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Conjoint Analysis versus Contingent Valuation: estimating Risk
Values and Death Risk Equivalents in Road Traffic
Krister Hjalte
Department of Economics, Lund University, Sweden,
e-mail: [email protected]
Anna Norinder
The Swedish Institute of Health Economics, Lund, Sweden,
e-mail: [email protected]
Anna Traw~n
Department of Technology and Society, Lund University, Sweden,
e-mail: [email protected]
1. Introduction
Eliciting preferences for non-market goods has traditionally been done by help
of stated preference methods, which could be seen as a group of direct
methods in comparison to those indirect methods usually denoted revealed
preference methods. The direct valuation methods include variants like
contingent valuation (CV) and conjoint analysis (CA). The CV method has
since long time been the most used technique in environmental economics
but is also used in health and transport economics, even though more
sparsely. The dominant technique in transport economics for example in
valuation of travel time savings has been the CA or closely related methods,
in the transport economics literature usually named stated preference
methods. An extension of the original CA is sometimes called choice
experiments that involve more experimental and involved analysis of choice
behaviour. The terminology as well as the advantages and drawbacks with
these methods are not quite clear demanding additional research for example
comparing different stated preference methods according the influence of the
context of the questionnaire and according the psychological process of
making choices in different formats. For instance, it may be quite different to
state a maximum willingness to pay for a specified good than choosing among
goods with assigned prices.
The purpose of this pilot study is to compare two direct valuation methods, the
CV and CA, for estimating risk values for fatal and non-fatal road traffic
accidents as well as death risk equivalents. The risk value for a fatal accident,
the value of a statistical life, is defined as the average willingness to pay for a
specified risk reduction divided by that risk reduction. Accordingly similar risk
values are calculated for serious disabling injuries and slight injuries. With
death risk equivalents, the health effects are valued in a common metric
where the injury is presented in relation to death. A fatal injury has the value
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one and good health has the value zero. This implies that for example two
injuries which both are valued to 0.5 death risk equivalent each, together
correspond to one fatal injury.
2. Case study
Results from two studies, one CV (Persson et al. 2000) and one CA (Traw~n
et al. 1999) are compared, both conducted in Sweden 1998 respectively
1999. The CV study was designed as a postal questionnaire and the CA study
was performed as an assistant based pilot study with portable computers. The
main purpose of the former study was to estimate the value of a risk reduction
in road traffic for a slight, a serious disabling and a fatal injury. The purpose of
the second one was to compare the results from the CV study with those from
other stated preference methods for example CA. It was also possible to
estimate the death risk equivalents for these injuries with both methods.
All individuals in both studies were asked identical questions on background
factors such as gender, age, household income, level of education,
experience of road accidents etc. The respondents were provided with
information on annual, baseline risks of dying or getting hurt in road traffic
accidents. In order to make the risk perception scenarios as clear as possible
each of these baseline risks were illustrated as a number of black filled
squares in a square net encompassing 100,000 squares.
2.1 Contingent valuation
The CV study was sent to 5,650 randomly chosen Swedes between the ages
18 and 74 (Persson et al. 2000) in 1998. An open-ended willingness to pay
(WTP) question was chosen as the main valuation format and the individuals
were asked to state how much he/she was willing to pay for a certain
reduction in the risk of being killed in a traffic accident. The study was split into
different sets of questionnaires where the size of the risk reduction was either
30 or 50 percent.
Before asking the WTP question the pure private character of the risk
reduction was carefully pointed out for the user, also stressing that it was
confined to safety perse. This was described as:
The safety device is not inconvenient, ugly or complicated to wear.
Actually you do not notice it. However, # is only you personally who can
benefit from it. The risk reduction has duration of just one year and will
only affect your death risk. Other people's risk is not affected and an
accident will not have any impact on your financial situation as we
assume that all expenditures and financial losses will be covered by the
insurance system.
All respondents were asked to make voluntary direct payments for a safety
device and to estimate their own annual risk for a fatal injury. A risk reduction
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was then valued from the individuals' own perceived annual risk for death in a
traffic accident.
The CV question had the following appearance:
How much would you at the most be willing to pay for reducing your own
annual risk of dying in a traffic accident by one third (one fifth)?
SEK ..................... per year
2.2 Conjoint analysis
The CA study included 200 interviews and was performed with portable
computers in Lund, Sweden. Respondents were found in parks and
supermarkets during the summer of 1999. This study included not only CA
questions, but also standard gamble and risk-risk trade off methods (Traw6n
et al. 1999). For each of these methods, games with questionnaires were
constructed for a slight, a serious disabling and a fatal injury, in total ten
games. However, each individual answered only three games for one kind of
injury, for example: risk-risk trade of, standard gamble and CA safety device,
for a serious disabling injury.
Before the game the respondents answered a '~est question" where one of the
alternatives was dominant, i.e. one alternative was most advantageous
regarding all attributes. This test was done to make sure that the respondents
had understood the question and to test for non-logical answers. In the games
however, alternatives that were dominated were not included in the choice
set.
The respondents answered two different CA games, each involving pair-wise
choices with six choice pairs. In each choice pair, alternative descriptions of
living areas or safety devices were presented according to a number of
attributes with various levels. The alternatives were presented randomly and
the respondents were asked to select alternative A or alternative B.
In one of the CA games the respondents were asked to choose between two
similar safety devices. The price and the risk reduction that the safety device
would bring differed in the two scenarios. The price of the safety device varied
between SEK 20, 100, 500, 1,000, 2,500, 5,000 and 10,000 and the risk
reduction varied between 10, 30, 50 and 99 percent. The s~ifety device was
described as:
It is not inconvenient, ugly or complicated to wear. The risk reduction has
duration of just one year and it is only you personally who can benefit
from it.
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An example of the CA question regarding safety devices is illustrated below.
SUPPOSE YOU SHOULD PURCHASE A SAFETY DEVICE. WHICH
AL TERNA TIVE DO YOU PREFER?
ALTERNA TIVE A
Price of the safety device
SEK1, 000
Reduces your annual risk for a
fatal injury with 30%
1)
2)
AL TERNA TIVE B
Price of the safety device
SEK5,000
Reduces your annual risk for a
fatal injury with 50%
Alternative A
Alternative B
In the other CA game the respondents were asked to choose between two
alternative living areas, which were described by three attributes: cost of
accommodation, travel time to work/school and risk for a road accident. The
alternatives that the respondent chose between were described as
comparable with the original situation of living, i.e. the respondent should
imagine that the size and standard of the flatJhouse and living area in the
alternatives correspond to the original situation of living. To make the
hypothetical choice as realistic as possible, the cost of accommodation and
travel time varied depending on what the respondent had answered earlier
about his/her own situation. Those respondents who had a travel time to
work/school of 0-20 minutes had to choose between the levels 10, 20 and 30
minutes in the game. Respondents with a travel time of 21 minutes or more
had to choose between the levels 20, 40 and 60 minutes. In the similar
manner, the level of the cost of accommodation in the game depended on the
respondents' real cost. For example, for a respondent with a cost of
accommodation between SEK0 and 33,000 per year the levels in the game
vary between SEK26,000, 27,000 and 29,000, see Table 1.
Table 1: Levels of the cost of accommodation in the CA game, SEK
Real cost of accommodation Leve~ ~ ~ e ~ame
26,000
27,000
0-33,000
48,000
50,000
34,000- 60,000
77,000
80,000
61,000- 100,000
130,000 135,000
101,000 and m o ~
29,000
56,000
87,000
149,000
The levels of the risk attribute for a fatal injury varied between 2, 7 and 13 in
100,000 per year. In the game with a serious disabling injury the risk varied
between 23, 40 and 52 in 100,000 per year, and for a slight injury between
200, 500 and 800 in 100,000 per year.
These attributes and levels form a lot of possible combinations. Therefore an
experimental design with 9 alternatives, was constructed. Each respondent
was presented with 6 pairs of alternative descriptions of living areas.
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The CA question regarding living areas had the following appearance:
SUPPOSE YOU SHOULD MOVE. WHICH ALTERNATIVE DO YOU
PREFER? Imagine that you use same transport mode to work/school as
you do today.
ALTERNATIVE A
Cost of accommodation
SEK48,000 per year (SEK4,000
per month)
Travel time to work~school 30
minutes
Risk for a fatal road accident 13 in
100,000
1)
2)
ALTERNATIVE B
Cost of accommodation
SEK50, 000 per year (SEK4,167
per month)
Travel time to work~school 20
minutes
Risk for a fatal road accident 7 in
100,000
Alternative A
Alternative B
3. Results
Both the CV and the CA methods have been used to estimate a value of
statistical life (VOSL), i.e. the marginal rate of substitution of wealth for risk as
well as death risk equivalents. In both methods the value of risk reduction for
a fatal, serious disabling and slight injury due to a traffic accident was
estimated.
Because we do not know in what way the respondents understood the
attributes risk and cost of accommodation, we estimated the VOSL in three
ways for the CA game where the respondents chose between living areas, in
the calculations of VOSL with the CA method named "living area/family" the
attributes cost of accommodation and risk for a road accident are used as
they are presented in the game. In "living area/consumer" the estimates are
made regard to the households' consumption, i.e. the cost of accommodation
in the alternatives is divided by a weight of consumption for the family. This
weight of consumption depends on the number of famUy members and the
age of the children (SCB 1997). If the respondents care about their families,
the risk for the family should be included in the analysis. In "living area/family
risk", the risk in the alternatives is multiplied with the numbers of family
members. Thereby we suppose that the family members exhibit the same risk
and that the risks are independent of each other.
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Table 2: Value of risk for different injuries, million SEK
Method
Slight injury
CV 30% risk red.
Mean
0,9
CV 50% risk red.
0,5
Serious disabling
injury
Median Mean Median
0,4
20,0
8,8
0,2
11,0
5,3
Fatal injury
Mean
95,2
Median
33,3
82,7
40,0
CA safety device
2,3
39,0
208,2
CA living area /
family
2,4
66,8
242,0
CA living area /
consumer
1,4
38,4
137,6
CA living area /
family risk
1,2
29,7
113,7
1The VOSL for fatal injury in the CV study is calculated for the respondents that stated their
own perceived annual risk to 5 in 100,000 while the VOSL in the CA study is calculated with
the average risk 6 in 100,000.
The estimates of a statistical life, i.e. the marginal rate of substitution of wealth
for risk, from the CA study are all higher than those estimated by the CV
method see Table 2. With the CV method the VOSL is between SEK33 and
95 million depending on the risk reduction and if you look at the mean or
median value. The corresponding values with the CA method vary between
SEK114 and 208 million.
The health effects may be valued in a common metric, death risk equivalent,
where the seriousness of the injury is presented in relation to death. The
death risk equivalents are calculated as the risk value for a non-fatal injury
divided by the risk value for a fatal injury.
Table 3: Death risk equivalents for different injuries
Method
CV 30% risk reduction
CV50% risk reduction
CA safety device
CA living area / family
CA living area / consumer
CA living area / family risk
Slight injury
Mean
0.009
0.006
Serious disabling
injury
Median -Mean Median
0.012
0.210
0.264
0.005
0.133
0.133
0,011
0,010
0,010
0,011
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0,187
0,276
0,279
0,261
Our empirical findings indicate that the ratios for different states of health in
relation to death, the death risk equivalents, are quite similar for the methods
compared, see Table 3. The death risk equivalent for a serious disabling injury
varies between 0.133 and 0.279. The lowest value is calculated by the CV
method with 50 percent risk reduction while the highest value is estimated by
the CA method with consideration to the households' consumption. For a
slight injury the death risk equivalent varies between 0.005 and 0.012. The
former value represents the median value from the CV method with 50
percent risk reduction. The highest value is also calculated by the CV method
but with a risk reduction of 30 percent.
4. Discussion
The comparison between estimates of the value of a statistical life derived by
the CV method and the CA method showed that the latter method yielded
higher values than the former. Likewise, when estimating risk values for nonfatal injuries, those derived by the CA method were higher than those derived
by the CV method. There are several possible explanations for these results.
First, the theoretical foundation differs between the elicitation methods. The
values from the CA method are calculated by use of the random utility theory.
This would have been comparable to the CV method, had this been a binary
choice question (Boxall et al. 1996). However, the CV study used open-ended
questions and therefore the results are not theoretically equivalent.
Second, another aspect, highlighted by Boxall et al. (1996), is that in a CV
format the respondents fail to consider substitutes, a problem that is obviously
not apparent in a CA format. This has also been discussed in Stevens et al.
(2000) where CV estimates are compared to CA. Contrary to Boxall et al.,
Stevens et al. argue that because CA estimates are sensitive to the model
specification the method should be used with care, even though Stevens do
not use the same pair-wise choice model as Boxall, but in stead use a ranking
exercise. The model specification also includes levels of the cost attributes
chosen. These levels determine the upper and lower boundaries-in between
which the WTP estimates will fall. Hanley et al. (1998) also compare the CV
and CA methods and conclude that CA is a promising method although many
design issues are unresolved.
Third, in our CA model no status quo alternative was included. This means
that the respondents were forced to choose an alternative where they had to
pay something. In the CV study, however, we accepted an answer of zero
crowns. If these zero-bids are excluded in the calculations of mean values, the
mean values from the CV method rise to approximately the same level as the
ones from the CA method. A disadvantage of including a status quo
alternative is that it could give a downward bias, since it is preferred to nonfamiliar alternatives (Salkeld et al. 2000).
In our CA model we tried to replicate the scenario described in our CV study
in order to compare the methods of eliciting a value of a statistical life. Despite
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our efforts, the comparison failed in a number of aspects. The first lesson from
the exercise is that comparing CV and CA estimates demands a binary choice
CV format. Constructing a binary choice bid vector is equally difficult as
choosing the levels of the attributes in a CA format. Likewise, constructing a
hypothetical scenario entails the same difficulties in both formats. However,
the CA approach does not suffer from a "yea-saying" behaviour often
connected to a binary choice CV, and the consideration of substitutes to a
scenario is clearer in a CA format. Finally, an advantage with the CA format is
that it could be conceptually more easily understood, and therefore yield
values more consistent with a choice in a real life situation.
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