Choice modelling and Conjoint Analysis

1. Choice modelling and Conjoint Analysis

2. Nuclearsurvey –pilotstudy • clogit choice dist waste emi billp bills, group(card) • Conditional (fixed-effects) logistic regression Number of obs = 532 • LR chi2(5) = 102.01 • Prob > chi2 = 0.0000 • Log likelihood = -133.37213 Pseudo R2 = 0.2766 • ------------------------------------------------------------------------------ • choice | Coef. Std. Err. z P>|z| [95% Conf. Interval] • -------------+---------------------------------------------------------------- • dist | .1119275 .020544 5.45 0.000 .071662 .152193 • waste | .4667233 .1328826 3.51 0.000 .2062782 .7271684 • emi | .4195618 .1244275 3.37 0.001 .1756884 .6634352 • billp | .6496312 .1336078 4.86 0.000 .3877647 .9114978 • bills | -.0785639 .1334266 -0.59 0.556 -.3400752 .1829474 • ------------------------------------------------------------------------------

3. . clogit choice di1 di2 di3 waste emi billp1 billp2 bills, group(card) • Conditional (fixed-effects) logistic regression Number of obs = 532 • LR chi2(8) = 104.28 • Prob > chi2 = 0.0000 • Log likelihood = -132.23584 Pseudo R2 = 0.2828 • ------------------------------------------------------------------------------ • choice | Coef. Std. Err. z P>|z| [95% Conf. Interval] • -------------+---------------------------------------------------------------- • di1 | -1.719337 .334741 -5.14 0.000 -2.375417 -1.063257 • di2 | -1.105394 .2842124 -3.89 0.000 -1.66244 -.5483482 • di3 | -.873054 .2915378 -2.99 0.003 -1.444458 -.3016504 • waste | .4940897 .1377777 3.59 0.000 .2240504 .764129 • emi | .4191198 .1262851 3.32 0.001 .1716055 .6666341 • billp1 | -1.242816 .2724375 -4.56 0.000 -1.776784 -.7088487 • billp2 | -.9297961 .3057129 -3.04 0.002 -1.528982 -.3306098 • bills | -.0418188 .1377645 -0.30 0.761 -.3118322 .2281947 • ------------------------------------------------------------------------------

4. 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

5. Conjoint Analysis

6. 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.

7. If we want to use conjoint analysis techniques for valuation purposes, one of the attributes must be the “price” of the alternative or the cost of a public program to the respondent. • If the “do nothing” (or “status quo” option—i.e., pay nothing and get nothing) is included in the choice set, the experiments can be used to compute the value (WTP) of each alternative. • Note that we only learn which alternative is the most preferred, but we do not know anything about the preferences for the options that have not been chosen  the exercise does not offer a complete preference ordering.

8. 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 Site A Site B hunting area Distance from home to 50 km 50 km hunting area Quality of road from Mostly gravel or dirt, Mostly paved, some home to hunting area some paved gravel or dirt Newer trails, Newer trails, Neither Site A or Access within hunting Site B area passable with a 2WD passable with a 4WD vehicle vehicle No hunters, other than Encounters with other Other hunters, on I will NOT go those in my hunting hunters ATVs, are encountered moose hunting party, are encountered Some evidence of No evidence of Forestry activity recent logging found logging in the area Evidence of less than 1 Evidence of less than 1 Moose population moose per day moose per day Check ONE and only one box Example of conjoint choice question from Boxall et al. (1996).

9. Conjoint choice question from Hanley et al. (2001)

10. Example of conjoint choice question from San Miguel et al. (2000).

11. Example of conjoint question from Alberini et al. 2005 English 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 new moorings No new moorings No new buildings Yes new buildings No connections Yes connections 350 new jobs 350 new jobs

12. 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.

13. Designing a Conjoint Analysis Study • 1st task: select the attributes that define the good to be valued. The attributes should be selected on the basis of what the goal of the valuation exercise is, prior beliefs of the researcher, and evidence from focus groups. • For valuation, one of the attributes must be the “price” of the commodity or the cost to the respondent of the program delivering a change in the provision of a public good. • Attributes can be quantitative, and expressed on a continuous scale, such as the gas mileage of a car, or the square footage of a house. The price or cost attribute should be on a continuous scale. Attributes can be of a qualitative nature, such as the style of a house (e.g., Cape Cod, ranch, colonial) or the presence/absence of a specified feature. • It is also important to make sure that the provision mechanism, whether private or public, is acceptable to the respondent, and that the payment vehicle is realistic and compatible with the commodity to be valued.

14. 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.

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

16. Attributes and levels from San Miguel et al. (2000).

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

18. 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.

19. 4th task: Once the experimental design is created, the researcher needs to construct the choice sets. The choice sets may consist of two or more alternatives, depending on how simple one wishes to keep the choice tasks. • The “status quo” should be included in the choice set if one wishes to estimate WTP for a policy package or a scenario. • This can be done in a number of different ways. For instance, one can ask the respondent to choose between A and the status quo, then B and the status quo, etc. Alternatively, one can ask the respondent to choose directly between A, B, and the status quo. Or, respondents may first be asked to indicate their preferred option between A and B (the so-called “forced choice”), and then they may be asked which they prefer, A, B or the status quo. • When grouping alternatives together to form the choice sets, it is important to exclude alternatives that are dominated by others. For example, if house A and B were compared, and the levels of all attributes were identical, but B were more expensive, A would be a dominating choice. • Such pairs should not be proposed to the respondents in the questionnaire, although some researchers believe that this is a way of checking if respondents are paying attention to the attributes of the alternatives they are shown.

20. Complexity Should increase with: • the number of attributes • the number of possible levels for an attribute, • how different the alternatives in each choice set are in terms of the level of an attribute, • how many attributes differ across alternatives in each choice set, • the number of alternatives in a choice set (A and B, or A v. B v. C v. D), • the number of choice tasks faced by the respondent in the survey. Fatigue or learning?

21. Model for the Conjoint Analysis It is assumed that the choice between the alternatives is driven by the respondent’s underlying utility. The respondent’s indirect utility is broken down into two components. The first component is deterministic, and is a function of the attributes of alternatives, characteristics of the individuals, and a set of unknown parameters, while the second component is an error term. Formally, 1) where the subscript i denotes the respondent, the subscript j denotes the alternative, x is the vector of attributes that vary across alternatives (or across alternatives and individuals), and  is an error term that captures individual- and alternative-specific factors that influence utility, but are not observable to the researcher. β is a set of parameters – see next slide.Equation (1) describes the random utility model (RUM).

22. We can further assume that the deterministic component of utility is a linear function of the attributes of the alternatives and of the respondent’s residual income, (y - C): 2) where y is income and C is the price of the commodity or the cost of the program to the respondent. The coefficient is the marginal utility of income. Respondents are assumed to choose the alternative in the choice set that results in the highest utility. Because the observed outcome of each choice task is the selection of one out of K alternatives, the appropriate econometric model is a discrete choice model expressing the probability that alternative k is chosen. Formally, 3) where signifies the probability that option k is chosen by individual i.

23. nij=3 because in each choice task there are 3 options Dataset in LIMDEP Left alternative Right alternative Status quo Taxes*castello 12 obs per respondent because each respondent answers 4 choice questions and each choice question has 3 alternatives (A,B and status quo) Status quo is chosen Right alternative is chosen Castello = dummy =1 if respondent lives in castello

24. Marginal Prices and WTP Once the model is estimated, the rate of trade off between any two attributes is the ratio of their respective  coefficients. The marginal value of attribute l is computed as the negative of the coefficient on that attribute, divided by the coefficient on the price or cost variable: The willingness to pay for a commodity is computed as: where x is the vector of attributes describing the commodity assigned to individual i.

25. Is conjoint analysis better than contingent valuation? • Several analysts believe that conjoint analysis questions reduce strategic incentives, because individuals are busy trading off the attributes of the alternatives and are less prone to strategic thinking (Adamowicz et al., 1998). • The same reasoning and the fact that conjoint choice questions may appear less “stark” than the take-it-or-leave options of contingent valuation has led other researchers to believe that “protest” behaviors are less likely to occur in conjoint analysis surveys.