Does the exposure to natural hazards affect risk and time preferences?
Some insights from a field experiment in Perú.
Mohamed Ali Bchir, Marc Willinger
Abstract
People who have been living for a long time in areas where they are threaten by natural
hazards are likely to have adapted in various ways to their hazardous environment. In this
study, we investigate whether risk and time preferences of individuals exposed to a
background risk differ with respect to the preferences of individuals that are not exposed to
such risk. For that purpose, we conducted a field experiment in Arequipa (Perú). The city is
exposed to the threat of lahars, i.e. mudflows that come down from a volcano. Our results
show that poor are more risk seeking and more impatient in exposed areas than in unexposed
area. However, for higher income categories, risk and time preferences seem to be unaffected
by the exposure to lahars risk. We also show, in line with previous findings, that risk and time
preferences are negatively correlated in individuals: more risk-averse individuals are also less
patient.
Keywords: Background risk, risk preference, time preference, risk perception, natural
hazards.
JEL classification: C93; C91; D81; Q54
Acknowledgement
This article received the support of the French National Research Agency within the project "Laharisk" (ANR09-RISK-005).
 Corresponding author: ENGEES, UMR GESTE, 1 quai Koch BP 61039, 67070 Strasbourg, France.
[email protected]; telephone: +33 (0)3 88 24 82 49, fax:+33(0)3 88 24 82 84
 UFR d’Economie, UMR LAMETA, Av. Raymond Dugrand, C.S. 79606, 34960 Montpellier Cedex 2, France.
[email protected]
-1-
1 Introduction
In contrast to the standard hypothesis in economics stating that individual preferences are
fixed, a growing literature is revealing an endogenous link between individuals’ preferences
and their living environment. In particular, experimental investigations related to risk
preference suggest that an individual’s environment determines to some extent his risk
attitude. For example, Eckel et al. (2009) showed that just after hurricane Katarina individuals
turned out to be significantly more risk loving, especially women. Similarly, Voors et al.
(2012) found that individuals exposed to civil war in Burundi became more risk seeking,
exhibited higher discount rates and behaved more altruistically. A few other papers also report
that past experience of dramatic events such as tsunamis, earthquakes or violent conflicts can
have long-lasting effects on individuals' preferences (Cameron and Shah 2012, Cassar et al.,
2011, Castillo and Carter, 2011). These findings suggest that individuals’ preferences are
partly shaped by their social, institutional or natural environment.
While there are many relevant types of environments that are likely to shape risk preferences,
natural hazard are of particular interest for several reasons. First, traumatic events such as
tsunamis, major earthquakes or volcanic eruptions can deeply affect individuals often with
irreversible consequences. In coming years, probably as a consequence of climate change,
some of these natural disasters will become more frequent and will affect densely populated
areas, especially in developing countries. Second, a better understanding of the link existing
between natural hazards and individual preferences might prove helpful to better target
prevention, protection and emergency policies. Indeed, self-protection, emergency
preparedness and self-insurance behavior are key issues related to individuals’ risk
preferences. Third, from a theoretical point of view natural hazards can be likened to
background risk. Such types of risks are difficult to insure and hard to mitigate. Yet there is
no clear theoretical prediction about how individuals’ preferences are affected by background
risk. According to expected utility theory (EUT), the most likely prediction is riskvulnerability, that is individuals behave as if they were more risk-averse with respect to a
given foreground risk in the presence of a harmful background risk than without (Gollier and
Pratt, 1996). However, non-EUT might predict just the opposite (Quiggin, 2003).
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One way to answer how the exposure to a natural hazard shapes risk preferences is to
investigate empirically variations of background risk. So far, empirical investigations about
the impact of background risk on risk-taking behaviour have mainly focused on financial
decisions (Guiso et al., 1996; Guiso and Paiella, 2008; Heaton and Lucas, 2001). Several
laboratory experiments (Beaud and Willinger, 2012; Lee, 2008; Lusk and Coble, 2008) and
field experiments (Harrison et al., 2007; Herberich and List, 2012) showed that most people
are risk-vulnerable, in accordance with EUT. However, there is an important issue with
respect to the magnitude of the impact of background risk. While Lee (2008) and Lusk and
Coble (2008) found a very small effect, Beaud and Willinger (2012) reported a very strong
impact. Since these findings are grounded on laboratory experiments, it is an open question,
whether background risk affects individuals’ risk-taking behavior in the field.
For that purpose, we designed an incentivized field experiment that was implemented in an
area where a portion of the population is exposed to lahars hazard. Lahars are mudflows that
come down from a volcano. Highly exposed areas are close to the beds of the watercourses.
We chose to collect our data in the city of Arequipa (Perú), which is settled down the volcano
El Misti, and where many people are threaten by particularly damageable lahars (Vargas
Franco et al., 2010). In order to isolate the impact of the background lahars risk on
respondents risk attitudes, we elicited the risk preferences of people living in exposed areas
and contrasted them with those of respondents living in safe areas. We also investigated the
impact of background risk on time preferences. Our motivation for investigating such an issue
is the burgeoning literature showing a strong correlation between risk-preferences and timepreferences within individuals (Anderhub et al., 2001; Burks et al., 2009; Carpenter et al.,
2011; Dohmen et al., 2010; Eckel et al., 2004). We hypothesized that exposure to background
risk may increase individuals’ impatience because of a shorter life-expectancy.
A key aspect of our experimental setting is the introduction of exposure as a treatment
variable. We carefully chose the exposed areas based on geographical data and advice of local
institutions. Previous studies by Cameron and Shah (2012), Eckel et al. (2009) and Castillo
and Carter (2011) showed that after having been affected by a natural hazard such as a
hurricane, a tsunami or an earthquake, individuals’ preferences changed more or less deeply.
In contrast to these studies which focused on the ex post impact of a background risk after the
occurrence of an adverse outcome, in this paper we are interested in whether and how the ex
ante exposure to background risk shapes people’s preferences. For this reason we chose to
study a population that is strongly exposed to natural hazards but has not been affected for a
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long time by an adverse outcome. The last damageable lahar occurred in 1997 (Vargas Franco
et al., 2010).
Our results show that poor individuals living in exposed areas are significantly more riskseeking and more impatient than those living in unexposed areas. However, larger income
categories are not affected by the presence of a background risk. In accordance with other
studies, we found a negative correlation between risk preferences and time preferences within
individuals: more risk-seeking individuals are also more patient. This relation is stable with
respect to the exposure to background risk.
The remainder of the paper is organized as follows. In section 2 we introduce our
experimental design and in section 3 we present our results. Section 4 concludes.
2 Experimental design
We trained a local team of geographers in Arequipa and in Lima in order to collect data and
run incentivized experiments. Each session was divided into three parts: in the first part,
respondents had to answer a survey questionnaire about their perception of lahars risk, part 2
consisted of several experimental games, including the elicitation of subjects’ risk and time
preferences, which we report in this paper. Finally, part 3 consisted in a short questionnaire
intended to collect socio-demographic data (income, age, birth, etc.). In this investigation, we
use only the data of parts 2 and 3.
We first describe how we selected the exposed and non-exposed areas. In a second subsection
we introduce the elicitation methods that were used. Finally in subsection 3 we address
several issues with respect to possible confounds that could explain risk and time preference
variability in our sample and how we control for them.
2.1
Zoning
Most people who are highly exposed to lahars hazard live close to the edge of a quebrada (the
watercourses spanning Arequipa). However, there is substantial spatial heterogeneity of
exposure to lahars hazard because the quebradas that cross the city have differing
characteristics and some highly exposed areas are also located on hills’ tops. Therefore, we
needed to establish a precise mapping of the most highly exposed areas. This zoning was
achieved with the help of geographers and experts of the Defensa Civil of Arequipa. Our data
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was collected from the three main quebradas of the city and, inside each quebrada, with
respect to different levels of exposure to lahars1. Preferences of individuals who live in
exposed areas were contrasted with preferences of individuals who are protected against the
risk of lahars, i.e. mainly those who live apart or far from quebradas.
As the city of Arequipa is settled down the active volcano El Misti, most inhabitants are also
exposed to a second natural hazard: the threat of an eruption. With the presence of this second
background risk, the contrast in risk-preferences between individuals exposed and unexposed
to lahars risk may fade away under the assumption of risk vulnerability. It is likely that the
volcanic threat is perceived as a much stronger background risk than the lahars risk. The
impact of the volcanic background risk on peoples’ risk-aversion may therefore hide the
impact of the lahars background risk on risk-attitudes. In order to control for the volcanic
background risk effect, we identified a location in Perú where the volcanic threat is absent,
but some people are exposed to a risk akin to lahars. With the help of geographers we located
a suburb of Lima where a fraction of the population is exposed to muddy flows that share
many characteristics of lahars2. Furthermore, the population of this suburb has similar
characteristics than the population living in the slums of Arequipa.
2.2 Elicitation of risk and time preferences
We elicited risk preferences by using a multiple price list method, similar to the one
introduced by Binswanger (1980). Individuals were offered a list of binary lotteries with
uniform probability, from which they were asked to pick up one. The lotteries were pictured
with bills and coins to make the outcomes more easily palpable, especially for illiterate
participants (see appendix for a sample illustration). At the end of the session, if the lottery
task was randomly chosen to be played out for real, individuals had to toss a fair coin in order
to determine the outcome of their chosen lottery. The advantage of this protocol lies in its
simplicity with respect to the low educated level of participants. Using uniform probabilities
guarantees that probability weighting is uniform across lotteries and therefore does not affect
the choice of the lottery, in contrast to other methods such as Holt and Laury (2002). Table 1
reports the different lotteries and the corresponding Constant Relative Risk Aversion (CRRA)
interval.
1
The localization of sessions on maps is available upon request.
2
The localization of sessions on maps is available upon request.
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Table 1 : Lottery choices (1)
Expected
Risk aversion
CRRA(2)
10
Extreme
[6.40 +∞,[
18
13.5
Very strong
[2.00,6.40[
8
24
16
Strong
[0.81,2.00[
Lottery 4
6
30
18
Moderate
[0.31,0.81[
Lottery 5
2
38
20
Light
[0, 0.31[
Lottery 6
0
40
20
Negative
]-∞, 0[
Option A
Option B
Lottery 1
10
10
Lottery 2
9
Lottery 3
return
(1) See appendix 1 ; (2) CRRA : Constant Relative Risk Aversion
Time preferences were elicited through a standard multiple price list for which participants
were asked to choose between a “sooner and lower” (SL hereafter) amount or a “later and
larger” (LL hereafter) amount (Coller and Williams, 1999; Harrison et al., 2002). The SL
amount was kept constant while the LL amount was increased stepwise in order to identify the
question number for which the respondent eventually switched from SL to LL. As with
lotteries, we pictured bills and coins to make outcomes more realistic (see Appendix 2). The
switching question number provides an estimate of the individual’s subjective rate of time
preference or impatience. The finer the interval between two adjacent questions the more
precise the estimate. We define as “patient” an individual who switches from the choice of SL
at question 1 to the choice of LL at question 2. Similarly, an individual who switches from SL
in the second question to LL in the third question is called “slightly patient”, etc. Note that
individuals who never switch are called “Extremely impatient” (always SL) or “Extremely
patient” (always LL). Table 2 summarizes all the categories. For practical reasons we had to
introduce a very short time interval (3 days). In order to avoid a biased preference in favour of
the SL, the SL and the LL amounts were both delayed, the SL being available in one day and
the LL in four days. The advantage of such a method is its simplicity. However, the data of
individuals who switch several times is useless. In our sample the category of inconsistent
respondents represents 31.45%.
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Table 2 : Time choices (1)
Question
(SL)
(LL)
Number
Tomorrow
In 4 days
1
10
10
--
2
10
11
Patient
3
10
12
Slightly patient/impatient
4
10
13
Impatient
5
10
14
Very impatient
6
10
15
Highly impatient
Patience category (2)
(1) See appendix 2; (2) There are two other patience categories where an individual never switches: Extremely patient: (always LL) and
Extremely impatient (Always SL)
All experimental games were incentivized. Participants received a small flat participation fee
in addition to their earning for the randomly selected game. Participants were aware that only
one of the games would be randomly determined at the end of the session to be played out for
real, and that their choice for this particular game would be implemented and paid out in cash.
The average earning was 17 soles. This is a substantial amount in poor areas (a wage between
1 and 3 days of work).
2.3 Selection bias
We organized experimental sessions in 18 different locations with an average of 16
participants per session. Out of the 309 participants, 214 lived in exposed areas (12 locations)
and 95 in unexposed ones (6 locations). All the participants were randomly selected in each
location. The characteristics of the sample are summarized in Table 1. The length of exposure
to background risks may be an important determinant for risk preferences and for time
preferences. We needed therefore to control for the duration of settlement and the age
distribution in our two study sites (Arequipa and Lima). On average people were settled for a
longer time period in unexposed areas compared to exposed areas (24 versus 21 years) an
insignificant difference ( (t=1.18; p=0.23) overall, (t=0.85; p=0.85) for Arequipa and (t=0.97;
-7-
p=0.33) for Lima ). Therefore, our experimental protocol based on zoning criteria is not
altered with the random selection of participants. Second, we observe the same age
distribution in exposed and unexposed areas (Kolmogorov Smirnov test, D=0.07; p=0.94): the
average age is 45 years in both areas (with a standard deviation of 16 years and 18 years
respectively). Since age is a key determinant of risk and time preferences, it was important to
control for difference in age distributions by locations. In our case since the distributions
cannot be distinguished, age does not account for observed differences in preferences.
However, we observe that participants living in unexposed area more educated (58.5% had
graduate level against 28.56%, Chi2=27.71; p<0.01) and have a higher income than those in
exposed area: 30.94% earn more than 600 soles against 11.94% in exposed areas
(Chi2=18.31; p<0.01). This fact is not surprising since people who can afford a more
expensive house do not choose to settle in hazardous areas. Since we were aware of this issue
when we were collecting data we tried to balance by choosing unexposed areas that are
socioeconomically similar to the exposed areas. However, we could not wipe out completely
the difference by our sample selection, so that we need to control for it in our data analysis. In
particular, there are several slum settlements in exposed areas where individuals live with a
very low income. Hereafter, we call “poor” individuals that earn less than 300 soles per
month. According to the World Bank, in 2010 30.8% of the Peruvian population lived under
the national poverty threshold, which is less than 272 Soles (3.5 dollars per day). For practical
reasons during the survey, we set the lowest income category to 300 Soles (4 dollars), that is,
twice the level of the World Bank threshold of 2 dollars per day. Note that during our
investigation, the minimum wage was equal to 550 Soles. Finally, we observe that we have
more women (67.31%) in the whole sample than men (32.69%): women represent 75.78% of
the sample in unexposed vs 63.55% in exposed areas. Women were more willing and
available to participate in our sessions than men. Nonetheless, we do not observe a significant
difference in the choices of the lotteries between men and women in the exposed areas
(Kolmogorov Smirnov test, D=0.12; p=0.39)) and in the unexposed areas (Kolmogorov
Smirnov test (D=0.22; p=0.27)) 3. That is the higher proportion of women in the whole sample
cannot explain the differences between the locations.
3
Similar differences hold for time choices in the exposed areas (Kolmogorov Smirnov test, D=0.12; p=0.59) and
the unexposed areas (Kolmogorov Smirnov test, D=0.13; p=0.93).
-8-
Definition of the variables
Lottery_choice: A categorical variable of risk aversion from one (Extreme) to six (Negative).
Switching_question: A categorical variable from zero (Extremely patient) to six (Extremely
impatient).
Riskyarea: A dummy that takes zero for the unexposed area and one for the exposed.
City: A dummy that takes zero for Arequipa and one for Lima.
Sex: A dummy that takes zero if the respondent is a man and one if she is a woman.
Age: A continuous variable.
Income_: A categorical variable from one “<300 soles per month” to four “>1200 soles per
month”.
Edu_: Four education levels from one “elementary level” to four “university”.
-9-
Table 3: Summary statistics (*)
Variable
Exposed Area
Total
Arequipa
Lima
(N=309)
(N=207)
(N=102)
Unexposed Area
Exposed Area
Unexposed Area
Exposed Area
Unexposed Area
(N=214)
(N=95)
(N=144)
(N=63)
(N=70)
(N=32)
Men
36.44%
24.21%
38.88%
20.63%
31.42%
31.25%
Women
63.55%
75.78%
61.11%
79.36%
68.57%
68.95%
45
44
46
43
42
45
Elementary
22.85%
18.08%
26.95%
17.74%
14.49%
18.75%
Highschool
48.57%
23.40%
43.26%
30.64%
59.42%
9.37%
Education
18.09%
30.85%
18.43%
38.70%
17.39%
15.62%
University
10.47%
27.65%
11.34%
12.90%
8.69%
56.25%
<300 soles
58.41%
50.52%
52.77%
49.20%
70.00%
53.12%
300< <600 soles
31.30%
22.10%
34.72%
28.57%
24.28%
9.37%
600< <1200 soles
8.87%
17.89%
11.11%
20.63%
4.28%
12.5%
1200 soles>
1.40%
9.47%
1.38%
1.58%
1.24%
25.00%
21
24
25
22
19
22
Gender
Average age (years)
Education
Income categories
Average settling period in
the area (years)
- 10 -
3 Results
Result 1: The distribution of risk preferences is similar in exposed and unexposed
areas, but poor individuals are significantly more risk seeking in exposed areas
and this difference is strongest in areas that are both exposed to lahars and
volcanic hazards.
Figure 2 depicts the distribution of lottery choices. It shows a larger choice frequency of
riskier lotteries in exposed areas in comparison to unexposed areas (+8.51% for lottery 4 and
+2.21% for lottery 6) and a lower choice frequency of safer lotteries (-2.94% for lottery 1 and
-7.04% for lottery 2). But, these differences are not statistically significant. A Chi-square test
does not reject the null hypothesis of equal distributions of choice frequencies in the two areas
(Chi2=5.91; p=0.31). These differences become however more pronounced if we focus on
poor individuals. Indeed, poor individuals are more likely to settle in exposed areas,
especially in locations where exposure is highest as described in subsection 2.3. Figure 2 also
depicts the distribution of the lottery choices of poor individuals. Lottery 6 is 11.15% more
frequently chosen by poor individuals and lottery 4 10.05% more frequently in exposed areas
than in unexposed areas. Similarly, the safer lottery 2 is 19.25% less frequently chosen in
exposed areas than in unexposed areas, while the choice frequency of lottery 1 is the same in
both areas. The Chi-square test rejects the null hypothesis of equal distributions of choice
frequencies between the exposed and the unexposed areas for poor individuals (Chi2= 12.91 ;
p=0.02).
A further analysis confirms this finding with respect to poor individuals. We run an ordered
logit4 regression to explain the likelihood of lottery choices: the dependent variable
lottery_choices is regressed towards the explanatory variables riskyarea and city. The dummy
variable Riskyarea equals 1 in the individual lives in an exposed area. The residuals are
clustered by session. Table 2 reports the results of the regression for the whole sample and for
poor individuals separately. It shows that for the whole sample the exposure to lahars risk
4
It is important to point out that we are not interested in the absolute level of CRRA. Rather, it is the existence
or not of a significant variation between exposed and unexposed areas that matter. The same remark is relevant
for time preferences.
- 11 -
does not affect the likelihood of the lottery choices but it does so for poor individuals for
which there is a strongly significant relation. Poor individuals settled in exposed areas are
significantly more risk loving than those settled in unexposed areas. In addition, this relation
is stronger whenever the exposed area cumulates both background risks (lahars and volcanic
threat) than in the case of a single background risk (lahars). Finally, controlling for
demographic variables (age, sex and education) does not affect these findings. Older and
higher educated individuals’ select more risky lotteries than younger and less educated. There
is also a negative gender effect (sex) on risk-taking but insignificant.
Table 4 : Ordered logit regression explaining risk preferences by exposure to lahars risk.
(1)
Risk
Lottery_ch~e
Riskyarea
City
(2)
Risk demog~r
0.194
(0.69)
-0.223
(-1.41)
0.0115
(0.04)
0.0176**
(2.52)
0.633**
(2.19)
0.783**
(2.32)
0.0537
(0.15)
0.0282
(0.10)
0.0555
(0.15)
0.400
(0.53)
0.495***
(2.78)
-0.449***
(-2.87)
0.495**
(2.02)
-0.590***
(-2.86)
-0.0608
(-0.18)
0.0277**
(2.40)
0.781**
(2.02)
1.073**
(2.15)
0.118
(0.20)
.
.
.
.
.
.
-1.930***
(-7.26)
-0.799*
(-1.82)
-1.824***
(-7.29)
-0.312
(-0.46)
-0.850***
(-3.41)
0.334
(0.71)
-0.683***
(-4.33)
0.913
(1.29)
-0.0145
(-0.06)
1.237**
(2.57)
0.0385
(0.26)
1.730**
(2.38)
0.891***
(3.53)
2.204***
(5.09)
1.152***
(5.47)
2.977***
(4.02)
1.826***
(7.69)
3.244***
(6.69)
2.202***
(8.12)
4.228***
(5.00)
309
292
173
162
Age
Edu_2
Edu_3
Edu_4
Income_2
Income_3
Income_4
cut2
_cons
cut3
_cons
cut4
_cons
cut5
_cons
N
(4)
Risk Poor ~r
0.223
(0.98)
-0.165
(-1.14)
Sex
cut1
_cons
(3)
Risk Poor
t statistics in parentheses
* p<0.10, ** p<0.05, *** p<0.01
- 12 -
Result 2: The distribution of time preferences in exposed areas does not differ
from the distribution of time preferences in unexposed areas. However, poor
individuals in exposed areas are significantly more impatient than individuals
living in unexposed areas.
The comparison of the distributions of switching questions between exposed and unexposed
areas shows that the proportion of very impatient individuals (always choosing SL) is larger
in exposed areas (+6.35%) than in unexposed areas while the proportion of very patient
(always choose LL) is lower (-2.94%). Despite these differences the distribution of switching
questions are not significantly different between the two areas (Chi2=1.15; p=0.97).
Interestingly, as already observed for risk preferences, these differences are exacerbated for
the poor category (+8.55% always choose SL and -7.43% always choose LL). But, in contrast
to risk preferences, we need to control for gender, age and education specific effects in order
to establish significance of the distributions of switching questions. These findings suggest
that the risk preferences of the poorest are more strongly shaped by background risk(s) than
their time-preferences. Table 3 reports the results of an ordered logit regression in which the
dependent variable is the switching_question. Explanatory variables are riskyarea, city and
demographic variables (age, sex, education and income). Residuals are clustered with respect
to session. Table 3 shows that poor individuals are significantly more impatient in exposed
areas than in unexposed ones. This impatience is also stronger in the Arequipa sample where
respondents are both exposed to lahars and volcanic hazard than in the Lima sample where
respondents are only exposed to lahars hazard. Impatience is higher with poor women and
increases with age and education.
- 13 -
Table 5 : Ordered logit regression explaining time preferences by exposure to lahars risk.
(1)
Time
Switching_~n
Riskyarea
City
(2)
Time demog~r
0.341
(1.60)
-0.601***
(-3.11)
-0.356
(-1.12)
0.0158*
(1.86)
0.729
(1.49)
0.984*
(1.77)
1.417***
(3.04)
0.298
(1.08)
-0.184
(-0.44)
-0.539
(-0.68)
0.358
(1.23)
-0.615*
(-1.74)
-1.496***
(-7.45)
-0.0175
(-0.02)
-1.326***
(-3.72)
0.558
(0.57)
-0.611***
(-3.75)
0.862
(1.20)
-0.501*
(-1.87)
1.436
(1.47)
-0.109
(-0.68)
1.410**
(2.03)
0.0683
(0.37)
2.069**
(2.34)
0.151
(0.76)
1.657**
(2.37)
0.345
(1.64)
2.348***
(2.74)
0.598***
(2.95)
2.133***
(2.99)
0.825***
(3.76)
2.865***
(3.48)
0.924***
(4.44)
2.495***
(3.51)
1.210***
(5.60)
3.313***
(3.96)
218
206
118
111
Age
Edu_2
Edu_3
Edu_4
Income_2
Income_3
Income_4
cut2
_cons
cut3
_cons
cut4
_cons
cut5
_cons
cut6
_cons
N
(4)
Time Poor ~r
0.199
(0.95)
-0.630***
(-2.65)
Sex
cut1
_cons
(3)
Time Poor
t statistics in parentheses
* p<0.10, ** p<0.05, *** p<0.01
- 14 -
0.626**
(2.02)
-0.740**
(-2.51)
-0.922***
(-2.68)
0.0232**
(2.10)
1.017
(1.61)
1.471**
(2.18)
2.072***
(3.76)
.
.
.
.
.
.
Figure 1: Risk and time preferences in exposed and unexposed areas.
Risk preference
Unexposed
0 1 2 3 4 5 6
0 1 2 3 4 5 6
0 5
Percent
0 5
Percent
Exposed
10 15 20 25 30 35 40
Unexposed
10 15 20 25 30 35 40
Exposed
Time preference
1 2 3 4 5 6
1 2 3 4 5 6
Lottery
Switching question
Poor
Unexposed
0 1 2 3 4 5 6
0 1 2 3 4 5 6
10 20 30 40
Exposed
0
Percent
0
Percent
Poor
Unexposed
10 20 30 40
Exposed
1 2 3 4 5 6
1 2 3 4 5 6
Lottery
Switching question
N=309, Pool
N=173, Poor: Income <300 soles
1 : Risk aversion Extreme
6 : Risk aversion Negative
N=218, Pool
N=118, Poor: Income <300 soles
0 : No switching, Extremely patient
6 : No switching, Extremely impatient
Result 3: Individuals who are more risk seeking are also more patient. Exposure
to natural hazard does not affect this relation.
Are risk and the time preferences related in individuals as suggested by several recent
experimental papers? Several investigations found a negative correlation between risktolerance and impatience (Anderhub et al., 2001; Burks et al., 2009; Carpenter et al., 2011;
Dohmen et al., 2010; Eckel et al., 2004)5. The findings of these contributions are consistent
even though they are based on very different samples, different methods and various
incentives. Our result 3 provides additional evidence to this stream of literature, based on a
very different sample. It also provides a new insight about whether the relation is affected by
background risk or not. We find a significantly negative correlation between risk and time
preferences. Individuals who are more risk seeking are also more patient (Chi2=50.35; pvalue=0.01). Interestingly, background risk does not affect this relation. Table 4 reports the
result of an ordered logit regression where the switching_question is the dependent variable.
5
Carpenter et al. (2011) found however a positive correlation.
- 15 -
Explanatory variables are Lottery_choice, riskyarea, city, and demographic variables. Each
lottery corresponds to a dummy variable: lottery 1 is the least risky one and lottery 6 the most
risky one. We observe that individuals who choose a more risky lottery choose also a lower
switching question. But at the same time the explanatory variable riskyarea is not significant
which means that the relation is independent on the exposure to lahars risk. However, by
reducing the level of background risk (dummy variable city) individuals become significantly
more patient all other things equal. Threfore the level of overall background risk seems to
play a role: a lower exposition to natural hazard affects the intensity of the relation between
risk and time.
Table 6 : Ordered logit regression explaining the switching question by the lottery choices.
(1)
Pool
Switching_~n
Lottery_2
Lottery_3
Lottery_4
Lottery_5
Lottery_6
Riskyarea
City
(2)
Pool demog~c
-1.571***
(-5.17)
-0.815**
(-2.18)
0.158
(0.35)
-1.323***
(-3.49)
-0.314
(-0.62)
.
.
.
.
-0.368
(-1.21)
0.0162*
(1.70)
0.822
(1.51)
0.935
(1.57)
1.271***
(2.60)
0.270
(1.03)
0.00348
(0.01)
-0.673
(-0.91)
-1.594***
(-3.27)
-0.950*
(-1.83)
0.318
(0.53)
-1.343***
(-2.93)
-0.414
(-0.58)
0.129
(0.36)
-0.730**
(-2.22)
-1.464***
(-4.84)
-0.909*
(-1.67)
0.503
(0.89)
-1.174**
(-2.40)
-0.508
(-0.81)
.
.
.
.
-0.932**
(-2.28)
0.0258*
(1.95)
1.168
(1.45)
1.742**
(2.09)
2.096***
(2.92)
.
.
.
.
.
.
-2.424***
(-5.46)
-0.882
(-1.03)
-2.394***
(-3.65)
-0.128
(-0.12)
-1.477***
(-3.60)
0.0587
(0.07)
-1.487***
(-2.74)
0.834
(0.81)
-0.914**
(-2.36)
0.670
(0.82)
-0.819
(-1.59)
1.572
(1.56)
-0.622
(-1.45)
0.947
(1.13)
-0.502
(-0.91)
1.895*
(1.86)
-0.130
(-0.31)
1.470*
(1.74)
0.0205
(0.04)
2.464**
(2.42)
1.870**
(2.20)
0.445
(0.83)
2.959***
(2.79)
Age
Edu_2
Edu_3
Edu_4
Income_2
Income_3
Income_4
cut2
_cons
cut3
_cons
cut4
_cons
cut5
_cons
cut6
_cons
N
(4)
Poor demog~c
-1.627***
(-4.06)
-0.953***
(-2.58)
0.0179
(0.04)
-1.338***
(-4.28)
-0.141
(-0.33)
0.112
(0.41)
-0.703***
(-2.95)
Sex
cut1
_cons
(3)
Poor
0.232
(0.54)
218
206
118
t statistics in parentheses
* p<0.10, ** p<0.05, *** p<0.01
- 16 -
111
4 Discussion and concluding remarks
We relied on experimentally elicited measures of risk preferences to analyze how exposure to
background risk affects individuals’ risk and time preferences. We studied the population of
Arequipa (Perú) which is exposed to two background risks (lahars hazard and volcanic
eruption) and the population of a suburb of Lima which is exposed to a single background risk
(lahars hazard).
We found that poor individuals are significantly more risk-seeking and significantly more
impatient when they live in exposed areas rather than in unexposed areas. However, at the
whole sample level (i.e. for all income levels) there is no significant difference in the
distribution of preferences between exposed and unexposed areas. The latter observation is
inconsistent with recent findings of laboratory experiments about the impact of background
risk on risk preferences. For instance, Lee (2008) and Beaud and Willinger (2012) found
strong evidence about increased risk-aversion with the presence of background risk, i.e.
evidence for risk-vulnerability, in contrast to Lusk and Coble (2008). Several field
experiments also found evidence of permanent increases in risk-aversion after experiencing
background risk (Cameron and Shah, 2012; Castillo and Carter, 2011). However, these papers
were concerned by ex post alteration of preferences after the occurrence of an adverse
outcome of the background risk. In contrast, our study focused on the impact of ex ante
exposure to background risk on individuals’ preferences. Clearly, further investigations are
still needed in order to better understand the link between risk preferences and exposure to
natural hazard. In particular, a within design (before / after) the hazard will allow a more
accurate measure of the exposure although it might be difficult to implement in the field (see
Beaud and Willinger, 2012 for a lab experiment).
There is also mixed evidence about the impact of background risk on time preferences. Voors
et al. (2012) found lower patience after exposure to violent conflicts. Callen (2011) found
increased patience after the 2004 earthquake tsunami in Sri Lankan workers although Cassar
et al. (2011) found no effect of the same event on Thai villagers. At best, these mixed results
point towards the need to more carefully designed experiments, including lab experiments, in
order to understand better the impact of changes in background risk on time-preferences and
discounting.
- 17 -
Our empirical analysis was also aimed to demonstrate that experimental methods are relevant
for investigating the role of preferences in field studies. Even if many economists are already
convinced about their usefulness, the experimental methodology is not yet widespread in
other disciplines social sciences, such as geography or sociology. The main advantage of
experiments is that they allow revealing individuals’ preferences on the basis of real
incentives rather than observing their stated preferences based on hypothetical survey
questions. Of course, experimental methods have also their limitations. In the present case,
the level of the stakes is an important methodological issue since natural hazards entail
situations where the stakes can be extremely high whereas standard experiments typically rely
on low stakes. The gap between the “down-scaled” risk involved in the experiment and the
real life risk can be extremely large. Even though we set the payments in the experiment at
levels that matched several days of salary, we are still very far away from dramatic events
such as injury. The methodological issue is whether it is possible to capture attitudes towards
large risks with experiments involving small risks. Answering this external validity issue
requires a broader investigation program. However, our results, which are in line with other
recent findings, show that the exposure to natural hazards represents a relevant variable that
participates in the shaping of individuals’ preferences at least for a non-negligible fraction of
the population. This is encouraging news for carrying out deeper investigations about the
impact of background risk on peoples’ preferences.
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- 19 -
Appendix 1: Lottery choices
A
Head
Tail
10 SOLES
10 SOLES
B
Head
Tail
9 SOLES
18 SOLES
- 20 -
C
Head
Tail
8 SOLES
24 SOLES
D
Head
Tail
6 SOLES
30 SOLES
- 21 -
E
Head
Tail
2 SOLES
38 SOLES
F
Head
Tail
0 SOLES
40 SOLES
- 22 -
Appendix 2 Time choices
TOMORROW
IN 4 DAYS
10 SOLES
10 SOLES
1
TOMORROW
IN 4 DAYS
10 SOLES
11 SOLES
2
- 23 -
TOMORROW
IN 4 DAYS
10 SOLES
12 SOLES
3
TOMORROW
IN 4 DAYS
10 SOLES
13 SOLES
4
- 24 -
TOMORROW
IN 4 DAYS
10 SOLES
14 SOLES
5
TOMORROW
IN 4 DAYS
10 SOLES
15 SOLES
6
- 25 -