conjoint Analysis

• Each alternative represents potential fuel profile (i.e. mix of fuel types used)

• Different profiles embody different levels of externalities (attributes) imposed on society

• Impacts of profile measureable and capable of aggregation into an index for each externality

Methodology: Conjoint Analysis

Attribute Levels of Attribute ComponentsEnv. Damage 37.5, 45, 50, 55, 62.5

Nat. Res Use 37.5, 45, 50, 55, 62.5

Hum. Health Risk 37.5, 45, 50, 55, 62.5

Price ($/gallon) -10%, -5%, 0%, 5%, 10%

Methodology: Conjoint Analysis

• Based in RUM Framework

• Respondent chooses 1 of 3 alternatives

• Attributes: Environmental DamageNatural Resource UsageHuman Health RiskPrice

Methodology: Conjoint Analysis

Envi-ron-

mental Damage

Natural Resource

Use

Human Health Risk

0

20

40

60

80

100

50 50 50

$[GASPRICE] per gallon

Current Fuel Mix

Methodology: Conjoint Analysis

Envi-ron-

mental Damage

Natural Resource

Use

Human Health Risk

0102030405060708090

100

62.537.5 50

$[GASPRICE] per gallon

Fuel Mix A

Methodology: Conjoint Analysis

RUM frameworkVi

j = V(xij , β) + εi

j

i = individualj = alternativex = vector of attributes and characteristics ε = stochastic error term

Methodology: Conjoint Analysis

RUM Formalized: Linear and IID

Vij = β0 + xi

j β1 + (Mi - pi

j) β2 + εij

M = Incomep = price

Methodology: Conjoint Analysis

Probability of K chosen over j, for all j≠k

Pr(dVij>0) = ϑ (Δ(x) β1 – Δ(p) β2)

(See Kanninen 2007)

Results

SurveyRepresentative Sample of 857 Ohio AdultsCompleted by 537 (62.5%), 532 useable; met criteria of

(1) Adult Resident of Ohio(2) Estimate Vehicle MPG(3) Estimate price of fuel at last fill-up

Results

• Homeowner, Older, and Driver (more likely)

• Price (self-reported)mean = $1.88min = $1.00max = $2.99

• Attribute means 49.9(ED), 50.2(NR), 50.3(HH)

ResultsAttribute Conditional Logit Parameter EstimatesPrice -1.722*Env. Damage -0.099Nat. Res. Use -0.427*Hum. Health Risk 0.142(Environmental Damage)2 -0.0003(Nat. Res. Use)2 0.003*(Hum. Health Risk)2 -0.002*EnvDam × NatRes 0.003NatRes × HumHea 0.002HumHea × EnvDam 0.001EnvDam×NatRes×HumHea -0.0001

ResultsAlternative (Difference from Current) WTP ($/Alternative)10% Reduction in Each Attribute $0.84/gal25% Reduction in Each Attribute $2.98/gal

Attribute MP ($/Alternative)Environmental Damage Reduction $0.030/galNatural Resource Use Reduction $0.035/galHuman Health Risk Reduction $0.036/gal

Conclusions

• Demand (WTP) for reduction in externalities related to transportation fuel usage exists

• Current (baseline situation) reveals one class of externality is not viewed as more important

• Starting point for policy discussions

Limitations

• Price increase still necessary (political will)• Less impact, result in more driving?• Do respondents accurately understand and

value indexes?• Accurate measurement and combination of

attribute components into indexes• Uncertainty of externality impacts

Future Research

• Income element of utility function may be non-linear

• Fatigue/Learning Effects• Exploration of demographic differences (mixed

logit)• Relaxation of IIA (multinomial probit)

Special Thanks

• National Science Foundation

• Agricultural, Environmental, and Development Economics: The Ohio State University

• Wisconsin Economic Association