The Contingent

Valuation Method

Introduction to the

contingent valuation method

Theoretical basis for CVM

▪ WTP measures give an estimate of compensating variation

for welfare improving moves and of equivalent variation for

welfare decreasing moves.

▪ Example with indirect utility function where utility indirectly

depends on prices, income and environmental quality:

For z1 > z0

u0 = V(p, y, z0) = V(p, y - CV, z1) (WTP)

u1 = V(p, y, z1) = V(p, y + EV, z0) (WTA)

For z1 < z0

u0 = V(p, y, z0) = V(p, y + CV, z1) (WTA)

u1 = V(p, y, z1) = V(p, y - EV, z0) (WTP)

Further reading: Champ et al. (2003): Chapter 2.

Brief description

The words contingent valuation (CV) is derived from the

application of the method since respondents are asked a value

contingent upon the occurrence of a hypothetical situation.

Quality of the CV questionnaire is extremely important.

Present a scenario that describes a certain change in the

provision of a public good. The scenario also describes how the

respondent would pay for this provision. Then respondents are

1) Closed-ended: Asked to say Yes or No to a certain cost

(bid), or

2) Open-ended: Asked to state how much they would be

willing to contribute.

Steps in the develpment of a CV survey

1) Identify the valuation problem

2) Construct the scenario

3) Design elicitation question

4) Draw a sample of the population

5) Collect data (including collection of e.g. socio-

economic characteristics)

6) Estimate econometric models and test validity

(estimate the WTP and analyse its determinants)

7) Extrapolate these responses to the whole

population

However, in reality

▪ Information gathering about the problem (talk with

experts, read materials)

▪ First draft of scenario

▪ Feedback from experts

▪ Revision of scenario

▪ Focus groups

▪ Pilot study

▪ If substantial revisions: new pilot

▪ Revisions

▪ Main study

▪ Analysis

Note:

Focus groups and pilot studies are extremely valuable tools.

Three problems with CVM in developing

countries

1. Poor survey implementation

Poorly executed surveys a major

problem. Researchers are badly trained at this

and it is difficult! Enumerators are critical link.

Train them and treat them well. Check their

impact with dummies in the analysis.

Three problems with CVM in developing

countries

2. Poorly crafted scenarios

Important and difficult hurdle – not easy!

Construct an interesting deal to be accepted or

rejected. Respondent should be sufficiently

intrigued and understand it as intended.

Put yourself in the respondents place! Does the

question make economic sense?

There is a link between the good to be valued,

the scenario and the elicitation method to be

used. Many possible pitfalls.

Three problems with CVM in developing

countries

3. Failure to test design

General recommendation to use split-samples to

test designs to:

▪ Gain insights if the right choices were made in the

design process (responses should be sensitive to

design).

▪ Opportunity to learn what works and what does not

work – particularly for funding agencies etc that are

interested in low-cost robust methods.

What could go wrong?Problem 1

Individuals answer willingness to pay questions

inaccurately due to …

▪ Problems with the method itself, and/or

▪ Problems with survey administration.

Problem 2

Miscommunication between the interviewer and the

respondent due to poorly designed questionnaire.

Problem 3

Problems involved with aggregation of individual

responses to CV questions.

▪ Due to non-random samples;

▪ Insufficient sample size;

▪ Poor econometrics.

What should we do?

▪ Design better survey instruments;

▪ Manage the survey implementation better

(e.g., training and supervision of

enumerators);

▪ Better research design, sampling,

econometric analysis.

Standard components of a CV questionnaire

▪ An introductory section that helps set the general context for

the decision to be made;

▪ A scenario which includes a detailed description of the good,

service, or project to be offered to the respondents as well as

the manner in which the respondent is to pay for the good,

service, or project;

▪ The institutional setting in which the good will be provided;

▪ A method by which the questionnaires elicits the

respondent’s preferences for (or against) the good or service;

▪ Debriefing questions about why respondents answered

certain questions the way they did;

▪ Questions about the characteristics of the respondent,

including socioeconomic and attitudinal information.

Design of contingent

valuation survey instruments

General issues in questionnaire design

▪ Time constraints

▪ Negotiating questions to include

▪ Sequence of modules

▪ Revealed preference data

▪ Time-to-think experiments

▪ Multiple income proxies

▪ Tag-on on other surveys

Again, steps in the develpment of a CV survey

1) Identify the valuation problem

2) Construct the scenario

3) Design elicitation question

4) Draw a sample of the population

5) Collect data (including collection of e.g. socio-

economic characteristics)

6) Estimate econometric models and test validity

(estimate the WTP and analyse its determinants)

7) Extrapolate these responses to the whole

population

1. Identification of the problem

▪ What is the problem?

▪ How to change the environmental

quality?

▪ What will the effect be of the changed

quality?

2. Construction of the scenario

▪ Story => reason for payment

▪ Conceptual model – CBA rule

▪ Theoretically accurate?

▪ Extent of “market”? (should be those with

economic and legal standing)

▪ Sensitive to amount and kind of information

regarding environmental change

▪ Explaining risk, probabilities

The scenario ...

Does the story make sense?

▪ Policy relevant?

▪ Plausible/reasonable?

▪ Understandable?

▪ Meaningful?

▪ Pre-test, pre-test, pre-test

Notes: differences b/w pre-test and pilot?

More specifically in the scenario

▪ When the service will be available?

▪ How will they pay? (payment vehicle)

▪ How much others will be expected to pay?

▪ Who will pay?

▪ What are conditions for the provision of the good?

(e.g. more than 50% says Yes);

▪ What institutions will be responsible for the delivery of

the good or service?

▪ Quality and reliability of the service?

▪ Include “cheap talk” script?

▪ Remind them about budget constraint

What is cheap talk?

▪ Cheap talk refers to a text script which

is shown to respondents prior to

completing an elicitation question or an

experiment. The script emphasizes the

importance of the respondent’s answer,

despite the hypothetical nature of the

designed task (Fifer et al., 2014).

Example of cheap talk script

The experience from previous similar surveys is that

people often respond in one way but act differently. It is

particular common that one states a higher willingness

to pay than what one actually is willing to pay for the

good in the store. We believe this is due to the fact that

one does not really consider how big an impact an extra

cost actually has to the family budget. It is easy to be

generous when one does not really need to make the

choices in a store. If you have another idea or comment

on what this behavior depends on, please write this

down on the last page of the questionnaire.

Follow-up of the scenario

▪ Test understanding

▪ Give them time to think (Cook et al, 2011)

▪ Debriefing questions; identifying “scenario

rejecters”

Informational content

We often face the conflict between how much

information we CAN give and how much information

we NEED to give.

Most respondents can not handle to much information.

In the case of environmental issues, but also health

issues:

▪ How much, or little, does the respondents know in

advance about the problem?

▪ How do we communicate complicated matters

such as risks, ecosystems, etc?

Example: Risk description I

We will consider the risk for people between the ages of 18 and 70 years. There

are currently 6 million people in this age group in Sweden. Although the number

of people that die in accidents within this age group varies from year to year, we

are still able to calculate the average risks based on statistics from different

accident types.

For example: During a ten year period, 600 adults die in fire accidents in

Sweden. We can then say that the average risk of dying in a fire accident is 600

in 6 million.

This is the same as saying that the average risk of dying in a fire accident is 6 in

60.000. For road traffic accidents, the risk of dying during a ten year period is 35

in 60.000 while for drowning accidents the risk is 8 in 60.000.

We will now require you to answer a number of questions. In each question,

there is the possibility of taking a measure that will reduce your own personal

risk of dying in a particular accident type. In each question, we assume that your

current level of risk is given. This risk will vary between questions and you are

only able to reduce this risk by taking the accident prevention measure. Please

take this risk as given, even if you believe that you are able to influence this risk

in some other way.

Example: Risk description II

The payment vehicle

▪ Should be perceived by the respondent to be linked directly

to the provision of the good.

▪ Should be viewed as mandatory once a commitment has

been made, so that respondents take the notion of payment

seriously.

Typical mechanisms:

▪ Tax

▪ Utility bills, for example water and electricity

▪ Donations

The first two are coercive and the last is voluntary. In general,

we use coercive payment vehicles.

▪ Lump sum or monthly?

▪ Money, labor or rice?

3. Design elicitation questions

▪ Open ended

▪ Payment card

▪ Bidding game

▪ Binary/dichotomous choice/single-bounded

▪ Double-bounded/triple-bounded

Further reading: Bateman et al. (2002): Chapter 4.

Elicitation methods I

Open-ended

How much would you at most pay as solid waste management

fee in order for you household to participate in the solid waste

management program? Express in per month and per

household:

........................... Rs per month

Single-bounded

If the solid waste management fee for your household is

Rs............ per month would your household participate in the

solid waste management program?

1. Yes 2. No

The amount they are offered is called the bid. Everybody does

not face the same bid. The bid is drawn from the bid vector.

Elicitation methods II

Double-bounded15. If the solid waste management fee for your house hold is Rs X.

per month would your household participate in the solid waste

management program?

1. Yes Go to 15b

2. No Go to 15c

15b. What if the fee for your household instead was Rs 2*X per

month, would your household participate in the solid waste

management program?

1. Yes 2. No

15c. What if the fee for your household instead was Rs 0.5*X per

month, would your household participate in the solid waste

management program?

1. Yes 2. No

Open-ended versus single-bounded

Open-ended▪ More information about WTP

▪ No hint/cue about the cost

▪ More difficult to answer

▪ Not incentive compatible

Single-bounded▪ Easy to answer

▪ Resemble market transaction

▪ Incentive compatible

▪ In the public good case, this resembles a vote for the public

good.

▪ Less information, which means larger sample size is required,

and more complicated econometrics.

▪ Yea-saying/Nay-saying. The bid does not affect the behaviour.

Single-bounded versus double-bound

Double-bounded/bidding game

▪ More information, i.e. smaller sample sizes

▪ Not incentive compatible

▪ Evidence that the preferences are not stable over

the experiment. The preferences reflected in the

first question are not the same as the preferences

in the second question.

▪ More difficult to answer.

Design of the bid vector

▪ How many bids should be used and what should the bids be?

▪ The goal of experimental design is to find the bid vector that

provides maximum possible information about the

parameters of the WTP distribution, given the size of the

sample.

▪ One fundamental problem of optimal design is that we need

to make an assumption about the true underlying WTP

distribution.

▪ This assumption should be based on extensive pilot studies.

▪ Use focus groups, pre-test and iterative surveys with updated

designs.

▪ In practice, CVM practitioners seem to rely more ad-hoc

rules, or perhaps are taking precaution against wrong priors.

Debriefing questions

▪ Did the respondent really care?

▪ Identify respondents with zero WTP - Why

zero WTP? True zero or protest? (compare

“cannot afford” versus “the government should

pay”).

▪ Measuring uncertainty

(If no WTP) Why don't you want to pay

anything for the provision of public good?

1. We are too poor.

2. The government should pay.

3. We are exempted from use.

4. We don't get anything/not much from the good.

5. We have good access to other sources.

6. Man is not present to decide.

7. We don't have cash.

8. Village committee has to decide.

9. Because the present situation gives us enough.

(Interviewer) Did the respondent:

1. Give the questions prolonged consideration in an

effort to arrive at the best possible value?

2. Give the questions careful consideration, but the

effort was not prolonged?

3. Give the questions some consideration?

4. Give the questions very little consideration?

5. Other (specify):

Tests of design and other hypotheses

Use various split samples to test for:

▪ Variations in the goods

▪ Variants of scenario

▪ Design and bias tests

▪ Survey implementation tests

Implementation of survey

4. Sampling

▪ Determine the target population

▪ Create the sampling frame

▪ Draw the sample from the sampling frame

• Random sample

• Stratified sample? (eg by income or ethnicity)

• Sample size?

– Depends on elicitation format

– Budget issue

Sampling frame

▪ Look out for poor/landless/women

▪ Choose household or individual household head

often the relevant level of disaggregation.

Common approaches:

▪ Village roster

▪ Census, tax or voting rosters

▪ Random sample in village (or “every fifth door”)

▪ Be careful with marginalized groups, e.g.

schedule caste in separate hamlets.

Sampling frame: Example

▪ I made village maps, and identified each

household

▪ Used random function in EXCEL

▪ Gave lists of hh:s and maps to

enumerators including extra numbers

(better to track down the first choice

because of potential selection bias)

Delivery mode

▪ Mail/telephone/in-person/www (mail is

cheapest and most common in

developed countries).

▪ Personal interview the only possible

option in many cases. Also considered

the best, but not neutral, as we will see.

▪ Be careful with training of enumerators in

order to decrease problems with biases.

Training enumerators

▪ Understanding the subject matter of the

survey.

▪ Understanding the CVM and the

research design.

▪ Imparting skills to be a good interviewer.

(If no WTP) Why don't you want to pay anything for the public good?

1. We are too poor.2. The government should pay.3. We are exempted from use.4. We don't get anything/not much from the good.5. We have good access to other sources.6. Man is not present to decide.7. We don't have cash.8. Village committee has to decide.9. Because the present good gives us enough.

Training Enumerators (cont.):Do’s and don’ts

1. Read every question exactly as written – do not improvise.

2. Read the questions slowly enough so that respondents can understand.

3. Wait for the respondent to answer.4. If the respondent cannot answer, repeat the question.5. Remain absolutely neutral about the respondent’s

answers.6. Do not act embarrassed about a respondent’s answers

to sensitive questions.

Training Enumerators (cont.):Do’s and don’ts

7. Never suggest an answer.

8. Don’t repeat the respondent’s answers.

9. Conduct the interview in private.

10. Do not give advice to respondents on personal matters.

11. Answer directly any questions the respondent may have about the purpose of the survey.

12. Listen carefully to the respondent’s answer.

▪ Contact a coding company to check the questionnaires for coding.

▪ Try to pre-code as much as possible.▪ Think about coding of zero, not applicable, no

answer.▪ Definition of household▪ When do respondents have time to respond?

(season of year, time of day, etc)▪ Transportation/accommodation▪ Double entry of data

Logistics

Pilots

▪ About 10-20% of final sample. Update bid

vector.

▪ Bring computer and printer – continuously

update coding.

▪ Make sure you have time between the

pilot study and the main survey to

implement any changes.

▪ You should even be prepared to do a

second pilot if things go wrong.

5. Collecting data

▪ Don’t let others (e.g. village leaders)

participate. Do it fast. Be sensitive to

local situation (e.g. funeral, problem

makers).

▪ Be careful with compensation of

respondents and villages.

▪ Different starting prices can lead to

confusion and reactions.

Pilots …

▪ First test is to see if bids can be explained

by variables proposed by economic

reasoning/model (construct validity).

▪ (Old rule of thumb from Mitchell and

Carson is an adjusted R2 of 0.15)

Validation

Checklist of Questions to Consider in the

Design of CV Survey Instruments

1. Should you have the respondent sign an

“informed consent” form?

2. Does the introductory material convey a sense

that the respondent’s answer will be meaningful

in policy matters and thus encourage the

respondent to take the interview seriously

(consequentiality)?

Checklist of Questions to Consider (cont.)

3. Are the questions in the introductory section

“easy” for the respondent to answer? Are they

“warming up” the respondent to think about the

valuation question(s)? Do they cover existing

attitudes and practices about the good or service

to be valued?

4. Does the CV scenario include questions to break

the presentation of material to the respondents?

Do they collect useful information?

Checklist of Questions to Consider in the

Design of CV Survey Instruments (cont.)

5. Does the CV scenario include a “cheap talk”

script to encourage the respondent to tell the

truth?

6. Are you satisfied that the payment vehicle is

credible?

7. Do you want to follow-up the valuation

question(s) with questions designed to gauge

how certain the respondent is of her/his answer?

Checklist of Questions to Consider (cont.)

8. Are debriefing questions included to better

understand the respondent’s motivations for his/her answers to

the valuation questions? Are the coded answers to the

debriefing questions sufficiently detailed to include most

respondents’ answers?

9. Is there a sequence of questions that clearly

identifies who is “in the market” and who is not (in

case you need to estimate a spike model)?

10. Are there multiple questions that can be used to

classify respondents’ income levels (socioeconomic

status)? Don’t forget to obtain the respondent’s

electricity and other infrastructure bills, if relevant!

Checklist of Questions to Consider (cont.)

11. Is there a question that requires the enumerator to compare

the respondent’s WTP response with his/her income and

confirm that the WTP response is reasonable? Can the

enumerator record a revised WTP answer?

12. If a discrete choice referendum valuation question

is used, is there a debriefing section at the end of the

questionnaire that tells the respondent the purpose of the

split-sample experiment and why some respondents were

offered one price and others will be offered a different price

(in order to avoid spreading misinformation)?

13. Is there a section at the end of the questionnaire to record

the enumerator’s assessment of the

interview?

Readings?

Bateman et al. 2002. Economic valuation

with stated preference techniques: A

manual. Edward Elgar. Chapters 3, 4.

Other Issues with

Contingent Valuation

Method

Whittington, D. (2010). What Have We Learned

from 20 Years of Stated Preference

Research in Less-Developed Countries?

Annual Review of Resource Economics, (2)

pp.209-236.

1. CVM in developing countries

Carlsson, F. (2010). Design of stated preference

surveys: Is there more to learn from

behavioral economics? Environmental and

Resource Economics, (46) pp.167-177.

2. Design of stated preference surveys

Cook, J., M. Jeuland, and D. Whittington.

(2012). Giving stated preference respondents

‘’Time to Think’”: Results from four countries.

Environmental and Resource Economics,

(51) pp.473-496.

3. Time to think

Venkatachalam, L. (2004).The contingent

valuation method: A review. Environmental

Impact Assessment Review, (24) pp.89-124.

4. A review of CVM

Choice Experiments

Introduction

What is it?

In a survey, setting people choose their preferred alternative

from a choice set.

Repeated choices! So each individual answers more than one

question.

The alternatives are described by a number of attributes,

including a monetary (cost) attribute.

So

▪ Each choice question is called a “choice set”.

▪ Each alternative is described in terms of a common set of

“attributes”.

▪ Alternatives are differentiated by the “levels” taken on by

the attributes.

Example 1:

Wetlands in

Sweden

Your choice

(Mark your choice)

Wetland Alternative 1

Simple ponds

Alternative 2 Alternative 3

Surrounding vegetation

Fish

Cray fish

Biodiversity

Walking facilities

Fence

___________________

Total cost per citizen

Your Choice

Forest

No actions

No introduction

Low

No walking

facilities

No

______________

SEK 0

Forest

Good conditions

Introduction

Low

No walking

facilities

No

_____________

SEK 850

Meadow-land

No actions

No introduction

High

Walking

facilities

Fence

______________

SEK 400

Example 2: Contract choice in China

Contract 1 Contract 2 No contract

Tenure length 25 years 50 years

I don’t get a

contract for the

plot.

In other words,

I prefer not to

contract the plot.

First right for contract

You have priority

to renew the

contract. But you

don’t know the

price.

You don’t have

priority to renew

the contract.

Enforcement

The contract can

be prematurely

ended. The risk is

5 out of 100

contracts.

The contract

cannot be

prematurely ended.

Harvest right

Half the times you

get it when you

want it. If you do

not get it you have

to wait 1 year.

Half the times you

get it when you

want it. If you do

not get it you have

to wait 4 years.

Total payment

Annual payment

700

28

500

10

Your choice

Example 3: Flood insurance in Vietnam

The “history” of CE

▪ CE popular in transport and marketing. Valuation of

different attributes. Which became an interest in

environmental economics.

▪ At the same time CVM criticized; a hope that CE could

”solve” some of the problems.

▪ A combination of the knowledge about surveys from

CVM and a development of econometric techniques

resulted in a rapid development of CE in environmental

economics.

▪ But, the hope that CE would solve the biases of CVM

did not really realize. Same problems as with CVM.

But why do CE and not CVM?

CVM and CE are to a greater extent complements

than substitutes. CVM originates from the welfare

approach, while CE from the marketing approach.

CVM: Estimate WTP for a certain scenario.

CE: Estimate marginal WTP for attributes of a

scenario.

So what method we should use depends more on

what we want to do.

Designing a Choice

Experiment

The steps of a CE survey

1) Design the questionnaire including the scenario;

2) Draw a sample of the population;

3) Ask the sample to answer the choice sets;

4) Estimate the parameter of the utility function and

estimate welfare measures (usually MWTP);

5) Extrapolate these responses to the whole

population.

Very much the same procedure as the design of CV

surveys. For example, the scenario is equally

important here. So we will focus on additional aspects.

Design of the choice experiment

1) Definition of attributes and levels;

2) Experimental design;

3) Context, validity tests and

questionnaire development;

4) Sample and sampling strategy.

1. Attributes and levels

1) Do focus groups and pilot studies. Extremely important.

2) Attributes should be expected to affect peoples’ choices.

3) Policy relevant attributes.

4) Do people understand the attributes? This can be a

particular problem for environmental attributes.

The general recommendation is to identify all relevant

attributes. We do not have to include all relevant attributes, but

in that case we need to inform respondents that those not

included are held constant.

The choice of attributes and levels is often a compromise

between what we want to know and complexity for the

respondent.

Attributes continued

Krupnick (2011) describe the need to construct attributes as

“endpoints” or items that are directly incorporated in the utility

functions or household production functions of consumers.

Thus, in a passive use valuation of endangered species

programs, habitat improvements may not be the relevant

attribute as that is not the “endpoint”. The effect on species

survival or species status (threatened, endangered, etc.) is

more likely the endpoint.

Schultz et al (2012) develop a set of standards for attributes or

indicators in stated preference studies. These include

measurability (quantifiable), interpretability (can be understood

by a non-scientist), applicability (linked to endpoints) and

comprehensiveness (a complete description of the outcomes or

endpoints).

2. Experimental design

Suppose that we are going to run a CE with three 2-level

attributes and two 3-level attributes 23*32 = 72 possible

combinations. And this is only for one of the alternatives.

Optimal statistical design is a method for reducing the number

of choice sets. Two key factors are:

1) Identification: We must make sure that the effects or the

parameters of the utility function are estimable given our

design.

2) Precision: We whish to estimate the parameters as

precisely as possible.

BUT LET US WAIT WITH THIS….

3. Context and Questionnaire development

The scenario is extremely important.

- Excellent work in the CVM literature.

- Simple and clear, but at the same time necessary information has

to be given.

- Particularly important and difficult for environmental and health

problems.

Alternative specific versus generic presentation of alternatives

- Alternative specific: easier to understand but can create biases

- Generic: focus on the trade-off between attributes.

The possibility of opt-out

- Do not distort the welfare measure (?)

- If we focus on the trade-off between attributes then no need for an

opt-out.

Alternative-specific vs. generic

This has to do with the labeling of the alternatives. In most

cases in environmental economics, generic designs are used.

But not always.

Park A Park B

Distance to

volcano50 meters 400 meters

Chance to

see crater

2 out of 10

days

9 out of 10

days

Price $5 $10

Poas Park Arenal Park

Distance to

volcano 50 meters 400 meters

Chance to

see crater2 out of 10

days

9 out of 10

days

Price $5 $10

What information do we get

In the generic:

How visitors value the two attributes in “general”. In principle

this can be transferred to any of the parks in Costa Rica.

In the alternative-specific:

How visitors value the two attributes for the two parks +

difference in valuation between the two parks. More difficult to

transfer the values to other parks.

So the format you use depends on the information you want.

However, by using generic design we face the risk that we miss

something crucial for a particular park (for example that they

don’t have a volcano ☺).

Opt-out or not

Park A Park B No park

Distance

to volcano

50

meters

400

metersNothing

Chance to

see crater

2 out of

10 days

9 out of

10 daysNothing

Price $5 $10 0

Park A Park B

Distance to

volcano50 meters 400 meters

Chance to

see crater

2 out of 10

days

9 out of 10

days

Price $5 $10

What information do we get?

With opt-out

How many people that would not buy any version for that given

combination?

The marginal WTP for the attributes for those that do not choose the

opt-out.

Without opt-out

The marginal WTP for the attributes for all respondents.

What should you go for?

Forced choice in reality? For example, choice of water/energy

contract.

Are you particularly interested in non-demanders?

Do you think that non-demanders would make different trade-offs

than those who demand?

Constructing validity tests

▪ Transitivity: if APB and BPC then APC.

▪ Stability: are preferences stable over the

experiment (test learning).

▪ Bozo test: if A is strictly better than B, then

everybody should prefer A.

Empirically: In general, people do not violate

the test. Again, this depends on the

complexity of the experiment. But we will get

back to this.

4. Sampling

The sample size is of course for researchers partly a

budget issue.

Things to think about:

▪ Sample size depends on the ”size” of the choice

experiment.

▪ Sample size also depends on how many sets each

respondent answers.

In theory discrete choice models required a lot of

observations to receive large sample properties. We

rarely have that many observations.

2. Going back to the experimental design

Suppose that we are going to run a CE with three 2-level attributes

and two 3-level attributes 23*32 = 72 possible combinations (full

factorial design). And this is only for one of the alternatives.

Optimal statistical design is a method for reducing the number of

choice sets. Two key factors are:

1) Identification: We must make sure that the effects or the

parameters of the utility function are estimable given our design.

2) Precision: We whish to estimate the parameters as precisely as

possible.

Fractional factorial design:

▪ Orthogonal design

▪ Efficient designs

Some examples: Johnson et al, Chapter 7.

Some examples: Johnson et al, Chapter 7.

Some examples: Johnson et al, Chapter 7.

Some examples: Johnson et al, Chapter 7.

Some examples: Johnson et al, Chapter 7.

Some examples: Johnson et al, Chapter 7.

Some examples: Johnson et al, Chapter 7.

Some examples: Johnson et al, Chapter 7.

Orthogonal main effects design

One fundamental rule that is most often used is that the design

should be orthogonal: the variation of the attributes should be

uncorrelated.

Furthermore, in many cases we are only interested in

estimating the main effects: the effect of each attribute on utility,

and not the interaction between them.

With these two principles we have a orthogonal main effects

design.

Suppose we have a very simple design problem with three

attributes, two of the attributes have 4 levels and one has 2

levels. There are thus 32 possible combinations.

Creating the choice sets

So far we have only generated levels combinations

for one alternative. Now we must create the choice

sets.

There are a number of alternatives available, all more

or less using different computer packages. Examples

of such designs are:

1) Comparing each alternative of the design with a

base alternative.

2) Cyclical design

3) Random designs

4) Creating the choice sets simultaneously.

Cyclical Design/Foldover

From an orthogonal array create additional alternatives in each choice set

by cyclically adding alternatives to the set. For example, the levels of these

added attributes add one to the level of the previous alternative. When the

highest level is attained, the level of the attribute is set to its lowest level.

Only for generic designs.

Alternative 1 Alternative 2

Choice set X1 X2 X3 X1 X2 X3

1 1 3 3 0 0 0

2 1 2 2 0 3 3

3 1 1 1 0 2 2

4 1 0 0 0 1 1

5 0 3 0 1 0 1

6 0 2 1 1 3 2

7 0 1 3 1 2 0

8 0 0 2 1 1 3

Simultaneous design

Suppose that there are two alternatives in

each choice set.

We would then generate an orthogonal design

with 2*number of attributes, thus (2142)*(2142)

possible combinations.

Suitable for alternative specific designs.

Blocking

Even if we have reduced the number of choice

sets with statistical design principles. The number

of sets might be to many to answer for each

respondent.

What we then do is to block the design so that

different respondents answers different subsets of

the main design.

We can do this randomly or using design

principles.

Summary orthogonal designs

Advantages:

Orthogonal and smaller than the full factorial.

Disadvantages:

Might still be too large, but this can be solved by

blocking.

May contain choice situations that does not provide

much information.

Other designs can yield more reliable parameter

estimates.

Readings?

Bateman et al. 2002. Economic valuation

with stated preference techniques: A

manual. Edward Elgar. Chapter 6.

Hoyos, D. 2010. The state of the art of

environmental valuation with discrete

choice experiments. Ecological

Economics, (69) pp.1595-1603.

Other Issues with

Choice Experiments

1. Hypothetical bias

Fifer, S., J. Rose, and S. Greaves. (2014).

Hypothetical bias in stated choice experiments:

Is it a problem? And if so, how do we deal with

it? Transportation Research, pp.164-177.

Hensher, D,A. (2010).Hypothetical bias, choice

experiments and willingness to pay.

Transportation Research, (44) pp.735-752.

2. Status quo bias

Meyerhoff, J., and U. Liebe. (2009). Status quo

effect in choice experiments: Empirical

evidence on attitudes and choice task

complexity. Land Economics, (85), pp.515-528.

Whittington, D., and W. Adamowicz. (2011).The

use of hypothetical baselines in stated

preference surveys. Environment for

Development, Discussion Paper Series.

3. Dominant preferences

Scott, A. (2002). Identifying and analyzing

dominant preferences in discrete choice

experiments: An application in health care.

Journal of Economic Psychology, (23), pp.383-

398.

4. Ordering effects in choice experiments

Carlsson, F., M.R. Morkbak, and S.B. Olsen.

(2012). The first time is the hardest: A test of

ordering effects in choice experiments. Journal

of Choice Modelling, (5), pp.19-37.

5. Effects coding in choice experiments

Bech, M., and D. Gyrd-Hansen. (2005). Effects

coding in discrete choice experiments. Health

Economics, (14), pp.1079-1083.

6. ANA in choice experiments

Thanh Cong Nguyen, J. Robinson, J.A. Whitty, S.

Kaneko, and Nguyen The Chinh. (2015). Attribute

non-attendance in discrete choice experiments: A

case study in a developing country. Economic

Analysis and Policy, (47), pp.22-33.

Erdem, S., D. Campbell, and A.R. Hole. (2014).

Accounting for attribute-level non-attendance in a

health choice experiment: Does it matter? Health

Economics.