Conjoint Analysis and Contingent Valuation in Environmental Economics, Slides of Environmental Economics

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Choice Modeling and Conjoint

Analysis

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Choice Modelling

• CM is a non-market valuation technique that is becoming increasingly popular in environmental economics, but also in other fields, such as management of cultural goods, planning, etc.
• Stated-preference technique—elicits preferences and places a value on a good by asking individuals what they would do under hypothetical circumstances, rather than observing actual behaviors on marketplaces.
• Survey-based technique.
• Contingent valuation is a special case of choice modeling
• 3 main approaches to elicit preferences with choice modeling:
  • ranking (choose the most preferred, then the second most preferred, etc.)
  • rating (give to each alternative a number from 1 to X to indicate strength of preference)
  • choice (choose the most preferred“conjoint choice”)

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Limitations of ranking approach

• Heavy cognitive burden
• It is probably easy to identify the most preferred and the least

preferred options, but it might be not so easy to rank the options in the middle  “noise”

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Contingent Rating

Respondents are shown different representations of the good and are asked to

rank each representation on a numeric or semantic scale.

On the scale below, please rate your preferences in buying the following car.

Car attributes

Fiat Punto 1.2 16V ELX Price £ 9, Number of Seats 5 Cubic capacity 1242 Gear Manual Maximum speed 172 km/h Number of doors 3 Consumption (liters/100 km)

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Baggage car 1.080 dm^3 1 2 3 4 5 6 7 8 9 10 Very high preference Very low Preference

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Conjoint Analysis

Suppose you are facing the choice of buying a new car. Choose one of the following cars according to your preferences. You may even choose not to buy any of these cars.

Cars attributes (^) 1.2 16V ELXFiat Punto^ Ford Focus 1.6 16V^ Volkswagen Polo 1.4 16V

Price £ 9,750 £ 10,120 £ 12,

Number of Seats 5 5 5

Cubic capacity 1242 1596 1390

Gear Manual Manual Automatic

Maximum speed 172 km/h 185 km/h 171 km/h

Number of doors 3 5 3

Consumption (liters/100 km) 6 6.8 6.

Baggage car 1.080 dm^3 1.205 dm^3 1.184 dm^3

Which would you buy?

Fiat Punto? □

Ford Focus? □

Volkswagen Polo? □

Would you prefer not to buy any of these cars? □

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Conjoint Analysis

(conjoint choice analysis,

choice experiments,

conjoint choice experiments)

• In a conjoint choice exercise, respondents are shown a set of alternative representations of a good and are asked to pick their most preferred.
• Similar to real market situations, where consumers face two or more goods characterized by similar attributes, but different levels of these attributes, and are asked to choose whether to buy one of the goods or none of them.
• Alternatives are described by attributes—the alternatives shown to the respondent differ in the levels taken by two or more of the attributes.
• The choice tasks do not require as much effort by the respondent as in rating or ranking alternatives.

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Example of conjoint choice question from Boxall et al. (1996).

Assuming that the following areas were the ONLY areas available, which one would you choose on you r next hunting trip, if either?

Features of the hunting area

Site A Site B

Distance from home to hunting area

50 km 50 km

Quality of road from home to hunting area

Mostly gravel or dirt, some paved

Mostly paved, some gravel or dirt

Access within hunting area

Newer trails,

passable with a 2WD vehicle

Newer trails,

passable with a 4WD vehicle

Encounters with other hunters

No hunters, other than those in my hunting party, are encountered

Other hunters, on ATVs, are encountered

Forestry activity

Some evidence of recent logging found in the area

No evidence of logging

Moose population Evidence of less than 1 moose per day^ Evidence of less than moose per day^1

Neither Site A or Site B

I will NOT go moose hunting

Check ONE and only one box

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Conjoint choice question from Hanley et al. (2001)

Which route would you prefer to visit in the summer, given the two routes

described below?

Characteristics of Route Route A Route B

Length of climb 100 meters 200 meters

Approach time 3 hours 2 hours

Quality of climb 2 stars 0 stars

Crowding at route Crowded Not crowded

Scenic quality of route Not at all scenic Not at all scenic

Distance of route from

home

160 miles 110 miles

Prefer Route A?

Prefer Route B?

Stay at home? (Choose neither?)

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Example of conjoint question from Alberini et al. 2005

1) Land use

2) Moorings

3) New Buildings

4) Fast connections with other parts of the city

5) New jobs created

6) Cost (regional tax for year 2004)

No connections Yes connections

350 new jobs 350 new jobs

No new moorings No new moorings

No new buildings (^) Yes new buildings

English

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Why is conjoint analysis useful?

• Useful in non-market valuation, because it places a value on goods that are not traded in regular marketplaces.
• It can also be used to value products, or improvements over existing products—popular technique in marketing research.
• Allows one to estimate WTP for a good that does not exist yet, or under conditions that do not exist yet—for example, a lake after water pollution has been reduced, but people have always seen the lake as a polluted body of water.
• Allows one to elicit preferences and WTP for many different variants of goods or public programs, and so it can help make decisions about environmental programs where the scope of the program has not been decided upon yet (e.g., EPA’s arsenic in groundwater rule—should it be 50 ppb, 25 ppb, 10ppb?)
• An advantage of conjoint choice is that researchers usually obtain multiple observations per interview, one for each choice task from each respondent. This increases the total sample size for statistical modeling purposes, holding the number of respondents the same.

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• 2nd step : choose the levels of the attributes.
• the levels of the attributes should be selected so as to be reasonable and realistic, or else the respondent may reject the scenario and/or the choice exercise.

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Attributes and levels used in the moose hunting study from Boxall et al. (1996).

Attributes Levels Evidence of < 1 moose per day Evidence of 1-2 moose per day Evidence of 3-4 moose per day

Moose population

Evidence of more than 4 moose per day Encounters with no other hunters Encounters with other hunter on foot Encounters with other hunter on ATVa

Hunter congestion

Encounters with other hunter in trucks No trails, cutlines, or seismic lines Old trails passable with ATVa Newer trails, passable with 4-wheel drive vehicle

Hunter access

Newer trails, passable with 2-wheel drive vehicle

Forestry activity Evidence of recent forestry activity No evidence of forestry activity

Road quality Mostly paved, some gravel or dirt Mostly gravel or dirt, some paved sections 50 km 150 km 250 km

Distance to site

350 km aAll-terrain vehicles

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Attributes and levels from Alberini et al. (2005).

Attribute Level 1 Level 2 Level 3 Level 4

Land use (4 levels)

Shipbuilding, research, offices, museum

Housing, research, museum

Hotels, museum, research

Shipbuilding, research, museum

Use of the water areas (2 levels) No new moorings^ 200 new moorings

New buildings in the Northeast portion of the Arsenale (2 levels)

No new buildings

Presence of new buildings on the 25% of the allowable area

Access (fast transportation links with other areas of Venice, the airport, the mainland, other islands) (2 levels)

Available Not available

Number of new jobs created (3 levels) 150 250 350

Cost to the respondent in Euro (4 levels) 25 50 100 150

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• 3rd task : be mindful of the sample size when choosing attributes and levels.
• The sample size should be large enough to accommodate all of the possible combinations of attributes and levels of the attributes, i.e., the full factorial design.
• To illustrate, consider a house described by three attributes:
• square footage,
• proximity to the city center, and
• price.
• If the square footage can take three different levels (1500, 2000, 2200), proximity to the city center can take two different levels (less than three miles, more than three miles) and price can take 4 different levels (£200,000, £250,000, £300,000, and £350,000), the full factorial design consists of 3× 2 ×4=24 alternatives. Fractional designs are available that result in fewer combinations.
• No of useful observations (no of individualsxchoices per individual) should be at least 1000