Can users price real-time contextual information
CAN USERS PRICE REAL-TIME CONTEXTUAL INFORMATION?
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BERNADETTE KAMLEITNER, WU Vienna University of Economics and Business STEPHAN DICKERT, Queen Mary University of London HAMED HADDADI, Queen Mary University of London
Personal data (PD) markets tend to exclude data subjects because they are considered incapable of consistent PD valuations. But perhaps users simply struggle to comprehend the data they are trading. Assessing real-time contextual information (RTCI) via an app, we show that in principal users are capable of consistent valuations; but observe this consistency only for data about social contexts. Moreover, consistency occurs when willingness to protect PD co-varies with perceptions of data sensitivity and ownership. This suggests complex novel mechanisms driving users’ valuations of PD. Insights facilitate the design of ecosystems that actively include users in data markets. • Security and privacy➝Human and societal aspects of security and privacy • economics of security and privacy➝ social aspects of security and privacy Additional Key Words and Phrases: endowment effect, app experiment, information sensitivity, psychological ownership, situational influence, WTA, WTP, value of data
INTRODUCTION
Mobile devices enable comprehensive snapshots of every moment of an individual’s life. Call logs, location data [Cheung 2014] and social media apps often provide real time contextual information (RTCI) on-the-go, i.e. information on where individuals are, what they do and with whom they do so. Upon download, many apps request permissions to obtain personal data (PD) including data that allow inferring RTCI, and they are basing their business models on trading these PD [Ducarroz et al. 2016; Hoofnagle and Whittington 2013]. The markets for PD are thriving (e.g., Spiekermann et al. 2015). The asset sold in these markets is something all natural persons are endowed with because it is about individuals. Arguably PD are also individuals property [Etzioni 2011; Purtova 2015]. Yet, the players in PD markets are corporate. They consist of businesses harvesting PD from the individual and of businesses buying, aggregating, and commercialising PD, often in bulk. These players determine the monetary value of PD in a market place that excludes those eventually providing the data. Individuals are, if at all compensated (e.g., by getting access to a mobile app), not usually paid for their PD. Harvesting and trading user data without user involvement is the norm. Two streams of research suggest that this practice may, in fact, be necessary. One stream of research centers on the notion of privacy calculus. It presumes that users engage in a tradeoff between the costs to their privacy and the services obtained so that whatever compensation they agree to adequately reflects their valuation of PD [Dinev and Hart 2006; Xu et al. 2009]. In essence, this stream assumes that users are already part of the data market and that the market also reflects user’s own data valuation. Rather than assuming that data volunteering equals adequate compensation, a second stream of research suggests that users are not even capable of becoming actors in the data market. To actively participate in a market and enable negotiations, actors need to consistently evaluate the good being traded. A comprehensive body of evidence suggests that this is where individuals
This work was supported by the EPSRC Pump-Priming grant number EP/J501360/1 at Queen Mary University of London awarded to Hamed Haddadi and Bernadette Kamleitner. Author’s addresses: B. Kamleitner, Institute for Marketing & Consumer Research, WU Vienna, Welthandelsplatz 1, 1020 Vienna, Austria; Stephan Dickert & Hamed Haddadi, Queen Mary University of London E1 4NS, London, UK. Permission to make digital or hardcopies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credits permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or
[email protected]. © 2016 ACM DOI:http://dx.doi.org/10.1145/0000000.0000000
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struggle. Valuations by users show substantial variations across value elicitation methods and contexts [Acquisti et al. 2015; Acquisti et al. 2013; Grossklags and Acquisti 2007; Rose 2006] suggesting that users are either unable to price their own PD or do not value it highly [Beresford et al. 2012]. So while coming to a very different conclusion in terms of users roles and capabilities, this stream of research also provides arguments that support the practice of excluding users from having an active share in data markets. Here, we re-inquire into this second stream of evidence and question whether users are in fact generally incapable of pricing their PD. We take a novel perspective and suggest that part of the reason why users struggle to consistently evaluate PD may be because they have no clear notion of what it is they evaluate. Often users are asked to agree to pass on a vast set of data points encompassing data about uncertain future events. This is, for example, the case when providing permission to track location data. What users are asked to share or evaluate is a comprehensive and ambiguous data bundle (e.g., all locations a person may be in at numerous points in time for an unspecified period). Anticipating and comprehending the entire scope of these bundles is at best cognitively challenging and at the worst impossible. Our premise is that the inability to value a boundless bundle does not also implicate an inability to evaluate the bundle’s components [Greenleaf et al. 2016; Hsee et al. 2013]. Consequently, we investigate whether users are perhaps able to consistently evaluate well-specified pieces of information. Specifically we look at RTCI, i.e. information comprising what people do, where, and with whom. RTCI focuses on the very moment, so users should have a nuanced and concrete understanding of it [Bhatia and Walasek 2016; Liberman et al. 2007] and be able to value it consistently. To assess RTCI, we developed a real-time, longitudinal mobile survey application. To assess consistency of valuations, we manipulated the way valuations were elicited following the endowment effect paradigm [Grossklags and Acquisti 2007; Knetsch and Sinden 1984]. Comparing valuations of RTCI across elicitation modes, we show, for the first time, that the premise of users’ inability to consistently value PD does not universally hold. In doing so we add substantive arguments to a growing body of endeavors that aim to include users in PD markets [Chaudhry et al. 2015; Crabtree and Mortier 2015]. In addition and building on the fact that RTCI is as heterogeneous as the situations people experience, we identify situations in which evaluations of RTCI are particularly consistent. This facilitates a more nuanced understanding of when users’ capability of pricing RTCI is particularly high and contributes to a growing body of research documenting contextual malleability of the perception of privacy and PD [Acquisti, et al. 2015]. Finally we aim to enhance current theorizing about why people struggle to consistently value PD. We exploring the role of perceptions of data sensitivity and ownership and identify novel mechanisms that point towards the power of contextual interdependence. We conclude that individuals’ struggle to consistently value PD is pervasive but not universal, and that the key to including users in markets for PD may reside in ensuring that people better understand what data are collected and who they might concern.
CONCEPTUAL BACKGROUND Perspectives on the purported inability to value PD
The premise that users cannot evaluate the value of their private data in an unbiased way is the backbone of several business models. It is so deeply ingrained in the market place as to perhaps even become a self-fulfilling prophecy. Current market practices do anything but help users to think about data as an asset with monetary value. Many online business models rely on additional revenue streams generated through users providing their data without ever encouraging users to think about what it is that they provide or what its value may be. Rarely is attention drawn to the fact that the data requested also act as compensation. For example, when downloading a mobile app users will rarely ever know which of the permissions they are asked to grant are needed purely for the app to work properly, which permissions may be needed to harvest data of commercial value, and which permissions may be needed for both, or which permissions may have no real purpose at all [Au et al. 2012]. The starting point for any discussion about users’ ability to valuate data is, thus, a market in which the seller may often not even fully be aware of her role. By virtue of their very design (e.g. asking individuals to put a price on data or to decide between options that differ in terms of PD requests and price), studies on users’ capabilities of data
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valuation ensured that awareness of PD as part of the actual transaction was at least somewhat heightened. Yet, they consistently find that users are unable to consistently price their PD [Acquisti, et al. 2013] and in turn often trade them for ludicrously small conveniences or gains [e.g., $1 off a DVD; Beresford, et al. 2012; Preibusch et al. 2013]. The implications are potentially far reaching [Acquisti et al. 2016]. Data have no expiry date and they gain in information value as more data become available, thus, allowing for their aggregation into commercially but also socially viable insights [Chaudhry, et al. 2015; Conitzer et al. 2012; Kostkova et al. 2016]. How much value eventually becomes extracted from what data is a dynamic parameter. This makes it hard to determine how fair or unfair a users’ de facto compensation (e.g. usage of an app) eventually turns out to be. One implication of the current practice of obtaining data is, however, clearly disadvantaging users: the practice of obtaining wholesale permissions for classes of data (e.g., location data). This practice implies that additional data can be harvested from the same person at (nearly) no extra cost [Rifkin 2014]. Put differently, companies are obtaining permission for a near endless data refill. This consideration draws attention to the fact that much PD is traded in large bundles. Users are generally asked to permit access to all of a class of data rather than to individual data points. This likely holds implications for people’s ability to consistently value data. A large body of research suggests that people generally struggle to comprehend anything that comes in larger scopes and that with increasing scope they become increasingly insensitive to variations in scope [Loureiro et al. 2013; Urminsky and Kivetz 2011]. At least in part this is because they lose trace of what this bundle actually contains. As a consequence people value the whole differently than they would evaluate the sum of its parts were they valued in isolation. Given the scope of personal data, as well as the fact that it has yet to accrue, failure to consistently evaluate such data bundles appears logical. In fact, Carrascal et al. [2013] show that users assign similar values to individual pieces of PD as they do for entire bundles of PD. However, if the bundle cannot be evaluated that does not necessarily mean that the things in it cannot be evaluated either. One proven way to increase evaluability is to single out units of information (Hsee et al. 2013). For example, when asked to volunteer their location information people are likely thinking of specific locations rather than of the fact that their every move implies a change in location. It is likely impossible to anticipate all the numerous coordinates this will eventually entail. Asking people to evaluate each location separately and sequentially would very likely lead to more nuanced and, therefore, likely also more reliable valuations than asking for the faceless commodity of location data. Beyond a mere issue of scope, the data are collected in units that may hold little meaning for the individual. People may face the challenge of evaluating something that is hard to grasp and that they never thought about. Research on people’s ability to value non-market goods—which, like data, are often hard to grasp—has shown that identifying single units plus embedding them in a bigger meaningful context helps curb exaggerated valuations [Hausman 2012; Kahneman and Knetsch 1992; Sælensminde 2003]. Location data, for instance, would come in the form of geographical coordinates. It is only once we give a name to this location and once we imbue it with a context that location data becomes meaningful. For example, “51,30’N,7’W” may mean little to most but “being in a shop in London doing the daily grocery shopping” does. The integration of data points into information is what eventually provides value for users and companies alike [Ackoff 1989]. Perhaps users could consistently evaluate holistic and meaningful pieces of PD. Dynamically accruing PD entail issues of scope and of a lack of embeddedness at their extreme. On top of it, they entail issues of uncertainty arising from the fact that many of the data harvested arise in and capture a future that is unknown. If we want to discern whether people are truly incapable of consistently valuing PD, it appears necessary to present and assess them in a manner that strips away these issues and turns PD into something people can emotionally and intellectually grasp. Following this logic, we ask what happens when people are asked to evaluate well-specified PD holistically and in real-time. Specifically, we look at a class of PD that becomes more important as the amount of sensors in human surroundings increases [Cukier and MayerSchoenberger 2013; Sundmaeker et al. 2010]. We look at what we call real-time contextual information (RTCI), i.e. PD comprising situational snapshots of a person’s live including what she does where and with whom. RQ1: Are users unable to consistently value RTCI?
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Contextual malleability
From waking and brushing one’s teeth in the morning, to working in the office, to going to the gym, to meeting friends, to relaxing at home, the situations we experience are numerous and ever changing. RTCI reflects the manifold situations each person dynamically finds herself in. As a consequence, RTCI is as heterogeneous as the situations people experience. A growing amount of evidence in the domain of privacy perceptions [Acquisti, et al. 2015; Martin and Shilton 2016; Zafeiropoulou et al. 2013] suggests that the value people assign to privacy and therefore also to PD is contextually malleable. In some situations people seem more privacy concerned than in others. Even the mere presence of specific cues such as a happy user interface [Kehr et al. 2015] or a hint that others disclosed information [Acquisti et al. 2011], suffices to alter privacy concerns in otherwise unchanged situations. The valuation and the consistency of valuations of RTCI about different contexts is, thus, likely to see variations1. Provided these variations are systematic, insights about them provide hints as to what makes situations hard to evaluate and how different classes of PD (e.g., activity and location) interact in bringing about privacy concerns and valuations. In this research we thus also investigate whether there are situational contexts that are inducive to particularly consistent valuations of the respective RTCI. RQ2: Does the consistency of valuations of RTCI vary across contexts? Select drivers of (in)consistent valuations of PD
If there are contextual variations in the evaluability of RTCI, this raises the question as to what drives them. Addressing this question may help identify strategies to increase consistency of valuations and because it may help unearth simple proxies for the conditions under which userinclusive data markets could work. The last set of questions we address in this paper, thus, relates to potential factors that may cause (in)consistent valuations of PD. Specifically, we explore the effect of perceptions of data sensitivity and ownership. Both perceptions have been shown to account for variations in valuation consistency [Baron and Spranca 1997; Reb and Connolly 2007], including in the context of data [Bauer et al. 2012]. Perceived data sensitivity
Sensitivity has been suggested as a needed boundary to the monetization of information [e.g., Nissenbaum 2004] but also as a proxy for the value of PD [Hirschprung et al. 2016; Kosa et al. 2011; Preibusch 2013]. Indeed there is evidence that people are unwilling to make trade-offs for sensitive issues and goods [Baron and Spranca 1997; McGraw et al. 2003] or as Boyce et al. [1992] put it: for goods with intrinsic value. If asked to evaluate a good, people appear to use the degree to which this good is perceived as sensitive as a cue for its value. Notably, past evidence indicates that the perception of a good as sensitive primarily affects the price people demand when selling this good [Anderson et al. 2000; McGraw and Tetlock 2005]. It has less effect on the price people are willing to pay for the same good. In other words sensitivity tends to have differentially strong effects on valuations depending on how these valuations are elicited. It is thus likely, that perceived sensitivity plays an essential role whenever people do not consistently evaluate data. Importantly sensitivity estimates themselves also appear to be malleable. For example, the same good becomes more sensitive and elicits different selling prices depending on who it has been acquired from [McGraw and Tetlock 2005] and whether it is morally charged [Anderson, et al. 2000]. Given that estimations of sensitivity vary and that they co-vary with the valuation of data [Hirschprung, et al. 2016] it appears plausible that perceived sensitivity and perhaps also uncertainty about the degree to which an issue is sensitive relates to the extent to which people are capable of providing consistent valuations. RQ3: Do user’s sensitivity perceptions of RTCI relate to the extent to which they are capable of consistently valuing RTCI?
Note, however, that when also assessing real-time valuations in the context of web browsing PD. Carrascal, et al. [2013] find no variations across webcontexts.
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Perceived data ownership
Closely related to the sensitivity and intimacy of a good is the extent to which people feel that it belongs to them. The perception of something as “mine” has also been termed “psychological ownership” [Pierce et al. 2003]. It is a phenomenon that stretches beyond physical goods and legal rights [Kamleitner and Dickert 2015]. For example, Kirk et al. [2015] show that psychological ownership determines whether people recommend an investment, Lee and Chen [2011] find that it predicts engagement in virtual worlds, Baer and Brown [2012] demonstrate that it determines how people react to feedback, and both Kim et al. [2016] and Raban and Rafaeli [2007] find that it predicts sharing in social networks. There is no doubt that the experience of ownership is powerful [for reviews see Jussila et al. 2015; Kamleitner 2014; Pierce and Jussila 2011]. In particular, ownership has been identified as a robust predictor of monetary valuations [Brasel and Gips 2014; Kamleitner and Feuchtl 2015; Reb and Connolly 2007; Shu and Peck 2011] including in the context of PD [Spiekermann and Korunovska 2016]. Taken together the evidence suggests that the perception of ownership predicts valuations of data but at the same time increases people’s willingness to share them. Notably, existing research also suggest that the perception of ownership acts as a predictor in particular if and when users consider themselves as the main or only owner of a good [cf. Kamleitner and Rabinovich 2010; Raban and Rafaeli 2007]. When it comes to PD and in particular to RTCI this last insight may be particularly important. RTCI captures a person’s situational makeup and people are rarely truly alone. Much of our waking hours are spend either with others or in a manner that can be observed by others. For example, any time somebody is leaving their house it is likely that they will be seen by other people. Even when at home many people will still be surrounded by other members of their household. While being an instance of PD, RTCI is often accessible to other human beings and therefore also information they are privy to. This raises the possibility that the perception of ownership of RTCI but also the degree to which people use considerations of ownership when making valuations of RTCI varies across situations. RQ4: Does the perception of RTCI as owned relate to the extent to which people are capable of consistently valuing RTCI?
Assessing consistency: contrasting willingness to accept (WTA) with willingness to protect (WTP)
To capture people’s capacity to evaluate RTCI we draw on a valuation paradigm used in economics and psychology: the endowment effect (Thaler, 1990 or Acquisti et al. 2013 in the context of data). Applying this paradigm allows highlighting the degree of consistency of evaluations by contrasting the minimum amount requested to sell an object (willingness to accept, WTA) with the maximum amount paid to obtain or (in the context of PD) protect the same object (willingness to pay/protect, WTP). The endowment effect is the consistent observation that the WTA/WTP ratio tends to be greater than 1. This signals a general unwillingness to part with objects and a widespread struggle to consistently evaluate goods.. Endowment effects are a robust phenomenon and have been found for both tangible and intangible goods [e.g., Ashby et al. 2012; Horowitz and McConnell 2002; Johnson et al. 2007], including personal data [Acquisti, et al. 2013; Grossklags and Acquisti 2007; Hui and Png 2006]. While the mere presence of the effect is insufficient to question people’s valuation ability, its scope is. Goods that individuals find easy to evaluate, i.e. ordinary private market goods such as mugs or shoes, exhibit WTA/WTP ratios of around 2 to 3. In contrast, goods that individuals struggle to evaluate, i.e. so called non-market goods such as health and safety, exhibit WTA/WTP ratios that are considerably higher [Horowitz and McConnell 2002; Nataf and Wallsten 2013]. The observed strength of the endowment effect can hence serve as a proxy for whether individuals are capable of somewhat consistently evaluating and in turn trading a good in a market. Ranging from a ratio of 5.47 for information on how participants spent money on a gift card [Acquisti et al. 2009] to more than 100 for information on the number of past sexual partners [Grossklags and Acquisti 2007], thus far the WTA/WTP ratios observed for PD are in fact on the high side, suggesting users inability to evaluate them. Nonetheless, given all the afore-mentioned arguments the evidence to date appears insufficient to allow for a final verdict. To the best of our knowledge there is no study that employed the endowment effect paradigm to dynamic and well-
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specified PD such as RTCI. While it is hard to anticipate the actual size of the effect for RTCI, we anticipate it to be lower than that observed in prior studies on PD. The endowment effect emerges by contrasting two different value elicitation methods, WTA and WTP. An interesting question asks which of these two elicitations it is that brings the effect about, both or only one of them? Existing research consistently shows that it is in particular when asked to determine a selling price that people’s valuations become volatile [Anderson, et al. 2000; Kahneman et al. 1991]. This has at least two reasons. First in contrast to WTP, WTA is not restricted to the funds available to a person. If a person does not want give up a piece of data, the most she could offer to protect it is whatever she has available. Before this background, WTP is, for example, also more insensitive to scope [Olsen et al. 2004]. In contrast, asking prices face no such restrictions. Second and more importantly, the elicitation of WTA shifts the cognitive frame [Morewedge and Giblin 2015] such that an individual is put into the position of a seller. This implies holding property rights and accordingly also moral responsibility for the fate of the good [Boyce, et al. 1992]. In turn, the elicitation method may have consequences also for the proposed processes of perceived sensitivity and ownership. Both perceptions are somewhat malleable. A focus on the proprietary reference point entailed in WTA may thus heighten them compared to the more suppliant reference point entailed in WTP. Beyond a potential main effect of the elicitation method on perceived sensitivity and ownership it is also likely that variations in both perceptions affect the strength of the endowment effect, again mostly by influencing WTA. Studies suggest that the amount people request for a good is strongly influenced by its perceived sensitivity and moral value [Boyce, et al. 1992; Hirschprung, et al. 2016] and its perceived ownership [Chatterjee et al. 2013; Morewedge et al. 2009; Shu and Peck 2011]. We thus expect to observe in particular variations of WTA, both across contexts and in response to variations in perceived sensitivity and ownership.
MOBILE APP STUDY
To examine the extent to which individuals exhibit biases in valuations of their private data, a mobile Android app was built, made available in the Google playstore, and brought to participants’ attention via a media campaign and word of mouth. Upon installation an initial questionnaire assessed participants’ profile (including demographics). The mobile app then popped up at random times twice a day for a maximum of ten days. Sample screen shots of the app can be found in the appendix. Each time it asked for self-reports of current activity, social context, and location. Using the format of a mobile app it was important to ensure that these self-reports were easy to make while also providing sufficient situational detail. In a multistep-procedure that preceded app development we, thus, identified and classified a large range of activity and location contexts. To do so we conducted a diary study with 10 volunteers. All participants were personally handed an especially designed diary in which they documented all situations (activity, location, persons present) they found themselves over the course of an entire week. Participants were instructed to document their exact activity for every single waking hour within that time frame. This procedure allowed us to capture a wide range of activities that people would normally engage in. All volunteers were compensated for their time with online shopping vouchers. In a next step participants reports were grouped and coded and enriched by activity categories known from prior evidence on human activities [Kahneman et al. 2004]. Finally, two of the authors and a research assistant iteratively condensed and classified the resulting activities locations and social contexts into larger sets of categories. Eventually we identified 36 types of activities which were grouped in 7 main categories of activity, 12 location types which were grouped into 3 main categories of location, and 6 types of companions participants interacted with. In the app participants could then first select a main category which opened up a new set of sub-options. Usability and comprehensiveness of this classification process was eventually verified by 3 volunteers who agreed to test the app and its user-friendliness for some days before it was launched. Most importantly and after asking participants to self-report their current situation, the app asked for the £-value people attach to this RTCI information. In order to gauge potential endowment effects, in a between subject variation participants were randomly assigned to different versions of the app. For the purposes of this paper WTP and WTA versions are compared. In the WTP condition valuations were elicited by asking “What is the maximum amount that you would
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be willing to pay to restrict others from accessing this information?”. In the WTA condition valuations were elicited by asking “What is the minimum amount that somebody would have to pay you to get access to this information?”. In addition to valuations, participants reported on the potential processes affecting valuations. First, in a single item they reported the extent to which they felt they personally own the data in question (“To which extent to you feel this is just YOUR information?” from ‘not at all’ to ‘very much’ on 100 grid points). Next they reported the extent to which they perceived information about this situation as sensitive. Because specific components of the situation may be differentially sensitive we asked for the perceived sensitivity of location, social company and activity data with one item each on a slider scale (100 grid points). All items loaded on one factor that explained 86 per cent of variance. Items were thus averaged into a scale (Cronbach’s alpha=.91). In addition to the variables of interest to this paper we assessed mood and the perceived value of these data for specific stakeholders. Sample and data preparation
Ninety-five participants (mean age = 29 years, 15% female, 48% full-time employees) provided a price for their information at least once. Overall, 775 valuations were obtained. As generally found in PI valuations [e.g., Bauer, et al. 2012] the distribution of values provided was positively skewed (M = £24 , Median = £3) necessitating non-parametric statistical procedures for some of the analyses. To facilitate interpretation, valuations were grouped into five meaningful categories. The first category comprises responses indicating that participants consider this information ‘free’. The second category comprises of valuations of up to £5, we named this category ‘low’ valuations. The ‘medium’ and ‘high’ valuation categories contained price indications of up to £10 and £50, respectively. The final category ‘no sale’ comprises all prices in excess of £50. These valuations are so high as to indicate little actual willingness to give the data away. Notably, substantive results do not change if category boundaries are shifted. A chi² test contrasting the distribution across valuation categories between the first valuation and later valuations also shows no difference in valuations across time (Chi²(df=4) = 2.80, ns). This indicates the absence of learning effects. To capture the holistic nature of a situation and to avoid issues related to low cell sizes, distinct situation profiles were inferred by means of a two-step cluster analysis across specific activities, locations and company. This yielded five interpretable homogenous situation clusters (Table 1). Table I. Description of situation clusters based on dominant RTCI per cluster.
Cluster1 Name
Social Time
Cluster2 Family Matters
Cluster3
Cluster4
Cluster5
Home and Alone
Out and About
Work Time
Typical Location
no dominant location
home
home
public space
work place
Typical Activity
visiting/ study/eat
eat/ drink
watch TV/ read
get somewhere
work
friends
family
alone
alone
alone/ colleagues
158
132
175
98
145
Typical Company n
Results and discussion Overall endowment effect
Table 2 provides a comprehensive summary of results. We first investigate results across situations before drilling into specific situation profiles. Overall, there is evidence for an endowment effect. A Mann-Whitney U-Test suggests a significant difference in mean ranks across conditions. Contrasting the median valuations suggests an average WTA to WTP ratio of 5. This is higher than that observed for most market goods but still lower than what is often found for nonmarket goods such as health.
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Table II. Valuations across elicitation methods and situations
situation
Valuation categories
across situations
social time
family matters
home and alone
WTA
WTP
overall
WTA
WTP
WTA
WTP
WTA
free £0
18%
28%
23%
24%
24%
14%
18%
15%
low £0.01-5
40%
42%
41%
38%
47%
53%
46%
44%
medium £5.01-10
16%
14%
15%
8%
14%
18%
22%
high £10.01-50
12%
12%
12%
18%
11%
2%
no sale £>50
15%
4%
9%
12%
5%
12%
Valuation Categories (Χ²) Median Valuation (£) WTAMedian/WTPMedian Z-value (U-Test)
32.49** 5.00
1.00
6.06 3.00
3.50
2.00
5.00
work time
WTA
WTP
WTA
WTP
8%
28%
22%
30%
40%
42%
37%
25%
46%
18%
12%
29%
12%
16%
12%
13%
5%
12%
13%
22%
14%
5%
1%
18%
5%
8%
2%
23%
7%
12.11* 1.20
WTP
out and about
31%
14.59** 5.00
2.00
11.85* 7.50
1.50
14.95** 9.00
1.00
5.0
1.8
2.5
4.2
5.0
9.0
-5.75**
-1.20
-0.56
-3.00**
-2.24*
-3.59**
Note. *…p
