the effect of power outages and cheap talk on willingness to pay to reduce outages
TRANSCRIPT
Energy Economics 33 (2011) 790–798
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Energy Economics
j ourna l homepage: www.e lsev ie r.com/ locate /eneco
The effect of power outages and cheap talk on willingness to pay to reduce outages
Fredrik Carlsson a,1, Peter Martinsson a,⁎, Alpaslan Akay a,b,2
a Department of Economics, School of Business, Economics and Law, University of Gothenburg, Box 640, 405 30 Gothenburg, Swedenb IZA, Schaumburg-Lippe-Str. 5-9, D-53113, Bonn, Germany
⁎ Corresponding author at: Tel.: +46 31 786 52 55.E-mail addresses: [email protected]
[email protected] (P. Martinsson), aka1 Tel.: +46 31 786 41 74.2 Tel.: +49 228 38 94 508.3 This is related to the literature on experience of for e
see e.g., Cameron and Englin (1997) and Whitehead et aindividuals are not self-selected as in the case of a visit
0140-9883/$ – see front matter © 2011 Published by Edoi:10.1016/j.eneco.2011.01.004
a b s t r a c t
a r t i c l e i n f oArticle history:Received 3 September 2008Received in revised form 15 January 2011Accepted 16 January 2011Available online 27 January 2011
JEL classifications:C25D12Q41
Keywords:Contingent valuationCheap talkExperiencePower outages
Using an open-ended contingent valuation survey, we analyze how (i) experience of a power outage due toone of the worst storms ever to hit Sweden and (ii) a cheap talk script affect respondents' WTP to avoid poweroutages. Experience significantly increases and a cheap talk script significantly decreases the proportion ofrespondents with zeroWTP, and as a result statedWTP is lower with experience and the cheap talk script. Thepaper concludes with a discussion on the use of valuation studies shortly after the occurrence of anundesirable event.
(F. Carlsson),[email protected] (A. Akay).
xample visiting national parks;l. (1995). However, in our caseto a national park.
4 The storm is caknown as Erwin. Th2005.
lsevier B.V.
© 2011 Published by Elsevier B.V.
1. Introduction
Valuation methods, such as contingent valuation and choiceexperiments, are importanteconomic tools toelicit thevalue individualsput on non-market goods and services. In many cases, valuation of non-market goods takes placeafter amajor changehas occurred, for exampleafter a natural disaster. The objective of the present paper is to study therespective effects of (i) experience of a natural disaster and of (ii) acheap talk script on stated willingness to pay (WTP). We investigateSwedish households' WTP for avoiding power outages of variousdurations up to 24 h by using an open-ended contingent valuationstudy. First, we study the effect of experience by comparing WTP foravoiding power outages one year before and immediately after theworst storm ever to hit Sweden in terms of its effect on power outages(Energimyndigheten, 2006). Experiences from such an event mightaffect not only people's perceptions about the consequences of poweroutages, but also their preferences.3 In behavioral economics, a numberof studies show that people take actions that potentially are contrary totheir own interest (see, e.g., Lowenstein, 1996). Furthermore, studies
show that people have difficulties forecasting their future utility of astimulus (Kahneman and Snell, 1992). This has led to a discussion onwhether public policy should be guided by so-called experience utilityor decision utility (see, e.g., Kahneman et al., 1997; Kahneman andSugden, 2005). Conducting a contingent valuation study on subjectsbefore and after an event is therefore a good opportunity to compareresponses of inexperienced and experienced respondents. Second, westudy how WTP is affected by the introduction of a cheap talk script tosome of the households that received the survey after the storm. Inaddition to explaining that respondents, for different reasons, maydeviate from their true WTP, the script stresses that individuals'responses are sometimes expressions of particular views, in our casethat everyone should have the right to power without outages(Kahneman et al., 1999). This is explained in greater detail later in thetext.
On the evening of 8 January 2005, the storm “Gudrun” hit thesoutheastern part of Sweden.4 Due to the storm, approximately75 million m3 of trees fell. In the worst affected areas, the amount oftrees that fell in a few hours was equivalent to the amount normallyharvested in five years. Falling trees destroyed approximately20,000 km of power lines, severely impacting the distribution ofpower. The storm caused power outages in 663,000 of the approx-imately 4.5 million households in Sweden (i.e., every seventh
lled Gudrun in the Nordic countries, while it is internationallyis storm made its way from Ireland to Russia during 7–9 January
8 The clearing of forest was dangerous work. About ten people were killed andseveral injured. Another related issue discussed was the risk of an invasion of vermin,especially bark beetles, unless the timber was removed from the forest fairly soon afterthe storm.
9 In 1996, the Swedish electricity market was reformed from being a completelyregulated domestic market to a completely liberalized Nordic (including Norway,Denmark, and Finland) market. To create an incentive to make investments to reduceboth the number and length of power outages, a performance assessment model hasbeen developed for evaluation of the quality of the supply of electricity. One part of theelectricity bill paid by the households relates to the tariff for transmission of electricity,and the network companies determine the level of the tariff since there is no marketfor transmission of electricity. However, the tariffs charged by the network companieshave to be “reasonable” according to Swedish Law, utilizing the network performanceassessment model for this purpose. For example, the network companies have tosubmit reports to the Swedish National Energy Administration, and the report has toinclude the amount of transmitted electricity, the tariffs charged for transmission,where their customers are located etc. In addition, the report has to include qualityaspects such as the number of outages and the duration of the outages. Based on this
791F. Carlsson et al. / Energy Economics 33 (2011) 790–798
household) (Statens Energimyndighet, 2005). Over half of the affectedhouseholds had their power back within 24 h, while 68,000 were stillwithout power eight days after the storm and 10,000 households stilldid not have power after three weeks. In fact, there were some caseswhere it took over a month before power returned. Power outages doof course occur in Sweden from time to time, but the geographicalspread and the lengths of the outages were unique.5
We conducted the original contingent valuation study in May2004, and used an open-ended contingent valuation survey to elicitindividuals' maximum WTP for avoiding power outages of variousdurations up to 24 h. Conducting exactly the same study after thestorm (hence using exactly the same design), but on a differentrandom sample of respondents, gives us the opportunity to study theinfluences of the storm on people's valuations of power outages.6
Personal experience could result in an increased WTP for somepeople, but it could also have the opposite effect for individuals whocome to realize that they did not suffer from the outages as much asthey had expected. WTP may also decrease for people who takepreventive action to avoid the negative consequences of futureoutages by, for example, purchasing candles, batteries, and/or alcoholstoves. Overall, it is not clear what effect personal experience ofGudrun has on WTP, if any. Moreover, people generally do have someprevious experiences of outages, although on average they have beenlocal and much shorter. The views expressed in the media were to alarge extent focused on the inability of the power companies tohandle the situation and that customers have the right to have thepower back as soon as possible irrespective of the costs to the powercompanies.
To conduct our studywe used an open-ended contingent valuationsurvey, despite the fact that practitioners generally prefer the close-ended elicitation format.7 Two important reasons for the skepticismtoward open-ended questions are that they are more difficult toanswer and that the question format is not incentive compatible(Carson et al., 2000). However, the open-ended format also has manyadvantages: in particular that the exact WTP information is obtainedfrom each respondent, and that there are no potential anchoringeffects on the bid. In addition, the general tendency found in theliterature is that the open-ended format results in lower WTPestimates than the close-ended format when used in a hypotheticalsetting (e.g., Brown et al., 1996; Kriström, 1993). Furthermore,experiments comparing hypothetical and actual WTP have shownthat the hypothetical bias is not higher, but actually lower, for theopen-ended format compared with the close-ended format (e.g., Listand Gallet, 2001; Balisteri et al., 2001). However, it may also be thecase that not all respondents want to overstate their WTP in ahypothetical survey situation. In particular, this has been suggested tobe the case in many open-ended studies; a respondent whoseexpected cost of a scenario is larger than his or her WTP hasincentives to state a zero WTP. In many open-ended studies, thefraction of respondents with zero WTP is non-negligible. At the sametime, respondents with an expected cost lower than the WTP haveincentives to overstate their WTP in an open-ended WTP.
During the month after the storm, newspapers, radio, and TV werefull of reports about the effects of the storm — both direct effectsamong those suffering and indirect effects (for example, it took onemonth to get the trains operating on a normal schedule again). Then,when most of the damages had been repaired, the media started to
5 On average, a Swedish household experiences one outage per year. An averageouting lasts around 20 min for households located in populated areas and 200 min forhouseholds in sparsely populated areas (Svenska Kraftnät, 2002).
6 It would of course have been interesting to include questions directly related tothe storm, but the trade-off would have been to give up the idea of having exactly thesame survey as before. Since we decided to use exactly the same survey, we can ruleout that respondents were affected differently by different questionnaire formats.
7 Using a WTP question implies that the customers do not have the right to power.Thus, the choice of elicitation format relates to property rights.
focus on the responsibility that the power companies have to providetheir consumers with power. The other main issue discussed in themedia was clearing the forest of fallen trees.8 One reason why thestorm had such a devastating effect on the power provision was thatpower in many places, especially in less densely populated areas, istransmitted in above-ground power lines that are easily hit by fallingtrees. Many people interviewed by the media blamed the powercompanies both for indirectly causing the power outages by makinginsufficient investments, for example by not burying power lines, aswell as for the long duration of the outages due to lack of personneland equipment to handle the crisis.9 In the backwash of the storm, thegovernment both increased the minimum levels of power-outagecompensation paid to households and shortened the outage durationrequired before compensation has to be paid.
Besides studying the effect of experience on respondents' WTP, weinvestigate the effect of reducing hypothetical bias by using cheaptalk. Cheap talk has been one of the most successful attempts in termsof reducing hypothetical bias in close-ended surveys (Cummings andTaylor, 1999). The original idea was to bring down the hypotheticalbias in close-ended contingent valuation surveys by thoroughlydescribing and discussing the propensity of respondents to exagger-ate their WTP. Indeed, a number of papers have shown that cheap talkscripts can successfully reduce the hypothetical bias in close-endedcontingent valuation surveys (e.g., Cummings and Taylor, 1999; List,2001; Brown et al., 2003; Bulte et al., 2005; Aadland and Caplan,2006). However, the effect and the formulation of a cheap talk scriptfor open-ended valuation questions are not necessarily the same as inthe close-ended case, not least because of the incentives to understatethe trueWTP. Given the discussion and views presented in the media,we stressed that individuals' responses are sometimes an expressionof a particular view, in our case that everyone should have the right topower without outages. The script also pointed out that individualssometimes state either a lower or a higher WTP than they are actuallywilling to pay. We hypothesize that a cheap talk script will decreasethe share of respondents stating a zero WTP. The effect on theconditional WTP is less clear, since the script should make somerespondents decrease and some increase their WTP.
The rest of the paper is organized as follows. Section 2 describesour contingent valuation survey, focusing especially on the threedifferent treatments: the contingent valuation survey (i) before thestorm and without the cheap talk script, (ii) after the storm and
information, it is possible, by using the network performance assessment model, tocalculate the value to the households of the services provided by the networkcompanies. The value is reduced if for example outages have occurred. In order tocalculate whether the tariffs charged to the households are reasonable or not, theactual tariffs charged are divided by the calculated value of the services from thenetwork performance assessment model. If the obtained ratio exceeds unity, then thenetwork company has charged more than the value of the services provided to itscustomers and vice versa. In cases when the network companies have overchargedtheir customers, they have to pay back the same amount to them. It should also benoted that since the reform of the electricity market in Sweden, there has been closureof reserve capacity as a means to reduce costs (Svenska Kraftnät, 2002).
792 F. Carlsson et al. / Energy Economics 33 (2011) 790–798
without the cheap talk script, and (iii) after the storm and with thecheap talk script. Section 3 presents and discusses the results. Weanalyze the effect of experience of the storm by comparing treatments(i) and (ii), and the effect of the cheap talk script by comparingtreatments (ii) and (iii). Finally, Section 4 concludes the paper.
2. The Survey
The final questionnaire consisted of three parts. The first partcontained questions about housing conditions, for example whetherthe homes could be heated without electricity. The second partconsisted of the contingent valuation survey and the last part of socio-economic questions. The questionnaire was developed in collabora-tion with representatives from various power companies and fromengineering researchers on power outages.10 The questionnaire wastested in several smaller focus groups and in a pilot study mailed to200 individuals at the beginning of 2004.
In the contingent valuation survey, the respondents were asked tostate their maximum WTP for avoiding nine different power outagesof certain durations starting at 6 pm on a January evening, which issupposed to represent a worst case scenario.11 We included planned(announced at least 3 weekdays in advance) and unplanned outages,each type lasting for 1, 4, 8, and 24 h. For unplanned outages, we alsoincluded a casewhere the duration of the outagewas uncertain in a 2–6 h interval, with an equal probability of ending at any time duringthis period. Each respondent answered all nine valuation questions (4planned, 5 unplanned) described above. The respondents wereexplicitly told that for each question, they should state the maximumamount their households would be willing to pay in Swedish kronor12
(SEK) to avoid one power outage of the kind stated in the question.The original contingent valuation survey was conducted in May
2004.13 While we were analyzing the results, the storm Gudrun hitparts of Sweden in January 2005. Therefore, we decided to conduct afollow-up contingent valuation study with exactly the same design asthe survey sent out before the storm. This means that we did notinclude any specific questions about the storm and to what extent therespondents had been affected by it. Although doing so would haverevealed interesting information, we wanted to keep the surveysidentical to make them completely comparable. In addition, we sentout a questionnaire that included a cheap talk script, but that in allother aspects was exactly the same as the original survey. Thecontingent valuation part of the questionnaire including the cheaptalk treatment is presented below in Fig. 1 (the version without thecheap talk script did not include the box with bold text).
Thus, our study contains three treatments: (i) before the stormwithout cheap talk, (ii) after the stormwithout cheap talk, and (iii) afterthe stormwith cheap talk. In the analysis, we test for the effect of havingexperienced the stormby comparing treatments (i) and (ii), andwe testfor the effect of the cheap talk script by comparing treatments (ii) and(iii).
3. Results
Our samples were randomly drawn from a register containing allindividuals aged 18–75with a permanent address in Sweden. The firsttreatment was sent out to 3000 individuals in May 2004. Each of thetwo other treatments was sent out to 500 individuals in April 2005.The response rates were 56% for the original treatment, 47% for thetreatment after the storm without the cheap talk, and 49% for the
10 The complete questionnaire is available upon request.11 Our scenario is similar to the previously conducted 1994 study in Sweden onpower outages (Svenska Elverksföreningen, 1994).12 At the time of the survey in mid-May 2004, the exchange rate was 1 USD=7.70SEK, while in mid-April 2005 1 USD=7.10 SEK.13 For details on that survey and its results, see Carlsson and Martinsson (2007).
cheap talk script treatment. Comparing the sample statistics of thethree treatments with Swedish population statistics, we find nostatistical difference at the 5% level in terms of gender composition(proportion of females are 0.49, SCB, 2004) and geographic repre-sentation (based on the postal codes). However, there is a slight andsignificant overrepresentation of older people in all three treatments(average age is 44.8, SCB, 2004). There is no significant differenceamong the three treatments with respect to gender composition,geographic representation, and age. The descriptive statistics of themain socio-economic characteristics of the three samples arepresented in Table 1.
There are no significant differences in socio-economic character-istics among the three samples. Note that there is no significantdifference in the number of self-protecting measures taken betweenthe samples before and after the storm. Hence, this cannot explain anypotential difference in WTP.
Table 2 below presents the descriptive statistics of the WTPresponses to each of the nine different valuation questions. We reportthe mean WTP for the overall sample, the proportion of respondentswith zeroWTP, and the meanWTP conditional on a positiveWTP. Theoverall results are as expected: Overall mean WTP increases and theshare of zero WTP decreases as the length of the power outageincreases, and the overall WTP is higher and the proportion of zeros islower for unplanned outages compared to planned outages. More-over, the overall WTP for the uncertain power outage with anexpected duration of 4 h is higher than the overall WTP for a poweroutage that will definitely last for 4 h. The conditional WTP, i.e., theWTP given it exceeds zero, follows the same pattern as the overallWTP.
To compare the different treatments, we conduct both non-parametric and regression analyses. Our regression models aim toanalyze both the probability of a positive WTP and the conditionalWTP (i.e., given that WTP is positive), by also controlling for observedand unobserved individual characteristics of the individuals. Anumber of regression models can be applied to this type of data,where one main decision relates to how to treat the zero WTPresponses; see, e.g., Carlsson and Johansson-Stenman (2000) andMoeltner and Layton (2002). Our regression analysis is based on theidea that a zero WTP is not necessarily a genuineWTP response of theindividuals. This results in a need to control for the correlationbetween the probability of stating a positive WTP response and theconditional WTP using a sample-selection model. Therefore, themodel that we specify aims both to control for the potential sample-selection bias due to the zero WTPs and to exploit the panel aspect ofthe data (controlling for the unobserved individual-effects simulta-neously) as specified and discussed in, for example, Vella and Verbeek(1999), Zabel (1992), Kyriazidou (1997) and Vella (1998). The datagenerating process of the unobserved WTP* is specified as follows
WTP� = xitβ + ui + εit ; ð1Þ
rit = 1 zitγ + vi + ωit N 0ð Þ; ð2Þ
WTPit = WTP�it × rit ; ð3Þ
where i indicates the individuals and t indicates the repeatedcontingent valuation values for an individual. Eq. (1) is the targetequation (regression corrected for the sample selectivity) that isaimed to be estimated by taking the selection equation in the accountwhich is given by Eq. (2). The asterisk on the WTP indicates that theactual WTP is latent and it is incidentally truncated by the selectionEq. (2) with the selection indicator rit, which is equal to unity whenWTP is greater than zero otherwise it is equal to zero. Eq. (3) gives theselected sample with which the selective outcome of WTP is to becalculated; xit and zit are vectors of the exogenous variables and, β andγare the parameters to be estimated; ui and vi are the unobserved
We will now ask some questions regarding your household’s willingness to avoid power outages. Imagine that there is a service with a backup electricity board that can be used in case of a power outage. This electricity board will cover your household’s need for electricity during the whole outage. You will only pay the power company if an outage actually occurs. If you do not want to pay anything, your household will experience power outages since you will then not be connected to the backup electricity board. There are two types of outages, and we would like your answers for both of them:- Planned outage: An outage that you have been notified in advance about.- Unplanned outage: An outage that comes as a surprise and that you have not been notified in advance about.
Imagine that an outage occurs on an evening in January and that the outage starts at 6 pm. For each question we ask you to answer how much your household at most would be willing to pay in order to avoid this outage by using the backup service. We ask you to consider your answers as carefully as possible and to remember that it is also possible to answer zero kronor.
Experiences from similar studies show that people have a tendency to answer one thing but in reality may want to do something else. For example, you may state a lower amount of money than what you would actually be willing to pay, e.g. that you answer zero kronor. We believe that this sometimes can be due to one wanting to express a view that one for example has the right to have uninterrupted distribution of power. Others may answer a higher amount than what they actually would be willing to pay. We do not want you to think in this way when answering our questions. There might also be other reasons why you answer the way you do. If you have any thoughts about this, please write down your thoughts at the end of the survey.
Planned outagesHow much would your household at most be willing to pay in order to avoid a power outage that starts at 6 pm on an evening in January? You know in advance that the outage will occur. We ask you to answer all 4 questions below.
Duration of the outage I am at most willing to pay (round to whole numbers)
Question 1 1 hour kronorQuestion 2 4 hour kronorQuestion 3 8 hour kronorQuestion 4 24 hour kronor
Unplanned outagesHow much would your household at most be willing to pay in order to avoid a power outage that starts at 6 pm on an evening in January? You do not know in advance that the outage will occur. We ask you to answer all 5 questions below.
Duration of outage I am at most willing to pay (round to whole numbers)
Question 5 1 hour kronorQuestion 6 4 hours kronorQuestion 7 8 hours kronorQuestion 8 24 hours kronorQuestion 9 Between 2 and 6 hours. It is equally likely that the power
returns after 2 hours as after 6 hours, or at any time in between.kronor
Fig. 1. Scenario and WTP question, with cheap talk script.
14 The models are estimated with simulated maximum likelihood estimator with1000 GHK simulator replications in each case.
793F. Carlsson et al. / Energy Economics 33 (2011) 790–798
individual-effects and they are assumed to be orthogonal to botherror-terms εitand ωit, and to observed individual characteristicsfollowing the standard random-effects assumption. The error terms intarget Eq. (1) and selection Eq. (2) are assumed to have bivariatenormal distribution with zero mean vector and covariance structurewhich is given below as
Ωεω =1 ρεωσω
ρεωσε σ2ε
" #: ð4Þ
Eq. (4) implies that the selectivity is assumed to show-up throughthe correlation of the error-terms ρεω=corr(εit,ωit). Note that the
variance of the error-term in selection Eq. (2) has been normalized tounity to be identified.14
A simultaneous focus on the probability of a non-zero WTP andactual WTP immediately implies that one has to find some identifyinginstruments for the individuals with a non-zero WTP. A credibleanalysis of selection requires one or more instruments (exclusionrestrictions), i.e., a group of variables that is correlated with thedecision to report zero WTP, but not with the actual WTP. In thispaper, we use the regional variation of the impact of the Gudrun-storm as the identifying instrument. The main motivation is that thezero WTP is reported not as an actual response of the individuals, but
Table 1Descriptive statistics of the socio-economic variables.
Variable Description Before Gudrunno cheap talk
After Gudrunno cheap talk
After Gudruncheap talk
Mean Std. Mean Std. Mean Std.
City = 1 if more than 10,000 inhabitants; 0 otherwise 0.644 0.479 0.612 0.488 0.637 0.482House = 1 if detached or terraced house; 0 otherwise 0.631 0.482 0.618 0.487 0.632 0.483Cannot heat = 1 if dwelling cannot be heated during outage; 0 otherwise 0.391 0.489 0.397 0.490 0.415 0.494Age Age in years 48.15 15.04 47.95 14.56 46.63 14.04Female = 1 if female respondent 0.484 0.500 0.485 0.501 0.505 0.501Income Monthly household income after tax, in 10,000 SEK. 2.485 1.208 2.523 1.167 2.542 1.178Self-protection Number of self-protective measures taken 2.302 1.086 2.245 1.082 2.434 1.114Number of respondents 1518 204 212
794 F. Carlsson et al. / Energy Economics 33 (2011) 790–798
it is reported as a “protest” due to frustrations after inconvenienciescreated by Gudrun and the inabilities of the power companies toproperly deal with the situation. We argue that the storm affected theindividuals to a different extent depending on where they live andhence that a zeroWTP is correlatedwith the degree of exposure by thestorm. One of the natural instruments to use to be able to identify the“protest” effect is the “proximity of the storm zone”. Our instrument istherefore the proximity of the house of the respondent (based on zipcodes) to the storm zone based on metrological maps showing windspeed. The maps categorized the wind speed from the storm into fivegroups. In the analysis, we transformed this variable to three dummyvariables to deal with small cell sizes and then use them as exclusionrestrictions in the selection equation.15
Moreover, we also report the results from another model thattreats a zero WTP response as an actual outcome to check for thesensitivity of the results.16 To address this, we estimate a random-effects Tobit model for the overall WTP responses. The index functionfor the latent WTPit⁎ given by Eq. (1) and it is modeled with theobservation criterionWTP=max(WTP⁎, 0). This implies that the zeroWTP is actually a corner outcome and the model is censored frombelow at zero WTP. The error terms of this model are assumed to benormally distributed with zero mean and a positive variance.
As explanatory variables, we include eight dummy variables foreach of the different contingent valuation questions except for theone-hour planned power outage, which is used as a reference case inthe analysis. To test for the effect of having experienced Gudrun, weinclude a dummy variable. This approach assumes that experienceresults in a shift of the preferences. We also estimate models in whichsocio-economic variables were included. In order to consider theeffects of the cheap-talk script, we include dummy variables in asimilar fashion as for experience.
3.1. Before and After the Storm
Let us beginwith a comparison between before and after the stormwithout the cheap talk script. Table 3 summarizes the before–afterdifferences in the overall WTP, proportions of respondents with apositiveWTP and conditionalWTP. In addition, we report the p-values
15 We have very restricted number of variables, which we can use as exclusionrestriction. We also experimented with some other variables and combinations ofthose variables. For instance, we combined the proximity of the house to the affectiveareas of the storm with some other variables such as not having a heating system orhouse characteristics. The results mainly differ in terms of the correlation betweenequations and the magnitude of some parameters. However, we do not observe achange in the sign of any parameters. We also thank an anonymous referee for his/hervaluable suggestions on the selection of the instruments.16 In order to test the sensitivity of the results given with the sample selection model,we also estimate a random-effects probit and a linear random-effects regression modelfor the conditional WTP without controlling for the sample-selectivity. This way ofmodeling WTP should be robust to weak instrument problem (Staiger and Stock,1997). Our results differ slightly, but the main conclusion of the paper does not changewith this approach. The results can be obtained from the authors upon request.
of the tests of the null hypothesis of no difference betweentreatments, i.e., before and after the storm. For the overall WTP andthe conditionalWTPwe use theMann–Whitney test, and for the shareof zero WTP we use a Chi-square test. These results are shown in thethree middle columns of the table (Change in experience). For alloutage durations, the overall mean WTP is higher for the sample thatreceived the survey before the storm. This result seems to beexplained by an increase in the proportion of respondents with zeroWTP, which in all nine cases is lower for the treatment before thestorm. As shown in column 4 (Change in experience— column OverallWTP), there is a significant difference in the overall WTP at the 5%significance level in five out of the nine valuation questions.Interestingly, the null hypothesis of an equal proportion of zeroWTP is rejected in the same five cases. Moreover, if we look at theconditional mean WTP, i.e., the mean WTP for respondents with apositive WTP, there is no statistically significant difference at anyconventional level between before and after the storm in any of thenine questions (see column 6 – Change in experience – WTPN0).Thus, the higher overall WTP before the storm is due to a lowerproportion of respondents with zero WTP.
The first regression model presented in Table 4 is based on theresponses from the treatments before and after the storm without acheap talk script. As mentioned above we do not consider a zero WTPas a genuine response and thus we estimate a sample selection modelas specified in Eqs. (1)–(4). The first model, i.e., Model (1) in Table 4,analyzes whether or not a positive WTP has been reported and WTPsimultaneously. The marginal effects in the selection equation(estimated at the sample means) are obtained from the random-effects probit model. In sample selection models, several types ofmarginal effects can be obtained. In Table 4, we report total-effects forthe WTP equation, which is the sum of the direct effect and theindirect effect due to selection term.17 The effects are as expected andin line with the descriptive statistics presented in Table 1, i.e., theprobability of a positiveWTP increases with duration and is higher forunplanned than for planned outages. We reject the null hypothesis ofno Gudrun effect on the probability of stating a positiveWTP since theGudrun variable (indicating that the valuation was conducted post-Gudrun) is significant.We find a significant effect of experience on thelevel of WTP in the regression model correcting for sample selectionbias. Most interesting from the regressions that include socio-economic variables is that we find that people living in morepopulated areas are significantly more likely to have a positive WTP,but the effect on WTP is insignificant. Both living in a detached orterraced house and living in a dwelling that cannot be heated duringan outage have a significant and positive impact on the WTP. Age andbeing female have a negative and significant impact on the probabilityof stating a positive WTP. Income has, as expected, a positive andsignificant effect on the WTP, while it has no significant effect on the
17 See Greene (2008) for detailed explanation of the marginal effects of the sampleselection model.
Table 2Descriptive statistics of the overall stated WTP in SEK, proportion of zero WTP, and conditional WTP in SEK (standard deviations in parentheses).
Before Gudrunno cheap talk
After Gudrunno cheap talk
After Gudruncheap talk
OverallWTP
Prop.WTP=0
WTP(WTPN0)
OverallWTP
Prop.WTP=0
WTP(WTPN0)
OverallWTP
Prop.WTP=0
WTP(WTPN0)
Planned 1 h 6.3(39.2)
0.90(0.31)
60.2(107.4)
3.0(14.9)
0.93(0.26)
39.7(40.2)
10.2(52.9)
0.88(0.33)
83.1(131.7)
4 h 28.5(99.9)
0.74(0.44)
108.0(171.2)
24.3(124.8)
0.84(0.37)
148.9(280.4)
30.3(73.4)
0.73(0.44)
112.7(104.3)
8 h 84.4(202.0)
0.51(0.50)
170.7(260.5)
82.1(233.9)
0.57(0.50)
190.0(326.7)
71.6(144.9)
0.49(0.50)
141.3(178.0)
24 h 189.3(377.1)
0.39(0.49)
308.3(441.6)
157.7(350.6)
0.43(0.50)
280.8(428.6)
185.8(360.2)
0.35(0.48)
285.5(413.7)
Unplanned 1 h 9.4(45.1)
0.86(0.35)
65.6(102.9)
4.8(28.3)
0.93(0.25)
72.4(87.7)
14.2(52.6)
0.84(0.37)
87.7(104.2)
4 h 37.3(101.9)
0.68(0.47)
117.5(152.7)
30.6(115.7)
0.79(0.41)
144.4(217.9)
46.6(111.8)
0.68(0.47)
144.7(157.6)
8 h 108.1(239.5)
0.46(0.50)
198.3(295.6)
96.2(259.7)
0.55(0.50)
214.8(354.6)
103.7(209.6)
0.44(0.50)
184.3(251.8)
24 h 223.0(430.6)
0.36(0.48)
350.8(496.9)
188.9(382.3)
0.41(0.49)
322.3(454.9)
237.3(446.8)
0.31(0.46)
345.3(503.5)
Between 2 and 6 h 68.8(168.2)
0.59(0.49)
167.6(229.0)
64.8(190.8)
0.68(0.47)
202.3(295.5)
68.6(160.7)
0.58(0.50)
163.0(214.9)
Number ofrespondents
1518 204 212
795F. Carlsson et al. / Energy Economics 33 (2011) 790–798
probability of stating a positive WTP. Model (2) is the random-effectsTobit model, which interprets a zeroWTP as a genuine response of theindividuals. The Gudrun variable is negative and significant and thesocio-economic characteristics have the same sign as in the probitmodel.
3.2. With and Without a Cheap Talk Script
Next we analyze the effect of the cheap talk script, which wasincluded in half of the treatments sent out after the storm. As shownin Table 3, the overall mean WTP is significantly different at the 5%level between the two treatments in three out of the nine valuationquestions. Moreover, the share of respondents stating zero WTP isconsistently lower with the cheap talk script, and the difference inshares is statistically significant at the 5% level in six cases out of nine
Table 3Difference in mean WTP in SEK and p-values from non-parametric tests of the null hypoth
Change in experience: before–
Overall WTP Prop. WTP=
Planned 1 h Difference 3.3 −0.03p-value 0.19 0.14
4 h Difference 4.2⁎⁎⁎ −0.10⁎⁎⁎
p-value 0.00 0.008 h Difference 2.3 −0.06
p-value 0.18 0.0924 h Difference 31.6 −0.04
p-value 0.16 0.19Unplanned 1 h Difference 4.6⁎⁎⁎ −0.07⁎⁎⁎
p-value 0.00 0.004 h Difference 6.7⁎⁎⁎ −0.11⁎⁎⁎
p-value 0.00 0.008 h Difference 11.9⁎⁎ −0.09⁎⁎
p-value 0.02 0.0124 h Difference 34.1 −0.05
p-value 0.19 0.18Between 2 and 6 h Difference 4.0⁎⁎ −0.09⁎⁎
p-value 0.04 0.02
We applied theMann–Whitney test for overallWTP and conditionalWTP to test the null hypois applied for the proportions.
⁎⁎⁎ Significant at the 1% level.⁎⁎ Significant at the 5% level.⁎ Significant at the 10% level.
(see Table 3, column 8). There is no statistically significant differenceat the 5% level between the two treatments in any of the ninequestions with respect to conditional WTP (see Table 3, column 9).The results of the non-parametric analysis of the difference betweenwith andwithout cheap talk in Table 3 are confirmed in the regressionanalysis in Table 4. The effect of the cheap talk script is parametricallyanalyzed in the same way as experience. In Model (3) in Table 4, theprobability of stating a positive WTP and WTP response is simulta-neously analyzed in a sample-selection model. As in the case ofexperience, i.e., Model (1), we report the marginal effects of the firststage probit model (selection equation) and the total marginal effectsin the second state WTP equation. There is a significantly higherprobability of stating a positive WTP, while there is only a partiallysignificant difference in the realized WTP between the two treat-ments. People living in more populated areas, living in a house or
esis of no difference between the treatments.
after Introduction of cheap talk: with–without
0 WTP (WTPN0) Overall WTP Prop. WTP=0 WTP (WTPN0)
20.5 7.2 −0.05 43.40.92 0.94 0.10 0.42
−40.9 6.0⁎⁎ −0.11⁎⁎ −36.20.87 0.01 0.01 0.30
−19.3 −10.5 −0.08 −48.70.50 0.31 0.12 0.22
27.5 28.1 −0.08 4.70.64 0.16 0.07 0.75
−6.8 9.4⁎⁎⁎ −0.09⁎⁎⁎ 15.30.79 0.00 0.00 0.57
−26.9 16.0⁎⁎ −0.11⁎⁎ 0.30.81 0.01 0.01 0.39
−16.5 7.5⁎ −0.11⁎⁎⁎ −30.50.90 0.05 0.00 0.61
28.5 48.4 −0.10⁎⁎ 230.80 0.11 0.03 0.69
−34.7 3.8⁎ −0.10⁎⁎ −39.30.44 0.06 0.04 0.83
thesis that the two treatments come from the same distribution, while a Chi-square test
Table 4Marginal effects for estimated models on experience and cheap talk (standard errors in parentheses).
Experience Cheap talk
Model (1) Model (2) Model (3) Model (4)
Random-effects sample-selection model Random-effectsTobit model
Random-effects sample-selection model Random-effectsTobit model
Selection equationP(WTPN0)
Corrected regression(total-effects)
OverallWTP
Selection equationP(WTPN0)
Corrected regression(total-effects)
OverallWTP
Planned 4 h 0.197⁎⁎⁎
(0.024)9.296(12.56)
56.89⁎⁎⁎
(4.938)0.170⁎⁎⁎
(0.037)51.99⁎
(29.11)47.16⁎⁎⁎
(8.502)Planned 8 h 0.412⁎⁎⁎
(0.023)57.56⁎⁎⁎
(11.32)146.0⁎⁎⁎
(6.544)0.398⁎⁎⁎
(0.013)104.8⁎⁎⁎
(25.58)130.0⁎⁎⁎
(11.55)Planned 24 h 0.526⁎⁎⁎
(0.025)171.9⁎⁎⁎
(11.17)244.18⁎⁎⁎
(7.867)0.516⁎⁎⁎
(0.040)195.9⁎⁎⁎
(28.09)214.6⁎⁎⁎
(14.00)Unplanned 1 h 0.058⁎⁎
(0.024)−5.622(17.23)
13.63⁎⁎⁎
(4.105)0.028(0.041)
1.751(46.32)
7.028(6.989)
Unplanned 4 h 0.252⁎⁎⁎
(0.028)25.30⁎⁎
(12.47)79.56⁎⁎⁎
(5.397)0.219⁎⁎⁎
(0.047)82.27⁎⁎⁎
(30.16)69.52⁎⁎⁎
(9.469)Unplanned 8 h 0.456⁎⁎⁎
(0.082)82.48⁎⁎⁎
(11.87)172.5⁎⁎⁎
(6.966)0.427⁎⁎
(0.198)131.8⁎⁎⁎
(28.71)153.36⁎⁎⁎
(12.39)Unplanned 24 h 0.552⁎⁎⁎
(0.029)192.4⁎⁎⁎
(11.01)265.5⁎⁎⁎
(8.113)0.548⁎⁎⁎
(0.046)222.9⁎⁎⁎
(25.80)245.44⁎⁎⁎
(14.78)Unplanned between 2 and 6 h 0.344⁎⁎⁎
(0.020)64.21⁎⁎⁎
(11.46)126.71⁎⁎⁎
(6.406)0.313⁎⁎⁎
(0.023)104.2⁎⁎⁎
(28.04)106.67⁎⁎⁎
(11.07)City 0.095⁎⁎⁎
(0.011)5.528(3.631)
22.70⁎⁎⁎
(5.609)0.171⁎⁎⁎
(0.007)−59.67⁎⁎⁎
(7.246)30.08⁎⁎⁎
(10.06)House −0.007
(0.011)48.05⁎⁎⁎
(3.622)11.99⁎⁎
(6.072)0.052⁎⁎⁎
(0.010)29.66⁎⁎⁎
(7.682)21.82⁎
(11.40)Cannot heat 0.011
(0.010)45.46⁎⁎
(3.503)14.89⁎⁎⁎
(5.552)0.038⁎⁎⁎
(0.010)40.346⁎⁎⁎
(6.792)7.477(10.36)
Age −0.003*(0.002)
0.240(0.676)
−0.003⁎⁎⁎
(0.001)−0.004(0.009)
0.2326⁎
(0.1385)−0.388⁎
(0.226)Female −0.039⁎⁎⁎
(0.009)−11.10⁎⁎⁎
(3.034)−12.86⁎⁎
(5.095)−0.053⁎⁎⁎
(0.009)−10.90⁎
(6.357)−7.580(9.655)
Income 0.022(0.021)
39.17⁎⁎⁎
(1.267)12.59⁎⁎⁎
(2.228)0.003(0.047)
39.44⁎⁎⁎
(2.638)8.193⁎
(4.406)Gudrun (post Gudrun valuation) −0.165⁎⁎⁎
(0.042)−25.48⁎⁎⁎
(5.214)−16.37⁎⁎
(6. 871)– – –
Cheap talk script – – – 0.067⁎⁎⁎
(0.011)10.58⁎
(6.183)17.15⁎
(9.519)Severity of the storm(exclusion restrictions)
Low impact 0.108⁎⁎⁎
(0.047)– – 0.010
(0.010)– –
High impact 0.083⁎⁎
(0.051)– – −0.035⁎⁎⁎
(0.008)– –
Means of random constants −2.151⁎⁎⁎
(0.197)98.81⁎⁎⁎
(18.39)−2.340⁎⁎⁎
(0.399)−54.17(38.93)
Scale parameters of random constants(implied standard deviations ofunobserved individual-effects)
3.000⁎⁎⁎
(0.043)207.4⁎⁎⁎
(1.536)306.9⁎⁎⁎
(7.754)2.870⁎⁎⁎
(0.091)185.8⁎⁎⁎
(2.947)286.6⁎⁎⁎
(14.66)
Rho-correlation between equations −0.367⁎⁎⁎
(0.027)−0.476⁎⁎⁎
(0.058)Variance explained by unobservedindividual effects in Tobit model
0.776⁎⁎⁎
(0.009)0.818⁎⁎⁎
(0.016)Log-likelihood function −41,051.53 −39,699.17 −9248.46 −8872.38Number of respondents 1721 1721 415- 415Number of observations 14,698 14,689 3502 3502
Marginal effects of the parameters are obtained by evaluating the first derivative of the conditional expectations at the mean values of the explanatory variables (the marginal effectage-squared is not reported).We used different numbers of Gaussian–Quadrature points in the estimation of the Tobit model to test the sensitivity and the results stabilized after 60–70. The sample selection model with random-effects is estimated with simulated maximum likelihood estimator and GHK simulator is used. Number of random draws are 1000 ineach case. In the estimations, we deleted outliers, which we defined as WTP above 1000 SEK, and they corresponded to 0.8% of the total number of observed WTP answers.
⁎ Significant at 10% level.⁎⁎ Significant at 5% level.⁎⁎⁎ Significant at 1% level.
796 F. Carlsson et al. / Energy Economics 33 (2011) 790–798
cannot heat the dwelling if an outage occurs are significantly morelikely to have a positiveWTP, and also significant effect on conditionalWTP except for living in city with a negative and significant effect.Income has, as expected, a positive and significant effect onconditional WTP but the marginal effect of the income on a positiveWTP is not significant. In the Tobit model, the cheap talk variable ispositive and partially significant as in the first model and the socio-
economic characteristics have the same sign as in the sample selectionmodel.
4. Conclusions
By using an open-ended contingent valuation survey, we haveanalyzed the effects of (i) having experienced the storm Gudrun and
797F. Carlsson et al. / Energy Economics 33 (2011) 790–798
of (ii) using a cheap talk script on people's stated maximum WTP toavoid power outages. Having experience resulted in a significantincrease in the proportion of respondents stating a zero WTP, and asignificantly lower WTP. This cannot be explained by an increase inself-protective measures after the storm. One reasonable explana-tion is that the experience of an outage actually affected thepreferences of some respondents. This relates to the literature on thedifference between decision and experience utility (Kahneman andSnell, 1992). Although outages do occur from time to time, they arenot frequently experienced in Sweden. This means that respondentscould have difficulties in predicting the negative experiences of anoutage. Another possible explanation for the difference in WTPbefore and after the storm is that the respondents, in line with theview presented in the media, disagree with the implied propertyrights, i.e., that consumers do not have the right to power withoutoutages. In light of this, it might seem tempting to apply acompensation question and instead ask for minimum willingnessto accept (WTA), which would then change the property right to theconsumers. However, research on WTA has shown a large discrep-ancy between these two measures. For example, Horowitz andMcConnell (2002) found a mean WTA/WTP ratio of around seven ina meta-study using 200 previously conducted contingent valuationstudies. Thus, applications of WTP can be seen as a conservativevaluation approach. Our results point to the fact that it is difficult touse a particular question format, since different respondents havedifferent views on property rights. This is a general problem whenapplying valuation to cases where property rights are ambiguous.The cheap talk script, which was mainly intended to avoidunderestimation of WTPs, seems to have counteracted the effect ofexpressing an attitude since it increased the proportion of non-zeroresponses.
The cheap-talk script resulted in a decrease in the number ofrespondents stating a zero WTP. The share of zero WTP responsestends to be large in open-ended CV studies, and this is the case in ourstudy as well. One explanation for this is strategic behavior, whichthen results in a lower WTP. If this is the case, then our cheap-talkscript seems to reduce the hypothetical bias and increase WTP. Theliterature presents mixed effects of using cheap talk scripts, but thereis evidence that a neutral script such as the one used here can increaseWTP and increase hypothetical bias (Aadland and Caplan, 2006).However, it is important to distinguish between different elicitationformats. Most studies use a close-ended format, where a positivehypothetical bias is expected. In our study, an open-ended format isused, where the direction of hypothetical bias is less clear.
From a policy perspective, our results are interesting since theyshow that valuation may differ before and after a negative event.This can be compared with the results in Ajzen et al. (1996), whofound a strong information effect on WTP for a good that is relevantfor the respondent compared to an irrelevant good. The changes invaluation are likely to be driven by the expression of new attitudesand more experience, but the a priori net effect on WTP is unknown.A related issue is the long-term effect; e.g., Kunreuther (1996) foundthat people hit by flooding or other natural disasters are more likelyto purchase insurance, but as time passes they cancel their policiesand regress to their previous behavior. Whether this behaviorextends to power outages is an empirical question. Thus, forvaluation studies taking place shortly after a major negative eventsuch as a natural disaster, it is important to test the sensitivity in theelicited valuation by conducting studies at more than one point intime and by using a cheap talk script. Moreover, as shown in Aadlandand Caplan (2006), the length of the cheap talk seems to beimportant, but of course the wording of the script is also important.Thus, there is clearly a need for more research on applications ofcheap talk as an instrument to improve the quality of preferenceselicited from stated preference surveys. This paper has shed somelight on the question of whether people's stated preferences are
affected by a negative event and on the effect of different strategiesto investigate this issue.
Acknowledgments
Financial support from Elforsk, Vetenskapsrådet (Swedish Re-search Council) and Tom Hedelius and Jan Wallanders Foundation isgratefully acknowledged. The paper has benefited from comments byPeter Fritz, Lennart Hjalmarsson, Anders Johansson, Olle Hansson,Per-Olof Nilsson, Håkan Nyberg, Matz Tapper, and two anonymousreferees.
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