#  Doing the right thing (or not) in a lemons-like situation: on the role of
social preferences and Kantian moral concerns ††thanks: I.A. acknowledges
funding from the European Research Council (ERC) under the European Union’s
Horizon 2020 research and innovation programme (grant agreement No 789111 -
ERC EvolvingEconomics), as well as IAST funding from the French National
Research Agency (ANR) under grant ANR-17-EURE-0010 (Investissements d’Avenir
program). We are grateful to Lony Bessagnet, Jean-François Bonnefon, Eric
Danan, Pierre Fleckinger, Peter Hammond, Astrid Hopfensitz, Nicolas Jacquemet,
Pau Juan-Bartroli, Fabrice Le Lec, Thierry Magnac, Pauline Morault, Esteban
Muñoz-Sobrado, Sébastien Pouget, Régis Renault, Angelo Secchi, Sigrid Suetens,
Ao Wang and Boris van Leeuwen for helpful discussions, and to Stéphane Cézera
for invaluable assistance in the lab. We thank audiences at CY Cergy Paris
University, IAST, the Paris School of Economics Summer School, Toulouse School
of Economics, Catholic University of Uruguay, Max Planck Institute, Economic
Science Association, Uruguayan Central Bank, Universidad de la República and
University of Gothenburg for feedback.

Ingela Alger
José Ignacio Rivero-Wildemauwe Toulouse School of Economics, CNRS, University
of Toulouse Capitole, and Institute for Advanced Study in Toulouse, France,
and CEPR<EMAIL_ADDRESS>of Business and Administration,
Catholic University of Uruguay, Montevideo, Uruguay.
<EMAIL_ADDRESS>
( September 3, 2024)

Abstract: We conduct a laboratory experiment using framing to assess the
willingness to “sell a lemon”, i.e., to undertake an action that benefits self
but hurts the other (the “buyer”). We seek to disentangle the role of other-
regarding preferences and (Kantian) moral concerns, and to test if it matters
whether the decision is described in neutral terms or as a market situation.
When evaluating an action, morally motivated individuals consider what their
own payoff would be if—hypothetically—the roles were reversed and the other
subject chose the same action (universalization). We vary the salience of role
uncertainty, thus varying the ease for participants to envisage the role-
reversal scenario. We find that subjects are (1) more likely to “sell a lemon”
in the market frame, and (2) less likely to do so when the role uncertainty is
salient. We also structurally estimate other-regarding and Kantian moral
concern parameters.

JEL codes: C91, D01, D91.

Keywords: market framing, lemons, social preferences, Kantian morality,
experiment.

##  1 Introduction

There is by now robust evidence from experimental games played by subjects in
laboratory settings in a host of countries, that a large share of individuals
give up own material payoff to increase that of others (e.g., Zelmer (2003);
C. Engel (2011)), and exhibit lying aversion (e.g., Gneezy, Rockenbach Serra-
Garcia (2013); Abeler, Nosenzo Raymond (2019)). What are the implications of
this evidence for behaviour in real market settings? In particular, is there
reason to believe that sellers are less willing to sell goods at a price above
their value than many theoretical models assume (Akerlof, 1970)? We aim at
providing some insights by conducting a laboratory experiment that we designed
with two objectives in mind: (1) to examine behavior of subjects in a
situation reminiscent of that of a seller of a “lemon”, using both a neutral
and a market frame; and (2) to disentangle the role of other-regarding, or
distributional, preferences from Kantian moral concerns in this context.

Subjects in our experiment face a series of anonymous binary choices, each of
which affects the payoff of a randomly drawn other subject, hence avoiding
that subjects’ decisions be influenced by strategic or repeated interaction
considerations, (Roth Schoumaker, 1983; Andreoni Miller, 1993), and social
image or reciprocity concerns (Bénabou Tirole, 2006; DK. Levine, 1998;
Charness Rabin, 2002). In each binary choice one of the options—the selfish
option—entails a gain for self and a loss for the other, compared to the other
option—the status quo option (in other words, each decision situation is a
Dictator game, with taking rather than giving (Dreber, Ellingsen, Johannesson
Rand, 2013). The payoff consequences of this choice thus capture a lemons-like
situation, in which the selfish option amounts to selling an object of low
quality at such a price that the seller gets better off but the buyer gets
worse off, while the status quo option amounts to keeping the object.

To achieve our first objective, we let half of the decisions be taken in a
standard neutral frame and the other half in a market frame, keeping the
payoff consequences identical under both frames. Only the wording used to
describe the situation differs. Specifically, in the market frame subjects get
the following information: “As you can see on the decision screen above, the
Buyer would be better off if you chose Not Sell, while you are better off if
you Sell. Think of this as representing a situation in which the good that you
sell has a defect which makes the Buyer enjoy owning the good less than you
do.” The wording was chosen to mimic a situation in which a seller can sell a
“lemon” to an uninformed buyer (who in the experiment is passive). In the
neutral frame the options are simply referred to as X and Y, and no context is
given.

Our second objective is to disentangle two distinct motivations for refraining
from the selfish option. The first motivation is consequentialist: since the
selfish option increases the payoff gap between the decision-maker and the
passive subject, the status quo may be selected due to an other-regarding
concern in the form of altruism or inequity aversion, formalized as a weight
$\beta$ on the payoff gap (Becker, 1974; Fehr Schmidt, 1999). The second
motivation relies on a (possibly partial) universalization argument: it makes
the subject evaluate each decision in the light of what his payoff would be,
if—hypothetically—the other were to take the same decision as the subject
himself with some probability $\kappa$, if the roles were reversed. This
motivation, which is reminiscent of a Kantian moral concern, can be expressed
with Homo moralis preferences, where $\kappa$ is the individual’s degree of
morality (Alger Weibull, 2013). We posit that subjects have preferences that
combine other-regard with a Kantian moral concern. Such a combination is
indeed predicted by recent theoretical work on the evolutionary foundations of
preferences in a model where interactions yield payoff consequences with
negligible effects on overall reproductive success (Alger, Weibull Lehmann,
2020), and it has also received empirical support in another experimental
study (Van Leeuwen Alger, ). Moreover, recent theoretical studies on the
effects of Kantian moral concerns on market outcomes that feature an unequal
distribution of information (including cases where product quality is the
relevant information asymmetry) document important effects of such moral
concerns on equilibrium outcomes (Rivero-Wildemauwe, 20231, 20232). In
equilibrium, Kantian motivations reduce the likelihood of “lemons” being
exchanged, or may even eliminate it altogether. Importantly, these references
also underline that other-regard may have similar effects. In light of this,
it is of particular relevance to be able to distinguish between both
behavioral drivers.

Because the Kantian concern is triggered by consideration of a possible role
reversal, the experimental design consists in varying the salience of role
reversal. Specifically, half of the decisions we observe are taken in a VOI
frame and the other half in a non-VOI frame, where VOI stands for veil of
ignorance. In the VOI frame, a subject is told that (s)he stands an equal
chance of being the decision-maker and the passive subject. In the non-VOI
frame (s)he is simply told that (s)he is the decision-maker. In reality,
however, the two situations are the same, since upon entering the experiment
any subject effectively stands an equal chance of being the decision-maker and
the passive subject in any given match. Hence, the only difference is that we
make this role uncertainty explicit in the VOI frame. Our theory thus predicts
that if subjects are driven by a Kantian moral concern and if they are not
fully aware of the arbitrariness in the role distribution in the non-VOI
decisions, they are more likely to sell in non-VOI decisions than in VOI
decisions. We also estimate the contribution of the other-regarding and the
Kantian motivation to the subjects’ decisions, and examine whether the
estimated motivations differ in the market and the neutral frames.

We find that, on average, subjects are significantly more prone to selecting
the selfish option in the market frame compared to the neutral frame. In line
with our theoretical prediction, they also select the selfish option
significantly more often in non-VOI than in VOI decision situations. Comparing
the effect of the VOI frame on the propensity to select the selfish decision
between the market frame and the neutral frame, the magnitude in absolute
terms is larger in the market frame, but the effect of VOI relative to non-VOI
decisions is the same in both frames. The effects of the market frame and of
the VOI frame are larger in magnitude in the within-subjects regressions,
suggesting significant heterogeneity across subjects.

We also structurally estimate the preference parameters $\beta$ and $\kappa$,
in line with several earlier experimental studies (e.g., Fisman, Kariv
Markovits (2007); Bruhin, Fehr Schunk (2018); Van Leeuwen Alger ()). A novelty
of our study compared to existing ones, is that we obtain estimates of the
preference parameters in two different frames: the market frame and the
neutral frame. Overall, we find differences in both the degree of aheadness
aversion and the Kantian morality estimates across the market and the neutral
frame, some of the differences being large.

Our study contributes to three strands of the literature. First, it adds
insights into an old question: do market interactions render people more
selfish (Bowles, 1998)? Some authors have relied on lab-in-the-field
experiments to compare the behavior of individuals with varying degrees of
exposure to market interactions (e.g., Henrich . (2005, 2016) and Agneman
Chevrot-Bianco (2022)), while others have used laboratory to examine how
variation in the competition between subjects affects behaviors (e.g.,
Cabrales, Miniaci, Piovesan Ponti (2010); Sutter, Huber, Kirchler, Stefan
Walzl (2020); J. Engel Szech (2020); Dufwenberg, Johansson-Stenman, Kirchler,
Lindner Schwaiger (2022); Bartling Özdemir (2023)). Our experiment explores a
different possibility, by adopting a design where we examine the effect of
framing the task as the sale of an lemon (recall the wording above), compared
to a standard neutral framing, the objective being to quantify the effect of
the market frame (for a recent discussion of the use of frames in economic
experiments, see Alekseev, Charness Gneezy (2017)). Our hypothesis is close to
that put forward elsewhere (e.g., Bowles (1998); Kirman Teschl (2010)):
preferences may be endogenous and thus shaped differently by market
interactions than by, say, interactions with friends and family. Some
experiments lend support to the importance of frames, for example the famous
study that showed that a Prisoner’s dilemma generates more cooperation if it
is labeled as “the community game” than if it is called “the Wall street game”
(Liberman, Samuels Ross, 2004) (see also (Kay Ross, 2003)). Other studies,
however, found small or no effects (Dreber, Ellingsen, Johannesson Rand, 2013;
Dufwenberg, Gächter Hennig-Schmidt, 2011). By contrast to other studies that
use some kind of market-related framing (e.g., Dufwenberg, Gächter Hennig-
Schmidt (2011); Thöni Gächter (2015)), our instructions to the participants
explicitly describe an action as the sale of a lemon, and we compare behavior
under this wording to behavior under neutral wording. Furthermore, our design
allows us to compare the estimated preference parameters between the neutral
and the market frames.

Second, our study adds to a recent set of experiments that have sought to
detect moral concerns as drivers of behavior, where moral concerns are
distinct from distributional preferences (Capraro Rand, 2018; Bursztyn,
Fiorin, Gottlieb Kanz, 2019; Miettinen, Kosfeld, Fehr Weibull, 2020; Chen
Schonger, 2022; Feess, Kerzenmacher Timofeyev, 2022; Bénabou, Falk, Henkel
Tirole, 2023, 2024; Van Leeuwen Alger, ). The most closely related experiments
are those by Miettinen, Kosfeld, Fehr Weibull (2020) and Van Leeuwen Alger (),
who also posit a utility function with a Kantian moral concern à la Homo
moralis (Alger Weibull, 2013), and who also seek to disentangle this concern
from distributional preferences. Our study differs from both of them in two
important ways. First, while they only use neutral wording, our experimental
design allows us to test whether preferences differ between the neutral and
the market frame. Second, they rely exclusively on what we call VOI decisions;
our comparison of decisions in VOI and non-VOI decisions gives an indication
about the extent to which the explicit mention of the role uncertainty helps
trigger the Kantian moral concern.

This last point brings us to the third literature to which our study
contributes: that which examines if decisions depend on whether they are
elicited using the direct-response or the strategy method — i.e., whether they
are taken after or before subjects learn the role distribution. Several
studies find that decisions differ significantly, while other studies find
mixed or no effects (see the surveys by Brandts Charness (2011) and Grech, Nax
Soos (2022)). In the studies where an effect appears, decisions taken behind
the veil of ignorance tend to be more pro-social on average (Sutter Weck-
Hannemann, 2003; Iriberri Rey-Biel, 2011; Huang, Greene Bazerman, 2019; S.
Levine, Kleiman-Weiner, Schulz, Tenenbaum Cushman, 2020; García-Pola, Iriberri
Kovářík, 2020; Ortiz-Riomalo, Koessler Engel, 2021; Herne, Hietanen,
Lappalainen Palosaari, 2022). Our findings are in line with this empirical
regularity. But while previous studies have sought to explain the greater pro-
sociality in decisions taken under role uncertainty by referring mainly to
aspects of the decision-making process (e.g., number of decisions taken,
complexity of the information presented, likelihood that emotions are
triggered by the task), our experiment relies on a preference-based theory
which predicts and thus explains it. According to our theory, the explicit
mention of role uncertainty awakens or reinforces a Kantian moral concern,
which triggers pro-social behavior in our dictator game design.

The next section describes the experimental design and procedures, and in
Section 3 we test whether behaviors are different under the market and the
neutral frames, and under the VOI and the non-VOI frames. In Section 4 we
report the results from the structural estimations, and Section 5 concludes.

##  2 The experiment

###  2.1 Game protocols and preferences

In the experiment subjects are matched into pairs to play anonymous one-shot
interactions. In each interaction one subject is assigned to the Player 1 role
and the other to the Player 2 role, each role assignment being equally likely.
Player 1 has to choose between two actions, call them $X$ and $Y$. If she
chooses $X$, both individuals obtain their initial endowment, denoted $e_{1}$
and $e_{2}$, respectively, while if she chooses $Y$, she gets $e_{1}+G>e_{1}$
while Player 2 gets $e_{2}-L<e_{2}$, where $e_{1}>e_{2}$, $G>0$, and $L>0$.
That is, Player 1 makes a net gain $G$ from choosing $Y$ rather than $X$,
while this choice entails a net loss $L$ for Player 2. Note that whether she
chooses $X$ or $Y$, Player 1 receives a higher payoff than Player 2. The game
tree for any matched pair of subjects—here called $i$ and $j$— is depicted in
Figure 1.

NatureSubject $i$ is Player 1$(e_{1},e_{2})$X$(e_{1}+G,e_{2}-L)$Y$1/2$Subject
$j$ is Player 1$(e_{2},e_{1})$X$(e_{2}-L,e_{1}+G)$Y$1/2$ Figure 1: The game
protocol used in the experiment. The payoff vectors state subject $i$’s and
$j$’s payoff as the first and second component, respectively.

We use two frames in the experiment: the Neutral frame and the Market frame.
In the latter, we replace Player 1 by Seller and Player 2 by Buyer, and we
describe actions $X$ and $Y$ by Not Sell and Sell, respectively. Indeed, the
payoff structure captures in a stylised manner the case where a seller must
decide whether to sell a low-quality item to a buyer in exchange for the going
(fixed) price in the market—a classic “lemons” situation, in which $Y$ amounts
to selling and $X$ to keeping the good. The Buyer has no say here: we adopt
this simplification to concentrate on the Seller’s willingness to sell a
lemon. To facilitate the exposition, we will henceforth use the term Selfish
option to refer to Sell in the Market frame and action $Y$ in the Neutral
frame, and the term Status quo option to refer to Not Sell in the Market frame
and action $X$ in the Neutral frame. We will also refer to the two roles as
the active and the passive roles.

In addition to the aforementioned frames, we vary the information that
subjects receive about the decision situation. In the VOI decision situation
subjects are asked to state their choice in the active role before learning
the role distribution. Participants are told that they have an equal chance of
being cast in either role. Being thus explicitly informed about the whole game
tree in Figure 2, they are led to reason behind the “Veil of Ignorance” (VOI)
with respect to the role distribution. In the non-VOI decision situation, a
subject is asked to state her decision after being informed that she is in the
active role and is therefore not behind the “Veil of Ignorance”. Hence, even
though the game actually being played between any given matched subject pair
in the experiment is the one depicted in Figure 2 (since each subject stands
an equal chance of being handed the active role), in a non-VOI decision
situation a subject cast in the active role is given explicit information from
us only about part of the game tree (the part highlighted in red in Figure 2,
should the active player be if $i$ is the active subject).

NatureSubject $i$ is Player 1$(e_{1},e_{2})$Y$(e_{1}+G,e_{2}-L)$X$1/2$Subject
$j$ is Player 1 $(e_{2},e_{1})$Y$(e_{2}-L,e_{1}+G)$X$1/2$ Figure 2: The
matched subjects ($i$ and $j$) are informed about the whole game tree in a VOI
decision, but only about the realized role distribution in a non-VOI decision
(if the Player 1 role is assigned to subject $i$, she only receives
information about the part highlighted in red).

The decisions being anonymous and one-shot, the experimental design removes
motivations such as social image concerns, repeated interaction, and
reciprocity effects. Hence, a subject’s decision should be driven by his or
her intrinsic preferences and beliefs. We posit a utility function that
combines material self-interest, other-regard, and a Kantian moral concern. As
we will now show the key point is that the Kantian moral concern is expected
to be fully triggered in a VOI decision situation, but only partially so or
not at all in a non-VOI decision situation. The reason is that in the former,
subjects explicitly take into account the possibility that they may end up in
either role, while in the latter individuals are either fully unaware or only
partially aware of the arbitrariness of the role allocation.

Formally, consider subjects $i$ and $j$ and denote their decisions by
$x\in\\{0,1\\}$ and $y\in\\{0,1\\}$ respectively, where $x=1$ if $i$ chooses
the selfish option and $x=0$ if she selects the status quo option when active,
while $y=1$ if $j$ selects the selfish option and $y=0$ if she chooses the
status quo option when active. We posit the following (expected) utility
obtained by $i$ in the VOI decision situation:

$\displaystyle U_{i}\left(x,y\right)$ $\displaystyle=$
$\displaystyle(1-\kappa_{i})\cdot\Big{[}\frac{1}{2}\cdot(e_{1}+xG)+\frac{1}{2}\cdot(e_{2}-yL)\Big{]}$
$\displaystyle+$
$\displaystyle\kappa_{i}\cdot\Big{[}\frac{1}{2}\cdot(e_{1}+xG)+\frac{1}{2}\cdot(e_{2}-xL)\Big{]}$
$\displaystyle-$
$\displaystyle\frac{1}{2}\cdot\beta_{i}\cdot[e_{1}-e_{2}+x(G+L)]$
$\displaystyle-$
$\displaystyle\frac{1}{2}\cdot\alpha_{i}\cdot[e_{1}-e_{2}+y(G+L)]$

The term inside the square brackets in the first line is $i$’s expected
material payoff: with probability $1/2$ $i$ gets the active role, and $i$’s
decision $x$ determines his material payoff; with probability $1/2$ $j$ gets
the active role, and $j$’s decision $y$ determines $i$’s material payoff. The
term inside the square brackets in the second line captures $i$’s Kantian
moral concern: it is the expected material payoff that $i$ (in fact, any
subject in this interaction) would get if—hypothetically—the strategy $x$ was
universalized. The parameter $\kappa_{i}\in[0,1]$ is $i$’s degree of
morality.111Mathematically, the Kantian moral concern in Homo moralis
preferences has a similar effect as the false belief that one’s action affects
the action of the opponent, a belief known as magical (or quasi-magical)
thinking (Shafir Tversky, 1992; Daley Sadowski, 2017). Moreover, under the
special case of Homo moralis preferences where the degree of morality $\kappa$
is equal to one, predictions based on Nash equilibrium play sometimes coincide
with predictions based on Kantian equilibrium, an equilibrium concept
introduced by Roemer (2010). The preference-based approach adopted here
presents several advantages: it avoids reliance on false beliefs, it allows
for intermediate degrees of Kantian concern, it allows for the co-existence of
other-regarding and Kantian concerns, and it allows for the adoption of the
standard Nash equilibrium concept.

The third line measures the effects on $i$’s utility of being materially ahead
of the other when in the active role, while the last line measures the effects
on $i$’s utility of being materially behind the other when in the passive
role. The parameter $\beta_{i}$ represents $i$’s aheadness aversion (if
$\beta_{i}>0$) or love for aheadness (if $\beta_{i}<0$), where aheadness means
that $i$’s monetary payoff is larger than $j$’s.

Similarly, the parameter $\alpha_{i}$ represents $i$’s behindness aversion (if
$\alpha_{i}>0$) or love for behindness (if $\alpha_{i}<0$), where behindness
means that $i$’s monetary payoff is smaller than $j$’s. While it is natural to
include attitudes towards being both ahead and behind materially, in our
experimental design any active decision-maker is always ahead of the other,
passive subject; hence we henceforth omit the last term of the utility
function.222The utility function in (2.1) is the same as that posited in the
main analysis of (Van Leeuwen Alger, ). Moreover, such a utility function,
which describes the individual’s preferences over (potentially trivial)
material payoffs, has been shown to be favored by evolution by natural
selection (Alger, Weibull, and Lehmann, 2020).

To see why the Kantian moral concern is expected to generate different
decisions in the VOI and the non-VOI decision situations, consider now a
subject in a non-VOI decision situation, cast in the active role. Assuming
that this subject is completely unaware of the fact that she could have been
allocated to the passive role instead, her utility reduces to:

$\displaystyle
V_{i}\left(x,y\right)=e_{1}+xG-\beta_{i}\cdot[e_{1}-e_{2}+x(G+L)].$ (2)

Comparing this with (2.1), we see that as long as $L>0$, a subject may select
a different $x$ in VOI and non-VOI decisions if $\kappa_{i}>0$. Specifically,
Kantian morality reduces the subject’s willingness to select the selfish
decision: from (2.1) and (2), we see that she is willing to select the selfish
decision if

$G\geq\beta_{i}(G+L)$ (3)

in the non-VOI decision situation, but only if

$G-\kappa_{i}L\geq\beta_{i}(G+L)$ (4)

in the VOI decision situation. The VOI decision situation renders explicit the
fact that the selfish decision would hurt her if she were cast in the passive
rather than the active role and her strategy was universalized to the other
individual.

Importantly for our experiment, these two conditions imply that for a given
payoff vector $(e_{1},e_{2},e_{1}+G,e_{2}-L)$ a subject makes a switch from
the selfish action in the non-VOI decision to the status quo act in the VOI
decision situation if her aheadness aversion $\beta_{i}$ is not too
pronounced,

$\beta_{i}\leq\frac{G}{G+L}\equiv z,$ (5)

and her Kantian moral concern $\kappa_{i}$ is sufficiently pronounced,

$\kappa_{i}\geq\frac{G-\beta_{i}(G+L)}{L}=\frac{z-\beta_{i}}{1-z}.$ (6)

Figure 3 shows these threshold values for two payoff configurations used in
the experiment: the solid vertical and oblique lines show, respectively, the
threshold values $z$ and $(z-\beta_{i})/(1-z)$ for payoff configuration
$(e_{1},e_{2},e_{1}+G,e_{2}-L)=(150,100,165,90)$; the dashed lines for payoff
configuration $(e_{1},e_{2},e_{1}+G,e_{2}-L)=(200,190,210,100)$. To see how
variation of the payoffs enables to distinguish between different preference
types, note that the two solid lines and the two dashed lines divide the
$(\beta,\kappa)$-space into six regions. An individual with a preference type
$(\beta_{i},\kappa_{i})$:

  * •

in region A always selects the selfish option;

  * •

in region B selects the selfish option under payoff $(150,100,165,90)$, but
makes a switch under payoff $(200,190,210,100)$;

  * •

in region C makes a switch under both payoffs;

  * •

in region D selects the selfish option under payoff $(150,100,165,90)$, but
always the status quo option under payoff $(200,190,210,100)$;

  * •

in region E makes a switch under payoff $(150,100,165,90)$, but always selects
the status quo option under payoff $(200,190,210,100)$;

  * •

in region F always selects the status quo option.

$0.1$$0.6$$0.11$$1.5$EFDABC$\beta$$\kappa$
Figure 3:  Threshold values $z$ and $(z-\beta_{i})/(1-z)$ for payoffs
$(e_{1},e_{2},e_{1}+G,e_{2}-L)=(150,100,165,90)$ (solid) and
$(e_{1},e_{2},e_{1}+G,e_{2}-L)=(200,190,210,100)$ (dashed)

In the experiment we use 20 different payoffs $(e_{1},e_{2},e_{1}+G,e_{2}-L)$.
Table 1 shows, for each of these 20 payoffs, the threshold values $z$ and
$(z-\beta)/(1-z)$ (see (5) and (6)). Note that the 20 payoff configurations
used in the experiment entail 20 different values of $z$. Let $Z$ denote the
set of these 20 values. Figure 4 shows the lines $\kappa=(z-\beta)/(1-z)$ for
the 20 values of $z$. Except for individuals with $\beta_{i}\geq 0.6$ (who
always select the status quo option for all payoffs), and those with
$\beta_{i}\leq 0.03$ and $\kappa_{i}\leq 0.03-1.03\beta_{i}$ (who always
select the selfish option for all payoffs), these payoffs are expected to
generate behavioral variation across payoffs and/or across the two conditions
(VOI vs. non-VOI).

The payoffs were chosen so as to maximize statistical power in the between-
subject estimation of the VOI treatment’s effects (conditional on the fact
that we decided to have no more than 20 decisions in each sequence), where
power computations were based on the results presented by Van Leeuwen Alger
(). More precisely, we carried out 1000 simulations where in each one, the 109
subjects for which Van Leeuwen Alger () estimate individual preference
parameters were randomly assigned to either VOI (55 participants) or non-VOI
(54 people). Then, we computed their decisions based on the
$(\beta,\kappa)$-estimates obtained by Van Leeuwen Alger (), assuming that
subjects in the non-VOI treatment where completely unaware of the possibility
of role reversal. Finally, we estimated the effect of the VOI treatment on the
decision to sell, through the specifications presented in Section 3. The
results of this exercise indicate that the chosen payoffs generate behavioral
variations that our main specification is able to detect a significant effect
(at a 5% confidence level) 75% of the time.

Table 1: Payoff combinations $(e_{1},e_{2},e_{1}+G,e_{2}-L)$ (first four columns) and (approximate) threshold values for $\beta$ and $\kappa$ as per equations (5) and (6) (last two columns) Payoff number ($s$) | $e_{1}$ | $e_{2}$ | $e_{1}+G$ | $e_{2}-L$ | $z=G/(G+L)$ | $(z-\beta_{i})/(1-z)$
---|---|---|---|---|---|---
1 | 150 | 100 | 165 | 90 | 0.6 | $1.5-\beta_{i}\cdot 2.5$
2 | 150 | 100 | 160 | 90 | 0.5 | $1-\beta_{i}\cdot 2$
3 | 150 | 100 | 165 | 80 | 0.43 | $0.75-\beta_{i}\cdot 1.75$
4 | 150 | 100 | 165 | 70 | 0.33 | $0.5-\beta_{i}\cdot 1.5$
5 | 250 | 240 | 300 | 100 | 0.26 | $0.36-\beta_{i}\cdot 1.36$
6 | 250 | 240 | 300 | 90 | 0.25 | $0.33-\beta_{i}\cdot 1.33$
7 | 250 | 240 | 300 | 80 | 0.24 | $0.31-\beta_{i}\cdot 1.31$
8 | 250 | 240 | 300 | 70 | 0.23 | $0.29-\beta_{i}\cdot 1.29$
9 | 250 | 240 | 300 | 60 | 0.22 | $0.28-\beta_{i}\cdot 1.28$
10 | 150 | 120 | 170 | 20 | 0.17 | $0.2-\beta_{i}\cdot 1.2$
11 | 200 | 190 | 220 | 60 | 0.13 | $0.15-\beta_{i}\cdot 1.15$
12 | 200 | 190 | 220 | 50 | 0.125 | $0.14-\beta_{i}\cdot 1.14$
13 | 200 | 190 | 210 | 100 | 0.1 | $0.11-\beta_{i}\cdot 1.11$
14 | 200 | 190 | 210 | 90 | 0.09 | $0.1-\beta_{i}\cdot 1.1$
15 | 200 | 190 | 210 | 80 | 0.08 | $0.09-\beta_{i}\cdot 1.09$
16 | 250 | 220 | 265 | 20 | 0.07 | $0.08-\beta_{i}\cdot 1.08$
17 | 250 | 220 | 260 | 50 | 0.06 | $0.06-\beta_{i}\cdot 1.06$
18 | 250 | 220 | 260 | 10 | 0.05 | $0.05-\beta_{i}\cdot 1.05$
19 | 250 | 220 | 260 | 5 | 0.04 | $0.05-\beta_{i}\cdot 1.05$
20 | 250 | 220 | 255 | 60 | 0.03 | $0.03-\beta_{i}\cdot 1.03$
$-0.3$$0.5$$0.5$$1$$\beta$$\kappa$ Figure 4:  Each line represents the
threshold value $\bar{\kappa}(\beta)$ for one of the 20 payoffs.

In the analysis above we assumed that in a non-VOI decision situation a
subject is completely unaware of the complete game tree. However, some
subjects may in fact understand that they could have been in their
counterpart’s place with some non-null probability. The following equation
shows the utility of a subject $i$ who assigns a probability $\hat{p}_{i}$
that she was cast in the Seller role.

$\displaystyle\hat{U}_{i}\left(x,y\right)$ $\displaystyle=$
$\displaystyle(1-\kappa_{i})\cdot\Big{[}\hat{p}_{i}\cdot(e_{1}+xG)+(1-\hat{p}_{i})\cdot(e_{2}-yL)\Big{]}$
$\displaystyle+$
$\displaystyle\kappa_{i}\cdot\Big{[}\hat{p}_{i}\cdot(e_{1}+xG)+(1-\hat{p}_{i})\cdot(e_{2}-xL)\Big{]}$
$\displaystyle-$
$\displaystyle\hat{p}_{i}\cdot\beta_{i}\cdot[e_{1}-e_{2}+x(G+L)]$
$\displaystyle-$
$\displaystyle(1-\hat{p}_{i})\cdot\alpha_{i}\cdot[e_{1}-e_{2}+y(G+L)]$

The expressions in (2.1) and (2) correspond to the special cases
$\hat{p}_{i}=1/2$ and $\hat{p}_{i}=1$, respectively. We will say that a
subject with $\hat{p}_{i}=1$ is fully unaware, that one with
$\hat{p}_{i}\in(1/2,1)$ is partially aware, and that one with
$\hat{p}_{i}=1/2$ is fully aware of the full game tree.

A partially aware subject switches from the selfish option in the non-VOI
decision situation to the status quo option in the VOI decision situation if

$\beta_{i}<\dfrac{G+\frac{1-\hat{p}_{i}}{\hat{p}_{i}}\kappa_{i}L}{G+L}$ (8)

and

$\kappa_{i}>\frac{G-\beta_{i}(G+L)}{L}.$ (9)

Our experiment is based on the premise that in the non-VOI decisions at least
some subjects are partially aware or fully unaware, i.e., they hold some
belief $\hat{p}>1/2$, since a fully aware subject would never switch (i.e.,
conditions (8) and (9) are incompatible). We deliberately chose not to elicit
the subjects’ beliefs $\hat{p}$: eliciting them prior to the presentation of
the decision situations might have affected the choices, and eliciting them
afterwards would make no sense since the subjects would then have been exposed
already to VOI decisions, in which they are told explicitly that both role
distributions are equally likely.

###  2.2 Treatments

Each subject is asked to state his/her choice in two sequences, both of which
consist of the same set of 20 payoff configurations, listed in Table 1. Each
sequence of 20 decisions is either entirely non-VOI or entirely VOI.

Clearly, to avoid raising awareness of the arbitrariness of the role
distribution in the non-VOI decision situations, subjects should be presented
a non-VOI sequence before being exposed to a VOI sequence. However, always
letting a VOI sequence be preceded by a non-VOI one may lead to anchoring
and/or fatigue effects. To deal with these issues, and to enable meaningful
comparisons between the Neutral and the Market frames, we adopt the following
four treatments:

  1. 1.

Neutral (N): Non-VOI + VOI — neutral frame

  2. 2.

Market (M): Non-VOI + VOI — market frame

  3. 3.

Mixed Frame A (A): VOI neutral frame + VOI market frame

  4. 4.

Mixed Frame B (B): VOI market frame + VOI neutral frame

These treatments allow us to carry out a large number of comparisons: (1) a
within-subject comparison of decisions in the non-VOI and VOI sequences in the
Neutral treatment; (2) a within-subject comparison of decisions in the non-VOI
and VOI sequences in the Market treatment; (3) a between-subject comparison of
decisions in the non-VOI sequence in the Neutral treatment and the first
sequence in the Mixed Frame A treatment; (4) a between-subject comparison of
decisions in the non-VOI sequence in the Market treatment and the first
sequence in the Mixed Frame B treatment; and (5) within-subject comparisons of
decisions in differently framed VOI sequences, using treatments Mixed Frame A
and Mixed Frame B.

For further use below, let $D$ denote the set of eight decision sequences in
our four treatments: $D=\\{N1,N2,M1,M2,A1,A2,B1,B2\\}$, where a 1 refers to
the first sequence and a 2 to the second sequence of 20 decisions in the
treatment.

###  2.3 Procedures and data

We conducted the experiment between November 2021 and March 2022 at the
Toulouse School of Economics TSE Lab for Experimental Social Sciences. The
software oTree was used to program the experiment, and participants were
recruited via email using the Laboratory’s participant pool (people who had
signed up to be informed of laboratory experiments). Overall, we recruited 453
participants, all of whom participated in only one session. Tables A.1 and A.2
in Appendix A1 provide information on the total number of subjects in each
treatment and associated summary statistics. The experiment (the design and
the empirical analysis reported below) was pre-registered on aspredicted.org
on November 21st 2021, the first session taking place on November 22nd.

Each session was allocated to one treatment, and lasted between 25 and 45
minutes. After being randomized to the lab booths, participants read the
instructions on their desktop computer screens, were allowed to ask questions
privately, and completed a comprehension test. Then the two sequences of 20
decisions were presented, upon which the participants filled out a post-
experiment questionnaire (with questions on sex, age, nationality, and field
of study). The English version of the instructions are included in Appendix A3
(the experiment was conducted in French). In each decision situation the
participants had to answer questions about the payoffs correctly before being
allowed to state their decision (see an example of the two screens shown for
one decision in Appendix A3.4).

The participants’ payoffs were determined based on two randomly drawn matches,
one from each sequence. Each unit of the payoffs used in the instructions (see
Table 1) was converted to 2.5 eurocents. For VOI decisions, for the randomly
drawn match the role distribution was determined randomly, and the payoffs
were calculated based on the active subject’s decision. For non-VOI decisions,
for the randomly drawn match each subject received the payoff corresponding to
their decision, and also the payoff corresponding to the other’s decision (of
which they were not aware during the experiment). Participants who answered
all the questions received a show-up fee of 4 euros. Together with the payoffs
obtained from the tasks, participants earned on average 15.5 euros.
Participants privately received their payoffs in cash, upon which they left
the premises.

Table 2 below and Figures A.1-A.3 in Appendix A1 give a first glimpse of the
data, here pooled for all the $n=453$ participants’ decisions across all the
treatments and sessions. In the figures we define one observation as the
number of selfish options selected by one participant in one sequence of 20
decisions (there are thus two observations per participant). Figure A.1 shows
the observations from the Neutral frame on the left and those from the Market
frame on the right. The dashed (resp. solid) horizontal lines show the average
(resp. median) number of selfish choices. It appears that, overall, the
participants were more prone to select the selfish option in the Market than
in the Neutral frame. Figure A.2 compares the cumulative distributions of the
observations in the VOI decision sequences (solid line, $n=686$) and the
observations in the non-VOI decision sequences (dashed line, $n=220$).
Participants were more prone to select the selfish option in the non-VOI than
in the VOI decision situations. Figure A.3 confirms these two tendencies by
splitting the observations into the four different frame combinations: Neutral
VOI (black line), Market VOI (red line), Neutral non-VOI (blue line), and
Market non-VOI (green line). Finally, Table 2 provides the summary statistics
on the number of selfish decisions: in the first two rows for the Neutral and
the Market frames, followed by two rows for the VOI and non-VOI frames, and
finally the four different frame combinations. We see that the differences in
the total number of selfish decisions between the Neutral and the Market
frame, and between the VOI and non-VOI frame, are significantly different from
zero.

Table 2: Total number of selfish decisions per sequence of 20 decisions, by frame Frame | N | Mean | Median | Min | Max | Q1 | Q3
---|---|---|---|---|---|---|---
Neutral | 449 | 7.08 | 6 | 0 | 20 | 2 | 10
Market | 457 | 9.03 | 9 | 0 | 20 | 4 | 13
t-test and Wilcoxon test p-values |  | 0.00 | 0.00 |  |  |  |
VOI | 686 | 7.35 | 7 | 0 | 20 | 3 | 10
non-VOI | 220 | 10.30 | 10 | 0 | 20 | 6 | 15
t-test and Wilcoxon test p-values |  | 0.00 | 0.00 |  |  |  |
VOI and Neutral | 341 | 6.52 | 6 | 0 | 20 | 2 | 9
VOI and Market | 345 | 8.17 | 8 | 0 | 20 | 4 | 11
non-VOI and Neutral | 108 | 8.86 | 8 | 0 | 20 | 4 | 13
non-VOI and Market | 112 | 11.7 | 11 | 0 | 20 | 8 | 17

##  3 Hypothesis testing

In this section we perform regressions to test whether the Market and VOI
frames have significant effects on behavior in the experiment.

###  3.1 Does the Market frame significantly affect behavior?

To assess the effects of the Market frame we estimate linear models of the
form:

${x}_{izd}=\lambda_{0}+\lambda_{1}z+\lambda_{2}M_{izd}+\upsilon_{izd}$ (10)

where ${x}_{izd}\in\\{0,1\\}$ is the decision of subject $i$ in payoff
configuration $z=G/(G+L)$ in sequence $d\in\\{N1,N2,M1,M2,A1,A2,B1,B2\\}$,
where ${x}_{izd}=1$ if she chooses the selfish option and ${x}_{izd}=0$
otherwise; $M_{izd}$ is a dummy variable that takes value 1 if the frame is
Market and 0 if the frame is Neutral; and $\upsilon_{izd}$ is a mean-zero
random variable assumed to be uncorrelated with $z$ and $M_{izd}$. Our null
hypothesis is then that $\lambda_{2}=0$. Recalling also condition (5) for a
(fully unaware subject $i$ to choose the selfish option in a non-VOI decision,
our theory predicts that ceteris paribus an increase in $z=G/(G+L)$ should
lead to a higher propensity to choose the selfish option, and hence we
conjecture that $\lambda_{1}>0$. Because the values of $z$ are not uniformly
spaced, we also run regressions where instead of including $z$ as an
independent variable we include payoff fixed effects.

[b]  Dependent Variable: Sell (Yes or No) Model: (1) (2) (3) (4) (5) (6)
_Variables_ Frame 0.1413∗∗∗ 0.1413∗∗∗ 0.1413∗∗∗ 0.1413∗∗∗ 0.1413∗∗∗ 0.1425∗∗∗
(0.0149) (0.0144) (0.0396) (0.0149) (0.0396) (0.0403) z 0.8027∗∗∗ 0.8027∗∗∗
(0.0461) (0.0655) Socio-dem. controls No No No No No Yes Clustering of SE No
No Subject No Subject Subject and Payoff _Fixed-effects_ Payoff Yes Yes Yes
_Fit statistics_ Observations 4,400 4,400 4,400 4,400 4,400 4,400 R2 0.01998
0.08323 0.08323 0.09530 0.09530 0.10272 Within R2 0.02161 0.02161 0.02964
_Signif. Codes: ***: 0.01, **: 0.05, *: 0.1_

Table 3:  Effect of Frame on Selling under non-VOI conditions - between
subjects (N1-M1)

  * •

Note: Column (3) includes clustering of SE’s at the Subject level, column (4)
includes Payoff fixed effects, column (5) includes Payoff fixed effects with
clustering of SE’s at the Subject level, column (6) includes Payoff fixed
effects with clustering of the SE’s at the Subject and Payoff level. Socio-
demographic controls are: nationality, gender, couple status, previous
experience in experiments and attendance to a private school.

Overall, the results reported in Tables 3-5 show that subjects are more prone
to selecting the selfish option under the Market than under the Neutral frame,
that this effect is more pronounced in non-VOI than in VOI decisions, and that
within-subject effects are stronger than between-subject effects in VOI
decisions.

Specifically, Table 3 shows between-subject effects of the Market frame in
non-VOI decisions, based on the decisions in the first decision sequences of
the Neutral and the Market treatments (N1 and M1). The Market frame reduces
the propensity to select the selfish option, an effect which is consistent in
magnitude at 14.13 percentage points, and consistently significant across
specifications.

[b]  Dependent Variable: Sell (Yes or No) Model: (1) (2) (3) (4) (5) (6)
_Variables_ Frame 0.0319∗∗ 0.0319∗∗ 0.0319 0.0319 0.0319 0.0488 (0.0142)
(0.0138) (0.0367) (0.0185) (0.0368) (0.0362) z 0.7354∗∗∗ 0.7354∗∗∗ (0.0442)
(0.0546) Socio-dem. controls No No No No No Yes Clustering of SE No No Subject
No Subject Subject and Payoff _Fixed-effects_ Payoff Yes Yes Yes _Fit
statistics_ Observations 4,660 4,660 4,660 4,660 4,660 4,660 R2 0.00107
0.05714 0.05714 0.06439 0.06439 0.08209 Within R2 0.00114 0.00114 0.02004
_Signif. Codes: ***: 0.01, **: 0.05, *: 0.1_

Table 4:  Effect of Frame on Selling under VOI conditions - between subjects
(A1-B1)

  * •

Note: Column (3) includes clustering of SE’s at the Subject level, column (4)
includes Payoff fixed effects, column (5) includes Payoff fixed effects with
clustering of SE’s at the Subject level, column (6) includes Payoff fixed
effects with clustering of the SE’s at the Subject and Payoff level. Socio-
demographic controls are: nationality, gender, couple status, previous
experience in experiments and attendance to a private school.

Table 4 also shows between-subject effects of the Market frame, but now in VOI
decisions, based on regressions using only the first decision sequences of
Mixed Frame A and Mixed Frame B (A1 and B1): these subjects would thus not
have been exposed to the other frame. The effect of the Market frame is much
smaller in magnitude than for the non-VOI decisions, and also insignificant as
soon as subject-specific and/or payoff-specific effects are accounted for.

[b]  Dependent Variable: Sell (Yes or No) Model: (1) (2) (3) (4) (5) (6)
_Variables_ Frame 0.0700∗∗∗ 0.0700∗∗∗ 0.0700∗∗∗ 0.0700∗∗∗ 0.0700∗∗∗ 0.0700∗∗∗
(0.0100) (0.0096) (0.0181) (0.0096) (0.0181) (0.0082) z 0.8806∗∗∗ 0.8806∗∗∗
(0.0308) (0.0513) Socio-dem. controls No No No No No Yes Clustering of SE No
No Subject No Subject Subject and Payoff _Fixed-effects_ Sub. Yes Payoff Yes
Yes Sub.-Payoff Yes _Fit statistics_ Observations 9,320 9,320 9,320 9,320
9,320 9,320 R2 0.00517 0.08557 0.33028 0.09207 0.09207 0.66812 Within R2
0.11329 0.00567 0.00567 0.01535 _Signif. Codes: ***: 0.01, **: 0.05, *: 0.1_

Table 5:  Effect of Frame on Selling under VOI conditions - within subjects
(A1-A2-B1-B2)

  * •

Note: Column (3) includes clustering of SE’s at the Subject level, column (4)
includes Payoff fixed effects, column (5) includes Payoff fixed effects with
clustering of SE’s at the Subject level, column (6) includes Payoff fixed
effects with clustering of the SE’s at the Subject and Payoff level. Socio-
demographic controls are: nationality, gender, couple status, previous
experience in experiments and attendance to a private school.

Finally, Table 5 shows within-subject effects of the Market frame in VOI
decisions, based on decisions in the mixed treatments $A$ and $B$. Framing the
decision as a market interaction on average increases the likelihood of
selecting the selfish option by 7 percentage points, and the significance
level is consistently high across specifications. Although large, this effect
is about half as large as for the non-VOI decisions.

Note that, as expected, the estimate of $\lambda_{1}$ is positive and highly
significant (see columns (2) and (3) in the tables), and that its inclusion
substantially increases R2. The specifications which instead use “payoff fixed
effects” yield similar results.

###  3.2 Does the VOI frame significantly affect behavior?

To evaluate the effects of the VOI frame, we estimate linear models of the
form:

${x}_{izd}=\gamma_{0}+\gamma_{1}z+\gamma_{2}V_{izd}+\epsilon_{izd}.$ (11)

where $V_{izd}$ is a dummy variable that equals 1 if the decision situation is
VOI and 0 if the decision is non-VOI, and $\epsilon_{izd}$ is a mean-zero
random variable, assumed to be uncorrelated with $c$ and $V_{izd}$. Our null
hypothesis is then that $\gamma_{2}=0$. The results, reported in Tables 6-9,
show that this hypothesis is clearly rejected, for both within- and between-
subject analyses, and that the effect of VOI is large.

[b]  Dependent
Variable: Sell (Yes or No) Model: (1) (2) (3) (4) (5) (6) _Variables_ VOI
-0.1528∗∗∗ -0.1528∗∗∗ -0.1528∗∗∗ -0.1528∗∗∗ -0.1528∗∗∗ -0.1528∗∗∗ (0.0145)
(0.0140) (0.0277) (0.0140) (0.0278) (0.0122) z 0.7685∗∗∗ 0.7685∗∗∗ (0.0447)
(0.0828) Clustering of SE No No Subject No Subject Subject*Payoff _Fixed-
effects_ Sub. Yes Yes Payoff Yes Yes Sub.-Payoff Yes _Fit statistics_
Observations 4,320 4,320 4,320 4,320 4,320 4,320 R2 0.02513 0.08750 0.32862
0.09302 0.33415 0.65531 Within R2 0.11530 0.02696 0.03637 0.06795 _Signif.
Codes: ***: 0.01, **: 0.05, *: 0.1_

Table 6:  Effect of VOI on Selling under Neutral frame - within subjects
(N1-N2)

  * •

Note: Column (3) includes Subject fixed effects with clustering of SE’s at the
Subject level, column (4) includes Subject and Payoff fixed effects with
clustering of SE’s at the Subject level, column (5) includes Subject and
Payoff fixed effects with clustering of SE’s at the Subject level, column (6)
includes Subject by Payoff fixed effects with clustering of the SE’s at the
Subject by Payoff level.

Beginning with the neutral frame, Table 6 shows the results based on a within-
subjects comparison ($d\in\\{N1,N2\\})$, while Table 7 reports results from
the between-subjects comparison ($d\in\\{N1,A1\\}$). Independent of the
specification, the effect is highly significant, and on average the VOI frame
reduces the likelihood of selecting the selfish option by 15.28 percentage
points.333This indicates that the within-subjects treatment does not suffer
too much from subjects’ desire to act consistently in the VOI and the non-VOI
decision situations with the same payoff configuration. The between-subjects
effect is smaller, at 7.45 percentage points for most specifications, and also
less robust in terms of significance across the specifications.

[b]  Dependent Variable: Sell (Yes or No) Model: (1) (2) (3) (4) (5) (6)
_Variables_ VOI -0.0745∗∗∗ -0.0745∗∗∗ -0.0745∗ -0.0745∗∗∗ -0.0745∗ -0.0728∗
(0.0145) (0.0141) (0.0395) (0.0143) (0.0396) (0.0403) z 0.6780∗∗∗ 0.6780∗∗∗
(0.0451) (0.0586) Socio-dem. controls No No No No No Yes Clustering of SE No
No Subject No Subject Subject and Payoff _Fixed-effects_ Payoff Yes Yes Yes
_Fit statistics_ Observations 4,580 4,580 4,580 4,580 4,580 4,580 R2 0.00574
0.05257 0.05257 0.05625 0.05625 0.06045 Within R2 0.00604 0.00604 0.01047
_Signif. Codes: ***: 0.01, **: 0.05, *: 0.1_

Table 7:  Effect of VOI on Selling under Neutral frame - between subjects
(N1-A1)

  * •

Note: Column (3) includes clustering of SE’s at the Subject level, column (4)
includes Payoff fixed effects, column (5) includes Payoff fixed effects with
clustering of SE’s at the Subject level, column (6) includes Payoff fixed
effects with clustering of the SE’s at the Subject and Payoff level. Socio-
demographic controls are: nationality, gender, couple status, previous
experience in experiments and attendance to a private school.

One explanation for the difference between the within- and the between-
subjects effect of VOI appears to be heterogeneity across subjects, as
indicated by the fact that the between-subjects effect is only weakly
significant for the specifications which cluster the standard errors at the
subject level.

Turning now to the market frame, Tables 8 and 9 report, respectively, how VOI
affects the propensity to choose Sell within subjects ($d\in\\{M1,M2\\}$) and
between subjects ($d\in\\{M1,B1\\}$). Qualitatively, the within-subjects
results are similar to those in the neutral frame (see Table 6): the effect is
highly significant and identical in size across all specifications. However,
the effect is even stronger than in the neutral frame: the VOI wording reduces
the propensity to sell by 18.66 percentage points. Contrary to the neutral
frame, here the between-subjects effect is similar in size to the within-
subjects effect, and it is also robustly significant across the
specifications. Overall there is thus a large and robust effect of the VOI
wording on the propensity to choose Sell in the market frame.

[b]  Dependent Variable: Sell (Yes or No) Model: (1) (2) (3) (4) (5) (6)
_Variables_ VOI -0.1866∗∗∗ -0.1866∗∗∗ -0.1866∗∗∗ -0.1866∗∗∗ -0.1866∗∗∗
-0.1866∗∗∗ (0.0147) (0.0138) (0.0265) (0.0138) (0.0265) (0.0112) z 1.042∗∗∗
1.042∗∗∗ (0.0442) (0.0913) Clustering of SE No No Subject No Subject
Subject*Payoff _Fixed-effects_ Sub. Yes Yes Payoff Yes Yes Sub.-Payoff Yes
_Fit statistics_ Observations 4,480 4,480 4,480 4,480 4,480 4,480 R2 0.03483
0.14142 0.36707 0.15022 0.37586 0.72045 Within R2 0.18263 0.03938 0.05286
0.11080 _Signif. Codes: ***: 0.01, **: 0.05, *: 0.1_

Table 8:  Effect of VOI on Selling under Market frame - within subjects
(M1-M2)

  * •

Note: Column (3) includes Subject fixed effects with clustering of SE’s at the
Subject level, column (4) includes Subject and Payoff fixed effects with
clustering of SE’s at the Subject level, column (5) includes Subject and
Payoff fixed effects with clustering of SE’s at the Subject level, column (6)
includes Subject by Payoff fixed effects with clustering of the SE’s at the
Subject by Payoff level.

[b]  Dependent Variable: Sell (Yes or No) Model: (1) (2) (3) (4) (5) (6)
_Variables_ VOI -0.1839∗∗∗ -0.1839∗∗∗ -0.1839∗∗∗ -0.1839∗∗∗ -0.1839∗∗∗
-0.1879∗∗∗ (0.0147) (0.0141) (0.0367) (0.0220) (0.0368) (0.0402) z 0.8602∗∗∗
0.8602∗∗∗ (0.0451) (0.0609) Socio-dem. controls No No No No No Yes Clustering
of SE No No Subject No Subject Subject and Payoff _Fixed-effects_ Payoff Yes
Yes Yes _Fit statistics_ Observations 4,480 4,480 4,480 4,480 4,480 4,480 R2
0.03384 0.10642 0.10642 0.11804 0.11804 0.14127 Within R2 0.03695 0.03695
0.06231 _Signif. Codes: ***: 0.01, **: 0.05, *: 0.1_

Table 9:  Effect of VOI on Selling under Market frame - between subjects
(M1-B1)

  * •

Note: Column (3) includes clustering of SE’s at the Subject level, column (4)
includes Payoff fixed effects, column (5) includes Payoff fixed effects with
clustering of SE’s at the Subject level, column (6) includes Payoff fixed
effects with clustering of the SE’s at the Subject and Payoff level. Socio-
demographic controls are: nationality, gender, couple status, previous
experience in experiments and attendance to a private school.

As was the case for the regressions measuring the effects of the Market frame,
the estimate of the coefficient for $z$ is positive and highly significant
(see columns (2) and (3) in the tables), and its inclusion substantially
increases R2. Moreover, the results are similar in the specifications which
instead use “payoff fixed effects”.

###  3.3 Interaction effects

The results reported above show that the VOI frame has a stronger absolute
effect on the propensity to select the selfish option in the Market than in
the Neutral frame. However, from the descriptive statistics we also know that
subjects are on average more willing to choose the selfish option in the
Market than in the Neutral frame. Here we test whether the relative effect of
VOI is different under the two frames, by running regressions with a VOI-
Market interaction, as follows:

${x}_{izd}=\gamma_{0}+\gamma_{1}z+\gamma_{2}V_{izd}+\gamma_{3}V_{izd}\times
M_{izd}+\epsilon_{izd}.$ (12)

The results are reported in Table A.5 in Appendix A1. The coefficient on the
interaction VOI $\times$ Market is not significant, implying that, on average,
the effect of VOI relative to non-VOI is the same in the Market as in the
Neutral frame.

##  4 Structural estimation of $\beta$ and $\kappa$

Besides the reduced-form estimates of treatment effects specified above, we
structurally estimate the aheadness aversion and Kantian morality parameters
$\beta$ and $\kappa$.

###  4.1 Method

We run the estimations using the data obtained from subjects exposed to both
non-VOI and VOI sequences. We will first report the estimates of the
preference parameters, $(\hat{\beta},\hat{\kappa})$, and the choice
sensitivity, $\hat{\sigma}$, under the hypothesis that the observed data
pooled over all the subjects (in a given treatment) had emanated from a
representative agent. For non-VOI decisions we assume that any subject is
fully unaware of the possible role reversal, i.e., we posit $\hat{p}=1$ (see
(2.1)). By contrast, for VOI decisions we assume that any subject is fully
aware of the arbitrariness of the role distribution (by positing
$\hat{p}=1/2$), since we explicitly inform the subjects about it. These
assumptions are extreme, and we do not claim that they are in line with the
subjects’ subjective beliefs about the role distribution. However, the
advantage is that they imply a conservative estimate of $\hat{\kappa}$.
Indeed, under the assumption that $\hat{p}=1$ in non-VOI decisions, only a
positive $\hat{\beta}$ can explain why a subject would refrain from selecting
the selfish option. Hence, for any subject who is in fact fully or partly
aware of the role reversal in the non-VOI decisions (i.e., with
$\hat{p}=1\in[1/2,1)$) and who refrains from the selfish option in such a
decision due to a combination of aheadness attitude and Kantian morality (see
(8)), our assumption that $\hat{\kappa}$ cannot be at work leads to over-
estimation of $\hat{\beta}$ and under-estimation of $\hat{\kappa}$.

We use a standard random utility model, by assuming that the representative
agent’s true utility from using pure strategy $x$ is a random variable of the
form

$\tilde{U}(x;\beta,\kappa,\sigma,z)=2U(x;\beta,\kappa,z)+\varepsilon_{x}$ (13)

in a VOI decision situation with payoff configuration $z\in Z$ and

$\tilde{V}(x;\beta,\sigma,z)=V(x;\beta,z)+\varepsilon_{x}$ (14)

in a non-VOI decision situation with payoff configuration $z\in Z$, where the
function $U(x;\beta,\kappa,z)$ is that specified in (2.1), the function
$V(x;\beta,z)$ is that specified in (2), and $\varepsilon_{x}$ represents
randomness in the utility evaluation, assumed to be independent across all the
decisions. This random variable is taken to follow a type 1 extreme value
distribution with scale parameter $1/\sigma$. The factor 2 in equation (13)
avoids that the magnitude of noise relative to $z$, $\beta$, and $\kappa$ be
twice as large for VOI than for non-VOI decisions (recall the factor 1/2 in
(2.1)).

In a VOI decision situation, the selfish option is selected if

$\tilde{U}(1;\beta,\kappa,\sigma,z)\geq\tilde{U}(0;\beta,\kappa,\sigma,z),$
(15)

or

$2U\left(1;\beta,\kappa,z\right)-2U\left(0;\beta,\kappa,z\right)\geq\varepsilon_{0}-\varepsilon_{1},$
(16)

which reduces to

$G-\kappa L-\beta(G+L)\geq\varepsilon_{0}-\varepsilon_{1}.$ (17)

Likewise, in a non-VOI decision situation, the selfish option is selected if

$\tilde{V}(1;\beta,\sigma,z)\geq\tilde{V}(0;\beta,\sigma,z),$ (18)

or

$V\left(1;\beta,z\right)-V\left(0;\beta,z\right)\geq\varepsilon_{0}-\varepsilon_{1},$
(19)

which reduces to

$G-\beta(G+L)\geq\varepsilon_{0}-\varepsilon_{1}.$ (20)

Letting $\nu$ be a dummy variable that takes the value 1 in a VOI decision
situation and the value 0 in a non-VOI decision situation, it follows that the
probability that the selfish option is selected in payoff configuration $z$
equals (McFadden, 1974):

$\displaystyle\dfrac{1}{1+\exp\bigg{[}-\sigma\big{[}\nu\left[(1-\beta)G-(\beta+\kappa)L\right]+(1-\nu)\left[(1-\beta)G-\beta
L\right]\big{]}\bigg{]}},$ (21)

where $\sigma$ is the representative agent’s choice sensitivity with respect
to differences in deterministic utility. If $\sigma$ is close to 0, the agent
chooses either option with a probability close to 1/2, independent of the
difference in deterministic utilities. In contrast, arbitrarily large values
of $\sigma$ indicate that the probability of choosing the option that results
in the higher deterministic utility approaches 1. Noting that the expression
in (21) can be written as:

$\displaystyle
H\big{(}\sigma\big{[}\nu\left[(1-\beta)G-(\beta+\kappa)L\right]+(1-\nu)\left[(1-\beta)G-\beta
L\right]\big{]}\big{)},$

where $H:\mathbb{R}\rightarrow(0,1)$ is the logistic function, the probability
density function can be written

$\displaystyle f\left(\mathbb{x},\beta,\kappa,\sigma\right)=$
$\displaystyle\prod_{i\in I}\prod_{z_{i}\in
Z}H\left(\sigma\left(U(x^{(z_{i})}=1;\beta,\kappa,z_{i})-U(x^{(z_{i})}=0;\beta,\kappa,z_{i})\right)\right)^{\mathbbm{1}\\{x^{(z_{i})}=1\\}}$
(22)
$\displaystyle\times\left[1-H\left(\sigma\left(U(x^{(z_{i})}=1;\beta,\kappa,z)-U(x^{(z_{i})}=0;\beta,\kappa,z_{i})\right)\right)\right]^{1-\mathbbm{1}\\{x^{(z_{i})}=1\\}},$

where $I$ is the set of subjects, and $z_{i}$ refers to the situation in which
subject $i$ faced payoff $z$: this notation addresses the fact that here all
the subjects’ decisions are pooled and treated as if taken by one single
individual. In the expression, $\mathbb{x}$ is the vector of observed choices,
and $\mathbbm{1}\\{x^{(z_{i})}=1\\}$ is an indicator function that takes the
value 1 if $x^{(z_{i})}=1$ and 0 if $x^{(z_{i})}=0$. Using (22), we obtain the
log-likelihood

$\displaystyle\ln(L(\beta,\kappa,\sigma;\mathbb{x}))=\log(f\left(\mathbb{x},\beta,\kappa,\sigma\right)),$
(23)

and we will report the vector of estimates
$(\hat{\beta},\hat{\kappa},\hat{\sigma})$ that maximizes this log-likelihood.

Second, we use the finite-mixture approach used by Bruhin, Fehr Schunk (2018)
and Van Leeuwen Alger () to evaluate whether the data is reasonably described
by a finite set of estimated preference types, and if so, whether the Kantian
moral concern appears relevant for most of them. This approach consists in
estimating preference parameters for a given number $K$ of “preference types”.
For each type $k=\\{1,...,K\\}$, we estimate the preference parameters
$\left(\beta_{k},\kappa_{k}\right)$ and the noise parameter $\sigma_{k}$. The
log-likelihood is then given by:

$\ln(L(\bm{\beta},\bm{\kappa},\bm{\sigma};\mathbb{x}))=\ln\left(\sum_{k=1}^{K}\phi_{k}\cdot
f(\beta_{k},\kappa_{k},\sigma_{k};\mathbb{x})\right),$ (24)

where $\phi_{k}$ is the share of subjects of type $k$, estimated together with
the preference and noise parameters.

All structural estimations of the preference parameters $\beta$ and $\kappa$
are conducted both for the Neutral frame and for the Market frame, to examine
whether any differences appear. Furthermore, we carry out all our estimation
procedures for the full samples and for two subsets of the latter, which we
deem “Core 1” and “Core 2”. Our Core 1 samples consist of the 98 subjects in
the Neutral treatment and 100 subjects in the Market treatment who made at
least one decision switch between non-VOI and VOI. Recalling that our
theoretical model predicts that a subject should be less inclined to select
the selfish decision in VOI situations, we say that a subject switches in the
expected direction if (s)he selects the selfish option under non-VOI and the
status quo option under VOI, and in the unexpected direction if (s)he selects
the status quo option under non-VOI and the selfish option under VOI. Thus,
for our Core 2 samples we remove subjects who made at least as many unexpected
as expected switches from the Core 1 samples. This leaves us with 68 subjects
in the Neutral treatment and 79 subjects in the Market treatment. In the
following section, all reported estimates are obtained using the Core 1
samples, while results with the full and Core 2 samples can be found in
Appendix A1.

###  4.2 Results

####  4.2.1 Representative agent estimates

Table 10 shows the representative agent estimates of the preference parameters
$\beta$ and $\kappa$, as well as that of the choice sensitivity parameter
$\sigma$ for our Core 1 sample for the Neutral frame (first column) and the
Market frame (second column).

Table 10:  Estimated preferences for the representative agent in Neutral and
Market frames - Core 1 sample

|  Neutral |  Market |  $H_{0}$: Neutral = Market+
---|---|---|---
$\beta$: Aheadness aversion | 0.194∗∗∗ | 0.099∗∗∗ | 0.009
(0.030) - [0.012] | (0.020) - [0.008] |
$\kappa$: Degree of morality | 0.258∗∗∗ | 0.228∗∗∗ | 0.721
(0.075) - [0.032] | (0.037) - [0.018] |
$\sigma$: Choice sensitivity | 0.295∗∗∗ | 0.040∗∗∗ | 0.295
(0.009) - [0.003] | (0.010) - [0.003] |
Number of Observations | 3,600 | 3,720 |
Number of subjects | 90 | 93 |
Log likelihood | -2,196.136 | -2,255.419 |

  * •

Notes: Standard errors clustered at the individual level in parentheses. Non-
clustered standard errors in brackets. ∗∗∗ Significant at 1% using clustered
standard errors.

  * +

$p$-value of $z$-test, using clustered standard errors.

Both the aheadness aversion and the Kantian morality estimates significantly
differ from zero and are positive. They are also both higher in the Neutral
than in the Market frame: $\beta=0.194$ and $\kappa=0.258$ in the Neutral
frame while $\beta=0.099$ and $\kappa=0.228$ in the Market frame. The absolute
difference is thus larger for the aheadness aversion estimates than for the
Kantian morality estimates, and the difference is statistically different only
for the aheadness aversion estimates ($\beta$) as shown in the third column.

The estimates based on the full sample and the Core 2 samples, reported in
Tables A.6-A.7 in Appendix A1, are qualitatively similar, except that for the
Core 2 sample, for which the estimated aheadness aversion parameter is not
significantly different under the Market than under the Neutral frame.444This
result is partly driven by the clustering of the standard errors at the
individual level, as they generate wider confidence intervals compared to non-
clustered standard errors.

####  4.2.2 Finite-mixture estimates

Table 11 presents the finite mixture estimates using a two-type model ($K=2$
in (24)), as well as the shares of subjects who are classified either as Type
1 or as Type 2, depending on the type that is more aligned with their
choices.555The estimates based on the full sample and the Core 2 samples are
reported in Tables A.8-A.9 in Appendix A1. It is worth underscoring that both
types in either frame show some degree of Kantian morality, which is in line
with the significant VOI treatment effects presented in Section 3.

Table 11: Two-type Finite Mixture Model estimates, Core 1 sample

| Neutral | Market
---|---|---
|  Type 1 |  Type 2 |  Type 1 |  Type 2
$\beta$: Aheadness aversion |  0.327∗∗∗ |  -0.065 |  0.203∗∗∗ |  -0.143
|  (0.082) |  (0.258) |  (0.015) |  (0.117)
|  [0.023] |  [0.049] |  [0.007] |  [0.046]
$\kappa$: Degree of morality |  0.116∗∗ |  0.342∗ |  0.153∗∗∗ |  0.325∗∗∗
|  (0.049) |  (0.199) |  (0.023) |  (0.072)
|  [0.029] |  [0.070] |  [0.017] |  [0.056]
$\sigma$: Choice sensitivity |  0.046∗∗∗ |  0.025∗∗ |  0.068∗∗∗ |  0.030∗∗∗
|  (0.009) |  (0.012) |  (0.013) |  (0.010)
|  [0.005] |  [0.004] |  [0.005] |  [0.004]
Share |  0.554∗∗∗ |  0.446∗∗∗ |  0.560∗∗∗ |  0.440∗∗∗
|  (0.173) |  (0.173) |  (0.056) |  (0.056)
|  [0.058] |  [0.058] |  [0.052] |  [0.052]
Observations | 3,920 | 4,000
Number of subjects | 98 | 100
Log likelihood | -2,155.558 | -2,193.214

  * •

Notes: Standard errors clustered at the individual level in parenthesis, non-
clustered standard errors in brackets. ∗∗∗ Significant at 1%, ∗∗ Significant
at 5%, ∗ Significant at 10% using clustered standard errors.

Starting with the Neutral frame, Type 1, which accounts for 55.4% of the
sample, displays aheadness aversion that is almost twice that of the
representative agent (0.327 compared to 0.194), but a Kantian morality that is
only about half as large as the representative agent’s (0.116 compared to
0.238). By contrast, Type 2 (with a share of 44.6%) seems to be indifferent to
advantageous inequality (notice that the -0.065 estimate is not significant at
any of the usual confidence levels) and exhibits a much larger Kantian
morality estimate than the representative agent (0.342 versus 0.258).

Turning now to the Market frame, a similar pattern emerges. We again see a
type (Type 1) who is about twice as aheadness averse as the representative
agent –0.203 compared with 0.099– but less morally concerned, with a Kantian
morality estimate of 0.153 (versus 0.228 for the representative agent). In
addition, we estimates that 56% of subjects in the Market frame belong to Type
1. Type 2 appears to not display aheadness liking nor aversion (the negative
$\beta$ estimate of -0.143 is once more not statistically significant at the
usual levels). Meanwhile, the Kantian morality estimate of this second type is
larger than that of the representative agent (0.325 compared to 0.228). Here,
44% of the subjects are classified as this latter type.

The estimated parameter values described above allow us to endogenously
classify each subject into the preference type that best fits her observed
behaviour. More precisely, using Bayes’ rule, a subject $i$’s posterior
probability of belonging to Type $k=1,2$ is given by:

$\tau_{ik}=\frac{\hat{\pi}_{k}f(\hat{\beta}_{k},\hat{\kappa}_{k},\hat{\sigma}_{k};\mathbb{x}_{i})}{\sum_{m=1}^{2}\hat{\pi}_{m}f(\hat{\beta}_{m},\hat{\kappa}_{m},\hat{\sigma}_{m};\mathbb{x}_{i})}.$
(25)

These probabilities indicate the type with which the participant’s behavior is
the most compatible. A model that manages to capture the preferences and share
of the underlying types should give way to most posterior probabilities being
very close to 0 or to 1. Moreover, the share of subjects classified as
belonging to a given type according to these probabilities should be similar
to that same type’s share estimated by the model. Figure 5 shows that this is
indeed the case here.

Figure 5: Distribution of posterior probabilities of individual type-
membership in Neutral (first row) and Market frames (second row). Core 1
sample estimates.

According to our theory, subjects who are classified as Type 1 should display
behavior that is more consistent across the non-VOI and VOI-treatments, as
indicated by the relatively low value of $\kappa$. By contrast, those who are
classified as Type 2 ought to exhibit less consistent behavior across the non-
VOI and VOI-treatments, as suggested by the high value of $\kappa$. This is
clearly seen in Figure 6, where we show that the difference in the number of
selfish actions in the non-VOI and VOI treatments is more concentrated around
zero for participants classified as Type 1.666We refer to Figures A.4-A.7 in
Appendix A1 for the corresponding analysis for the full and Core 2 samples.

(a) Neutral frame

(b) Market frame

Figure 6: Difference in selfish actions non-VOI versus VOI. Core 1 sample.
Subjects classified as Type 1 if $\tau_{1}>0.95$ or Type 2 if $\tau_{1}<0.05$

We do not report estimates based on more than two types, since our
experimental design is better suited for estimation at the aggregate level
(i.e., treating the observations as if they emanated from one or a small
number of preference types) than for estimation of each individual’s
preference type $(\beta_{i},\kappa_{i})$ (we refer to Appendix A2 for a
detailed explanation).

##  5 Concluding remarks

In order to issue effective and desirable policy recommendations, economists
need to achieve a realistic understanding of the motivations of human
behavior. Indeed, insights about the functioning of markets and institutions
derived in theoretical models where humans are driven solely by material self-
interest may be misleading. We designed an experiment aimed at evaluating the
willingness of subjects to sell a lemon – i.e., a good that makes the buyer
worse off – and to disentangle the role of other-regarding concerns and
Kantian moral concerns in this decision. Our design further allows us to
structurally estimate preference parameters, and to test whether the obtained
estimates differ between decisions taken in a frame that describes the
situation as the sale of a lemon – the market frame – and those taken in a
standard neutral frame.

The subjects in our experiment were on average much more prone to “sell
lemons” in the market frame than they were to select the payoff-equivalent
decisions in the neutral frame. The propensity to sell lemons was, however,
strongly mitigated when the instructions made explicit reference to the
arbitrariness in the role distribution. This result is in line with our
theoretical model, according to which such instructions are expected to awaken
or strengthen subjects’ Kantian moral concerns. As per our structural
estimations of this Kantian moral concern and the aversion towards being ahead
materially compared to the other subject in a match, the difference in
behavior between the market and the neutral frame was driven mainly by a
weaker aheadness aversion in the market frame.

Our results are in line with those of other studies that have also shown that
preferences that combine a Kantian moral concern with other-regard
significantly enhances the explanatory power compared to preferences without
the Kantian moral concern (Miettinen, Kosfeld, Fehr Weibull, 2020; Van Leeuwen
Alger, ). Like Van Leeuwen Alger (), who also estimate aheadness aversion and
Kantian moral preference parameters, we find estimates which indicate that
some individuals appear to be driven by a combination of altruism (a positive
$\beta$) and a Kantian moral concern, and all the estimates of the Kantian
moral concern parameter are positive and significantly so. Interestingly,
under the Market frame some individuals appear to be driven by a combination
of spite (a negative $\beta$) and a Kantian moral concern. This suggests that
in our experiment the Market frame may have triggered more competitive
preferences.

Our study is the first to explore – and find support for – the hypothesis that
the Kantian moral concern is (at least somewhat) muted in situations where
role uncertainty is not made explicit. If externally valid, this result would
mean that the mere mention of role uncertainty in situations outside of the
experimental laboratory could be used to awaken Kantian moral concerns, and
thus trigger behavioral changes. In this line, Bowles (2023) suggests that
indeed, in order to improve the functioning of markets, policies ought to
combine both incentives and moral messages, leveraging synergies between the
two (Kranton, 2019). Field experiments might be appropriate to examine this
question in the future.

Our results further strongly suggest that both frames in our experiment
matter. On average decisions are more selfish under the Market than under the
Neutral frame, and the difference is large. However, the VOI wording also has
a stronger dampening effect on selfish actions under the Market frame.
Accordingly the estimates of the preferences based on subjects’ decisions in
market interactions differ from those based on decisions in interactions with
similar payoff consequences but presented with a neutral frame.

Such disparities in preferences across different social situations would be in
line with the theory of preference evolution, which predicts that as long as
humans in our evolutionary past could discriminate between classes of
interactions (say, market vs non-market), preferences would be expected to
depend on the specifics of each class of interaction (for surveys, see Alger
Weibull (2019) and Alger (2023)). As such, they strengthen the more general
case made elsewhere for further research on how the language used to describe
a task matters for subjects’ decisions (Alekseev, Charness Gneezy, 2017;
Capraro, Halpern Perc, 2024).

As a final note, it is worth mentioning that recent theoretical studies
highlight the role that Kantian moral concerns can play in mitigating
inefficiencies in equilibrium outcomes in bilateral trade situations plagued
by information asymmetries (Rivero-Wildemauwe, 20231, 20232). While our study
focuses solely on the behavior of potential “lemon” sellers, by shutting down
the buyers’ decisions, these references find that morality decreases or even
eliminates the exchange of “lemons” in equilibrium. Moreover, they document
that altruism has effects that go in the same direction, although they are
generally weaker. It would thus be worth extending our present work to capture
interactions between both sides of the market. With both sellers and buyers
being active, beliefs should also play a role; the evidence that preferences
may be affected by framing can help explain why beliefs would also be affected
by framing (Dufwenberg, Gächter Hennig-Schmidt, 2011).

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##  Appendix A1 Additional tables and figures

Table A.1: Participants’ socio-demographic characteristics

Variable | N | Mean |  Variable | N | Mean
---|---|---|---|---|---
Age | 453 | 21 |  Education | 413 |
Gender | 453 |  |  … Highschool | 37 | 8%
… Female | 193 | 43% |  … Undergrad. | 272 | 61%
… Male | 260 | 57% |  … Engineer | 5 | 1%
Background | 418 |  |  … Master 1 | 68 | 15%
… Economics | 339 | 81% |  … Master 2 | 44 | 10%
… Sciences, Eng. | 10 | 2% |  … PhD | 3 | 1%
… Political Science | 47 | 11% |  … Other | 19 | 4%
… Languages | 8 | 2% |  Experience | 453 |
… Literature, Philo. | 11 | 3% |  … Yes | 140 | 31%
… History, Geog. | 3 | 1% |  … No | 313 | 69%
Nationality | 453 |  |  Couple | 453 |
… French | 362 | 80% |  … Yes | 66 | 15%
… Other | 91 | 20% |  … No | 387 | 85%

  * •

For Age, Std. Dev. = 3.9, Min = 18, Pctl. 25 = 19, Pctl. 75 = 22, Max = 54.
Five participants did not report their education level, while 35 did not
report their background.

Table A.2: Participants’ socio-demographic characteristics

Treatment | N | M | A | B |
---|---|---|---|---|---
Variable | N | Mean | N | Mean | N | Mean | N | Mean | Test
Age |  108 |  21.2 |  112 |  22 |  121 |  20.5 |  112 |  20.3 |  F$:0.003^{***}$
Gender |  108 |  |  112 |  |  121 |  |  112 |  |  $\chi^{2}:0.29$
… Female |  53 |  49% |  44 |  39% |  54 |  45% |  42 |  38% |
… Male |  55 |  51% |  68 |  61% |  67 |  55% |  70 |  62% |
Education |  108 |  |  112 |  |  118 |  |  110 |  |  $\chi^{2}:0.12$
… Highschool |  10 |  9% |  7 |  6% |  11 |  9% |  9 |  8% |
… Undergrad. |  60 |  56% |  67 |  60% |  73 |  62% |  72 |  65% |
… Engineer |  1 |  1% |  2 |  2% |  1 |  1% |  1 |  1% |
… Master 1 |  25 |  23% |  18 |  16% |  17 |  14% |  8 |  7% |
… Master 2 |  10 |  9% |  13 |  12% |  11 |  9% |  10 |  9% |
… PhD |  1 |  1% |  0 |  0% |  2 |  2% |  0 |  0% |
… Other |  1 |  1% |  5 |  4% |  3 |  3% |  10 |  9% |
Background |  103 |  |  99 |  |  111 |  |  105 |  |  $\chi^{2}:0.034^{**}$
… Economics |  84 |  82% |  78 |  79% |  98 |  88% |  79 |  75% |
… Sciences, Eng. |  3 |  3% |  2 |  2% |  4 |  4% |  1 |  1% |
… Political Science |  9 |  9% |  16 |  16% |  7 |  6% |  15 |  14% |
… Languages |  1 |  1% |  0 |  0% |  1 |  1% |  6 |  6% |
… Literature, Philo. |  5 |  5% |  2 |  2% |  0 |  0% |  4 |  4% |
… History, Geog. |  1 |  1% |  1 |  1% |  1 |  1% |  0 |  0% |
Experience |  108 |  |  112 |  |  121 |  |  112 |  |  $\chi^{2}:0.023^{**}$
… Yes |  28 |  26% |  44 |  39% |  28 |  23% |  40 |  36% |
… No |  80 |  74% |  68 |  61% |  93 |  77% |  72 |  64% |
Couple |  108 |  |  112 |  |  121 |  |  112 |  |  $\chi^{2}:0.551$
… Yes |  15 |  14% |  13 |  12% |  22 |  18% |  16 |  14% |
… No |  93 |  86% |  99 |  88% |  99 |  82% |  96 |  86% |
Nationality |  108 |  |  112 |  |  121 |  |  112 |  |  $\chi^{2}:0.818$
… French |  83 |  77% |  90 |  80% |  99 |  82% |  90 |  80% |
… Other |  25 |  23% |  22 |  20% |  22 |  18% |  22 |  20% |

  * •

Standard deviations for Age are respectively: 3.6, 5.6, 2.9 and 2.4. The last
column reports the p-value of an independence test between each variable and
the treatment arm. An F test is used for the “Age” variable and “$\chi^{2}$”
for the remaining ones. Statistical significance markers: * $p<0.1$; **
$p<0.05$; *** $p<0.01$. Five participants did not report their education
level, while 35 did not report their background.

Figure A.1: Each dot represents one observation, defined as the total number of times that one subject selected the selfish option in one sequence of 20 decisions. Each subject was presented with two such sequences, hence each subject is represented by two observations. The left part shows the $n=453$ observations collected under the Neutral frame in sequences $N1$, $N2$, $A1$, and $B2$, while the right part shows the $n=453$ observations collected under the Market frame in sequences $M1$, $M2$, $A2$, and $B1$. The two-sided t-test for the difference in means results in a $p$-value of $0.0000004$. The Wilcoxon rank-sum test for the difference in medians results in a $p$-value of $0.0000004$. Figure A.2: Cumulative distributions of observations—defined as the total number of times that one subject selected the selfish option in one sequence of 20 decisions—for decisions collected in the VOI sequences (solid line, for the $n=686$ observations in sequences $N2$, $M2$, $A1$, $A2$, $B1$, and $B2$) and those collected in the non-VOI sequences (dashed line, for the $n=220$ observations collected in sequences $N1$ and $M1$). The Kolmogorov–Smirnov test’s $D$ statistic was 0.21206, while the p-value was .0000006231.  Figure A.3: Cumulative distributions of observations—defined as the total number of times that one subject selected the selfish option in one sequence of 20 decisions—for decisions collected in the Neutral VOI sequences $N2$, $A1$, and $B2$ (black line, $n=341$), the Market VOI sequences $M2$, $A2$, and $B1$ (red line, $n=345$), the Neutral non-VOI sequence $N1$ (blue line, $n=108$), and the Market non-VOI sequences $M1$ (green line, $n=112$). All pairwise Kolmogorov-Smirnoff tests result in $p$-values at or below 0.01, with the exception of Market VOI versus Neutral non-VOI, which gives a $p$-value of 0.6. Table A.3: Share of subjects choosing selfish action - By Frame | n-VOI - between (N1-M1) | VOI - between (A1-B1) | VOI - within (A1-A2-B1-B2)
---|---|---|---
Payoff | Neutral | Market | Neutral | Market | Neutral | Market
1 | 0.69 | 0.84 | 0.59 | 0.80 | 0.65 | 0.77
2 | 0.69 | 0.82 | 0.55 | 0.58 | 0.60 | 0.65
3 | 0.64 | 0.74 | 0.54 | 0.56 | 0.62 | 0.66
4 | 0.49 | 0.72 | 0.48 | 0.49 | 0.50 | 0.50
5 | 0.53 | 0.72 | 0.45 | 0.61 | 0.45 | 0.57
6 | 0.53 | 0.67 | 0.42 | 0.44 | 0.40 | 0.48
7 | 0.45 | 0.66 | 0.37 | 0.47 | 0.34 | 0.48
8 | 0.51 | 0.60 | 0.36 | 0.34 | 0.31 | 0.39
9 | 0.52 | 0.73 | 0.38 | 0.53 | 0.35 | 0.52
10 | 0.39 | 0.60 | 0.31 | 0.25 | 0.23 | 0.32
11 | 0.38 | 0.58 | 0.29 | 0.30 | 0.27 | 0.33
12 | 0.36 | 0.50 | 0.32 | 0.32 | 0.26 | 0.34
13 | 0.42 | 0.46 | 0.32 | 0.42 | 0.36 | 0.40
14 | 0.34 | 0.39 | 0.24 | 0.29 | 0.25 | 0.31
15 | 0.31 | 0.54 | 0.36 | 0.30 | 0.28 | 0.30
16 | 0.36 | 0.46 | 0.30 | 0.21 | 0.23 | 0.24
17 | 0.24 | 0.39 | 0.24 | 0.34 | 0.19 | 0.33
18 | 0.44 | 0.60 | 0.25 | 0.24 | 0.19 | 0.26
19 | 0.29 | 0.35 | 0.35 | 0.27 | 0.26 | 0.29
20 | 0.29 | 0.30 | 0.26 | 0.24 | 0.21 | 0.25
Mean | 0.44 | 0.58 | 0.37 | 0.40 | 0.35 | 0.42
Table A.4: Share of subjects choosing selfish action - By VOI | Neutral, bet. (N1-A1) | Neutral, within (N1-N2) | Market, within (M1-M2) | Market, bet. (M1-B1)
---|---|---|---|---
Payoff | non-VOI | VOI | non-VOI | VOI | non-VOI | VOI | non-VOI | VOI
1 | 0.69 | 0.59 | 0.69 | 0.60 | 0.84 | 0.83 | 0.84 | 0.80
2 | 0.69 | 0.55 | 0.69 | 0.42 | 0.82 | 0.74 | 0.82 | 0.58
3 | 0.64 | 0.54 | 0.64 | 0.60 | 0.74 | 0.83 | 0.74 | 0.56
4 | 0.49 | 0.48 | 0.49 | 0.40 | 0.72 | 0.61 | 0.72 | 0.49
5 | 0.53 | 0.45 | 0.53 | 0.40 | 0.72 | 0.53 | 0.72 | 0.61
6 | 0.53 | 0.42 | 0.53 | 0.31 | 0.67 | 0.46 | 0.67 | 0.44
7 | 0.45 | 0.37 | 0.45 | 0.30 | 0.66 | 0.41 | 0.66 | 0.47
8 | 0.51 | 0.36 | 0.51 | 0.28 | 0.60 | 0.34 | 0.60 | 0.34
9 | 0.52 | 0.38 | 0.52 | 0.30 | 0.73 | 0.39 | 0.73 | 0.53
10 | 0.39 | 0.31 | 0.39 | 0.23 | 0.60 | 0.29 | 0.60 | 0.25
11 | 0.38 | 0.29 | 0.38 | 0.26 | 0.58 | 0.31 | 0.58 | 0.30
12 | 0.36 | 0.32 | 0.36 | 0.19 | 0.50 | 0.27 | 0.50 | 0.32
13 | 0.42 | 0.32 | 0.42 | 0.29 | 0.46 | 0.32 | 0.46 | 0.42
14 | 0.34 | 0.24 | 0.34 | 0.23 | 0.39 | 0.26 | 0.39 | 0.29
15 | 0.31 | 0.36 | 0.31 | 0.23 | 0.54 | 0.26 | 0.54 | 0.30
16 | 0.36 | 0.30 | 0.36 | 0.19 | 0.46 | 0.21 | 0.46 | 0.21
17 | 0.24 | 0.24 | 0.24 | 0.17 | 0.39 | 0.27 | 0.39 | 0.34
18 | 0.44 | 0.25 | 0.44 | 0.15 | 0.60 | 0.25 | 0.60 | 0.24
19 | 0.29 | 0.35 | 0.29 | 0.15 | 0.35 | 0.18 | 0.35 | 0.27
20 | 0.29 | 0.26 | 0.29 | 0.12 | 0.30 | 0.20 | 0.30 | 0.24
Mean | 0.44 | 0.37 | 0.44 | 0.29 | 0.58 | 0.40 | 0.58 | 0.40

[b]  Dependent Variable: Sell (Yes or No) Model: (1) (2) (3) (4) (5)
_Variables_ VOI -0.1528∗∗∗ -0.1528∗∗∗ -0.1528∗∗∗ -0.1528∗∗∗ -0.1528∗∗∗
(0.0147) (0.0141) (0.0276) (0.0277) (0.0294) Market 0.1413∗∗∗ 0.1413∗∗∗
0.1413∗∗∗ 0.1413∗∗∗ 0.1411∗∗∗ (0.0146) (0.0140) (0.0396) (0.0396) (0.0403) VOI
x Market -0.0338 -0.0338∗ -0.0338 -0.0338 -0.0338 (0.0206) (0.0197) (0.0382)
(0.0383) (0.0412) z 0.9079∗∗∗ 0.9079∗∗∗ (0.0315) (0.0623) Socio-dem. controls
No No No No Yes Clustering of SE No No Subject Subject Subject and Payoff
_Fixed-effects_ Payoff Yes Yes _Fit statistics_ Observations 8,800 8,800 8,800
8,800 8,800 R2 0.04555 0.12801 0.12801 0.13370 0.13965 Within R2 0.04995
0.05648 _Signif. Codes: ***: 0.01, **: 0.05, *: 0.1_

Table A.5:  Effects of VOI (within) and Market frame (between) on Selling
(N1-N2-M1-M2)

  * •

Note: Column (3) includes clustering of SE’s at the Subject level, column (4)
includes Payoff fixed effects with clustering of SE’s at the Subject level,
column (5) includes Payoff fixed effects with clustering of SE’s at the
Subject and Payoff level. Socio-demographic controls are: nationality, gender,
couple status, previous experience in experiments and attendance to a private
school.

Table A.6: Estimated preferences for the rep. agent in Neutral and Market
frames - Full sample

|  Neutral |  Market |  $H_{0}$: Neutral = Market+
---|---|---|---
$\beta$: Aheadness aversion | 0.230∗∗∗ | 0.106∗∗∗ | 0.003
(0.034) - [0.013] | (0.023) - [0.008] |
$\kappa$: Degree of morality | 0.223∗∗∗ | 0.196∗∗∗ | 0.738
(0.071) - [0.030] | (0.035) - [0.019] |
$\sigma$: Choice sensitivity | 0.024∗∗∗ | 0.034∗∗∗ | 0.331
(0.007) - [0.002] | (0.007) - [0.002] |
Number of Observations | 4320 | 4480 |
Number of subjects | 108 | 112 |
Log likelihood | -2665.795 | -2845.855 |

  * •

Notes: Standard errors clustered at the individual level in parentheses. Non-
clustered standard errors in brackets. ∗∗∗ Significant at 1% using clustered
standard errors.

  * +

$p$-value of $z$-test, using clustered standard errors.

Table A.7:  Estimated preferences for the representative agent in Neutral and
Market frames - Core 2 sample

|  Neutral |  Market |  $H_{0}$: Neutral = Market+
---|---|---|---
$\beta$: Aheadness aversion | 0.147∗∗∗ | 0.089∗∗∗ | 0.159
(0.035) - [0.013] | (0.022) - [0.008] |
$\kappa$: Degree of morality | 0.441∗∗∗ | 0.255∗∗∗ | 0.120
(0.112) - [0.058] | (0.041) - [0.019] |
$\sigma$: Choice sensitivity | 0.025∗∗∗ | 0.045∗∗∗ | 0.254
(0.011) - [0.003] | (0.013) - [0.003] |
Number of Observations | 2,720 | 3,160 |
Number of subjects | 68 | 79 |
Log likelihood | -1.576.849 | -1,846.436 |

  * •

Notes: Standard errors clustered at the individual level in parentheses. Non-
clustered standard errors in brackets. ∗∗∗ Significant at 1% using clustered
standard errors.

  * +

$p$-value of $z$-test, using clustered standard errors.

Table A.8: Two-type Finite Mixture Model estimates, Full sample

| Neutral | Market
---|---|---
|  Type 1 |  Type 2 |  Type 1 |  Type 2
$\beta$: Aheadness aversion |  0.373∗∗∗ |  -0.057 |  0.188∗∗∗ |  -0.104
|  (0.086) |  (0.159) |  (0.012) |  (0.072)
|  [0.026] |  [0.036] |  [0.007] |  [0.034]
$\kappa$: Degree of morality |  0.125∗∗∗ |  0.309∗∗∗ |  0.229∗∗∗ |  0.136∗∗∗
|  (0.064) |  (0.054) |  (0.020) |  (0.040)
|  [0.034] |  [0.053] |  [0.020] |  [0.033]
$\sigma$: Choice sensitivity |  0.043∗∗∗ |  0.026∗∗∗ |  0.055∗∗∗ |  0.037∗∗∗
|  (0.009) |  (0.003) |  (0.011) |  (0.011)
|  [0.005] |  [0.004] |  [0.004] |  [0.005]
Share |  0.553∗∗∗ |  0.447∗∗∗ |  0.643∗∗∗ |  0.357 ∗∗∗
|  (0.134) |  (0.134) |  (0.026) |  (0.026)
|  [0.053] |  [0.053] |  [0.046] |  [0.046]
Number of Observations | 4,320 | 4,480
Number of subjects | 108 | 112
Log likelihood | -2297.761 | -2403.156

  * •

Notes: Standard errors clustered at the individual level in parenthesis, non-
clustered standard errors in brackets. ∗∗∗ Significant at 1%, ∗∗ Significant
at 5%, ∗ Significant at 10% using clustered standard errors.

Figure A.4: Distribution of posterior probabilities of individual type-
membership in Neutral (first row) and Market frames (second row). Full sample
estimates.

(a) Neutral frame

(b) Market frame

Figure A.5: Difference in selfish actions non-VOI versus VOI. Full sample.
Subjects classified as Type 1 if $\tau_{1}>0.95$ or Type 2 if $\tau_{1}<0.05$

Table A.9: Two-type Finite Mixture Model estimates, Core 2 sample

| Neutral | Market
---|---|---
|  Type 1 |  Type 2 |  Type 1 |  Type 2
$\beta$: Aheadness aversion |  0.287∗∗∗ |  -0.145 |  0.18∗∗∗ |  -0.305
|  (0.024) |  (0.194) |  (0.012) |  (0.283)
|  [0.020] |  [0.066] |  [0.007] |  [0.109]
$\kappa$: Degree of morality |  0.344∗∗∗ |  0.595∗∗∗ |  0.176∗∗∗ |  0.597∗∗
|  (0.085) |  (0.146) |  (0.020) |  (0.297)
|  [0.061] |  [0.124] |  [0.016] |  [0.143]
$\sigma$: Choice sensitivity |  0.047∗∗∗ |  0.023∗∗∗ |  0.073∗∗∗ |  0.029∗
|  (0.018) |  (0.006) |  (0.012) |  (0.015)
|  [0.007] |  [0.005] |  [0.005] |  [0.006]
Share |  0.522∗∗∗ |  0.478∗∗∗ |  0.623∗∗∗ |  0.377∗∗∗
|  (0.084) |  (0.084) |  (0.028) |  (0.028)
|  [0.065] |  [0.065] |  [0.056] |  [0.056]
Number of Observations | 2,720 | 3,160
Number of subjects | 68 | 79
Log likelihood | -1384.981 | -1617.195

  * •

Notes: Standard errors clustered at the individual level in parenthesis, non-
clustered standard errors in brackets. ∗∗∗ Significant at 1%, ∗∗ Significant
at 5%, ∗ Significant at 10% using clustered standard errors.

Figure A.6: Distribution of posterior probabilities of individual type-
membership in Neutral (first row) and Market frames (second row). Core 2
sample estimates.

(a) Neutral frame

(b) Market frame

Figure A.7: Difference in selfish actions non-VOI versus VOI. Core 2 sample.
Subjects classified as Type 1 if $\tau_{1}>0.95$ or Type 2 if $\tau_{1}<0.05$

##  Appendix A2 Preference parameter estimation at the individual level

Figures A.8 and A.9 show the distributions of the estimates of $\kappa$ at the
individual level, for the Market and the Neutral frames, respectively.

(a)

(b)

Figure A.8: Distributions of individual estimates of $\kappa$ for the 100
subjects for whom the decision was different under VOI and non-VOI for at
least one of the 20 payoffs, in the Market frame (treatment M). In panel (a),
no restriction was imposed on the estimates. In panel (b) the estimates were
restricted to the interval $[0,1]$. In panel (a) the estimates whose value is
above 2 in absolute value are lumped together at -2 and 2.

(a)

(b)

Figure A.9: Distributions of individual estimates of $\kappa$ for the 98
subjects for whom the decision was different under VOI and non-VOI for at
least one of the 20 payoffs, in the neutral frame (treatment N). In panel (a),
no restriction was imposed on the estimates. In panel (b) the estimates were
restricted to the interval $[0,1]$. In panel (a) the estimates whose value is
above 2 in absolute value are lumped together at -2 and 2.

These figures show that for a large number of subjects the estimates are
negative and/or exceed 2 in absolute value (this is especially true in the
Neutral frame). This suggests that our experimental design is not ideal to
structurally estimate individual preference parameters. To see why, consider
Figure A.10, which shows the threshold values for $\beta$ and $\kappa$ (recall
(5) and (6)) for three of the payoffs used in the experiment: those labeled 1,
4, and 20 in Table 1. As indicated by the numbers in the blue balls, the solid
lines correspond to payoff 1, the dashed ones to payoff 4, and the dotted ones
to payoff 20. We discuss the challenge of identifying $(\beta_{i},\kappa_{i})$
for several different behavioral scenarios (a discussion which clearly
generalizes to subjects taking decisions in the 20 payoff configurations of
the experiment). For this discussion, recall that, for any given payoff, a
subject $i$: (a) does not sell under either non-VOI or VOI if
$(\beta_{i},\kappa_{i})$ is to the right of the vertical line; (b) sells under
both non-VOI or VOI if $(\beta_{i},\kappa_{i})$ is below the downward-sloping
curve; and (c) makes a switch (i.e., sells under non-VOI but not under VOI) if
$(\beta_{i},\kappa_{i})$ is between the vertical and the downward-sloping
curve (recall Figure 3).

The first issue is the lack of bounds on $\beta$ and/or $\kappa$ inherent in
individual estimations. Consider a subject who switches under payoff 1 and
never sells under payoffs 4 and 20. Such behavior is consistent with any
$(\beta,\kappa)$ in zone I (where the red ball labeled I appears). While the
value of $\beta$ is thus bounded below by 0.33 and above by 0.6, the value of
$\kappa$ is bounded below only. In a similar vein, a subject who switches
under payoff 20 and sells under payoffs 1 and 4 is consistent with any
$(\beta,\kappa)$ in zone II: for this subject, the value of $\beta$ is bounded
above only, while the value of $\kappa$ is bounded below by the downward-
sloping dashed and dotted lines. As a third example, a subject who switches
under the three payoffs is consistent with any $(\beta,\kappa)$ in zone III.
Again, identification of this subject’s $(\beta,\kappa)$ is clearly
impossible.

The second issue arises for subjects whose behavior is compatible with
disjoint sets of values of $(\beta,\kappa)$. For example, consider a subject
who switches under payoff 1 and 20, but not under payoff 4. Such a subject
could have a $(\beta,\kappa)$ in zone I (consistent with the switch under
payoff 1), in which case (s)he would have made a large mistake by switching
under payoff 20; or (s)he could have a $(\beta,\kappa)$ in zone II (consistent
with the switch under payoff 20), in which case (s)he would have made a large
mistake by switching under payoff 1.

By contrast, estimation of $(\beta,\kappa)$ at the aggregate level is
possible. To see why, consider again Figure A.10. Suppose that 20% of the
subjects switched under all the payoffs (zone III), 25% switched under payoffs
1 and 4 and did not sell under payoff 20 (zone IV), 35% switched under payoff
1 and did not sell under payoffs 4 and 20 (zone I), while the remaining 20%
switched only under payoff 20 and sold under payoffs 1 and 4 (zone II). If
these decisions are interpreted as emanating from one single individual who
sometimes makes mistakes, the estimated $(\beta,\kappa)$ of this hypothetical
individual would likely be in zone V, or in zone IV close to zone V. The
between-subject heterogeneity in behavior would thus help put boundaries on
the estimates of $\beta$ and $\kappa$.

$3\cdot 10^{-2}$$0.33$$0.6$$1.5$1420IIIIIIIVV$\beta$$\kappa$
Figure A.10:  The numbers in the blue balls indicate the payoff to which the
V-shaped lines correspond (see Table 1). The numbers in the red balls indicate
zones with different switching behaviors between non-VOI and VOI.

##  Appendix A3 Experimental instructions

Screen 1 [common to all the treatments]:

Welcome to this experiment!

Please read the following instructions carefully and from now on, do not
communicate with any of the other participants. If you have any questions
during the experiment, please raise your hand and wait until we come to you to
answer your questions in private.

All the participants here today will be asked to take decisions. These
decisions will generate points. At the end of the experiment points will be
converted into money. Every point is worth 0.025 euros. Each participant also
receives 4 euros for attending until the end and answering all questions.

Your decisions during the experiment are anonymous. They will not be linked to
your name in any way. Other participants can never trace your decisions back
to you. Moreover, the amount of money you receive at the end will be handed
over to you in an opaque envelope, and no other participant will see what is
inside the envelope.

During the experiment, your cell phone should be switched off and out of
reach. And remember not to talk with the other participants. We would need to
exclude you from the experiment (and the payment) if you breach these rules.

The experiment consists of two parts, followed by a short questionnaire. At
the beginning of each part, you will receive new instructions. Your decisions
made in one part will never affect outcomes in the other part, so you can
treat both parts as independent.

###  A3.1 Remaining screens for the Market treatment

Screen 2:

Part I

In this part you will be asked to make choices in 20 different decision
situations, which represent an interaction between a Seller and a Buyer.

In each decision situation you will be paired with one of the other
participants here today, each time with a different participant.

In each decision situation you are the Seller and the person you are paired
with is the Buyer. You will be asked to choose one of two options, Sell and
Not Sell, while the Buyer has no choice to make. Think of this as representing
a situation in which the Buyer is willing to pay the price for a good that you
own. Here is an example of a decision screen:

The number of points that you get if you choose Not Sell can be interpreted as
the value you attach to owning the good. The number of points that you get if
you choose Sell can be interpreted as the amount of money you have if you Sell
the good.

The number of points that the Buyer gets if you choose Not Sell can be
interpreted as the amount of money he/she has initially. The number of points
that the Buyer gets if you choose Sell can be interpreted as the value he/she
attaches to owning the good.

As you can see on the decision screen above, the Buyer would be better off if
you chose Not Sell, while you are better off if you Sell. Think of this as
representing a situation in which the good that you sell has a defect which
makes the Buyer enjoy owning the good less than you do. At the end of the
experiment one of these 20 decision situations will be randomly drawn. Your
decision in this situation (and only this situation) will have an effect on
the number of points you and the person you were paired with will get. This
other participant will make no decision that affects your number of points,
however.

Remember that:

  * •

all your decisions are anonymous, and no participant will ever learn with whom
he/she was paired in any decision situation;

  * •

all the decision situations are equally important, in the sense that they are
all equally likely to count towards the amount of money you and the person you
were paired with will receive at the end;

  * •

each point will be converted to 0.025 euros at the end of the experiment.

Screen 3 [comprehension quiz]:

Screens 4-33: //the first 20 decision situations//

Screen 34:

Part II

In this part you will be asked to make choices in 20 different decision
situations.

In each decision situation you will be paired with one of the other
participants here today, each time with a different participant.

In each decision situation there are two roles: the Seller role and the Buyer
role. Either you or the person you are paired with will be assigned to the
Seller role, while the other will be assigned to the Buyer role. The two
different role assignments are equally likely. The person assigned to the
Seller role chooses between two options, Sell and Not Sell, while the other
person has no choice to make. However, because the role assignment will only
be made at the end of the experiment, both you and the person you were paired
with will be asked to state what you would do in Role A in each decision
situation. Here is an example of a decision screen:

The number of points that the Seller gets from choosing Not Sell can be
interpreted as the value that the Seller attaches to owning the good. The
number of points that the Seller gets from choosing Sell can be interpreted as
the amount of money the Seller has if he/she Sells the good. The number of
points that the Buyer gets if the Seller chooses Not Sell can be interpreted
as the amount of money the Buyer has initially. The number of points that the
Buyer gets if the Seller chooses Sell can be interpreted as the value the
Buyer attaches to owning the good.

As you can see on the decision screen above, the Buyer would be better off if
the Seller chose Not Sell, while the Seller is better off if he/she chooses
Sell. Think of this as representing a situation in which the good that the
Seller sells has a defect which makes the Buyer enjoy owning the good less
than the Seller does.

At the end of the experiment one of these 20 decision situations will be
randomly drawn. The role assignment for this decision situation will be
randomly drawn. Recall that the two roles are equally likely. If you are
assigned to the Seller role (this happens with probability 1/2), it is your
decision in this situation that will have an effect on the number of points
you and the person you were paired with will get. If you are assigned to the
Buyer role (this happens with probability 1/2), it is the decision of the
person you were paired with in this situation that will have an effect on the
number of points you and the person you were paired with will get.

Remember that:

  * •

all your decisions are anonymous, and no participant will ever learn with whom
he/she was paired in any decision situation;

  * •

all the decision situations are equally important, in the sense that they are
all equally likely to count towards the amount of money you and the person you
were paired with will receive at the end;

  * •

each point will be converted to 0.025 euros at the end of the experiment.

Screen 35 [comprehension quiz]:

Screens 36-65: the 20 decision situations for Part II

###  A3.2 Remaining screens for the Neutral treatment

Screen 2:

Part I

In this part you will be asked to make choices in 20 different decision
situations.

In each decision situation you will be paired with one of the other
participants here today, each time with a different participant.

In each decision situation you will be asked to choose one of two options, X
and Y, while the other participant has no choice to make.

Here is an example of a decision screen:

At the end of the experiment one of these 20 decision situations will be
randomly drawn. Your decision in this situation (and only this situation) will
have an effect on the number of points you and the person you were paired with
will get. This other participant will make no decision that affects your
number of points, however.

Remember that:

  * •

all your decisions are anonymous, and no participant will ever learn with whom
he/she was paired in any decision situation;

  * •

all the decision situations are equally important, in the sense that they are
all equally likely to count towards the amount of money you and the person you
were paired with will receive at the end;

  * •

each point will be converted to 0.025 euros at the end of the experiment.

Screen 3 [comprehension quiz]:

Screens 4-33: the 20 decision situations for Part I

Screen 34:

Part II

In this part you will be asked to make choices in 20 different decision
situations.

In each decision situation you will be paired with one of the other
participants here today, each time with a different participant.

In each decision situation there are two roles: Role A and Role B. Either you
or the person you are paired with will be assigned to Role A, while the other
will be assigned to Role B. The two different role assignments are equally
likely. The person assigned to Role A chooses between two options, X and Y,
while the other person has no choice to make. However, because the role
assignment will only be made at the end of the experiment, both you and the
person you were paired with will be asked to state what you would do in Role A
in each decision situation.

Here is an example of a decision screen:

At the end of the experiment one of these 20 decision situations will be
randomly drawn. The role assignment for this decision situation will be
randomly drawn. Recall that the two roles are equally likely. If you are
assigned to Role A (this happens with probability 1/2), it is your decision in
this situation that will have an effect on the number of points you and the
person you were paired with will get. If you are assigned to Role B (this
happens with probability 1/2), it is the decision of the person you were
paired with in this situation that will have an effect on the number of points
you and the person you were paired with will get.

Remember that:

  * •

all your decisions are anonymous, and no participant will ever learn with whom
he/she was paired in any decision situation;

  * •

all the decision situations are equally important, in the sense that they are
all equally likely to count towards the amount of money you and the person you
were paired with will receive at the end;

  * •

each point will be converted to 0.025 euros at the end of the experiment.

Screen 35 [comprehension quiz]:

Screens 36-65: the 20 decision situations for Part II

###  A3.3 Remaining screens for the two Mixed treatments

Each mixed treatment combines the Market VOI instructions (shown in Part II of
subsection “Remaining screens for the Market treatment”) and the Neutral VOI
instructions (shown in Part II of subsection “Remaining screens for the
Neutral treatment”).

###  A3.4 Example of the two screens shown for one decision