Environment and Development Economics 2013/19: Modeling demand for catastrophic flood risk Modeling demand for catastrophic flood risk insurance in coastal zones in Vietnam using choice experiments

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Modeling demand for catastrophic ood risk
insurance in coastal zones in Vietnam using
choice experiments

Roy Brouwer, Bui Duc Tinh, Tran Huu Tuan, Kristin Magnussen and Ståle Navrud

Environment and Development Economics / Volume 19 / Issue 02 / April 2014, pp 228 - 249

DOI: 10.1017/S1355770X13000405, Published online: 07 October 2013

Link to this article: http://journals.cambridge.org/abstract_S1355770X13000405

How to cite this article:

Roy Brouwer, Bui Duc Tinh, Tran Huu Tuan, Kristin Magnussen and Ståle Navrud
(2014). Modeling demand for catastrophic ood risk insurance in coastal zones in
Vietnam using choice experiments . Environment and Development Economics,
19, pp 228-249 doi:10.1017/S1355770X13000405 
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Environment and Development Economics19: 228–249 © Cambridge University Press 2013
doi:10.1017/S1355770X13000405
Modeling demand for catastrophic ﬂood risk
insurance in coastal zones in Vietnam using
choice experiments
ROY BROUWER
Department of Environmental Economics, Institute for Environmental
Studies, VU University Amsterdam, De Boelelaan 1087, 1081 HV,
Amsterdam, The Netherlands.Email: r.brouwer@vu.nl
BUI DUC TINH
College of Economics, Hue University, Vietnam.Email: bdtinh@yahoo.com.sg
TRAN HUU TUAN
College of Economics, Hue University, Vietnam.
Email: tuantranhuu@yahoo.com
KRISTIN MAGNUSSEN
Vista Analyse, Norway.Email: kristin.magnussen@vista-analyse.no
ST˚
ALE NAVRUD
School of Economics and Business, Norwegian University of Life Sciences,
Norway.Email: stale.navrud@umb.no
Submitted 26 September 2010; revised 24 October 2012, 18 April 2013; accepted 16 June 2013;
ﬁrst published online 7 October 2013
ABSTRACT. In a choice experiment, households in Vietnam are offered ﬂood insurance
to mitigate increasing catastrophic ﬂood risks due to climate change. Participants are
asked to choose their most preferred insurance policy given expected future ﬂood and
mortality risks, insurance cover and associated insurance premiums. Although not
affordable to everyone, there exists substantial demand for ﬂood insurance. Insurance
demand is spatially differentiated, non-linear in ﬂood probabilities and mortality risks,
and subject to signiﬁcant preference heterogeneity. Since respondents are unfamiliar
with the concept of ﬂood insurance and education levels are low, choice consistency
tests were conducted. These show that choice consistency depends on a combination of
respondent characteristics, such as gender and education level, and experimental design
characteristics.
This paper is based on a case study carried out under the European Commis-
sion 6th Framework Research Programme funded project MICRODIS: Integrated
Health Social and Economic Impacts of Extreme Events: Evidence, Methods and
Tools (Contract No. GOCE-CT-2007-036877).

Environment and Development Economics229	
1. Introduction
The impact of natural catastrophes on societies and economies is likely to
increase in the future. Climate change is expected to increase the frequency
and severity of climate-related catastrophic events (IPCC,2007). Vietnam is
one of the countries in the world most prone to the effects of climate change
and natural disasters. With 3,000 km of coastline and sea level expected to
rise as a result of climate change, it faces natural hazards of various kinds,
most importantly tropical storms such as cyclones and typhoons, ﬂoods,
landslides and droughts. The country is expected to become more vulnera-
ble in the future as a result of an increase in the frequency of some of these
natural hazards, especially ﬂooding, and its fast growing economy (around
7 per cent annually over the past decade) and population (about 2 per cent
annually over the past decades) (GSO,2008).
Embankments are the dominant approach to ﬂood control in Vietnam
(Do and Bennett,2008). However, besides physical infrastructures, ‘micro
insurance’ is also increasingly considered worldwide as a complementary
ﬂood catastrophe mitigation mechanism. The body of literature related to
catastrophe insurance, and crop insurance in particular, is vast and rapidly
growing (e.g.,Hazell,1992;Kunreuther,1996,2006;Kleindorfer and Kun-
reuther,1999;Browne and Hoyt,2000;Vandeveer,2001;Botzen and van
den Bergh,2008,2012;Brouwer and Schaafsma,2013). It has been argued
that crop insurance can play a vital role as a risk management instrument to
enable poor farmers in developing economies to cope with weather-related
production risk, hence contributing to poverty alleviation (e.g.,Hazell,
2001).
However, empirical evidence of demand for catastrophe insurance and
household risk aversion is limited in a developing country context (Akter
et al.,2011). In this study, a choice experiment was therefore developed to
investigate household willingness and ability to pay for micro insurance
to reduce socioeconomic vulnerability and increase the coping capacity
of residents living in different ﬂood-prone coastal zones in Vietnam. The
design of the choice experiment is an extension of previous research on
micro insurance conducted in Bangladesh (Brouwer and Akter,2010). The
novelty of the study is found in the estimation of the economic value of
preventing a fatality, generally referred to as the value of a statistical life
(VSL), under different catastrophic ﬂood probabilities. This is to our knowl-
edge the ﬁrst stated preference study trying to estimate the VSL related to
catastrophic ﬂooding. Most stated preference studies of VSL refer to mor-
tality risks due to air pollution, road safety or general environmental risks
(Dekkeret al.,2011).
The choice experiment was preferred over the contingent valuation
approach since it allowed testing of the conditions under which house-
holds favor micro ﬂood insurance. The contingent valuation method is
limited in the number of possible terms and conditions that can be pre-
sented to respondents. Although still hypothetical, the choice experiment
results provide important indications of household demand for micro ﬂood
insurance under different contractual design and provision conditions,
such as insurance sum and premium. Moreover, compared to the ‘one shot’
contingent valuation approach, through choice repetition respondents are

230Roy Brouwer et al.
expected to be capable of making more precise and consistent decisions:
they learn about the survey format, the associated hypothetical market and
their own preferences (e.g.,DeShazo and Fermo,2002).
The paper is organized as follows. Section 2 presents the design of the
choice experiment and section 3 the underlying econometric model. Section
4 introduces the case study area and describes the survey conducted to
collect the empirical data. The choice experiment results, including willing-
ness to pay (WTP) for micro ﬂood insurance and the VSL under different
catastrophic ﬂood probabilities, are presented in section 5. Finally, section
6concludes.
2. The choice experiment
Stated preference methods are rarely used in ﬂood risk valuation stud-
ies (Brouweret al.,2009;Botzen and van den Bergh,2012). Demand for
ﬂood insurance has mainly been examined using existing actuarial data
in countries where ﬂood insurance exists (e.g.,Kunreutheret al.,2009;
Michel-Kerjan and Kousky,2010).
In stated preference research, the general expectation underlying WTP is
that people are risk averse when their decision involves potential losses
under low probability–high impact conditions, and corresponding indi-
vidual choice behavior is motivated by a desire for security (Tversky and
Kahneman,1979). In this study, respondents are faced with a situation
where catastrophic events occur frequently with devastating effects on
poor rural populations, and we test if risk aversion also holds under these
circumstances. In theory, WTP for a reduction in risk exposure depends
on: (i) the realized level of riskR, which is determined by exogenous risk
Xand self-protection activitiesSP; (ii) individual characteristics such as
disposable incomeYto be able to protect oneself against risk; and (iii) an
individual’s disutility from risk exposure (risk aversion) and hence utility
Sfrom a risk reduction (Batemanet al.,2005). This is shown in equation (1).
WTP	
i=f(Y	i,Si,R(X,SP	i))(1)
Most environmental risks faced by individuals are to a degree endoge-
nous, since people can take actions to reduce the likelihood of an unde-
sirable event occurring (self-protection) and the costs of the event to
them if it occurs (self-insurance) (Shogren and Crocker,1991). In equi-
librium, individuals equate the marginal beneﬁts of self-protection or
insurance (expected avoided disutility) with the marginal costs (price of
self-protection or insurance), subject to their budget constraint. In the case
of fatal risks, i.e., where people run the risk of dying, for instance due to
catastrophic ﬂooding, the concept of VSL can be used to measure the rate
at which people are willing to trade off money income for reductions in
mortality risk (Alberini and Chiabai,2007). The VSL is calculated by divid-
ing the maximum amount of money an individual is willing to pay for a
reduction in mortality risk by the change in the probability of dying.
Demand for ﬂood insurance is expected to increase as the frequency
of catastrophic ﬂooding increases, for instance due to climate change.

Environment and Development Economics231	
In the choice experiment, respondents are asked to value increases in catas-
trophic ﬂood probabilities from their initial (baseline) level. Included in the
description of these exogenously driven future risk levels in the choice
experiment is also the probability of dying as a result of a catastrophic
ﬂood. This allows us to estimate a VSL for different fatality probabilities
due to catastrophic ﬂooding. In general, WTP to reduce mortality risk is
expected to increase when the latter is higher (Lindhjemet al.,2011). Since
WTP is divided by the mortality risk to ﬁnd the VSL, the VSL decreases
with increasing mortality risk. FollowingBrouwer and Schaafsma(2013),
social disruption is included to represent the immaterial damage cost due
to catastrophic ﬂooding.
The variation in the endogenous component of realized risk is found in
this study in the offered insurance schemes in the choice experiment. These
insurance schemes are offered to respondents after they are ﬁrst presented
with different states of the world in terms of catastrophic ﬂood probabil-
ity, fatality and social disruption. The insurance schemes are subsequently
described in terms of insurance provider, cover and premium. In this way,
we are able to value both the characteristics of different states of the world
and the characteristics of the insurance policy.
The insurance coverage sum, i.e., the loss paid directly to the insured by
the insurer for ﬁrst-party coverage, varies from VND (Vietnamese Dong)
25 million to VND 100 million per catastrophic ﬂood event. In accordance
with previous ﬁndings (Brouwer and Akter,2010), risk adverse households
are expected to prefer higher damage cover. Furthermore, the catastrophic
ﬂood insurance can be provided either by a public (government) or pri-
vate operator (insurance company of the respondent’s own choice). No a
priori expectations exist regarding household preferences for either public
or private insurance provision. The premium for the insurance consists of
monthly household installments varying from VND 2,500 to VND 20,000.
The experimental design is summarized in table1. Baseline conditions
for catastrophic ﬂood probabilities, including mortality risks, are included
in the design to test if the VSL is increasing in the level of baseline risk
(e.g.,Eeckhoudt and Hammitt,2001). In the case of catastrophic ﬂooding
the baseline level is once every 100 years and for the probability of a fatality
this is one in every 10,000 people. Due to climate change these probabilities
are expected to increase in the case study area.
Alternative states of the world and insurance schemes are created by
combining the six variables presented in table1based on their levels.
Because respondents cannot be shown all possible combinations, the num-
ber of choice options is reduced to 16 sets of eight choice tasks each based
on a D-efﬁcient fractional factorial design using the software Sawtooth.
Hence, respondents are asked to choose between 256 alternatives presented
in pairs on 128 choice cards, which are divided over 16 sets of eight cards.
Each respondent is randomly shown one of these 16 sets of eight different
choice cards.
Finally, one of the main assumptions underlying stated preference
research is that respondents know their preferences and that these prefer-
ences are stable and coherent (e.g.,Brownet al.,2008). Hence, from a set of

232Roy Brouwer et al.
Table 1.Experimental design
Attributes Units Levels
1. Flood return period Once in everyxyears 100-50-20-10
2. Probability of fatality One in everyxpersons 10,000-5,000-2,000-1,000
3. Length of social
disruptionWeeks 1-2-4-8
4. Insurance provider Public or private Government-Insurance
company
5. Insurance cover Million VND/
catastrophic event25-50-75-100
6. Insurance premium Monthly installment in
VND2,500-5,000-7,500-
10,000-15,000-20,000
alternative insurance policies given for future states of the world, the indi-
vidual is assumed to be capable of selecting the most preferred insurance
policy based on its characteristics. However, lack of familiarity and experi-
ence with ﬂood risk insurance may undermine these a priori assumptions
(Shaikhet al.,2007). We examine choice consistency by presenting respon-
dents with the same choice task at the beginning and the end of the choice
experiment. So, a ninth card is added to each choice set, which is identical
to the ﬁrst choice card. Based on theory, choice behavior at the start of the
experiment is expected to be consistent with choice behavior at the end of
the experiment (Brouweret al.,2010).
3. Econometric model
The design of the choice experiment can be translated into the following
indirect utility function:
V	
ij=β	0+β	1FP	ij+β	2MR	ij+β	3SD	ij+β	4Iij+β	5Cij+β	6Pij+ε	ij	(2)
where indirect utilityVof respondentiis measured through his or her
observed choice for alternativejand an unobservable random termε. This
latter term is assumed to have an independent and identically Extreme
Value Type I error distribution (e.g.,Tr a i n,2003).
Each alternativejis described with the help of a vector ofkattributes,
whereβis the corresponding coefﬁcient vector. In this case study, the
parametersβ	
1toβ	3represent the various states of the world: the exoge-
nous increase in catastrophic ﬂood probability (FP), mortality risk (MR)
and social disruption (SD) due to climate change, whileβ	
4toβ	6refer to
the characteristics of each insurance alternativejto mitigate the impacts of
catastrophic ﬂood risks: the insurance provider (I), damage cover (C)and
insurance premium (P).
Respondent preferences and hence demand for micro insurance are
expected to be higher when risk exposure is higher. Under the assump-
tion that households are risk averse, they are expected to prefer higher

Environment and Development Economics233	
indemnity coverage. The insurance premium is expected to have a negative
effect on choice behavior: the higher the premium, the lower the probability
that someone will want to buy insurance. The expected direction of
inﬂuence of the attributes included in equation (2) is summarized below:
∂V/∂FP<0;∂V/∂MR<0;∂V/∂SD<0;∂V/∂C>0;∂V/∂P<0.(3)
Affordability is expected to play an important role in this speciﬁc devel-
oping country context too. Household income is theoretically expected to
signiﬁcantly inﬂuence choice behavior. Lower income groups are less likely
to be able to afford and hence choose insurance with higher premiums.
Interactions between attributes and individual socioeconomic characteris-
tics can be included in the utility function to account for such preference
heterogeneity. The utility function is estimated using mixed logit regres-
sion models (e.g.,Tr a i n,2003;Hyneset al.,2008), which allow us to capture
the expected heterogeneity in respondent preferences due, among others,
to differences in risk exposure as a result of the different locations where
respondents live.
4. Survey implementation
The study presented in this paper was conducted in the province Quang
Nam in Central Vietnam, about 300 km south of the old capital city, Hue.
Quang Nam is a tropical coastal highland with an average temperature of
25.4	
◦C and an average annual rainfall of 2,580 mm. The climate of Quang
Nam knows two seasons: the rainy season from September to December
and the hot dry season from February to April. Around 70 per cent of
Quang Nam’s topography is made up of mountains and hills. The coastal
zone of Quang Nam is plain land. The rivers Vu Gia, Thu Bon and Tam Ky
are the main water providers for socioeconomic activities in Quang Nam
(SRV,2007). In 2007 the province had more than 1.4 million inhabitants, of
which 83 per cent lived in rural areas and depended mainly on agriculture
and aquaculture for their livelihood (GSO,2008).
The study area is highly vulnerable to climatic hazards, such as trop-
ical storms, ﬂoods and droughts, due to its geographic location and
topographic conditions. These hazards cause losses to agriculture, infras-
tructure, households and life. Flooding is the most common of all environ-
mental hazards, especially in the rainy season due to heavy rainfall. Future
climate change is expected to worsen the situation. The most prominent
ﬂoods in the last decades were those of 1999 and 2007. The most recent
catastrophic ﬂood in 2007 is used in this study as a point of reference. This
extreme ﬂood also caused by far the highest damage costs. The total esti-
mated damage costs were around US$87 million and about 70,000 people
hadtobeevacuated(Scarpaet al.,2005). The ﬂoods furthermore killed 39
people, while 339 people were seriously injured.
Within Quang Nam, three different districts were selected, which are
located downstream from each other along the river Vu Gia: the upland dis-
trict Dai Loc, the midland district Dien Ban, and the coastal district Hoi An
where the river drains into the sea. In view of the fact that the province has

234Roy Brouwer et al.
a slope from west to east, during heavy rainfall this causes ﬂooding with
inundations especially in downstream areas. Hence, demand and WTP for
micro ﬂood insurance across the three risk-prone areas are expected to be
higher in the downstream areas Dien Ban and Hoi An.
Due to a lack of information about exact household numbers and lim-
ited information about household socio-demographic characteristics at the
district level, two communes were randomly selected from each district.
Households living in the villages within each commune were subse-
quently randomly selected from available lists with household names and
addresses at the commune level. In total, 540 households were selected in
this way, 180 households from each district, equally divided across the two
communes, which were more or less equally sized in terms of household
numbers based on the available household lists. The survey consisted of
face-to-face interviews lasting about 30 minutes and was conducted over a
two-month time period in August and September 2009 by trained inter-
viewers graduated from the University of Hue. The response rate was
close to 100 per cent. Thirty interviews were dropped due to incomplete
questionnaires, which resulted in a total number of 510 observations.
The questionnaire used for the survey consisted of three parts. The ﬁrst
part collected information about the household socio-demographic charac-
teristics (e.g., age of the household head, household composition, income
sources, etc.). The second part focused on household risk exposure levels,
in particular related to the 2007 catastrophic ﬂood event (e.g., frequency
of ﬂooding, inundation levels during regular ﬂood seasons and the catas-
trophic 2007 ﬂood, ﬁnancial damage as a result of the 2007 ﬂood, etc.). The
third and ﬁnal part included the choice experiment. The introduction to the
choice experiment is included in Appendix A. Special attention was paid to
the communication of the ﬂood and mortality risks. The questionnaire and
the choice experiment, including the risk levels, were pretested through 50
face-to-face interviews in the case study area prior to implementation of the
survey. Changes were made to the questionnaire and choice experiment
after the pretest, mainly to further clarify questions and the choice task.
The survey targeted rural residents with limited education. Therefore, the
attributes and their levels were conveyed to respondents on choice cards
with pictographs and as little text as possible. Choice cards were printed
on separate sheets of paper, laminated and bound together into a spiral
binder for multiple use.
Each choice card shows two alternatives describing a possible future
catastrophic ﬂood risk situation and a speciﬁc micro insurance scheme
to mitigate the possible ﬁnancial impacts of this future risk on house-
hold socioeconomic vulnerability along with the option to choose none
of the two (neither option A or B). The latter ‘opt-out’ option, as it was
explained to respondents, is the baseline alternative and implies facing the
increasing climate change induced catastrophic ﬂood risks in the future
and choosing not to mitigate them through the purchase of an insurance
policy. The additional mitigation costs in this opt-out alternative are there-
fore zero. The baseline alternative was not further speciﬁed since baseline
conditions were expected to differ across districts and individual respon-
dents. The inclusion of this alternative is instrumental to estimate welfare

Environment and Development Economics235	
measures that are consistent with economic demand theory (Hanleyet al.,
2001).
An example of a choice card is presented in Appendix B. Respondents
who chose the opt-out were asked in a follow-up question for their reasons.
In order to make sure respondents had a clear understanding of the choice
task, they were ﬁrst asked to make their choice using an instruction card.
Here, they were allowed to ask questions about the choice task before the
choice experiment started.
5. Results
5.1. Sample characteristics
The socio-demographic characteristics of the 510 households included in
our sample are summarized in table2. No signiﬁcant differences are found
for any of the socio-demographic characteristics between the three districts,
except for the share of crop income in total household income.	
1Compared
to the limited available regional statistics for the province Quang Nam,
the sample is fairly representative in terms of gender (52 per cent of the
whole population is female) and household size (the average in the whole
population is ﬁve family members).
Most respondents in our sample are female and married (54 per cent).
The average age of the respondent is around 40–50 years, with most respon-
dents (55 per cent) falling in the age groups between 30 and 50 years and
51 and 65 years (26 per cent). The average number of years of schooling is
six. Five per cent of the respondents are unable to read and write.
The households consist on average of ﬁve members, of which three earn
income. Average household income in the sample is slightly less than that
for the population as a whole (20 million VND or around $1,000 per year
based on an average exchange rate in 2009 of 18,465 VND per US$). Less
than 10 per cent have an annual income level that falls below the National
Poverty Line ﬁxed by the Ministry of Labour, Invalids and Social Affairs
at VND 1,200,000 (US$65) for rural households (MOLISA, 2009). This share
is lowest in Dien Ban and more or less the same in Dai Loc and Hoi An.
As for the whole population, most people depend on agriculture, i.e., crop
cultivation and livestock holding, for their livelihood. Less than half of the
households in the sample live in a brick house, 8 per cent live in a wooden
or bamboo constructed house. Everybody has access to electricity, but only
20 per cent to piped drinking water. Most households use tube well water.
On average, a household owns only 0.15 ha of land.
Turning to the risk exposure indicators in table2, all respondents live
within a radius of 5 km from the nearest river. On average, a house-
hold lives less than 1 km from the nearest river, and almost a quarter of
the respondents (23 per cent) live between 1 and 5 km from the nearest	
1The outcome of the non-parametric Kruskal–Wallis test rejects the null hypothesis
of equality across the three districts at the 1 per cent level (KW−χ	2=9.507;p<
0.009).

236Roy Brouwer et al.
Table 2.Summary of household characteristics across the three districts
Sample characteristics Dai Loc Dien Ban Hoi An
Socio-demographic characteristics
Gender (% male) 39.9 43.6 41.2
Average age 46.8 48.0 47.1
Average number of years of schooling 6.5 6.4 6.0
Average number of household members 4.6 4.5 4.5
Average number of working adults 3.0 3.0 2.8
Average number of children 1.1 1.2 1.3
Average household income (million VND/year) 18.1 19.4 17.9
Share households below poverty line (%) 7.7 1.8 6.8
Share crop income in total household income (%) 20.3 25.1 17.9
Risk exposure characteristics
Average distance to nearest river (km) 0.81 0.92 0.84
Share living in house with elevated ground
ﬂoor (%)24.7 22.4 23.7
Share who has ever been evacuated due to ﬂood (%) 41.0 41.5 44.1
Average household ﬂood damage 2007 (million
VND)2.6 5.9 4.6
Mode statistic inundation level in 2007 head shoulder head
Mode statistic inundation duration in 2007 (days) 3–4 3–4 3–4
Risk coping characteristics
Share taking ﬂood protection measures (%) 91.1 97.6 92.7
Average number of recovery days 2007 ﬂood 13.9 18.4 10.7
Share borrowing money after the 2007 ﬂood (%) 16.8 11.5 10.1
Share stating they cannot cope with ﬂood
disasters (%)16.4 20.7 21.1
Risk perception characteristics
Share who believes ﬂoods are main problem (%) 55.7 57.3 56.0
Share believing extreme ﬂoods increased past 10-20
years (%)79.2 81.2 76.3
Share believing that extreme ﬂoods will increase in
future (%)64.5 66.0 54.3
river. This is the same across all three districts. The only signiﬁcant dif-
ference is found between Dai Loc and Dien Ban.	2Three-quarters of the
sample live in a one-storey house, a quarter in a house with an elevated
ground ﬂoor. The ﬂood disaster in 2007 was more severe than regular
ﬂooding during the rainy season, with a majority of the households in
all three districts reporting shoulder-high inundation levels. The highest
inundation levels were reported in Hoi An, followed by Dien Ban. Most	
2The outcome of the non-parametric Mann–Whitney test rejects the null hypothesis
of equal distances to the nearest river between these two districts at the 1 per cent
level (MW−Z=−2.511;p<0.012).

Environment and Development Economics237	
households suffered from the inundations for 3–4 days, especially in Hoi
An and Dai Loc. In most cases, the ﬂoods did not last longer than one
week. Signiﬁcant differences are found for inundation levels between the
upland district Dai Loc and the coastal district Hoi An, and for inun-
dation duration between upland Dai Loc and the midland district Dien
Ban.	
3
The average household ﬂood damage in 2007 was highest in Dien Ban
(US$315), followed by Hoi An (US$245) and then Dai Loc (US$140). Despite
the fact that the damage was more than twice as high in Dien Ban than
in Dai Loc, the difference is not statistically signiﬁcant due to the high
standard deviations. The same applies if the self-reported ﬂood damage is
expressed as a percentage of household income. Only the ranking of most
severely struck districts changes in that case: the impact of the ﬂood dam-
age on household income is highest in the coastal district Hoi An (85 per
cent) and lowest in the midland district Dien Ban (61 per cent). The share
of the ﬂood damage costs of average household income in Dai Loc is 73
per cent. These relative ranks correspond with the ranking of the number
of recovery days after the disaster ﬂood in 2007. The number of recovery
days is signiﬁcantly higher in Dien Ban than in Hoi An, but not in Dai Loc.	
4	
Between 40 and 45 per cent of all households have experienced evac-
uations as a result of ﬂooding and the share of households taking ﬂood
protection measures is also very high. The most important measure before
and during the ﬂood events is to move property to a safer place. Besides
cleaning the house and yard, securing clean drinking water is one of the
most important activities after the ﬂood event. Almost half of the sample
are not satisﬁed with their current level of protection against catastrophic
ﬂoods. Around 20 per cent of the sample stated that they are unable to cope
with the impacts of ﬂood disasters like the one in 2007. Most frequently
heard reasons are that (i) they feel they live in an unstable house in a low
lying area close to the river, (ii) they are too old and weak to protect them-
selves and have no adult family member to help them, and (iii) they have
insufﬁcient ﬁnancial resources to recover from a catastrophic ﬂood. In the
latter case, many respondents reported that they still have unrepaired dam-
ages from the ﬂood in 2007. On average, 13 per cent borrowed money after
the extreme ﬂood in 2007. This share was highest in Dai Loc and more or
less the same in Dien Ban and Hoi An.
Finally, a majority of the respondents in all three districts consider
catastrophic ﬂooding the most important issue in their region. This is eas-
ily understood after examination of the high share of the sample facing
catastrophic ﬂood risks. A majority of 80 per cent of the respondents are
of the opinion that catastrophic ﬂoods have increased over the past 10–20
years. Sixty per cent believe that their frequency of occurrence will increase
3Mann–Whitney Z values are respectively−2.801 (p<0.005) and−2.558 (p<
0.011).
4The outcome of the Mann–Whitney Z-statistic is−1.665 (p<0.096) when com-
paring households from Hoi An and Dien Ban, 0.859 (p<0.390) for Dai Loc and
Dien Ban and−0.831 (p<0.406) for Hoi An and Dai Loc.

238Roy Brouwer et al.
further in the future. Forty per cent of the sample consider this likely and
45 per cent very likely, while 10 per cent are convinced that they will be
struck more often by catastrophic ﬂoods in the future.
5.2. Testing equality of choice models across the three districts
Our hypothesis that demand is higher in the downstream areas Dien Ban
and Hoi An was ﬁrst tested using theSwait and Louviere(1993)proce-
dure. Based on the socio-demographic and risk exposure characteristics
presented in section 5.1, the three districts seem fairly similar. However,
in order to ensure that the data collected in the three districts can be com-
bined in one aggregate model, we test whether the utility parametersβ
presented in equation (2) and the scale parametersμin the choice model
(e.g.,Louviereet al.,2000) are equivalent across districts. To this end, a
pair-wise comparison is performed between the districts. In a ﬁrst step, the
mixed logit models of each district provide us with efﬁcient estimates for
μ
1β1,...,μ	6β6in equation (2) and a likelihood function for each district.
Then the scale parameter of, for example, the ﬁrst district is normalized to
μ	
1=1 for identiﬁcation purposes.
In a second step, a pooled model including the ﬁrst and second district
is estimated, which has the effect of imposing equality on the preference
parameters(β	
1=β	2). In this model the relative scale parameterμ	2/μ	1is,
however, not set equal to 1. A search procedure over a range of relative
scale parameters is applied to estimate the combination of scale and pooled
preference parameters providing the best model ﬁt. At each possible rela-
tive scale parameter the data for the second district are rescaled such that
a mixed logit model can be estimated. After the best ﬁt model has been
identiﬁed, a standard chi-square (Likelihood Ratio) test using the log like-
lihoods of the models from step 1 and the best ﬁt model in step 2 can be
used to test the difference between the preference parameters in the two
districts under the null hypothesis that they are the same.
The third step, in which we test for differences in scale across districts,
is conditional on accepting the chi-square test in step 2. It requires the esti-
mation of a mixed logit model for the same pooled model as in step 2, but
with equality imposed on both preference and scale parameters this time
(β	
1=β	2andμ	1=μ	2). Again, a chi-square test can be applied to compare
the log likelihood of the estimated model to the log likelihood of the pooled
model with varying scale parameters. The results of this test procedure are
presented in table3.
Equality of preference parameters is convincingly rejected for the pooled
samples including the highland district Dai Loc (column 6), but cannot
be rejected at the 10 per cent signiﬁcance level for the two downstream
districts Dien Ban and Hoi An. Also equality of scale parameters cannot
be rejected for Dien Ban and Hoi An (last column 9). This implies that
the choice models for Dien Ban and Hoi An can be estimated using the
same pooled model. However, control has to be included for Dai Loc as
preference and scale parameters are signiﬁcantly different for respondents
from this district.

Environment and Development Economics239	
Table 3.Test results for equality of preference(β )and scale (μ) parameters between districts and between the ﬁrst and last choice task	
Relative Relative
LL	
Ci+j
LR-test Reject scale varianceLL
Ci+j
LR-test Reject
LL	
Ca
LLCb
(μCa
 =μ
Cb)a
(12 d.f.)H
0:βCa
=β
Cb?(μ
Cb/μCa)(σ
2
Cb/σ2
Ca)(μ
Ca
=μ
Cb)b
(1d.f.)H
0:μ
Ca
=μ
Cb?	
Comparison between districts
Dai Loc &
Dien Ban−736.983−715.412−1472.302 39.8 Yes 1.01 1.61−1472.31 0.02 No
Dai Loc &
Hoi An−736.983−779.014−1528.815 25.6 Yes 0.89 2.08−1539.91 22.19 Yes
Dien Ban &
Hoi An−715.412−779.014−1502.574 16.3 No 1.03 1.55−1502.64 0.13 No
Comparison between choice tasks
Ta s k 1 a n d 2−258.981−261.053−534.601 29.1 Yes 0.95 1.82−534.659 0.11 No
Ta s k 1 a n d 9−258.981−172.813−385.274 93.0 Yes 1.36 0.89−386.899 3.25 Yes	Notes: LL, log likelihood. Likelihood ratio (LR) tests performed at 10% signiﬁcance level.aPooled mixed logit model allowing scale parameters to vary;
bpooled mixed logit model keeping scale parameters constant.

240Roy Brouwer et al.
5.3. Choice consistency test results
In order to test choice consistency, respondents were shown the ﬁrst
card also at the end again without telling them.	
5When asked afterwards
whether they were aware of the fact that the ﬁrst and last choice card were
the same, 38 per cent of the respondents said that they noticed this, and
hence most respondents did not know that they were shown the same card
twice. When furthermore asked if they chose the same alternative on both
choice occasions, 86 per cent said yes. This is slightly higher than what was
actually observed. Almost 80 per cent of all respondents (78.6 per cent) con-
sistently chose the same alternative during the last choice task as they did
during the ﬁrst choice task. This high share suggests that preferences are
stable. However, when comparing the estimated choice models between
the ﬁrst and last choice task using the same Swait-Louviere test procedure
as in the previous section, both equality of preference parameters and scale
parameters is rejected at the 10 per cent signiﬁcance level (table3).
Comparing the error variance between the ﬁrst and the last choice task
and between the ﬁrst two choice tasks, evidence of preference reﬁnement is
found. The error variance is signiﬁcantly different when comparing choice
cards 1 and 9, but not between choice cards 1 and 2. The variance is reduced
by more than 50 per cent when going through the choice sequence from
choice card 1 to choice card 2 and then on to choice card 9. Hence, although
choices are consistent in a majority of cases, we ﬁnd evidence of preference
learning and reﬁnement.
When regressing the variable as to whether or not a respondent changed
his or her choice during the last choice task on a number of socio-
demographic and design characteristics, choice consistency appears to be a
function of both respondent socio-demographic and experimental design
characteristics (table4). The explanatory power of the estimated binary
logistic regression model is high, almost 50 per cent. Women are more likely
to change their choice, as well as higher educated respondents and respon-
dents who suffered more damage during the catastrophic ﬂood in 2007.
On top of these variables, a signiﬁcant effect of the experimental design
was also detected. Three of the 16 choice sets appeared to signiﬁcantly
reduce the likelihood of respondents changing their choice during the last
choice task compared to what they chose in the beginning. Dummy vari-
ables were created for the different choice sets to see if we could ﬁnd
a signiﬁcant relationship between speciﬁc choice sets and choice consis-
tency. Examining the levels of the attributes of the alternatives in these
three choice sets more carefully, the ﬁrst alternative in the ﬁrst (and last)
choice task consistently had the lowest insurance premium and lowest
catastrophic ﬂood probability (and in two of the three choice sets also
the highest indemnity cover). In other words, despite randomizing the
sequence in which choice cards were shown to respondents, these alter-
natives have the appearance of being dominant, hence explaining why
respondents might have been reluctant to change their choices. We also	
5Following the procedure outlined in Brouweret al. (2010), the ﬁrst card in each
choice set was randomly picked and shown to the respondent by the interviewer.

Environment and Development Economics241
Table 4.Estimated binary logistic model(1=changed choice in last choice task)	
Parameter Standard
Variables Description estimate error	
Experimental design characteristics
Choice set 6 Dummy−1.330 0.601	∗∗	
Choice set 7 Dummy−2.926 0.794	∗∗∗	
Choice set 16 Dummy−1.202 0.633	∗	
Respondent characteristics
Gender Dummy (1 = female) 0.955 0.362	∗∗∗	
Education level School years 0.065 0.036	∗	
Flood damage suffered in 2007 Million VND 0.281 0.095	∗∗∗	
Model summary statistics
−2 Log likelihood 179.662
Percentage correct predicted 78.8
R-square 0.488
N193
Notes: Signiﬁcance levels:	∗10%,	∗∗5%,	∗∗∗	1%, respectively.	
tested for possible interviewer effects on choice consistency, but could not
ﬁnd any.
5.4. Choice experiment and WTP results
Of the 510 respondents, 62 per cent are interested and willing to pay for
ﬂood insurance. Hence, WTP for ﬂood insurance is positive in a majority
of the cases. Of the 194 respondents (38 per cent of the whole sample) who
consistently chose neither of the two alternatives in the choice experiment,
a majority (81 per cent) were either not interested in ﬂood insurance or
unable to pay extra for ﬂood insurance given their limited ﬁnancial income.
These are considered legitimate reasons for refusing to choose between one
of the two hypothetical alternatives, because they correspond with theoret-
ical expectations (i.e., no preferences for the good in question and lack of
income). The protest rate in this study is 6 per cent. Protest rates are more
serious, because they provide important clues about the validity and relia-
bility of the choice experiment, often related to procedural bias. Although
no strict guidelines exist in the literature, a protest rate of 6 per cent is
considered acceptable. The most important reason to protest was that,
despite the extensive introduction to ﬂood insurance, respondents wanted
more information before they felt capable of making a decision whether
or not to buy it. When asked after the choice experiment which attribute
played the most important role in their decision to choose between the
two alternatives, the catastrophic ﬂood return period was mentioned most
often (32 per cent), followed by the insurance premium (21 per cent).
The choices were regressed on the choice attributes and other covariates
using a combined random parameters and error component logit model,
which was estimated in NLOGIT version 4.0, accounting for the panel data

242Roy Brouwer et al.
Table 5.Estimated choice model	
Standard
Parameter Standard deviation Standard
Variables estimate error random pars error	
ASC 1.725	∗∗∗	0.232
Choice attributes
States of the world
Flood return period−0.048	
∗∗∗	0.004 0.007	∗∗∗	0.001
Flood return
period-squared0.0003	∗∗∗	0.00004
Probability fatality−0.0003	
∗∗∗	0.00005 0.0001	∗∗∗	0.00002
Probability fatality-
squared0.232·10	−7∗∗∗	0.398·10	−8	
Social disruption−0.039	∗	0.021 0.230	∗∗∗	0.022
Risk mitigation
characteristics
Insurance provider−1.173	
∗∗∗	0.051 0.041 0.444
Insurance cover 0.072	∗∗∗	0.007 0.006	∗∗∗	0.002
Insurance cover-
squared−0.0006	∗∗∗	0.00006
Insurance cover x Dai
Loc district0.012	
∗∗∗	0.002
Insurance premium−0.00009	
∗∗∗	0.000005
Model summary statistics
Sigma error component 0.829	
∗∗∗	0.120
Log likelihood−2463.875
PseudoR-square 0.145
N2,622	
Notes: Signiﬁcance levels:	∗10%,	∗∗5%,	∗∗∗	1%, respectively.	
structure of the choice experiment. Panel data refer to the fact that we
have multiple choice observations from each individual since each respon-
dent answered a sequence of choice tasks in the choice experiment. The
estimated choice model controlled for possible correlation between the
observed choices across choice tasks. For efﬁciency purposes, the model
was furthermore estimated using a Halton sequence of 100 replications
in a quasi-Monte Carlo maximum likelihood simulation (Bhat,2001). In
our search for the statistically best ﬁt model, we systematically included
interaction terms between the attributes in our experimental design and
dummy variables for respondents living in the three different districts.
Only one of these interaction terms appeared to be statistically signiﬁcant,
namely for insurance cover in the upstream district Dai Loc (correspond-
ing with our test results in section 5.2). The estimated best ﬁt model is
presented in table5.
The model is highly signiﬁcant (outcome of theχ	
2is 833.374 with 17
degrees of freedom) with a pseudoR	2of around 15 per cent. The outcome

Environment and Development Economics243	
for the error component is signiﬁcant at the 1 per cent level and indicates
that respondents evaluated the two hypothetical alternatives distinctly
different from the existing situation (e.g.,Scarpaet al.,2005). The signiﬁ-
cant positive outcome of the alternative speciﬁc constant (ASC) suggests
that respondents prefer a change instead of no change from the current
situation.
Preference heterogeneity is accounted for by randomizing the choice
attributes across respondents. Different distributional assumptions were
tested. A normal distribution produced the statistically best ﬁt for the
continuous design variables, while a uniform distribution was used for
the dummy variable representing the insurance provider (seeHensher
et al.,2005for guidelines on the appropriate choice of distribution).
However, this latter random parameter appeared not to be statistically
signiﬁcant at the 10 per cent level. Once control is included for these
random effects, no additional signiﬁcant effects could be detected for
any of the other socio-demographic and risk characteristics discussed in
section 5.1.
All attribute parameters are statistically signiﬁcant and have the
expected signs. Respondents dislike higher ﬂood return periods, mortal-
ity risks and longer social disruption. A higher return period resultsceteris
paribusin a lower probability of an alternative being chosen. The same
applies to a higher mortality risk and longer lasting social disruption. An
interesting ﬁnding is that both the utility attached to catastrophic ﬂood
probability and the probability of dying are non-linear. Their quadratic
terms are highly signiﬁcant. As a consequence, reducing their probability
results in deceasing returns. The marginal values are hence not constant,
but a function of their levels. This is shown in ﬁgures1aand1b. Marginal
WTP values are negative because the alternatives in the choice experiment
represent increases in catastrophic ﬂood probabilities and mortality risks,
resulting in disutilities.
Especially in the case of mortality risk, the slope of the curve becomes
increasingly steep at higher probabilities of dying, reﬂecting a higher WTP
to avoid increasing mortality risk, but less than proportionally (and hence
a lower VSL). Increments in catastrophic ﬂood probabilities from current
baseline levels are valued signiﬁcantly less (and are in the case of mortality
risk even positive). Turning points reﬂecting saturation levels are located
at the lower end of the value domain used in the choice experiment: once
every 92 years in the case of catastrophic ﬂood probability and one in every
7,040 people in the case of mortality risk. WTP is VND 15 (US$0.001) per
household per year when reducing the probability of dying from one in
7,000 persons to one in 8,000 persons per catastrophic ﬂood event, and
VND 3,193 ($0.17) per household per year when reducing the probability
of dying from one in 1,000 persons to one in 2,000 persons.
Turning to the insurance design characteristics, respondents have a
strong preference for a government-supplied ﬂood insurance (the dummy
variable for insurance provider has the value 1 if the provider is a private
insurance company). As expected, a higher insurance premium results in a
lower likelihood that an alternative is chosen. For insurance cover, both a
signiﬁcant linear and quadratic effect is found, indicating that this variable

244Roy Brouwer et al.
Figure 1.(a) WTP for different catastrophic ﬂood probabilities, (b) WTP for different
mortality risks, (c) WTP for different insurance covers in the three districts and Dai Loc
has a saturation level too: VND 61.2 million (US$ 3,314) in the case of all
three districts together and VND 71.5 million (US$ 3,872) for the district
Dai Loc. These saturation levels are more than 10 times the average ﬂood
damage reported by households during the last catastrophic ﬂood in 2007
(see table2), suggesting a strong risk aversion. The loss functions for all
three districts together and Dai Loc are presented in ﬁgure1c.
As expected, WTP for ﬂood losses in ﬁgure1c is, ceteris paribus, signif-
icantly lower in the upstream district Dai Loc compared to Dien Ban and

Environment and Development Economics245	
Hoi An. Marginal WTP increases most when moving across the lower lev-
els of ﬂood damage. Across all three districts, household WTP is VND 685
(US$0.04) per year for the ﬁrst VND 10 million (US$540) damage suffered
as a result of a catastrophic ﬂood. This increases more than proportion-
ally to VND 5,506 (US$0.3) per year if the damage cover increases from
VND 10 to 20 million. The increase in marginal WTP then displays dimin-
ishing returns from VND 4,170 (US$ 0.23) to VND 160 (US$0.009) per year
if the indemnity cover increases from VND 20 to 30 and ultimately from
50 to 60 million per catastrophic ﬂood event, respectively. After the turn-
ing point marginal WTP is still positive, but decreases as insurance cover
increases further.
6. Discussion and conclusions
Discrete choice modeling has become increasingly popular in the economic
valuation domain. However, almost no applications exist related to climate
change and ﬂood risks. This study contributes to the existing literature in a
number of ways. From a practical policy point of view, we show that there
exists substantial demand for ﬂood insurance, even though a considerable
share of the sample population indicate that they are unable to afford to
pay for such insurance. After excluding these respondents from further
analysis, we ﬁnd no signiﬁcant effect of household income any more on
respondent choice behavior. Of all insurance design characteristics, only
the insurance sum is characterized by signiﬁcant preference heterogeneity.
Respondents seem risk averse as they like higher insurance cover, prefer-
ably provided by the government, not a private insurance company. How-
ever, demand for catastrophic ﬂood insurance has a ceiling. Respondents
do not wish to insure more than 15 times their self-reported ﬂood damage
suffered during the last catastrophic ﬂood in the case study area. More-
over, demand is spatially differentiated and is signiﬁcantly lower upstream
where ﬂood risks are lower than downstream where risks are higher.
The validity of our results is strengthened by the fact that we convinc-
ingly manage to embed mortality risks in a clear and understandable way
in the context of catastrophic ﬂood probabilities. Although WTP to insure
oneself against the occurrence of catastrophic ﬂoods and the probability
of dying due to a catastrophic ﬂood are not directly comparable (they are
measured in different units), the relative increase in WTP for ﬂood insur-
ance is higher for an increase in catastrophic ﬂood probability than for the
same relative increase in mortality risk compared to current baseline con-
ditions. The increase in WTP for ﬂood insurance is non-linear in increasing
ﬂood probabilities and mortality risks, and characterized by signiﬁcant
preference heterogeneity. The former means that catastrophic ﬂood risk
valuation and the derived VSL exhibit decreasing returns in the level of
the risk reduction. The latter conﬁrms that the WTP welfare measure is
not universally transferable. Risk perception and perspectives differ among
individuals in the same situation, therefore leading to different decisions
related to the purchase of catastrophic ﬂood insurance.
In view of the fact that most respondents are unfamiliar with the con-
cept of ﬂood insurance, some degree of learning was expected to inﬂuence

246Roy Brouwer et al.
their decisions during the choice experiment. This was conﬁrmed when
comparing the preference parameters of the estimated choice model across
the choice sequence. Although 80 per cent of the sample population passed
the choice consistency test when presented with the same choice task at the
beginning and the end of the experiment, preference parameters appeared
to be signiﬁcantly different and hence unstable during the choice sequence.
However, choices in the ﬁnal choice task were more stable than choices in
the ﬁrst choice task as measured through the lower error variance. Pref-
erence uncertainty is usually characterized by larger variance. From the
responses of those who consistently chose the opt-out in the choice exper-
iment, including those who protested, some indication was found that
decision making in the choice experiment was not easy. Six per cent of the
sample asked for more information before they felt capable of making a
decision whether or not to buy ﬂood insurance, while another 8 per cent
said they needed more time to think. Future research in this area would
beneﬁt from a more detailed assessment of the relationship between choice
complexity and preference stability.
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Appendix A: Introduction choice experiment
I’d like to inform you about the possibility of extreme ﬂood events occur-
ring in the future. In the future the frequency of extreme ﬂood events like
the one in 2007 is expected to increase due to climate change. Also the
damage associated with these events is expected to increase due to popula-
tion growth and economic development. In order to anticipate these future
ﬂood risks, we would like to ask you to consider a number of descriptions
of possible future situations and measures to mitigate the negative impacts
of the increasing risks of ﬂooding. We are interested to ﬁnd out which of
these measures you prefer given the expected future situation. All we ask
you to do is to look at the presented situation and tell us which situation
you prefer best given the expected increase in ﬂood risk and the proposed
measures to mitigate these risks. I will present you with an example card
ﬁrst to explain to you what the situations represent. Following this exam-
ple card, I will show you nine more of these cards and for each of these
cards you will be asked to indicate which situation you prefer most.
In each future situation the frequency of extreme ﬂood events is expected
to change, from currently once every 100 years to once every 10 years.
The chance that someone will die as a result of the extreme ﬂood event
ranges between one in ten thousand people to one in one thousand peo-
ple. This means that if an extreme ﬂood event occurs, one in ten thousand
or one in one thousand persons, including you and your family members,
faces the risk of dying. The length of the social disruption during the after-
math of the extreme ﬂood event, such as lack of electricity and clean water
may vary between one and eight weeks.
In order to deal with these increased ﬂood risks in the future, an
insurance scheme is proposed in your region to mitigate the ﬁnancial con-
sequences of extreme ﬂood events. The insurance will cover your ﬁnancial

Environment and Development Economics249	
damage costs as a result of future extreme ﬂood events like the one in 2007.
Having an insurance hence reduces the ﬁnancial burden your family faces
should an extreme ﬂood event occur. You are free to choose the sum of
money for which you wish to insure yourself and your family, ranging from
25 to 100 million VND per extreme ﬂood event. This latter sum of money
is the maximum amount of compensation you are entitled to in case you
suffer any damage due to a ﬂood.
You also have the option to choose from whom you wish to buy the
insurance. The insurance will be either provided by the central government
or a private insurance company. The standard insurance policy on offer
includes home insurance to cover any damage to your family home, crop
insurance to cover any damage to your crops, and a life insurance in case
you or one of your family members die as a result of catastrophic ﬂooding.
In order to obtain the insurance, your household will either have to pay
every month a ﬁxed insurance premium to the private insurance company
or an extra earmarked tax to the central government. The premium amount
or earmarked tax is the same; the only difference is that the insurance is a
private one if concluded with the private insurance company, and a public
one if concluded with the central government. The prices of the standard
insurance policy vary between 2,500 and 20,000 VND per household per
month.
The insurance policy applies to catastrophic ﬂooding only. This means
that a ﬂood event has to be categorized ofﬁcially as catastrophic by an
independent assessor. Also the damage claim after the catastrophe and
the indemnity paid will be evaluated and estimated by an independent
assessor. The terms and conditions of your insurance are protected by law.
Appendix B: Example of a choice card

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