SERIESPAPERDISCUSSIONIZA DP No. 6919

Truth-Telling: A Representative Assessment
Johannes Abeler
Anke Becker
Armin Falk

October 2012

Forschungsinstitut
zur Zukunft der Arbeit
Institute for the Studyof Labor

Truth-Telling:
A Representative Assessment
Johannes Abeler
University of Oxford
and IZA

Anke Becker
University of Bonn

Armin Falk
University of Bonnand IZA

Discussion Paper No. 6919
October 2012

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IZA Discussion Paper No. 6919
October 2012

ABSTRACT
Truth-Telling: A Representative Assessment *A central assumption of the canonical cheap talk literature is that people misreport theirprivate information if this is to their material benefit. Recent evidence from laboratory
experiments with student subjects suggests, however, that while many people do report thepayoff-maximizing outcome, some report their private information truthfully or at least do notlie maximally. We measure truth-telling outside the laboratory by calling a representative
sample of the German population at home. In our setup, participants have a strong monetary
incentive to misreport, misreporting cannot be detected, and reputational concerns are
negligible. Yet, we find that aggregate reporting behavior closely follows the expected truthful
distribution. Our results underline the importance of lying costs and raise questions regarding
the influence of the decision-making environment and the elicitation mode on reporting
behavior.

JEL Classification:
Keywords:

C93, D01, D82, D83

private information, cheap talk, honesty, lying costs, representative experiment

Corresponding author:
Johannes Abeler
University of Oxford
Department of Economics
Manor Road
Oxford OX1 3UQ
United Kingdom
E-mail: johannes.abeler@economics.ox.ac.uk*

Financial support from the German Science Foundation (DFG) through SFB/TR 15, the Bonn
Graduate School of Economics, the European Research Council (Starting Grant), and CESifo isgratefully acknowledged. We thank Steffen Altmann, Vince Crawford, Eddie Dekel, Holger Gerhardt,
Sebastian Kube, Theo Offerman, Paul Schempp, Matthias Wibral, and Florian Zimmermann for helpful
discussions. Valuable comments were also received from numerous seminar and conference
participants.

1

Introduction

A potential buyer of a car stating his willingness to pay, a self-employed shopkeeper
reporting her income to the tax authorities, an employee telling his boss that heworked the entire weekend: situations of asymmetric information are ubiquitous.
How people report their private information is thus of fundamental importance formany areas in economics.
Economic theory usually assumes that agents are rational and behave fully strategically when reporting their type (e.g., Crawford & Sobel 1982): agents therefore
misreport their private information if this is to their immediate benefit. In contrast,
telling the truth is one of the most important norms, postulated by all moral systems
and world religions.1 Deviating from this norm will evoke disutility or a psychological cost for people who value this norm and could therefore make them misreport lessthan predicted by standard theory. Lying could also cause disutility for other reasons, e.g., because utility is derived from identity (Akerlof & Kranton 2000, Bénabou& Tirole 2011) or from self-reputation (similar to Bénabou & Tirole 2006). A recent
theoretical literature is built on the assumption that agents face such a (psychological) lying cost when misrepresenting their type (e.g., Kartik et al. 2007, Chenet al. 2008, Kartik 2009, Saran 2011). These models capture the intuition that people are sometimes reluctant to lie. Yet, it remains an open empirical question how
people actually report their private information. Using observational field data tounderstand reporting behavior is notoriously difficult since an individual’s true type
is usually unknown and misreporting cannot be identified. Even the reports areoften not observed or are the consequence of equilibrium behavior which makes it
difficult to deduce individual motives.
The cleanest evidence so far on how people report their private information
comes from experiments conducted in tightly controlled laboratory situations. A
robust result is that many subjects misreport their private information to their own
advantage but that a substantial share of subjects refrain from reporting the payoffmaximizing type and that some are fully honest (e.g., Gneezy 2005, Charness &Dufwenberg 2006, Fischbacher & Heusi 2008, de Haan et al. 2011, Houser et al.
2011, Shalvi et al. 2011, Wibral et al. forthcoming, Serra-Garcia et al. forthcoming).
These studies are a strong first indicator that lying costs do influence behavior.
However, lab experiments do not allow for inferences with respect to the prevalence1

In few exceptional circumstances, some moral systems allow lying, e.g., if it serves a greater
cause. In this paper, however, we are concerned with the much more common situation in which
only the person who lies would benefit from misreporting.

1

of lying costs in the overall population since they have been conducted with student
samples only (DellaVigna 2009, Falk & Heckman 2009). Also, decision making took
place in an austere laboratory environment that is not representative of an everyday
decision-making context, rendering generalizations to the level of behavior elsewheremore difficult. It could thus be that there are systematic differences in behavior of
students in the laboratory and of non-student subjects outside the lab.
In this paper, we measure how people report their private information outside
the laboratory by calling participants on the phone at their home. Participants weredrawn randomly from the German population, yielding a representative sample. An
incentivized experiment was embedded in the interview. The experimental setup isrelated to the design of Fischbacher & Heusi (2008) and Bucciol & Piovesan (2011)
and is extremely simple: participants were asked to toss a coin and report their type,i.e., either “heads” or “tails”. Reporting tails yielded a payoff of 15 euros, which
participants could choose to receive in cash or as an Amazon gift certificate, while
reporting heads yielded a payoff of zero. Participants thus had a clear monetary
incentive to report tails regardless of their true type. It was obvious that the true
outcome was only known to the participants, as they tossed the coin privately athome. In this setup, we cannot draw reliable conclusions about the truthfulness of
any individual report. But we can learn about aggregate behavior by comparing the
distribution of reports to the true distribution of a fair coin (50 percent tails) andto the payoff-maximizing distribution (100 percent tails). This indirect observation
therefore allows us to study the behavior of subjects in a situation in which private
information is kept truly private and in which subjects do not face any risk ofdetection.2 Moreover, the decision is non-strategic and reputational concerns areminimized as the interviewer is a stranger with whom no future interaction can be
expected.3
2

In other studies concerning how people report their private information (e.g., Gneezy 2005,
Charness & Dufwenberg 2006) the experimenter knows or will later know the subject’s true type
(and the subject is aware of this) and can thus judge whether an individual was honest or not.
In our experiment, only the participant knows his or her private information. Our setup is thus
closer to situations in which information is truly private and only known by the individual, while
Gneezy’s and Charness & Dufwenberg’s setup is more representative of situations in which theprivate information is known by more than one person, e.g., when filing a joint tax declaration.
These papers are also interested in the interaction between sender and receiver, from which we
abstract.
3

Fischbacher & Heusi (2008) have conducted direct tests of the influence of reputation in thisexperimental paradigm. Subjects in their baseline condition rolled a die, reported their number,and were paid their report; in a double-blind control treatment, subjects were provided with enoughmoney for the maximal report, took whatever they decided to take, put the remaining money inan envelope, and dropped the envelope into a letter box when leaving. The letter box was only
opened after all subjects had left. A report could thus not be traced to any individual subject,2

If all our participants were rational money maximizers, we would expect that allof them reported tails. If behavior on the phone was similar to previous, comparable
laboratory experiments (e.g., Houser et al. 2011), we would expect about 75 percent
of subjects reporting tails.
In contrast to these predictions, we find that almost all participants report their
type honestly. If anything, participants report the payoff maximizing outcome lessoften than expected under truthful reporting. This latter effect, however, is small
and disappears in a second treatment in which participants were asked to report
the total number of tails in four consecutive coin tosses and received 5 euros times
the number of reported tails. The resulting distribution of reports in the 4-cointreatment is indistinguishable from the distribution under complete truth-telling.
Moreover, while previous studies (e.g., Dreber & Johannesson 2008) have foundcorrelations between individual characteristics, like gender, and truth-telling, we do
not find any robust correlations between individual characteristics and reporting
behavior. This is not surprising if almost all participants report truthfully. Reports
are thus solely determined by chance, namely the coin toss, which cannot be related
to any individual characteristic. At the same time, our results show that lying costs
are widespread and are influencing behavior regardless of gender, religious beliefs,
education, or age: participants forego considerable amounts of money to avoid lying.
Our results add to the doubts that previous laboratory experiments have cast on
the assumption of zero lying costs. If anything, lab experiments have understated theimportance and extent of lying costs. This suggests that studying the theoretical
implications of such costs (as in, e.g., Kartik et al. 2007) is a promising research
avenue. The difference in behavior in our study and in previous lab studies further
shows how malleable reporting behavior can be and opens many new questions
about how exactly reporting private information depends on the decision-making
context.4 Intuitively, different norms might apply when making such a decision at
home, representing a private and familiar environment. Similarly, people could be
more attentive to their own moral rules, e.g., abstaining from lying when at home. In
independent research, Waubert De Puiseau & Glöckner (2012) also find considerable
further reducing reputational concerns. Aggregate behavior, however, did not differ between the
two treatments suggesting that reputation did not play a role in the baseline treatment.
4

Previous research comparing behavior of student samples vs. non-students samples and behavior in the lab vs. outside the lab has mostly shown little differences, with some notable exceptions(e.g., Stoop et al. 2010, Falk et al. forthcoming). For an overview and critical discussion, see Falk
& Heckman (2009) or Camerer (2011). Our field results differ quite strongly from behavior incomparable lab experiments (e.g., Fischbacher & Heusi 2008), suggesting that truth-telling is more
context dependent than other behaviors.

3

truth-telling at home, though not as extreme as in our data, using an online panel
in which participants answered questions at home on a computer. Irrespective of
these differences between lab and field, our study establishes that lying costs are
more important than previously assumed and are strongly influencing behavior.
In the next two sections, we present the design of the study and our hypotheses.
Section 4 contains the results. We discuss policy implications in Section 5.

2

DesignThe computer-assisted telephone interviews were operated by the Institute for Applied Social Sciences (infas), a private and well-known German research institute.
They were conducted between November 2010 and February 2011.5 The averageinterview lasted 20 minutes (standard deviation: 5.5 minutes). Telephone numbers
were selected using a random digit dialing technique: numbers were generated randomly based on a data set of all potential telephone numbers in Germany. Only
landline numbers were used in this study, as 92 percent of German households havea landline number. The selection of the participant within each household was also
random: only the member of the household whose birthday was the most recent
among all household members was eligible to participate. We restricted participation to those aged between 18 and 70 years at the time of the interview.
The survey part of the study consisted of questions relating to the participants’socio-demographic background, their economic and political preferences, their current living and financial situation, their religious beliefs, their attitudes towards
opportunistic behavior and everyday crime, as well as their belief about other people’s behavior in the experiment.
The experimental part of the study was embedded in the survey. Before theexperiment started the participant was reminded that the resulting data would beanonymized, and that infas and the University of Bonn guaranteed the correct payment. The interviewer then asked the participant to take a coin and explained therules of the experiment: the task was to toss the coin and report whether heads
or tails came up. If the participant reported heads, they received no payment. If
the participant reported tails, they would receive 15 euros, either in cash via regularmail or as an Amazon gift certificate code. The alphanumeric 14-digit gift certificate
5

The interviews were conducted in the infas telephone studio. Infas ensures a high quality ofinterviews by supervising interviews randomly. Supervisors are present in the telephone studio at
all times and interviews can be monitored without the interviewer noticing this.

4

code could be received via email or directly on the phone at the end of the interview.
Then, the participant was asked to toss the coin and report the outcome. We willcall this treatment “1-coin treatment.” 658 people participated in this version of
our experiment. A translation of the exact experimental instructions can be foundin Appendix A.
In a second treatment, 94 people were interviewed and participated in the following variation of the experiment. Participants were asked to take a coin, toss
it four times, and report the number of times that tails came up. For each time
participants reported tails they received 5 euros. Thus, they could earn 0, 5, 10, 15,
or 20 euros. We will call this treatment “4-coin treatment.”

3

Hypotheses

The standard economic prediction in our setup is straightforward: depending on
the treatment, people will report tails one or four times, respectively. This is thepayoff maximizing outcome as there are no exogenous costs linked to misreporting,no possibility of detection and no fines. The setup is extremely simple and subjects
should have no trouble identifying the payoff maximizing choice. Moreover, the setup
is highly anonymous, discouraging any reputational concerns because of repeatedinteraction. Since the coin was tossed at the home of the participant, it was obviousthat the interviewer could not secretely observe the true outcome of the coin toss.
This might be a concern in otherwise similar laboratory experiments but not in ourphone study.
If, however, some participants incur a psychological cost or derive direct disutility
from falsely reporting their private information per se we should expect both heads
and tails to be reported in the experiment. There are a few recent theoretical papers
that assume such a cost. For example, Kartik (2009) and Kartik et al. (2007) build
on Crawford & Sobel’s (1982) cheap-talk model and derive predictions for the casethat some agents incur costs when misreporting their private information (see also,e.g., Saran 2011). Assuming some degree of heterogeneity in the incurred costs whenmisreporting, it is then a question of the trade-off between psychological costs andmonetary benefits of misreporting how many participants will report heads and how
many report tails.
The two treatments differ in crucial aspects and allow different types of lying
costs to influence behavior. On the one hand, marginal incentives to lie are higher
in the 1-coin treatment. If lying costs are compared to these marginal incentives,5the 1-coin treatment will favor lying more strongly than the 4-coin treatment. On
the other hand, lying costs might be related to self-reputation or identity arguments(e.g., Bénabou & Tirole 2006, Akerlof & Kranton 2000). Participants in the 1-cointreatment have to make a clear, binary choice whether to lie or not; lying in such asetting will impact self-reputation or identity and thus make lying more costly. Participants in the 4-coin treatment can make a finer choice between being fully honest,
exaggerating a little bit, or lying maximally; this could render small lies compatible
with a positive self-reputation and thus enhance lying (Mazar et al. 2008). Such
non-maximal lying has already been shown to be important by Fischbacher & Heusi(2008).
Regarding potential differences in reporting behavior according to individual
characteristics, we would expect that women are more honest than men (as alreadyshown by Dreber & Johannesson 2008, Houser et al. 2011). More religious participants would be expected to be more honest, since religious priming leads to lesslying and more pro-social behavior (Mazar et al. 2008, Shariff & Norenzayan 2007).
Income could be positively correlated with honesty because of the lower marginalutility of the monetary rewards or negatively correlated because of reverse causality. A similarly ambiguous hypothesis can be derived for education or the social
environment, e.g., the size of the community or family status.

4

Results

658 people were interviewed and participated in the 1-coin treatment of the experiment. Here, participants tossed the coin once and reported the outcome. A reportof tails yielded 15 euros, a report of heads 0 euros. In the 4-coin treatment, theyreported the outcome of four consecutive coin tosses. For each reported tails theyreceived 5 euros. 94 people participated in this treatment.
Result 1: In the 1-coin treatment, the distribution of actual reports is very
close to the truthful distribution; participants report the payoff-maximizing
outcome slightly less often than expected if everyone reported truthfully. In
the 4-coin treatment, the distribution of reports is indistinguishable from thetruthful distribution.
Figure 1 illustrates aggregate behavior (the dashed line corresponds to the expecteddistribution if every participant reported the true outcome of the coin toss). 55.6 percent of participants report heads as the outcome of the coin toss, yielding a payoff
6

of zero, the remaining participants report tails yielding a payoff of 15 euros. The
payoff-maximizing outcome is reported slightly less often than in 50 percent of thecases and although the difference is small in terms of effect size, it is significant(Binomial test, p < 0.01).6 Figure 2 shows aggregate behavior in the 4-coin treatment. Again, reporting behavior follows the expected distribution under complete
honesty extremely closely (the dashed line corresponds to the truthful distribution).
In fact, the distribution of reported outcomes is statistically indistinguishable fromthe truthful distribution (Kolmogorov-Smirnov test, p = 0.61; binomial tests of the
expected against the observed frequency, all five p > 0.13) and in particular, there
is no significant over-reporting of zero. Looking at behavior in both treatments wecan therefore summarize that the payoff-maximizing outcome is reported by much
fewer participants than expected if no one incurred lying costs. It is also reported
less often than suggested by previous lab experimental studies, which find sometruth-telling but also many instances of the payoff-maximizing report. Instead, it isclose to the distribution that would arise if every participant reported his or her type
truthfully. Overall, we therefore conclude that most participants in our experimentreported truthfully in the aggregate.
Previous studies have shown that truth-telling does correlate with observablecharacteristics, e.g. gender or religiosity (Dreber & Johannesson 2008, Houser et al.
2011, Mazar et al. 2008, Shariff & Norenzayan 2007). In contrast, if our conjecturethat almost all participants report truthfully is correct, an individual’s reported
outcome will only be driven by their random coin toss; if this is the case, reporting
cannot be correlated with any individual characteristic, as these are orthogonal
to the chance move. Therefore, if we do not find such a correlation, our finding of(almost) complete honesty is corroborated.7 More specifically, we conduct regression
6

We can only speculate about why some people obviously falsely claimed to be of the payoff
minimizing type. The design of the experiment allows to rule out reputational concerns towards
the interviewer as an important factor. Privacy concerns could drive this effect: reporting the typethat gives zero payoff makes it unnecessary to hand over one’s address. Note that we do not find any
such effect in the 4-coin treatment in which the option of reporting zero is less salient than in the
1-coin treatment. However, we ensured that privacy concerns were minimized in both treatments
by giving participants the opportunity to receive the payment as a gift certificate code directly via
the phone. Choosing this payment mode made it unnecessary to hand over any additional contact
details. Another possibility would be self-image concerns: refraining from easily and safely earning
15 euros could be a strong signal to oneself that one is not greedy and thereby flattering for one’s
self-image.
7

It could still be that a subgroup of people, which we cannot identify with our backgroundinformation, reports tails more often than actually true while another subgroup reports tails lessoften. This could result in the two effects offsetting each other, which would result in a similar
picture of aggregate behavior. However, we consider this to be extremely unlikely as our analysis
shows that this is not the case for any of the numerous subgroups that we can identify with ourdata. Such an effect would further need to recreate the distinct distributions of Figures 1 and 2,

7

Figure 1: Aggregate Behavior in the 1-coin Treatment. Reporting heads yielded
no payoff; reporting tails yielded a payoff of 15 euros. The dashed line corresponds
to the expected distribution if every participant reported the true outcome of theircoin toss.

Figure 2: Aggregate Behavior in the 4-coin Treatment. The payoff was 5 euros
times the number of tails reported. The dashed line corresponds to the expecteddistribution if every participant reported the true outcomes of their coin tosses.

8

analyses for the two experiments in order to examine whether there are systematiceffects of individual characteristics on reporting behavior. First, we regress thereport only on clearly exogenous variables such as age and gender, in a second
step adding religious denomination. We then include income, the size of the city
the individual lives in, and education dummies. Finally, we look at the effect of
an individual’s religiousness (interacted with denomination), their risk and trust
preferences, and their belief about the reporting behavior of other participants.
Result 2: There is no significant correlation between reporting behavior
and any individual characteristic.
First, we look for potential group differences in terms of reporting behavior in the
1-coin treatment by conducting Probit regressions of the reported outcome on the respective characteristics (see Table 1). No characteristic except for one’s belief aboutothers’ behavior is significantly associated with reporting in the experiment: participants who think many other participants report tails dishonestly, are less likely
to report tails themselves. This belief is, however, not significant if we include it as
the only explanatory variable (p = 0.15). Note in particular that neither gender nor
any religion-related variable is significantly correlated with reporting. Conducting
the same regressions as in Table 1 using OLS leaves the results unchanged. Next,
we check whether these results also hold in the 4-coin treatment. We run Ordered
Logit regressions of the reported number of tails on the same explanatory variables
as before. Table 2 illustrates the results from this estimation. Only the coefficientfor trust is significant. This effect is, however, not robust to the inclusion or exclusion of other explanatory variables. The effect is also not present in the 1-cointreatment. In contrast to the 1-coin treatment, the belief coefficient shows no significant association with reporting behavior in this treatment and the point estimate
has the opposite sign.
When we elicited beliefs about the behavior of other participants, most people
stated that they believed many people to make a false report in the experiment.
Participants in the 1-coin treatment believed that on average 46.7 percent (std. dev.:
28.5) of all participants would report tails when they actually had tossed heads (thiswould translate into a 73.4 percent share of tails if all tails are reported as tails).
In the 4-coin treatment participants believed that, on average, 43.7 percent (std.
dev.: 28.4) of all other participants reported higher types than actually true andthat 28.1 percent (std. dev.: 26.3) reported four times tails, the payoff-maximizing
report. Thus, participants’ beliefs strongly deviate from observed behavior. It is thus
which is again very unlikely.

9Table 1:

Covariates in 1-Coin Experiment (Probit Regressions)

Dependent Variable: 1 if Reported Tails

Age
1 if Female

(1)

(2)

(3)

(4)

(5)

0.002
(0.001)
0.041(0.039)0.002(0.001)0.040(0.039)0.010(0.046)0.006(0.048)0.002(0.002)0.079*(0.047)0.021(0.056)-0.004(0.057)0.002(0.005)0.041(0.062)-0.016(0.055)0.000(0.000)0.002(0.002)0.065(0.048)-0.038(0.140)-0.004(0.150)0.003(0.005)0.053(0.062)-0.009(0.056)0.000(0.000)0.029(0.020)-0.010(0.035)0.005(0.034)0.001(0.002)0.066(0.049)-0.084(0.141)-0.064(0.152)0.004(0.005)0.056(0.063)-0.015(0.057)0.000(0.000)0.023(0.020)0.004(0.036)0.013(0.035)-0.003(0.018)-0.028(0.018)-0.218**(0.092)

658

658

465

464

454

1 if Protestant
1 if Catholic
Income1 if Professional Education
1 if Academic Education
City Size
Religiousness
Religiousness*Catholic
Religiousness*ProtestantRisk Tolerance
Trust
Belief about otherParticipants
N.Obs.

Notes: Probit estimates. Marginal effects are shown, robust standard errors are in parentheses.
“Risk tolerance” is the answer to the general risk question of the GSOEP (“How do you consider
yourself? Are you in general a rather risk-loving person, or do you try to avoid risks? Use a scalefrom 1, meaning that you are not at all willing to take risks, to 7, meaning that you are absolutely
willing to take risks.”) and “trust” is the answer to the WVS trust question (“Generally speaking:Do you think one can trust other people, or that one should rather be careful when dealing withother people? Please indicate your answer on a scale from 1 to 7, with 1 meaning that one should be
careful when dealing with other people, and 7 meaning that one can trust other people.”). “Belief
about other participants” is the belief of this participant about the share of other participants whoreported “tails” while actually “heads” came up. Significance at the 1, 5, and 10 percent level isdenoted by ***, **, and *, respectively.

10

Table 2:

Covariates in 4-Coin Experiment (Ordered Logit Regressions)

Dependent Variable: Number of Reported Tails (0–4)

Age
1 if Female

(1)

(2)

(3)

(4)

(5)

-0.015(0.012)0.120(0.389)-0.017(0.012)0.159(0.399)-0.099(0.473)0.292(0.452)0.006(0.020)0.177(0.496)0.073(0.622)0.703(0.565)0.039(0.176)-0.591(0.587)-0.640(0.697)-0.002(0.002)0.008(0.023)0.072(0.526)-1.052(1.258)-0.407(1.373)0.042(0.184)-0.721(0.620)-0.757(0.693)-0.002(0.002)-0.271(0.238)0.328(0.318)0.336(0.283)-0.013(0.027)0.075(0.573)-1.588(1.269)-1.425(1.520)0.025(0.211)-0.759(0.693)-0.764(0.729)-0.002(0.002)-0.289(0.250)0.475(0.345)0.395(0.276)-0.324(0.251)
0.491**(0.215)0.715(1.311)

94

94

62

62

60

1 if Protestant
1 if Catholic
Income1 if Professional Education
1 if Academic Education
City Size
Religiousness
Religiousness*Catholic
Religiousness*ProtestantRisk Tolerance
Trust
Belief about otherParticipants
N.Obs.

Notes: Ordered Logit estimates. Robust standard errors are in parentheses. “Risk tolerance” isthe answer to the general risk question of the GSOEP (“How do you consider yourself? Are you
in general a rather risk-loving person, or do you try to avoid risks? Use a scale from 1, meaningthat you are not at all willing to take risks, to 7, meaning that you are absolutely willing to takerisks.”) and “trust” is the answer to the WVS trust question (“Generally speaking: Do you think
one can trust other people, or that one should rather be careful when dealing with other people?
Please indicate your answer on a scale from 1 to 7, with 1 meaning that one should be careful when
dealing with other people, and 7 meaning that one can trust other people.”). “Belief about otherparticipants” is the belief of this participant about the share of other participants who reported
four times “tails” while actually “heads” came up at least once. Significance at the 1, 5, and 10
percent level is denoted by ***, **, and *, respectively.

11

even more surprising that people decided to refrain from exploiting the opportunity
to receive a considerable amount of money when they believed that many others
would do so (see, e.g., López-Pérez 2010, López-Pérez 2012).
Two further aspects of our analysis are worth noting. First, when running OLS
regressions using the same predictor variables as above, we find that only one of the
10 specifications has an adjusted R2 above 0 (at 0.0146), all other adjusted R2 values
are negative. Moreover, the resulting adjusted R2 tend to decrease in the numberof included variables. This again underlines our conclusion: the tested predictor
variables do not increase explained variance in the dependent variable compared topure chance. Second, we also tested the correlation between reported number andanswers to the survey questions that we did not include in the main specificationsof Tables 1 and 2. These include a person’s citizenship and country of birth, various
personal characteristics, a person’s current job situation and their current or recent
position in the professional hierarchy, a person’s willingness to tell white lies indifferent situations, a person’s family status and living situation (whether one lives
with a partner and the number of people belonging to the household), the frequencyof church attendance, a person’s political preference, and the individual’s tendency
to behave in an opportunistic way as well as the belief about others’ willingness
to behave like that. Testing these variables as predictors in Probit and Ordered
Logit regressions in the two different data sets, akin to Tables 1 and 2, we findno robust association between any of them and reporting behavior. Summing up,
the overall picture is confirmed: no individual characteristic, whether exogenousor endogenous, is systematically associated with reporting behavior suggesting that
almost all participants in our study tell the truth.

5

Conclusion

Using a representative sample of the German population we conducted telephoneinterviews during which respondents participated in an incentivized experiment. Depending on the treatment, they could earn money by reporting tails as the outcomeof one or four coin tosses. We find that almost all participants report their cointoss(es) honestly: the distributions of reports are extremely close to the true distribution of a fair coin toss or four coin tosses, respectively. Moreover, reports are
not correlated with any individual characteristic, including gender which has beenshown in previous lab studies to predict honesty.
Our results strongly underline doubts about the generalizability of economic
12

models assuming that people always lie maximally when it is financially beneficial
and when they have the possibility to do so. Apparently, people do not only care forthe trade-off between expected costs (caused by some form of external punishment
when making a false claim and being detected) and expected benefits (namely themonetary gain from reporting the maximal type without being caught). Our results
reinforce the results of previous lab experimental research that many people facean intrinsic aversion to misreporting their private information, i.e., a lying cost. If
anything, these lying costs are stronger in our setting outside the lab. A fruitful
avenue for future research would therefore be to explore the implications of lying
cost models in different settings, e.g., how lying costs and concerns for reputationinteract. It is also not clear what drives such a lying cost. It could be based ona concern for self-reputation (Bénabou & Tirole 2006) or come from social norms(e.g., Pruckner & Sausgruber forthcoming, Abeler 2012) or preferences over identity(Akerlof & Kranton 2000, Bénabou & Tirole 2011).
At the same time, this study does not imply that everybody always reports theirprivate information truthfully. The level of lying costs seems to interact with thecontext in which people are asked to report their type (see also Mazar & Ariely
2006, Mazar et al. 2008). While we find that almost everyone reports the outcomeof the coin toss truthfully in the telephone study, previous studies run with student
subjects in the laboratory find that a substantial share of participants report higher
types or lie maximally. But even the lab experiments find evidence for truth-tellingor non-maximal lying. The difference in behavior on the phone and in the lab shows
how malleable reporting behavior can be.
Our results therefore point to important policy implications: institutions, liketax authorities or insurance companies, could make use of the context dependenceof reporting behavior when designing decision-making environments. As we findstrong evidence for widespread lying costs, appropriate mechanisms might be muchless complex than those resulting when assuming that agents have no qualms about
lying. It might be possible to change reporting behavior in simple and low-cost ways
in the spirit of libertarian paternalism (Thaler & Sunstein 2003). Further researchis necessary to uncover what the crucial aspects of the decision-making environment
are that induce truth-telling.
Methodologically, our findings suggest that survey responses are much more reliable than previously assumed: if people report the truth even when there is a strong
monetary incentive to lie, there is less reason to assume that they lie when there areno, or only non-monetary, incentives to do so.

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A

Experimental Instructions

These are the instructions for the 1-coin experiment translated into English. The
instructions for the 4-coin treatment were adapted according to the rules of the 4-cointreatment but otherwise identical.
The experiment’s purpose is without any exception scientific, and all legal regulations of data protection are strictly respected and the anonymity of the data
analysis is fully warranted. Your participation in the experiment is, of course, on a
voluntary basis. Infas and the University of Bonn guarantee the correct and proper
payment of the achieved amount of money.
In case the participant had doubts about any part of the experiment he or she
was given the phone number of the supervisor of the study at Infas, so that he or
she could make sure that everything was legitimate.
You can receive the payment in cash via regular mail (we will then need youraddress), or we will give you a gift certificate code which equals the respective
amount via email or via phone. The gift certificate is valid for a purchase at Amazon.
The address will only be used for mailing the payment of the money you earned andwill be deleted from our database afterwards. You will need a coin to take part in
the experiment (any coin). Please get the coin first. I will then explain the detailsof the experiment.
Now, I will explain the experiment. Your coin has one side showing a number,and another one, often showing some image. After receiving the instructions, I will
ask you to toss the coin, and you will have to tell me afterwards which side of thecoin came up. You receive 15 euros if “tails” comes up.8 You receive 0 euros if“heads” comes up.
The interviewers were instructed to react with the following sentence to any comments or questions that could possibly influence the participants decision: “I can only
give you the instructions. What side of the coin you report to me, is completely up
to you.” They were also reminded, to react neutrally towards any comment by theparticipant concerning the number they reported, and to refrain from making any
comments or jokes.
Once again: You toss the coin and tell me which side came up. If “tails” cameup, you receive 15 euros. Please don’t start just yet. Do you understand the rules?
If participant said that he or she understood the rules, the interviewer continued
8

The German word for “tails” is “Zahl”, literally “number”.

17with the experiment.
Please put aside your telephone receiver (but please do not hang up), and toss
the coin. Afterwards please tell me which side of the coin came up. Please toss thecoin now.
In case the participant hung up by accident he or she was called again immediately.
If necessary, the interviewer read the following question: Which side came up?

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