ISSN (Print) 0473-453X

Discussion Paper No. 1128

ISSN (Online) 2435-0982

NONVERBAL CONTENT AND TRUST:AN EXPERIMENT ON
DIGITAL COMMUNICATION

Zakaria Babutsidze
Nobuyuki Hanaki
Adam Zylbersztejn

April 2021

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, JapanNonverbal content and trust:An experiment on digital communication∗
Zakaria Babutsidze†‡

Nobuyuki Hanaki§

Adam Zylbersztejn¶kMarch 3, 2021

Abstract
We experimentally study the effect of the mode of digital communication on the emergenceof trust in a principal-agent relationship. We consider three modes of communication thatdiffer in the capacity to transmit nonverbal content: plain text, audio, and video. Communication is pre-play, one-way, and unrestricted, but its verbal content is homogenized acrosstreatments. Overall, both audio and video messages have a positive (and similar) effect ontrust as compared to plain text; however, the magnitude of these effects depends on the verbal
content of agent’s message (promise to act trustworthily vs. no such promise). In all conditions, we observe a positive effect of the agent’s promise on the principal’s trust. We also
report that trust in female principals is sensitive to the availability of nonverbal cues about
their partners.

Keywords: Digital communication; Trust; Hidden action; Nonverbal content; Principal-agent
relationship; Promises.
JEL Code: C72, D83∗
This is a revised version of GATE WP 1909 entitled “Digital communication and swift trust”. The authors
acknowledge the support from the following programs operated by the French National Research Agency (Agence
Nationale de Recherche): DigiCom as a part of U CAJEDI (ANR-15-IDEX-01) and LABEX CORTEX (ANR-11LABX-0042) as a part of Université de Lyon (ANR-11-IDEX-007), as well as the Joint Usage/Research Center
at ISER, Osaka University, and Grant-in-aid for Scientific Research, Japan Society for the Promotion of Science(15H05728, 18K19954, 20H05631). Ynès Bouamoud, Imen Bouhlel, Maxime David, Carla Lapointe, Ismaël Rafaï,Charlotte Saucet, Ann Thampi, Vincent Théroude, Mira Toumi, Dorian Vincileoni, and Charlène Wojerz haveprovided quality research assistance. Quentin Thévenet has provided valuable assistance with software programming. We thank Giuseppe Attanasi, Brice Corgnet, John Duffy, Martin Dufwenberg, Astrid Hopfensitz, Nicolas
Jacquemet, Annett John, Michal Krawczyk, Charles Noussair, Ro’i Zultan, Diego Zunino and other participants
to seminars, workshops and conferences at EMLyon, Université Côte d’Azur, Waseda University, Warsaw Economic Seminar, 2018 ASFEE Meeting in Nice, 2018 ESA World Meeting in Berlin, 2019 ECBA Workshop in Paris,and 2020 ESA Global Online Meeting for valuable comments. Adam Zylbersztejn acknowledges the VISTULA
Fellowship.
†
SKEMA Business School, Université Côte d’Azur (GREDEG), France‡
OFCE, Sciences Po Paris, France§Institute of Social and Economic Research, Osaka University, Japan¶
Univ Lyon, Université Lyon 2, GATE UMR 5824, F-69130 Ecully, FrancekVistula University Warsaw (AFiBV), Poland

11

Introduction

Trust is of utmost value for economic activities (Rousseau et al., 1998). It can be defined as “thebelief that others act in the interest of some measure of fairness or social welfare rather than theirown self-interest” (Bracht and Feltovich, 2009, p. 1036).1 Various institutions, spanning from
centralized justice and law enforcement systems to self-governance within a community, fosterand secure trust in economic interactions by means of incentives and punishment (Farrell and
Knight, 2003). But trust may also emerge through tacit social agreements and informal normsof behavior (Greif, 1993). Such informal trust often hinges on reputation which, in turn, requires
repeated interactions and ways of providing and receiving feedback (Bolton et al., 2004; Bracht
and Feltovich, 2009; Simpson et al., 2017). Importantly, however, even when reputation lacks
relevance, is hard to build, or when repeated interactions are unlikely, people may still exhibita specific form of trust towards others – known as “swift trust” (Meyerson et al., 1996). Swifttrust arises during a short process of interaction between previously unacquainted parties and(temporarily) affects their behavior.2
Recent experimental evidence suggests that face-to-face communication can be highly conducive in establishing trust in social interaction by facilitating the detection of other’s trustworthiness (He et al., 2016). Albeit most primal, this form of communication is multilayer and encompasses not only the verbal components (e.g., verbal content and style of the message), but also
the nonverbal components (e.g., a speaker’s facial displays, voice, body movements).3 However,1
As they note, trust and trustworthiness (i.e., the extent to which trust in a person is warranted) are commonly
considered as primary components of social capital (see also Knack and Keefer, 1997).
2
Swift trust differs from the general willingness to trust other people – an other-regarding preference that canbe measured either in behavioral or attitudinal manner (or both). A commonplace methodology for measuring
behavioral trust in laboratory settings is the incentivized, one-shot and anonymous trust game based on Berg et al.
(1995). In this context, trust is defined as “a willingness to bet that another person will reciprocate a risky move(at a cost to themselves)” (Camerer, 2003, p. 85) and captured by the amount transferred from the trustor to thetrustee; accordingly, trustworthiness is the amount subsequently re-transfered in the opposite direction. See Johnson
and Mislin (2011) for a meta-analysis of trust game experiments. Attitudinal trust, in turn, is often measured inlarge-scale surveys using items such as “Generally speaking, would you say that most people can be trusted, or thatyou can’t be too careful in dealing with people?” (World Values Survey; see, e.g., Knack and Keefer, 1997), “Ingeneral, one can trust other people” (German Socio-Economic Panel, also used herein), or “I assume that people
have only the best intentions” (Global Preference Survey being part of the Gallup World Poll; see Falk et al., 2018).
In addition, Falk et al. (2016) provide evidence that the two types of measures are correlated.
3
As noted by Chovil and Fridlund (1991), “[f]acial displays are a means by which we communicate with others.
Like words and utterances, they are more likely to be emitted when there is a potential recipient, when they are2the ongoing IT revolution that has been increasingly replacing direct face-to-face interactions withthose mediated by new digital technologies. Many strangers are now transacting in open online
marketplaces without ever meeting in person. People are donating to charity online (e.g. Qgiv )or contribute to online crowd-funding campaigns (e.g. Kickstarter ). Teenagers increasingly prefer
communicating with their friends indirectly via texting, social media, and video-chatting rather
than interacting face-to-face (Rideout and Robb, 2018). Furthermore, the proliferation of IT technologies has led to an upswing in “gig economy” and abundance of distributed enterprises. In these
new forms of organizations, communications among team members are less often face-to-face thanin the more traditional ones (see Marlow et al., 2017, for a recent review). Recently, the outbreakof COVID-19 and the resulting global lockdown have shown that a fast and sustainable digitaltransition in social and professional interactions is possible in a wide range of human activities.
Herein, we focus on the role of digital communication in building trust in human interactions.
The main question we are asking in this paper is: does the performance of a digital communication
tool in inducing trust depend on its capacity to transmit nonverbal content? 4 To answer thisquestion, we conduct an incentivized laboratory experiment that implements a classic principalagent paradigm (known as the hidden action game; see Charness and Dufwenberg, 2006) with
one-way, pre-play communication. Before making decisions, the principal receives a message fromthe agent which is transmitted in one of the following ways: plain text message, auditory message,or video message. We believe that the three communication protocols studied in this paper are
rather standard in the light of both the existing literature (see Bicchieri and Lev-On, 2007) andeveryday experience. As we discuss in the next section, the novelty of our design allows us tohomogenize the verbal content of communication across treatments, and thus isolate the causal
useful in conveying the particular information, and when that information is pertinent or appropriate to the social
interaction” (p. 163). This echoes a broad definition of communication used in animal studies which we also
adopt for the purpose of the present study: “behavior in [...] the sender [...] which evokes a response in [...] thereceiver” (Ekman, 2006, p. 21). Clearly, this definition includes both verbal and nonverbal forms or components ofcommunication.
4
Numerous experiments point to the importance of nonverbal content of communication. A survey by Bonnefon
et al. (2017) suggests that observing physical cues (such as facial displays or, more broady, bodily movements) may
help detect cooperation in various economic games. See, for instance, Fetchenhauer et al. (2010); Bonnefon et al.
(2013); Vogt et al. (2013); Centorrino et al. (2015); De Neys et al. (2015); van Leeuwen et al. (2017). Greiner et al.
(2012) and Tognetti et al. (2020) provide related evidence for auditory cues. Furthermore, Eckel and Petrie (2011)
and Zylbersztejn et al. (2020) document that people tend to exhibit a preference for receiving nonverbal cues insocial interactions.

3

link between the richness of nonverbal content transmitted through a communication channel andthe subsequent emergence of trust. We achieve this by inserting the recordings made in priorface-to-face interactions into our three communication treatments.
Our study aims to contribute to the experimental literature that highlights the importanceof communication in fostering trust between humans and building sound economic partnerships.
With some exceptions (that are discussed in the following section), it is commonplace in thisresearch to employ a minimalistic communication protocol in which written messages are transmitted between anonymous parties. By design, these studies are well suited for investigating therole of verbal content, but not the nonverbal content. In the context of the hidden action gamethat we use, several experiments highlight a particular feature of verbal messages – a voluntary,
non-binding promise to cooperate – as a credible signal of trustworthiness and an enhancer oftrust.5 This finding is also supported by field evidence from a TV show The Golden Balls, a highstake prisoner’s dilemma environment with a pre-play stage of natural face-to-face communication
moderated by the host (Belot et al., 2010; Van den Assem et al., 2012; Turmunkh et al., 2019).
Our controlled laboratory setting allows us to extend the experimental testbed to a wide setof communication protocols – ranging from the minimalistic text messages to the most complex
face-to-face interaction, and to focus on digital communication. For each of the studied environments, our experimental data corroborate the general finding that voluntary, non-binding promises
promote trust. Furthermore, we extend this literature by shedding light on the role of nonverbal
content of communication, as well as its interplay with the verbal content.
Our main findings are as follow. On aggregate, plain text messages engender less trust thaneither audio or video messages (which, in turn, yield similar trust rates). Providing auditory
cues about the speaker seems to play a key role in inducing trust, as compared to providing aplain text content of the message. Adding visual cues in the video-based communication increases
the observed trust rates only slightly (and not significantly). Furthermore, we also find evidence
5See, for instance, Charness and Dufwenberg (2006); Vanberg (2008); Ismayilov and Potters (2016); Schwartz
et al. (2019). For a systematic review of the experimental literature on promise-making, see Woike and Kanngiesser(2019). Other characteristics of messages may also matter. Using a modified two-person trust game with anadditional external observer, Chen and Houser (2017) report that other important features of a written message are
its length (which increases trust) and the fact of mentioning money (which is associated with untrustworthiness).
On the other hand, factors such as the use of encompassing words (like “we” or “us”) do not seem to matter.

4

that these aggregate effects are moderated by the verbal content of communication. Including
auditory cues generates a statistically significant increase in trust levels when the message conveys
a promise to cooperate, with no further effect of visual cues. However, in the the absence of sucha promise, a combination of auditive and visual cues is necessary to significantly increases trustlevels compared to the baseline textual communication.
Finally, in the additional analyses we follow some of the previous experiments on strategic
communication and focus on players’ gender. We observe systematic gender differences in theenvironments with a restricted access to nonverbal cues. Females are generally less trusting thanmales when solely the verbal content of a message is transmitted (which usually does not evensuffice to identify the other person’s gender). Adding nonverbal content based on auditory cues
helps close this gap. However, it also makes females display more trust towards other females
than towards males (which is a bias, since it stands at odds with the degree of trustworthinessobserved in both genders). These differences disappear once we further include visual cues about
the sender.
Altogether, our experimental evidence points to the positive effect of the transmission of nonverbal content of communication on trust, but also to the heterogeneity of this effect. It may besensitive not only to the factors that are endogenous to the process of communication (such as theverbal content that is transmitted by the sender alongside the nonverbal content), but also to theexogenous ones (such as players’ gender).

2Empirical strategy

The main objective of our study is to isolate the effect of the capacity to transmit nonverbal content on inducing trusting behavior in an experimental hidden action game with one-way, pre-playcommunication between players. The main virtues of our experimental design lie in the (i) ecological validity of the communication protocol (since we allow our subjects to address others in anunrestricted manner) while maintaining (ii) full control over the verbal content of communication
across different communication modes, so as to (iii) draw causal inference about the impact of the5different layers of nonverbal communication on trust in economic interactions.
Our experiment relates to the early articles by Brosig et al. (2003) and Bicchieri and Lev-On(2007) who compare the performance of different modes of pre-play communication in inducing
cooperation in public goods games. Like them, we employ a rich set of communication protocols:
plain text message transmitted through a computer interface, audio message, and video message.
The main findings from this literature are: (i) communication generally improves cooperation ascompared to a no-communication control condition, (ii) communication via plain text messages
and audio messages has a similar effect on cooperation, and (iii) communication via video messages
and face-to-face communication perform equally well and further enhance cooperation.6 However,such outcomes are far from being stable and the literature disagrees on the relative performance ofvarious communication channels in inducing cooperative behavior. In a related experiment withpublic goods games, Bochet et al. (2006) find no difference between a communication condition
with computerized plain text messages and another condition with face-to-face communication.
The same null result is observed for trust and trustworthiness in a trust game experiment by
Bicchieri et al. (2010).
Importantly, the determinants of behavior in this body of experiments are twofold: first, howpeople communicate (which is our variable of interest); second, what is the verbal content of theircommunication (which may impede the measurement of our variable of interest). Thus, these previous studies impose different communication channels as exogenous experimental treatments, butthey leave the verbal content of messages to be endogenous and to vary freely across treatments.7
Thus, behaviors observed in these studies may stem not only from an exogenous variation in the
6This echoes the findings from a meta-analysis by Balliet (2010). This study distinguishes between two forms ofcommunication and reports that cooperation is higher under face-to-face communication than under communication
via written messages. Other experiments provide similar evidence in the contexts of conflict resolution (Rockmann
and Northcraft, 2008) and bargaining (Valley et al., 1998); see also Charness and Gneezy (2008) for a related
discussion of earlier experimental evidence.
7
Recent studies that investigate the relationship between the mode of communication and lying behavior (see,for example, Abeler et al., 2014; Conrads and Lotz, 2015; Cohn et al., 2018) do better in controlling the content
of the communication across different channels. In these studies, subjects simply need to report the outcomes ofcoin tosses in all the communication channels (voice, text, online-form, or face-to-face). Abeler et al. (2014) find nodifference in the reported outcome between phone call and on-line form. Cohn et al. (2018) report that the presenceof a human counter part (who receives the reported outcome) is a more important determinant of lying than themode of communication (text vs. voice). Finally, Naquin et al. (2010) find that people are more likely to lie whensending e-mails than in hand-written messages.

6

modes of communication allowing for different transmission of nonverbal content, but also froman endogenous and uncontrolled variation in the verbal content of communication. We conjecturethat this may be the underlying cause of the diverging results in this literature. For the sake ofillustration of the interplay between the treatment variable, the content of communication, andthe eventual behavior, consider two examples drawn from the literature on promises.
Example 1. Using a set of communication conditions that is closely related to ours (spanningfrom computer chat to audio communication, then to face-to-face communication), Conrads andReggiani (2017) report that the outcome of communication (i.e., the rate of promise-making) varies
in a systematic way: the richer and more direct the communication protocol, the more likely asubject is to make a promise to the other person.8 At the same time, they observe little variationin the subsequent rates of promise-keeping.
Example 2. In the context of the hidden action game, the data from Charness and Dufwenberg(2006, 2010) point to an opposite phenomenon: in a richer communication environment, subjects
are less likely to make promises.9 However, the effects of communication (overall, as well as
conditional on a promise to be trustworthy) on trust and trustworthiness are stronger in the richer
communication environment.
We also note that the existing literature proposes some methods for controlling specific featuresof communication. One way to control the variability of its content is to restrict the allowed topics
of conversation (see Bouas and Komorita, 1996, for an early experiment applying this method).
For instance, on top of varying modes of communication (either computer chat or face-to-face)in the trust game experiment by Bicchieri et al. (2010), players are either allowed or not allowed
to make a promise. In a similar setting, Ismayilov and Potters (2016) allow pre-play written
message which can or cannot be related to the experimental game. However, while controlling the
8

Their experiment is based on an individual task rather than an experimental game. Communication takes
place between a subject and the experimenter (a research assistant). In all communication conditions, the latter
uses a standardized script presenting the task (an online survey to be filled in within 24h without any additional
gratification) and asking a subject whether he or she promises to perform it.
9
In those studies, authors consider two kinds of one-way, written messages: a fixed-form message (41/47 messages
contain a promise to cooperate) and a free-form, hand-written statement (24/42 messages contain a promise tocooperate). We use Fisher’s exact test to compare these two rates and obtain p = 0.004.

7

variation in the characteristic of interest, this method fails to control for the variation in othercharacteristics. For example, He et al. (2016) observe that when people are not allowed to talkabout the game they are about to play, some of them may resort to using signs and body language
to non-verbally transmit strategic content.
In this paper, we provide a simple yet novel experimental method for comparing the performance of different modes of digital communication, which solves the problem of the endogeneityof the verbal content of communication. We focus on one-way communication – from the agent
to the principal – in order to capture the causal effect of the transmission of nonverbal content ofcommunication on trust.10 In order to homogenize the communication content across treatments,
we first video-record messages transmitted in an experiment with direct (face-to-face, henceforth,F2F) interactions. Then, we use these recordings in our three main treatments. Therefore, alltreatments are based on resampling from the same set of messages, and thus the verbal content is homogenous across treatments, while the richness of nonverbal cues transmitted in thecommunication process varies across communication modes.
Going beyond its purely methodological scope, the design of our experiment is also motivatedby numerous real world situations – modern political, social, or commercial campaigns – in which
messages conveyed to a relatively small group of people in a direct face-to-face manner are recorded,
and then widely broadcasted via video-, audio-recording, or text transcripts. Importantly, therecent outbreak of COVID-19 and the resulting lockdown suggest that these forms of campaigning
could become increasingly prominent in the future. As a dramatic example, consider the 2020
presidential campaign in Poland most of which happened under general lockdown. These unusual
circumstance have greatly restricted live campaigning (e.g., election tours and partiasan rallys),leaving the candidates with little choice but to turn to small public events with large digital
media broadcast. Our experimental design closely mimics such environments and can deepen our
10

Anbarci et al. (2019) highlight the importance of two-way communication for enabling receivers to extract
information that is both “precise” and “consistent in its implication for decision making” in a strategic context. We,
however, note that when communication is real-time and multi-directional – like in Bochet et al. (2006), Bicchieri
et al. (2010), several treatments in Brosig et al. (2003), as well as other experimental data included in Bicchieri
and Lev-On (2007) and Balliet (2010) – each interacting group becomes an endogenously formed cell with nocounterpart in other conditions. In this case, it becomes even harder (if not impossible) to establish a causal linkfrom an individual message to behavior, since all the messages are correlated, and each player acts as both thesender and the receiver of messages.

8understanding of the effectiveness of applying different communication techniques therein.

3

Experimental design and procedures

Our three main experimental treatments are based on a set of video recordings from the preplay face-to-face communication from agents to principals gathered in our earlier experiment.
We employ a classic hidden action game by Charness and Dufwenberg (2006) in our experiment.
Participants acting as agents in those earlier sessions with face-to-face communication were clearly
informed that they would be video-recorded and that these recordings could be used in future
experiments. Participants to our three main treatments with digital communication are clearly
informed that the recorded messages they are about to receive (either as video recordings, audio
recordings, or text transcripts) come from the previous experimental sessions. Furthermore, theexperimental instructions handed out to all participants specify that their own payoffs may dependon the earlier decision of the agent who sent the message, but that their own decisions have nofurther impact on that agent’s payoffs.11 Below, we outline the experimental game and the generalprocedure of our experiment. In the latter, we first describe the face-to-face experiment used
to gather the recordings, and then our three main experimental treatments that exploit those
aforementioned recordings.

3.1

Experimental hidden action game

Figure 1 presents the classic hidden action game by Charness and Dufwenberg (2006). All payoffs
are in Euros. The game is played between two parties: the principal (henceforth, player A) andthe agent (henceforth, player B). Player A may either choose an outside option Out which yields
5 to both players and ends the interaction, or go In. Then, player B may either choose to Roll adie (which yields 12 to A and 10 to B with the probability of 5/6, and 0 to A and 10 to B withthe probability of 1/6), or not to Roll (yielding 0 to A and 14 to B with certainty). This game11

By the standard definition of trust adopted in the opening section – “the belief that others act in the interestof some measure of fairness or social welfare rather than their own self-interest”, the way we link player A’s payoff
to player B’s earlier decision allows us to measure player A’s trust in player B’s cooperative intentions even thoughboth parties do not directly interact.

9

Figure 1: Experimental hidden action game

A

In

Out

B
(5, 5)
Roll

Don’t roll

Chance(0, 14)P r = 5/6

(12, 10)P r = 1/6

(0, 10)provides a simple setting for studying principal-agent relationships with moral hazard: incentives
are not aligned between the two parties, and earning 0 is not perfectly informative for player A
about player B’s action.
Following Charness and Dufwenberg (2006), in the face-to-face implementation, we simultaneously elicit both players’ decisions. Namely, the player B makes a decision without knowing player
A’s decision. Player B’s decision is only implemented had player A gone In. In our three main
treatments the player B’s decision is pre-recorded, and again player A takes the decision without observing it. In addition, in all experimental conditions the game is preceded by a pre-playcommunication stage in which player B delivers a message to player A.

3.2

ProcedureFirst, we have collected the dataset for the direct face-to-face (F2F) communication condition. Thisauxiliary condition plays an instrumental role in the main experiment: the preliminary dataset
collected therein allows us to implement the three digital communication conditions. We havevideo-recorded all the pre-play statements made by player Bs, and kept records of their subsequent
choices (to Roll or not), as well as of the outcomes of die rolls. Based on that information, we
have then constructed our three treatments of interest – VIDEO, AUDIO, and TEXT – in which
we vary the channel through which player Bs’ messages are transmitted to player As.
10

Experiment begins with preliminary measurements (distributional preferences, risk preferences,cognitive skills, emotional intelligence, general trust towards others) and a basic socio-demographic
questionnaire. This is followed by treatment-specific experimental instructions. In VIDEO, AUDIO, and TEXT, those instructions also include a one-page summary of the F2F instructions.
Furthermore, after making the decision in the game (to go In or to stay Out), player As fill in ashort questionnaire about their perceptions of player Bs.12 Below, we overview the implementationof this hidden action game.

3.2.1

F2F

Each experimental sessions involves 6 player As and 6 player Bs. All player As remain in one
room during the whole experiment. They are seated in a single row, isolated one from another by
separators, and not allowed to talk. The space in front of them is left open and used by a player B
to make a brief statement. A video camera recording player Bs’ statement is discretely positioned
in the middle of the player A’s raw.
Player Bs enter the room one by one, so that player As play six rounds of the game (whichis common knowledge). Each time, player B faces the center of player As’ row, and all player As
have a clear view on the speaker. Player B also has a clear, unobstructed view on all six playerAs.
After making a statement, player B is invited to a separate room where s/he privately decides
whether to Roll a die or not. Then, s/he is asked to leave the laboratory and wait outside until
the end of the experiment. At the same time, each player A makes a decision whether to go In
or stay Out. All decisions are made on a sheet of paper, which is then put in an envelope, sealed,
and collected by the laboratory staff after each round. In addition, once player B has made adecision and left the separate room, a laboratory staff member rolls a die in private and marks
the outcome on player B’s sealed envelope.
12

Note that the experimental instructions use different labels for player A’s decisions than the one used byCharness and Dufwenberg (2006) – Right instead of In and Left instead of Out. In the paper we adhere to their
original nomenclature. An English translations of the instructions, a summary of the measured characteristics, andfurther details on how the experiment was implemented in the lab (including extra care we have put in to minimize
the likelihood that player As and player Bs know each other) are provided in Appendices A, B, and C, respectively.

11

At the end of the experiment, player As and player Bs are randomly and anonymously matchedin pairs. The outcome of the game for each pair is based on the payoff structure described in
Figure 1 and defined by the decision made by player A after player B’s statement, as well as thedecision made by player B in a private room had A chosen to go In. For B’s decision to Roll, theoutcome of the die roll is also taken into account.

3.2.2

VIDEO, AUDIO, and TEXTIn these three treatments, each participant acts as a player A and is exposed to 10 items randomly
drawn from the set of 41 recordings of player Bs’ statements made in F2F.13 Subjects are clearly
informed that these recordings have been gathered in experiments conducted in the past, and that
although their own decisions do not influence the payoffs of the player Bs behind those messages,
their own payoffs may depend on those player Bs’ antecedent decisions.
In each round, each participant receives a message (a statement made by player B). In the
VIDEO treatment, participants watch the video recording of the message. In AUDIO, they listen
to an audio recording. They can watch/listen to a given recording only once. In TEXT, precisetranscripts of the statements are displayed on player As’ computer screens for the amount oftime equal to the duration of the corresponding recording.14 We would like to emphasize that
the TEXT condition is not meant to measure the potential of text messages, or more generally
written statements, in inducing trust in humans. Rather, TEXT is the baseline condition throughwhich we control for the effect of merely providing verbal content of communication on subsequent
trust, so as to isolate the effects of nonverbal content. Hence, the transcripts keep track of vocal
parasites, slips of the tongue, sudden pauses, repetitions, etc., which are common for improvised
spoken language and come as an essential feature of what we consider as the verbal content ofcommunication (which, in turn, is meant to remain constant across treatments).15
13

We have conducted 7 F2F sessions. However, one player B in session 6 of F2F decided to quit after the
preliminary measurements and before receiving the instruction of the hidden action game, and was replaced by aresearch assistant unknown to player As. To avoid any contamination of player As’ behavior, that research assistant
acted as player B in the final round of the experimental game. The data from that round were dismissed and ourdataset from that session only covers 5 player Bs, and thus 41 player Bs in total.
14
Pilot tests suggested that this exposure time was sufficient for reading with comprehension a short statement
displayed on a computer screen.
15
Christenfeld (1995) shows that “ums” may matter for the receiver’s perception of the sender. However, we also12After receiving a message, each participant decides whether to go In or stay Out and fills in the
post-decision questionnaire. Participants are not informed about the choice made by the player B
whose message they have just received, or about the outcome of die roll.
After completing 10 rounds, each player A is randomly matched with one of the ten player
Bs. The payoff structure corresponds to player A’s payoffs in Figure 1. Player A receives 5 Euros
when staying Out, 0 Euros when going In with a player B who does not Roll, or either 12 Euros(with 5/6 chance) or 0 Euros (with 1/6 chance) if player B chooses to Roll.
The experiment took place at Laboratoire d’Economie Expérimentale de Nice (LEEN) of the
Université Côte d’Azur, France, between April 2017 and February 2018.16 All treatments were
implemented through a between-subject design. In addition to 83 subjects who participated in
F2F experiment, a total of 217 subjects participated in VIDEO (62), AUDIO (70), and TEXT
(85) treatments.17 Our subjects are predominantly students (296/300), their average age is 21.28(SD 3.69), 59.3% of them are females. See Appendix D for a summary of other characteristics of
our sample.

4

Results

4.1

Summary of player Bs’ behavior

Before investigating player As’ behavior, let us first briefly summarize the behavior observed among
their counterparts. 61% of player Bs in the F2F experiment decide to Roll, while a promise to
Roll is made by 51.2% of them. Following Charness and Dufwenberg (2006), a promise is definedas statement of intent to Roll.18 There is no significant difference between the duration of thebelieve that the mere presence of inarticulate sounds does not affect the interpretation of the strategic elementsin the verbal content of messages. Consistently with a large body of experiments using more “polished” free-form,
hand-written messages, in our TEXT condition with raw transcripts we identify a positive effect of promise-making
on trust (as discussed in the following section).
16
The computerized parts of the experiment are all programmed in z-tree (Fischbacher, 2007).
17
Number of participants in VIDEO, AUDIO, and TEXT varies due to the natural variation in show-up rates
across sessions. The recruitment of the participants has been carried out using ORSEE (Greiner, 2015).
18
As raised by Houser and Xiao (2011), the ex post interpretation of free-form messages is a major methodologicalchallenge for the experimenter. The literature still lacks a common consensus on whether this should involve content
analysis carried out by the experimenter (Charness and Dufwenberg, 2006), by independent coders (He et al.,2016), through an incentivized coordination game (Houser and Xiao, 2011), or by asking the subjects for their own
interpretation (Servátka et al., 2011). Echoing a recent study by Schwartz et al. (2019), herein we implement a13

messages with promise as compared to the ones without (average duration: 28.90 seconds vs. 23.75
seconds; p = 0.108 using ranksum Mann-Whitney test).
There is a large difference between the frequencies of Roll conditional on a promise to Roll(71%) and without such promise (50%). The result of a logit regression of the Roll dummy on
the Promise to Roll dummy (and session fixed effects) suggests that a promise to Roll is highly
predictive of a subsequent decision to Roll : the average marginal change of the likelihood ofRoll due to a Promise to Roll is estimated at 0.394 (SE=0.122, p = 0.001).19 In the additional
analyses reported in Appendix F, we also offer a screening exercise exploiting the information about
player B’s individual characteristics to investigate who makes a promise. We find that pro-social
preferences (measured by the SVO angle) stand out as a significant predictor of promise-making.
The Roll (promise-making) rates in our three treatments are as follow: 66.6% (53.9%) in
VIDEO, 64.7% (52.9%) in AUDIO, and 60.0% (49.3%) in TEXT. These small variations are
due to random sampling, and both rates are equally distributed across treatments.20 Albeit small,these sampling differences are nonetheless accounted for in the comparisons of aggregate trust rates
across treatments (see Section 4.2). First, we check if there are important differences in aggregate
trust rates for those player Bs that chose to Roll and those who did not. These differences arefound to be small (0.4, 2, and 3 percentage point in VIDEO, AUDIO, and TEXT, respectively)
suggesting that the observed treatment effects cannot simply stem from those random sampling
variations in player Bs’ trustworthiness. Second, echoing the previous experimental studies ofcommunication and trust, we condition the trust rates on the content of messages (i.e., promise
vs. no promise to Roll ).
method combining the first two approaches. Player Bs’ statements were classified as promises or non-promises by
two independent coders (research assistants). The first coder classified the content of messages (promise or not)while preparing the transcripts of player Bs’ statements. Then, another coder received the full list of 41 transcripts
and independently classified each of them as a promise or a non-promise. Ties were broken by one of the authors.
All the transcripts (translated from French to English) along with their classification (promise or non-promise) andthe resulting decision (Roll or Don’t roll ) may be found in the Appendix E.
19
Session dummies (7 sessions, first session dummy dropped) allow us to account for the fact that player Bs face
the same audience of player As within a session, but the audiences vary between sessions. These coefficients arejointly insignificant (p = 0.287). For details, see Table 7 in Appendix F.
20
To test whether each of these variables is equally distributed across the three treatments, for every player A we
calculate the frequency of decision Roll and the frequency of promise-making among the ten player Bs that person
faced during the experiment. Comparing the distributions of those frequencies across the three treatments using
Fisher’s exact test, we find p = 0.258 for Roll and p = 0.123 for the promise to Roll. Given that the size of eachcontingency table is relatively large (9 × 3), both p-values are computed using Monte Carlo simulations.

14

Figure 2: Average Trust Rate(a) Overall
0.6
0.5
0.4
0.3
0.2
0.1
0

(b) After a promise
0.6
0.5
0.4
0.3
0.2
0.1
0

VIDEO AUDIO TEXT(c) Without a promise
0.6
0.5
0.4
0.3
0.2
0.1
0

VIDEO AUDIO TEXTVIDEO AUDIO TEXT

Note. Each bar depicts the aggregate likelihood of a player As going In: across treatments (Panel (a)), and conditional on
the type of player B’s statement (Panels (b) and (c)). The error bar shows two standard errors range.

4.2

Trust and promises with digital communication

Figure 2 summarizes aggregate trust rates across our experimental conditions. The statisticalcomparisons of these rates are based on linear probability models (LPM) reported in Table 1. In
those models, we regress the trust indicator variable (= 1 if player A goes In, = 0 otherwise) on theset of condition indicator variables corresponding to various conditions (= 1 for a given condition,= 0 otherwise). The models are constructed such that each coefficient corresponds to the trust
rate observed in a given condition. We estimate cluster-robust standard errors (individual-levelclustering) to account for the potential unobserved correlation between individual observations
and the heteroscedasticity of residuals in the LPM.
The first model (Table 1a) uses treatment indicators as a set of explanatory variables. In thesecond model (Table 1c), these variables are further interacted with promise/non-promise indicators. For each model, the equality of trust rates between two (multiple) experimental conditions
is parametrically tested by comparing different coefficients through t-test (F -test). Finally, inTables 1b and Table 1d we compare aggregate trust rates across the experimental conditions.21
Panel (a) of Figure 2 provides the average trust rates, i.e. the likelihood that a player A
goes In, in VIDEO, AUDIO, and TEXT.22 Overall, we observe similar trust rates in VIDEO and
AUDIO (49.0% and 44.4%, respectively; p = 0.289) which are, in turn, significantly higher than21

As a robustness check, Table 9 in the Appendix G re-assesses the aggregate comparisons from Table 1b after
including player B fixed effects. This is a general parametric way to account for the fact that different player As
are exposed to a message coming from the same player B. All the results remain robust to this extension.
22
See Appendix H for details (as well as caveats) of the comparisons of these three treatments with the F2F
experiment. In a nutshell, the trust rate in F2F is 40.2% – descriptively higher than in TEXT, and lower than in
AUDIO and VIDEO; yet, none of those differences is found to be statistically significant.

15

Table 1: Statistical support for Figure 2 and trust rate comparisons

1[V IDEO] (α1 )1[AU DIO] (α2 )1[T EXT ] (α3 )

Rate
(SE)
0.490
(0.032)0.444(0.029)0.365(0.026)p< 0.001

VIDEO vs. AUDIO(H0 : α1 − α2 = 0)
VIDEO vs. TEXT
(H0 : α1 − α3 = 0)
AUDIO vs. TEXT
(H0 : α2 − α3 = 0)

< 0.001< 0.001(a) Trust rates and their SE from Fig. 2a1[V IDEO, N ON − P ROM ISE] (β1 )1[AU DIO, N ON − P ROM ISE] (β2 )1[T EXT, N ON − P ROM ISE] (β3 )1[V IDEO, P ROM ISE] (β4 )1[AU DIO, P ROM ISE] (β5 )1[T EXT, P ROM ISE] (β6 )

Rate
(SE)
0.420
(0.040)0.342(0.035)0.301(0.030)0.551(0.034)0.535(0.034)0.434(0.035)

Diff.
(SE)

p

0.046
(0.043)0.126(0.042)0.080(0.039)

0.289
0.003
0.042

(b) Comparing trust rates from Fig. 2ap< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001(c) Trust rates and their SE from Fig. 2b and 2c

Diff.
p
(SE)
Trust rates w/o a promise to Roll
VIDEO vs. AUDIO
0.077
0.151
(H0 : β1 − β2 = 0)(0.054)VIDEO vs. TEXT
0.119
0.020
(H0 : β1 − β3 = 0)(0.050)
AUDIO vs. TEXT
0.042
0.371
(H0 : β2 − β3 = 0)(0.046)
Trust rates after a promise to Roll
VIDEO vs. AUDIO
0.016
0.742
(0.048)(H0 : β4 − β5 = 0)
VIDEO vs. TEXT
0.117
0.016
(H0 : β4 − β6 = 0)(0.048)0.101
0.037
AUDIO vs. TEXT
(H0 : β5 − β6 = 0)(0.048)The effect of promise on trust
VIDEO
0.131
0.000
(H0 : β4 − β1 = 0)(0.036)
AUDIO
0.193
0.000
(H0 : β5 − β2 = 0)(0.038)
TEXT
0.133
0.000
(H0 : β6 − β3 = 0)(0.037)
Joint equality of diff.’s
0.420(d) Comparing trust rates from Fig. 2b and 2c

Note. (a) and (c): OLS estimates from linear probability models (LPM) regressing the trust indicator variable (= 1 if player
A goes In, = 0 otherwise) on the all-experiment set of condition indicator variables (= 1 for a given condition, = 0 otherwise).
The models are intercept-free, and each coefficient provides the aggregate rate (“Rate”) of trust in a given experimental
condition. p-values correspond to standard t-test of a coefficient’s nullity.
(b) and (d): coefficient comparisons based on the models (a) and (c), respectively. p-values correspond to t-tests for thenullity of the differences (“Diff.”) between a given pair of coefficients; the final test in (d) is an F -test for the joint nullity of
three differences.
In all models, observations are clustered and the individual level and estimated standard errors are cluster-robust. There 620
observations (62 clusters) in VIDEO, 700 (70) in AUDIO, and 850 (85) in TEXT.

16

in TEXT (36.5%, with p = 0.003 vs. VIDEO, and p = 0.042 vs. AUDIO).23
At the treatment level, these rates are insensitive to the conditioning on player B’s actual
trustworthiness: ex post, we hardly observe any variation between the aggregate rates of trust
displayed towards player Bs who decided to Roll as compared to those who did not (VIDEO:
49.2% vs. 48.8%; AUDIO: 45.5% vs. 42.5%; TEXT: 37.3% vs. 35.3%). This suggests that, inaggregate terms, player As’ trust is disconnected from player Bs’ trustworthiness. However, amessage may nonetheless convey a credible signal of player B’s trustworthiness that is taken into
account by player As: a voluntary promise to Roll.24
Panels (b) and (c) of Figure 2 separates the trust rates depending on whether player B’s
message contains a promise to Roll (Panel b) or not (Panel c). Trust rates following a promise do
not significantly vary between VIDEO and AUDIO (55% vs. 54%, p = 0.742), and they are bothsignificantly higher than 43% observed in TEXT (p = 0.016 vs. VIDEO, p = 0.037 vs. AUDIO).
Without a promise, trust rates vary from 42% in VIDEO to 34% in AUDIO to 30% in TEXT, andthis time only the difference between VIDEO and TEXT is significant (p = 0.020; other pairwise
comparisons yield p > 0.150). The effects of a player B’s promise to Roll on the overall trust rate
are positive (13 percentage points in VIDEO and TEXT, and 19 percentage points in AUDIO)
and statistically significant in all the treatments (p < 0.001). Furthermore, these effects do notvary significantly across the three treatments (p = 0.402, F -test).
To further investigate the robustness of the effect of player Bs’ promises on player As’ trust,Table 2 provides average marginal effects from a logistic regression of the In dummy on the
1[P romise] dummy, coupled with a set of individual-level control variables. These controls are
related to two questions: who is the subject in the role of player A that is more likely to trust,and how does that person perceive the participant in the role of player B?
23

Another way to look at those data is to take each player B as an individual observation and compare the trust
rates (i.e., the likelihood that a given player B is trusted by a player A) across the three experimental conditions
using two-sided signrank test. The main patterns in the data remain unchanged: trust rates are lower in TEXT
than in either AUDIO (p = 0.012) or VIDEO (p = 0.001), and do not differ significantly between the last two
conditions (p = 0.241). The respective comparisons conditional on a promise (on a non-promise) yield p = 0.013,
p = 0.005, and p = 0.715 (p = 0.390, p = 0.100, and p = 0.204).
24
This observation echoes Belot et al. (2010) and Van den Assem et al. (2012) who use prisoner’s dilemma datafrom a high-stake TV with a pre-play, free-form communication stage (with moderation from the host), and find(i) no evidence of a correlation in players’ decisions in aggregate, but also (ii) point to the importance of voluntary
promises in inducing individual cooperative behavior.

17

Table 2: Promises and individual predictors of trust: marginal effects from logit regression models

VIDEO
AUDIO
M E, (SE)p
M E, (SE)p
1[P romise]
0.102
0.001
0.171
< 0.001(0.030)(0.037)
Player A’s perception of player B’s characteristics (1-8 scale):beauty-0.0160.232-0.0100.449(0.014)(0.013)intelligence
0.033
0.070
0.019
0.352
(0.018)(0.020)trustworthiness
0.088< 0.001
0.058< 0.001(0.013)(0.015)self-confidence
0.009
0.465
-0.0010.920(0.012)(0.012)
Player A’s characteristics:
SVO angle-0.0020.249< 0.001
0.928(0.002)(0.002)
1[CRT > 0]
0.020
0.759
0.069
0.233
(0.066)(0.058)0.003
0.680-0.004
0.469
RMET score(0.007)(0.005)GP investment
0.001
0.285
0.003
0.013
(0.001)(0.001)
SOEP trust 1-0.0740.161-0.0720.172(0.053)(0.053)
SOEP trust 2-0.003
0.953
0.097
0.006
(0.050)(0.035)
SOEP trust 3
0.081
0.260
-0.0160.813(0.072)(0.068)
Political views-0.001
0.943
0.003
0.840
(0.017)(0.013)Pseudo-R2
0.142
0.100
Nb of obs./clusters
620/62700/70TEXT
M E, (SE)
p
0.099
0.002
(0.031)

0.066(0.010)0.033(0.009)-0.007(0.009)< 0.001< 0.001
0.461< 0.001
0.954(0.002)-0.0520.437(0.067)0.002
0.782(0.006)< 0.001
0.988(0.001)-0.0680.207(0.054)0.084
0.072(0.046)0.116
0.118(0.074)0.003
0.767(0.010)0.115
850/85Note. Average marginal effects from logistic regression models. First/second/third model: data from VIDEO/AUDIO/TEXT
treatment. Observations are clustered and the individual level and estimated standard errors are cluster-robust.

18

To empirically address the first question, we exploit the set of individual characteristics wehave gathered. For the second question, we exploit the information about player A’s perceptionsof player B’s characteristics: beauty (collected everywhere but in TEXT), intelligence, trustworthiness, and self-confidence, all measured on a 1-8 scale.25 Since this information has been elicitedfrom every player A in each round only after the decision-making stage of the game, we stress thecorrelational nature of its relationship with the dependent variable.26
The main result of this exercise is that the effect of a promise on trust remains positive andsignificant even after controlling for a wide range of individual-level variables. Not surprisingly, we
also find that player As are more likely to trust player Bs the more they see them as trustworthy.
We also observe a positive association between one’s trust and the perceived intelligence of theother person in most conditions (p = 0.070 in VIDEO; p < 0.001 in TEXT; insignificant in
AUDIO). The perceived beauty or self-confidence of a player B are not significantly predictive of aplayer A’s trust. Finally, in the absence of visual cues about player Bs, player As’ behavior seems
to be guided by their general sense of trust towards strangers: the marginal effect of “SOEP trust2” (i.e., the answer to the question “How much do you trust people you just met?” on a 1-4 scale;see Appendix B.1) is positive and significant at the 1% level in AUDIO (p = 0.006) and at the
10% level in TEXT (p = 0.072) models, but not in the VIDEO model (p = 0.953).
25

Although promise-makers receive slightly higher average scores from player As for each of the four characteristics (see Appendix I), we fail to detect any significant differences between promise and perceived trustworthinessacross treatments. For each condition, ranksum test using player Bs’ average score on trustworthiness as independent observation fails to identify significant differences between promise-makers (N = 21) and others (N = 20).
Regarding player Bs’ intelligence, the same test indicates significant differences in VIDEO and AUDIO.
26
We also note that the previous evidence suggests that at least part of this relationship may be causal. Ina related experiment, Greiner et al. (2012), introduce a three-person dictator game in which each of the tworecipients may independetly engage in a one-way communication with the decision-maker. They allow for different
pre-play communication protocols: either none or through (pre-recorded) muted video messages for both recipientsor through a muted video message for one recipient and a video message with sound for the other. Like us, theyelicit the decision-makers’ internal (i.e., post-decision) ratings of their interaction partners individual traits andcompare them with those provided independently by external rates. The conclude that “at least part of the internalratings are based on a general, unbiased, common impression, independent of previously made decisions”, so thatchoices are to a large extent driven by social evaluations, although “also idiosyncratic social tastes play a role, which[. . .] are indistinguishable from ex-post rationalization of previous [. . .] decisions” (pages 411-412; see their Result
4).

19

4.3

Additional analyses: gender differences in trust

In the previous section, we have shown how the role of the different layers of nonverbal communication depends on a factor that is endogeneous to the communication process, namely the verbal
content of statements, while also accounting for the receiver’s subjective perception of the sender.
Herein, we exploit the natural variation of players’ gender in our dataset to illustrate the possible
sensitivity of the effect of the nonverbal content of communication on trust to a characteristic thatis exogenous to the communication process. We see this exercise as an extension of the related
experimental study by Greiner et al. (2012) based on a sample of female subjects.
We note that the existing experimental literature on communication lacks a consensus on therelationship between gender and trust. Belot et al. (2010, 2012) and Van den Assem et al. (2012)
analyze a dataset from a high-stake prisoners’ dilemma game played in a TV show that females are
more likely to share the prize than males, and that external observers can accurately predict thisgender difference. Bicchieri et al. (2010), in turn, find no statistical evidence for gender differencesin trust and trustworthiness in a trust game played with different modes of communication (either
computer-mediated or face-to-face), and disregard this aspect in their analyses; similar conclusions
can be found in Chen and Houser (2017).
In our data, gender does not appear to be an important predictor of player Bs’ behavior. The
promise-making rates are fairly similar for both genders (females: 9/20; males: 12/21; Fisher’s
exact test yields p = 0.538). The average marginal effect of being a female on the likelihoodof choosing to Roll is negative (-0.113), yet insignificant (p = 0.479).27 Thus, we interpret any
difference in player As’ behavior that stems from player Bs’ gender as a behavioral bias.
We further investigate if there is a gender difference in player As’ trust, and whether the trustin our experiment is gender-biased. To answer these questions, let us suppose that people may
have different trust attitudes towards males and females, depending on their own gender (which is
known to the experimenter) and some other (unobserved) preferences. To capture these individual
trust attitudes, for each player A, we construct two variables: Ratio trust M and Ratio trust F.
27

This result stems from a logit regression with session fixed effects, and holds (marginal effect of -0.088, p = 0.534)
once we additionally control for the presence of a promise to Roll (the marginal effect of which is once again positive,
0.390, and highly significant, p = 0.001).

20

Table 3: Trust and gender: aggregate results

Ratio trust
Male player A
Female player A

VIDEO
F
M
54.1% 49.6%
48.9% 46.2%

AUDIO
F
M
53.5% 46.4%
50.4% 37.7%

TEXT
F
M
43.0% 43.7%
30.7% 32.4%

Note. For each player A, Ratio trust M (F ) provides the individual rate at with that player trusted male (female) player
Bs. The average individual rates in the sample are conditioned on treatment and player A’s gender.

They capture the fraction of decisions In in all the interactions with a player B of either gender(M stands for male, F stands for female).28 The sample means of those individual ratios are
summarized in Table 3.
We then conduct the following estimation exercise. For ith player A, the overall trust ratio
towards player Bs of gender g ∈ {F, M } depends on i’s gender in the following way:

Ratio_trustig = γ0 + γ1 × 1[F emale_Ai ] + uig ,(1)

where 1[F emale_Ai ] = 1 if an ith player A is a female, and = 0 otherwise. Since both Ratio_trustiF
and Ratio_trustiM may also stem from i’s unobserved preferences (so that uiF and uiM may becorrelated), these models are estimated as seemingly unrelated regressions (see, Cameron andTrivedi, 2005, pp. 209-210) and presented in Table 4. A notable difference between TEXT andthe remaining treatments is that in the former, player B’s gender remains mostly unknown toplayer As, so that Ratio_trustiF and Ratio_trustiM in that treatment should be similar.29
The main results are as follows. In TEXT (where player B’s gender is predominantly unobservable for player As), female player As are found to be generally less trusting than male player
As (H0 : γ1 = 0 for Ratio trust F : p = 0.041; for Ratio trust M : p = 0.055). For both male andfemale player As, we do not find a significant difference between the two rates: coefficients γ0 as
28
On average, a player A encounters a female player B in 4.71 rounds out of 10 in VIDEO, in 4.57 in AUDIO,
and in 5.08 in TEXT. Replicating the procedure described in footnote 14 for these data gives p = 0.287. Appendix
C provides further details on the gender composition of the experimental sample.
29
Only 3 out of 41 transcripts reveal the sender’s gender: twice as a female and once as a male. In French,
the grammar rule of accord may impose gender-specific forms of verbs (depending on the tense) and adjectives in
written which, however, are not always distinguishable in spoken (so that the masculine and the feminine forms
may sound exactly the same). In our transcripts, we respect the French grammar and follow the rule of accord.

21

Table 4: Trust and gender: seemingly unrelated regressions

Ratio trust
Intercept (γ0 )1[F emale_A] (γ1 )

VIDEO
F
M
0.541a0.496a(0.065) (0.061)-0.052-0.034(0.080) (0.076)

AUDIO
F
M
0.535a0.464a(0.067) (0.056)-0.031-0.087(0.081) (0.069)

TEXT
F
M
0.430a0.437a(0.046) (0.045)-0.123b -0.113c(0.060) (0.059)

Note. For each treatment, we provide estimated coefficients (and standard errors) of seemingly unrelated regression models.
The dependent variables are Ratio trust F and Ratio trust M, the explanatory variable is an indicator variable 1[F emale_A].
a/b/c indicate statistical significance at the 1/5/10% level.

well as γ0 + γ1 are not different in equations Ratio trust F and Ratio trust M (p = 0.871 for γ0
and p = 0.629 for γ0 + γ1 ). In AUDIO, male player As’ trust does not vary as a function of player
B’s gender (coefficients γ0 do not vary between the two equations, p = 0.283). However, female
player As now exhibit stronger trust towards player Bs of their own gender: the model suggeststhat γ0 + γ1 is statistically significantly higher in the equation Ratio trust F than in Ratio trust
M (p = 0.007). On the other hand, we also note that the effect of player A’s gender on trust in
AUDIO is less pronounced than in TEXT: for neither ratio, we detect a statistically significant
difference between male and female player As (H0 : γ1 = 0 in Ratio trust F : p = 0.701; Ratio trust
M : p = 0.208). Finally, all differences between trust ratios fade away in the VIDEO treatment.
To conclude, we find evidence for gender differences in trust that can be overridden by meansof providing nonverbal content in the process of communication. First, when no nonverbal content
is transmitted and player B’s gender remains unknown (TEXT treatment), we observe that female
player As are generally less trustful than males. The AUDIO treatment (in which the auditory cues
provide straightforward information about the speaker’s gender) helps close this gap. However, italso gives rise to a gender bias in trust: female player As tend to favor players Bs of their own
gender. Finally, any gender differences fade away in VIDEO that enriches the nonverbal content
by visual cues about the sender.

22

5

Discussion

A large body of experimental research in economics and other social sciences explores the roleof communication in various types of economic interactions, such as cooperation, coordination,
trust and reciprocity (see, among others, Ostrom et al., 1992; Cooper et al., 1992; Charness, 2000;
Duffy and Feltovich, 2002; Ellingsen and Johannesson, 2004; Duffy and Feltovich, 2006; Bochet
and Putterman, 2009; Bracht and Feltovich, 2009; Gangadharan et al., 2017). This body of
empirical evidence generally points to the conclusion that communication increases the efficiency
of interactions (Sally, 1995; Crawford, 1998; Balliet, 2010). Here, we complement this literature byasking a different question: how important is the nonverbal content of communication in inducing
trust? Overall, our experimental data highlight an interplay between verbal and nonverbal content
of communication in the process of inducing trust in human interactions.
To answer our research question, we use a controlled laboratory experiment based on a principalagent relationship with different modes of one-way communication. Our design allows us to homogenize the verbal content of communication across the experimental conditions. We documentthat the degree to which communication channels enable the transmission of nonverbal content isimportant for the emergence of trust in human interactions. In aggregate terms, providing auditory cues about the speaker seems to play a key role in inducing trust, as compared to providing
a plain text content of the message. Adding visual cues in video-based communication does notfurther lead to a statistically significant increase in the observed rates of trust, notwithstanding
the general findings in Bicchieri and Lev-On (2007). However, we find evidence that these effects
are moderated by the verbal content of communication. They hold for messages containing apromise to cooperate (which constitutes a significant and well recognized predictor of trustworthiness); in the absence of such a promise, a combination of auditive and visual cues is necessary tosignificantly enhance trust as compared to a plain text message.
These results complement and extend the previous evidence reported in Greiner et al. (2012).
Using three-person dictator game in which each of the two recipients may independently engage in
one-way communication with the decision-maker, they allow for different pre-play communication23

protocols: either none or through (pre-recorded) muted video message from each recipient, or
through a muted video message for one recipient and a video message with sound for the other.
While allowing for identification of the recipient via silent video does not affect the average levelsof donations as compared to the baseline condition, average donations increase when the auditory
channel is added. This result is consistent with our data in that it points to the importance (inaggregate terms) of auditory cues relative to visual cues.
Our study extends theirs in following ways. Unlike their dictator game, our hidden action gameinvolves strategic interactions. Their data do not reveal any impact of the content of the recipients’messages on the decision-makers’ behavior, which may be due to the fact that the conveyed
information necessarily lacks strategic importance. Our evidence suggests that when messages
can convey strategic signals (e.g., related to promise-making), the content of communication may
affect other-regarding behavior alongside the channel of communication.
Furthermore, Greiner et al. (2012) report that the decision-makers in their experiment form
impressions about the recipients and discriminate them on this basis. However, since their experiment employs a sample of female participants, an open question is how discrimination inother-regarding behavior may depend on both players’ gender.
In our additional analyses, we explore whether and how digital communication protocols may
give rise to gender differences in trust. The most restricted communication environment in which
players send plain text messages and that makes it virtually impossible to identify the other person(not even his or her gender), gives rise to a gender difference in trust: females tend to be generally
less trustful than males.30 Providing auditory cues that reveal the sender’s gender leads to agender bias in trust: female principals tend to trust other females more than to trust other males,
even though gender is not predictive of the agents’ trustworthiness in our experiment (if anything,females happen to be slightly, yet insignificantly, less trustworthy). All those differences disappear
once visual cues also become available.
We see two potential avenues for future research. First, economic models of strategic communication in cooperation problems focus on the content of messages. In particular, behav30
This stands in line with Buchan et al. (2008) who report that men are more trusting than women. However,they also find that women are more trustworthy which is not confirmed by our data.

24

ioral theories stipulate that communication may help building commitment to cooperate throughpromise-making (see, e.g., Ellingsen and Johannesson, 2004; Charness and Dufwenberg, 2006).
Combined with the aforementioned experimental studies, our experimental evidence suggests that
future theoretical developments should also account for the channel through which messages aretransmitted. Second, despite its methodological virtues, our study has one limitation that is
worth emphasizing in the closing lines. The stimuli used herein is based on a specific sample of
41 recordings that has been resampled and employed throughout the whole experiment. Thus,an improved experimental control and the ability to draw causal inference about the effect of thedigital communication mode on behavior comes with a price of reducing the natural heterogeneityof human communication (related to the variation in the content of messages, as well to the variety
of senders’ individual characteristics). Thus, there is certainly a need for further evidence, as wellas out-of-the-sample replications.

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Appendix
A

Experimental instructions

A.1

Preliminary instructions given to all subjects in all the treatments

You are about to take part in an experiment in which you can earn money. The amount of yourgains will depend on your decisions, as well as on the decisions made by other participants. In
addition, you will receive a fixed fee of [5 for player As, 10 for player Bs] EUR for completing theexperiment. Your total earnings will be paid privately in cash at the end of the experiment.
The experiment consists of several parts. Each part will involve tasks the rules of which will
be explained to you in due time. It is crucial that you understand and obey the rules of thisexperiment. Violation of these rules might result in an exclusion from the experiment and all
payments. Please raise your hand whenever you have questions or need assistance.
All the information you provide, as well as the amount of your gains from this experiment, will remain strictly confidential and anonymous.
We would now like to ask you to answer a series of preliminary questions. You will answer thesequestions using the interface on your computer screen. Some of these questions will generatemonetary gains. These gains will be determined and added to your overall earnings at the end of
the experiment.

34

Note: the following instructions were given only to player Bs in F2F for the preliminary recordings,
not reported in the paper.
Now, we would like to take a picture and make a video recording of you.
First, you will be asked to stand by the wall and look into the camera. Please, try to keep aneutral facial expression.
Second, you will be asked to read aloud the content displayed on the screen in front of you. While
reading, you will be video recorded.
All pictures and video recordings produced during this experiment will only serve strictly scientificpurposes of this research project. They may be used in other experimental sessions related to thisresearch project.

A.2

Specific instructions for F2F treatment

Rules of the gameYou will now play a game with monetary stakes. The rules of the game are as follows.
The game is played by two players: player A and player B. Each player must choose between two
possible actions. Player A chooses between actions “Left” and “Right”. Player B chooses whether
she want a six-sided die to be rolled (action “Roll”) or not (action “Don’t roll”).

You will play the role of player [A for player As, B for player Bs]

Each player’s payoff depends on the actions chosen by herself as well as the other player:• if player A chooses “Left”, then regardless of player B’s choice:– player A’s payoff is 5 EUR and player B’s payoff is 5 EUR;
• if player A chooses “Right” and player B chooses “Don’t roll”:
35

– player A’s payoff is 0 EUR and player B’s payoff is 14 EUR;
• if player A chooses “Right” and player B chooses “Roll”:– if the number of on the die is between 1 and 5, then player A’s payoff is 12 EUR andplayer B’s payoff is 10 EUR;
– if if the number of on the die is 6 player A’s payoff is 0 EUR and player B’s payoff is
10 EUR;

How the game proceeds
The game will consist of six identical rounds.
At the beginning of a round, one player B is asked to enter the room in which there are six players
As. Player As are separated one from another and are not allowed to talk.
Player B is then placed in front of player As and remains silent. Then, player B is allowed to talkfor no longer than 20 seconds, and then asked to leave player As’ room. While talking, player B
is video recorded and should look straight into the camera.
Once player B leaves player As’ room:• player B makes a decision in a separate room. Player B privately and individually indicatesher decision (either “Roll” or “Don’t roll”) on a separate answer sheet, puts it in an envelopeand seals the envelope. The experimenter collects the envelope and player B leaves the room.
Then, the experimenter privately rolls a six-sided die and marks the result on the envelope(without opening it). The outcome of the die roll will only be taken into account if player
A’s decision is “Left” and player B’s decision is Roll.
• each player A privately and individually indicates her decision (either “Left” or “Right”) on a
separate answer sheet, puts it in an envelope and seals the envelope. Then, all the envelopes
are collected by the experimenter. Player As are either asked to remain silent and await thenext player B, or informed that the experiment is over and given further instructions about
their payment.

36

No envelope will be opened before the end of the experiment.
At the end of the experiment, each player A is anonymously and randomly matched with one
player B. The outcome of the game for each pair of players is determined by the decisions madeby both playes (and also by the outcome of the die roll if the decisions in a pair are “Right’ and“Roll’) in the round in which the player B was in player As’ room. Players are only informed
about their personal payoffs, and not about the payoffs of or the decisions made by other players,or about the outcome of the die roll.

Additional information
Note that this set of instructions is provided to and read by each player A and each player B.
Furthermore, player Bs cannot communicate between themselves at any point of the experiment.
The same applies to the communication between player As.

You will play the role of player [A for player As, B for player Bs]

A.3

Specific instructions for VIDEO, AUDIO, and TEXT

Note: Below, the parts of instructions that are distinct for each treatment are marked with “(treatment name)”. Other parts are common to all three treatments.
(VIDEO) In this part of the experiment, you will watch a series of video recordings. In eachrecording, you will see a person speaking to other people. You will also answer a series of questions
after each recording; some of them will determine your gain from this experience.
(AUDIO) In this part of the experiment, you will listen to a series of audio recordings. In eachrecording, you will hear a person speaking to other people. You will also answer a series ofquestions after each recording; some of them will determine your gain from this experience.
(TEXT) In this part of the experiment, you will read a series of transcripts. Each transcript
comes from a person speaking to other people. You will also answer a series of questions after
each recording; some of them will determine your gain from this experience.

37

(VIDEO) Video recordings; (AUDIO) Audio recordings; (TEXT) Transcripts(VIDEO) Here is some information about the recordings you will be watching. The recording havebeen made during several laboratory experimental sessions.
(AUDIO) Here is some information about the recordings you will be listening to. The recording
have been made during several laboratory experimental sessions.
(TEXT) Here is some information about the transcripts you will read. The transcripts have been
made during several laboratory experimental sessions.
In each session, two groups of participants (six player As and six player Bs) were installed in two
different rooms. Participants in each room could not communicate with each other. Players were
informed that their decisions and earnings would remain private and anonymous, and would never
be disclosed to other participants.
Each session was organized as follows:
1. One by one, player Bs entered the room in which player As were sitting. Then, each player
B made a short speech in front of player As.
(VIDEO) All the speeches have been recorded, and you will be watching some of them.
(AUDIO) All the speeches have been recorded, and you will be listening to some of them.
(TEXT) All the speeches have been recorded, and you will read some of their transcripts.
2. After his speech, player B left player As’ room, and entered an empty room.
3. After player B’s departure, each player A made a decision (“Left” or“Right”) in private andindividually. At the same time, player B made a decision (“Roll” or “Don’t roll” a die) inprivately and individually.
4. Thereafter, player B left the room and waited outside the laboratory until the end of theexperiment. Meanwhile, a new player B was entering the player As’ room to make a speech.
The experiment ended when all the players had completed their task.

38

At the end of the experiment, each player A was anonymously and randomly matched with aplayer B. The outcome of the game for each pair of players was determined by the decisions madeby both players following player B’s speech:• if player A chose “Left”, then regardless of player B’s choice:– player A’s payoff was 5 EUR and player B’s payoff was 5 EUR;
• if player A chose “Right” and player B chose “Don’t roll”:– player A’s payoff was 0 EUR and player B’s payoff was 14 EUR;
• if player A chose “Right” and player B chose “Roll”:– if the number of on the die was between 1 and 5, then player A’s payoff was 12 EUR
and player B’s payoff was 10 EUR;
– if the number of on the die was 6, then player A’s payoff was 0 EUR and player B’s
payoff was 10 EUR;Your role(VIDEO) You will now watch ten recordings randomly selected from all the recordings describedabove. After each recording, you will make a decision: either “Left” or “Right”. This decision isimportant for your final earnings. You will also answer some questions about the person seen in
the recording. Finally, either you will proceed to the next recording, or you will be informed thatthe experiment is over.
At the end of the experiment, one of the recordings you have viewed will be drawn at random.
Your earnings will depend on the decision (“Left” or “Right”) you have made after watching thatrecording. It will also depend on the decision previously made by the person from the recording
following his or her speech in the laboratory session during which the recording was made:(AUDIO) You will now hear ten recordings randomly selected from all the recordings describedabove. After each recording, you will make a decision: either “Left” or “Right”. This decision is39

important for your final earnings. You will also answer some questions about the person heard in
the recording. Finally, either you will proceed to the next recording, or you will be informed thatthe experiment is over.
At the end of the experiment, one of the recordings you have listened to will be drawn at
random. Your earnings will depend on the decision (“Left” or “Right”) you have made after
listening to that recording. It will also depend on the decision previously made by the person fromthe recording following his or her speech in the laboratory session during which the recording was
made:(TEXT) You will now see ten transcripts randomly selected from all the transcripts describedabove. After each transcript, you will make a decision: either “Left” or “Right”. This decision isimportant for your final earnings. You will also answer some questions about the person that madethe speech presented in the transcript. Finally, either you will proceed to the next transcript, or
you will be informed that the experiment is over.
At the end of the experiment, one of the transcripts you have read will be drawn at random.
Your earnings will depend on the decision (“Left” or “Right”) you have made after reading thattranscript. It will also depend on the decision previously made by the person whose speech waspresented in the transcript, during the laboratory session in which that speech was made:• If you chose “Left”, then whatever the choice of the person from the recording: you earn 5
EUR ;• If you chose “Right” and the person from the registration has chosen “Don’t roll”: your earn
0 EUR ;
• If you chose “Right” and the person from the recording has chosen “Roll”:– if the number on the die was between 1 and 5, then you earn 12 EUR;
– if the number on the die was 6, then you earn 0 EUR;

40

B

Additional measures

B.1

Measures of individual characteristics

The preliminary stage of the experiment consists of basic socio-demographic questions (age, gender,
education, major, current occupation, score at the baccalauréat exam at the end of high school),as well as a battery of incentivized and non-incentivized computerized tasks designed to measurespecific individual characteristics. These tasks are administered in five blocks presented to eachindividual in random order. In the incentivized tasks, all payoffs were expressed in Experimental
Currency Units (ECU), with the exchange rate of 100 ECU = 2.50 EUR. No feedback on any
decision or answer is provided at any stage. Below, we describe the content of those parts (labelledA-E).31
Part A. Other-regarding preferences are measured using the Social Value Orientation (SVO)
slider task by Murphy et al. (2011). In each of six distributional tasks, the decision-maker chooses
an allocation of money for himself and for another person among nine possible allocations. These
choices are then transformed into a score, labelled as “SVO angle”: the higher the angle, thestronger one’s concerns about others’ welfare. We use the original set of distributional tasks (allthe amounts are expressed in ECU), and the strategy method to elicit responses in the role ofthe decision-maker from each participant. We also inform them that they will be randomly andanonymously matched in pairs at the end of the experiment, that in each pair one person will
be randomly chosen as the decision-maker, and that both players’ payoffs from this task willcorrespond to the decision-maker’s choice in a randomly selected task.
Part B. Cognitive skills are measured through standard (3-item) Cognitive Reflection Test (Frederick, 2005) which “measures cognitive reflectiveness or impulsiveness, respondents’ automatic response versus more elaborate and deliberative thought” (Brañas-Garza et al., 2012, p.255). The
questions are as follows:
31

Table 5 in Appendix D offers a summary of the elicited characteristics. We also provide evidence that the sample
has been properly randomized with respect to all of these characteristics across treatments (VIDEO, AUDIO, or
TEXT) and roles (player A or player B). Finally, we replicate several results that have been previously documents
by the experimental studies employing certain measures used herein. In addition, Table 6 summarizes the physicalcharacteristics in the sample of player Bs.

41

1. A notebook and a pencil cost 1.10 Euros in total. The notebook costs 1 Euro more than the
pencil. How much does the pencil cost?
2. If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines tomake 100 widgets?
3. In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48
days for the patch to cover the entire lake, how long would it take for the patch to cover halfof the lake?
Subjects are informed that this set of three questions should be answered within 30 seconds(although we allow them to provide answers even after this time has elapsed). In this way, subjects
can be classified according to their overall score (that is, the total number of correct answers) which
can range from 0 to 3. No monetary incentive was provided for giving correct answers to thesequestions.
Part C. The Reading the Mind in the Eyes Test is an advanced test of theory of mind (ToM)
developed by Baron-Cohen et al. (2001). As noted by Baron-Cohen et al. (2015), ToM entails
attribution and recognition of mental states in oneself or others [. . .] and to use such information
to make inferences and predict behaviour (p. 2). The test is non-incentivized and consists of
36 black-and-white photos of people. Each photo is cropped and rescaled so that only the areaaround the eyes can be seen. Photos are displayed one-by one and the participants are asked tochoose the word that best describes what the person in the photo is thinking or feeling among four
possible words shown underneath (only one of which is correct). Time is limited to 10 minutes.
Alongside a set of precise instructions about the content and duration of the task, the participants
go through one trial round with an immediate feedback on the accuracy of their answers; otherthan that, no feedback is provided.
Part D. Risk preferences are captured by the amount invested in the incentivized task based on
Gneezy and Potters (1997). A decision-maker holds an initial endowed of 100 ECU, some (or all)of which he can invest in the following lottery: 50% chance of multiplying the investment by thefactor of 2.5 times, and 50% chance of losing the invested amount. Any decision-maker who does42

not invest the whole amount is considered as being risk averse, and the invested amount is used
as a measure of risk aversion.
Part E. The non-incentivized measures of individual trust attitudes are based on the German
Socio-Economic Panel Study (SOEP), an ongoing longitudinal survey of German households. In
what follows, we present them in order of appearance. Variable SOEP trust 1 is the answer to “Ingeneral, one can trust other people” (4 point scale with 1 – “strong disagreement”, and 4 – “strong
agreement”); variable SOEP trust 2 is the answer to “How much do you trust people you just met?”(4 point scale with 1 – “no trust at all”, and 4 – “full trust”); variable SOEP trust 3 is the averagescore on 7 items measuring general trust in socio-economic institutions (“How much trust do you
have in market economy/large companies/courts/government/parliament/other citizens/banks ”,
same scale as for SOEP trust 2 ).32 Finally, political views are elicited using another SOEP
question: “In politics, people often talk about “left” and “right” when describing different politicalviews. When you think about your own political view, how would you rate them on the scale
below?” (11 point scale with 0 – “left”, and 10 – “right”).

B.2

Post-decision questionnaire

After every decision (and before moving on to the next round), player As fill in a short questionnaire(based on Centorrino et al., 2015). In all treatments but TEXT, player As are asked if they know
the person with whom they have just interacted. Then, they evaluate that person’s: beauty(inverse 1-8 scale: 1 – “beautiful”, 8 – “not beautiful”), trustworthiness (inverse 1-8 scale: 1 –“trustworthy”, 8 – “untrustworthy”), intelligence (1-8 scale: 1 – “not too intelligent”, 8 – “veryintelligent”), and self-confidence (1-8 scale: 1 – “shy”, 8 – “self-confident”). For the sake of thesimplicity of exposition, the statistical analysis reverses those scales for beauty and trustworthiness.
In F2F, the questionnaire is pen-and-paper. Each completed questionnaire sheet is put in an
envelope which is then sealed and collected by a laboratory staff member. In VIDEO, AUDIO,
and TEXT the questionnaire is embedded in the computer interface used in those treatments.
32

Cronbach’s α = 0.7 suggests that the individual answers are internally consistent. Tavakol and Dennick (2011)
describe internal consistency as the extent to which all the items in a test measure the same concept (here, trust
towards others). It is connected to the inter-relatedness of the items within the test (p. 53). In the literature,α = 0.7 is usually considered as acceptable (p. 54).

43

C

Implementation details

Recruitment of participants. We made sure that player Bs were aware of (and give theirconsent to) the fact that they would be recorded, as well as the future use of those recordings
for scientific purposes. The invitation email sent to player Bs included the following information:
During the experiment, your photos and video recordings will be taken. All pictures and video
recordings produced during this experiment will only serve strictly scientific purposes of this research project, but they will be used in other experimental sessions related to the research project.
Therefore, please register only if you agree that your photos and video recording will taken for thisscientific purpose.33 Similar information is repeated in the preliminary part of the experimental
instructions (see Appendix A). For obvious reasons, the invitation email emphasizes that subjects
who want to participate must be fluent French speakers.
In order to minimize the chance of subjects in the opposite roles knowing each other, we tried
to separate our subject pools and recruited participants from two different majors (player As: BA
students in economics and some other majors with the exception of public administration; player
Bs: BA students in public administration, MA students in economics, if necessary completed by
students from other departments). Acquaintance between player As and player Bs occurs at a
relatively low rate. In F2F, in 14.6% (36 in 246) of times a player A admits to know a player B.
In VIDEO and AUDIO, this rate is even lower (1.1% and 1.3%, respectively). We suspect thatthe difference between F2F and the two other conditions stems from the fact that in F2F subjects
in both roles physically participated in the same session. Thus, despite our attempts to limit
the correlation between player As and player Bs, factors such as shared courses, overlapping timeschedules, or simple word of mouth, may be still be at play. This problem happens to be marginal
in VIDEO and AUDIO in which we only recruit player As, and becomes irrelevant in TEXT.
Gender composition of the experimental sample. In order to homogenize the audience facedby player Bs in F2F and enhance our control over the experimental environment, we attempted tohave gender-balanced groups of player As in each session. We achieved this in 5 sessions out of 7.
In session 2, there are 2 males and 4 females, while the opposite holds in session 3. Despite these33

The recruitment of the participants has been carried out using ORSEE (Greiner, 2015).

44

small variations, altogether the sample of player As in F2F is gender-balanced. In the remaining
treatments, the gender-balance constraint is relaxed. Female player As account for 64.5% of thesample in VIDEO, 67.1% in AUDIO, and 57.1% in TEXT. Overall, this procedure does not produce
a statistically significant variation in the gender composition of player As’ samples. Comparing
the four samples using Fisher’s exact test yields p = 0.273.
Logistics of the F2F condition. For the sake of logistics and efficient time management, player
Bs arrive 30 minutes prior to player As. First, they are asked to take up several computerizedtasks that measure their preferences and characteristics (see Section B.1).34 Then, they are all led
to a waiting room. To avoid any communication or subjects overhearing what others are saying
or doing, each participant is seated in a separate cubicle, puts on a headphone and listens to aclassical music until further notice. Then, they are taken one by one to a separate room for amugshot picture and a short, standardized video recording.35
Then, each subject is seated back in his cubicle with headphones on. He now listens to an
audio file containing the experimental instructions (paper version is also provided). There is abrief comprehension quiz assisted by a laboratory staff member. Finally, he receives additional
paper instructions about the upcoming statement in front of player As, as well as a pen and an
empty sheet of paper, and is given approximately two minutes to prepare his message.36 After
that, a player B is invited to player As’ room where he delivers a statement, leaves for anotherroom, and the game proceeds as explain above in Section 3. The average duration of a message is
26.39 seconds (SD 2.09).
Upon their arrival to the laboratory, player As also take up the set of preliminary questionnaires. Then, they receive and read paper instructions for the experimental game, and finally they
fill in a short comprehension quiz. A laboratory staff member then reads aloud all the questions
from the quiz along with the corrects answers, and answers any remaining questions. Finally,
34

The computerized parts of the experiment are all programmed in z-tree (Fischbacher, 2007).
Like in van Leeuwen et al. (2017), subjects are asked to read neutral content (a short extract from a printerinstruction manual) and keep a neutral face expression. The recording takes about 30 seconds. This information is
not part of the present investigation and has been reported elsewhere (see Zylbersztejn et al., 2020).
36
Those additional instructions remind the subject about his role in the game; emphasize the fact that the message
may affect player A’s decisions and, consequently, the subject’s gain from the experiment; instruct the subject toavoid making a visual or verbal contact with the experimenter, to aim at communicating with all player As, andnot to introduce oneself or give any details about one’s own identity.
35

45

player As are asked to wait for the arrival of the first player B.
Recordings. Player Bs’ statements are recorded using a small, non-intrusive video camera set up
in the middle of player As’ row, right in front or player Bs’ zone, so that the perspective in the video
camera recording resembles the one of a player A. The camera is always adjusted to the height ofplayer B (so as to capture head, shoulders, and thorax), and to the luminosity in the room. The
sake of the quality of the video recordings, the background in player Bs’ zone is covered with light
canvas. While making a statement, each player B also has a portable microphone attached below
their face. The distance between player As and a player B is set to 2.50 meters. The information
recorded in the F2F treatment (i.e., player Bs’ messages and the content of their decisions sheets
and die rolls) is re-used in the remaining experimental conditions: VIDEO, AUDIO, and TEXT.
Duration and payoffs. For player As, the experiment takes roughly 1h. At the end of theexperiment, subjects are paid according to their earnings in the incentivized pre-experimental
tasks, earnings in the experimental game, in addition to the show-up fee of 5 euros. All payments
are made privately and in cash. For player Bs, the experiments takes 30 minutes longer, and theshow-up fee is increased to 10 euros. The average payoffs are as follows. Player Bs in F2F: 23.8
euros; Player As: 17.2 euros in F2F, 15.8 in VIDEO, 15.9 in AUDIO, 16.0 in TEXT.

D

Individual characteristics: randomization and key empirical patterns in the sample

Table 5 provides a summary of the individual measures outlined in Section B.1. To check if our
five experimental subsamples (player As in F2F, VIDEO, AUDIO, and TEXT, as well as player
Bs in VIDEO) have been properly randomized with respect to all of these characteristics, we
adopt a parametric procedure that is parsimonious (it involves a single test) and provides a simple
way to deal with the risk of false rejection due to multiple comparisons (see Veazie, 2006, for an
insightful discussion). We regress each outcome of interest (i.e, SVO angle, CRT and RMET scores,
investment in the Gneezy-Potters task, SOEP trust 1, 2, and 3, and political views) on the set offour experimental sample dummies (each set to 1 if an observation comes from a given sample,

46

Table 5: Individual characteristics measured in the experimental sample: average outcomes

N

SVO
angle

CRT
score

RMET
score

GP
inves’t

1

SOEP trust
2
3

Polit.
views

Player As:
F2F
VIDEO
AUDIO
TEXT
All player As

42
62
70
85
259

21.61326.97328.07826.48726.243

0.405
0.403
0.529
0.353
0.424

24.00026.79026.87126.43526.24348.64344.06543.27148.62446.089

2.310
2.339
2.443
2.412
2.386

1.905
1.984
2.129
2.047
2.031

2.255
2.306
2.422
2.380
2.354

4.452
4.645
4.143
4.094
4.297

Player Bs:

41

21.7970.415

25.95146.512

2.390

2.122

2.303

4.683

All participants:

300

25.6350.420

26.20346.147

2.387

2.043

2.347

4.350

and 0 otherwise; the fifth sample is the reference). These eight regression models are estimatedas seemingly unrelated regressions (SUR). This, in turn, enables us to perform F -test of joint
insignificance of all between-sample differences (i.e., joint insignificance of all sample dummies)
simultaneously across all the SUR models. The resulting joint test of 32 null hypotheses yields
p = 0.148.
We also replicate several results previously documents by the experimental studies employing
CRT and RMET. First, we find gender differences in both measurements: males score significantly
higher than females in the CRT (0.543 vs. 0.316; two-sided t-test: p = 0.005), and significantly
lower in the RMET (25.442 vs. 26.637; two-sided t-test: p = 0.019). This stands in line with thefindings by Brañas-Garza et al. (2012), Corgnet et al. (2015), and Hanaki et al. (2016) for the
CRT, and by Baron-Cohen et al. (2015) for RMET.
Second, we observe that most (74%) of our 300 participants fail to provide at least one correct
answer in the standard CRT (14.4% provide exactly one , 8.5% – two, and 3.1% – three correct
answers). Such finding is not new in the literature, and echoes a strongly left-skewed distributionof correct answers in the “lower bound” sample from the seminal study by Frederick (2005).37

37

In Frederick (2005), the respective frequencies are 64%, 21%, 10%, 5% (N=138). Similar findings are also
reported by Brañas-Garza et al. (2012) – 67%, 23%, 9%, and 1% (N = 191), and by Hanaki et al. (2016) – 70%,
16%, 8%, and 6% (N = 200).

47

Table 6: Player Bs’ physical characteristics

Mean age
Male (%)
Ethnicity (%):
African
CaucasianMiddle-easternSouth AsianNative speaker (%)
Wearing glasses (%)
Bearded male (%)
Female wearing hijab (%)
Female with clivage exposed (%)

22.51
51.22
7.32
63.41
26.83
2.44
80.49
29.27
31.71
7.32
34.15

a.

E

Translated transcripts, classification of player Bs’ statements
and subsequent decisions

Note. For player As, the experimental instructions use a neutral frame: “Right” or “Left”, which
is analogous to "In" or "Out" in the game from Figure 1.

1. So, I don’t know if you know about the experience that’s coming, but you have two choices
to make. So, you can choose between “Right” and “Left”. So, “Left” is the "safe" choice, thatis to say that when you earn 5e, I earn 5e. And “Right” is the risky choice, i.e. you may
win 0e and I may win 14e. On the other hand, it is also a choice that could make us all
earn money, in the sense that you would earn 12e if I remember correctly, and me 10. So
what does that mean? This means you have to have confidence. Well, that’s complicated, I
understand that, especially since we don’t know each other at all, and from what I’ve read,it’s going to be intermingled over there. All I can tell you is that no matter what choice
you make, I will choose to roll the die, because in any case this solution will best maximize
the profit of all of us. Whether you choose the "safe" solution, that is “Left”, or the other48

solution - “Right”, anyways, it’s in my best interest to play the die, because no matter what
you choose, I will earn money. As for you, you’re actually facing the risk of earning nothingat all. Do what you want, in any case I, do your statistics with those who will come next, I
in any case it will go full, full dice. That’s it. [Classification: promise; Decision: Roll]
2. Good morning, everyone. Just to let you know that I will always roll the die in every period.
[Classification: promise; Decision: Roll]
3. Dear player As, I encourage you to choose the option “Right”. I commit myself to choose theaction “Roll” in order to maximize all earnings, in a fair way, between you and me, since thisoption allows you to win the maximum in this game, that is 12e. [Classification: promise;
Decision: Roll]
4. So you have all read the rules of the game, just like me. I suppose you won. . . you came here,
I’m sorry, to make the most money. . . you didn’t come here for nothing. So I advise all of
you to choose the option “Right”, because I will choose the option. . . "win". . . no, the option“Roll”, sorry. That’s what it takes to make the most money. OK, I’m done. [Classification:
promise; Decision: Roll]
5. Hello everyone, I think you have all read the instructions, you know well that there is a way
allowing us to earn the maximum amount of money. Me and you, it’s a matter of trust,that’s why I simply invite you to choose. . . uh. . ... “Right”. Thank you for your confidence.
[Classification: non-promise; Decision: Don’t roll]
6. So. . . uh, I’m a competitor. I came here to play. Uh. . . so. . . uh, I’d like to have competitors
in front of me. And. . . always choose an action, and. . . I will always roll my. . . my game.
That’s it. [Classification: promise; Decision: Don’t roll]
7. Hello everyone, uh, I think that in life sometimes you need to stick your neck out and. . . here,
if you make that choice, you still have 5 chances out of 6 that it’s a good choice. Otherwise,
you can decide to stay in your comfort zone, and make sure you get the minimum. And
then, if everything is. . .. if everything is a matter of luck, then maybe today you will make49

the right choice by trusting a stranger. That’s it. [Classification: non-promise; Decision:Roll]
8. Hello. So what I’m proposing is that the best solution is to cooperate. So I’m going to choose
to roll the die every time. And if you can choose “Right”, if you are adventurous and not
afraid, the probability is still higher that you will win something. That’s it. [Classification:
promise; Decision: Roll]
9. Hello everyone, uh, so we’re all going to play a game with stakes. . .. monetary stakes. So
you’re player As, and I’m player B. So, um, you’re going to choose between “Left” or “Right”
and depending on that, so. . . uh. . . and I’m going to choose between rolling a die or not.
So, uh, if you choose “Left”, it’s likely we’ll all get the same gain. On the other hand, if
you choose “Right”, there are chances that I will not earn anything and that you will earnmore. But. . . whatever you choose, I wish you all the best, and thank you. [Classification:
non-promise; Decision: Don’t roll]10. Hello. Well, I think we’re all here to make money, so I’m counting on you to be players.
Don’t take the easy way out, let’s just leave it to chance, and then let’s have some fun.
That’s it. Thank you very much. [Classification: non-promise; Decision: Roll]
11. Well, hello there. So my choice is easy, that is, either I earn a lot or I earn a little less but
I help another person earn money. So it is a social ideal in the sense that I can create more
wealth for more people, even if it is not for my benefit. You, your choice is more difficult.
Of course, you can lose, while I have little to lose, and I have everything to gain from you
taking the step of the social ideal. So I wish you good luck to think beyond individuality,
and I look forward to seeing the results. [Classification: promise; Decision: Roll]
12. So this is the first time I’m taking part in an experiment like this. I didn’t expect it to beabout numbers. Uh, so it reminds me a little bit of the probability actually in. . . in highschool, with math. Even though I haven’t being doing it for three years now, it reminds meof that, actually. [Classification: non-promise; Decision: Roll]

50

13. Hello. I advise you, player As, to choose action “Right”. If you take this decision, be awarethat your gains from the experiment will be greater. If you choose the alternative action,
you will not have all the odds play in your favor. [Classification: non-promise; Decision:
Don’t roll]
14. Hello everyone. Uh, so trust and solidarity. If you are. . . if you trust me, I will act insolidarity. If you choose “Right”, I’ll roll the die, we’ll all be winners. So that’s it, you haveto take risks. And with me, there’s no risk, if you go “Right”, I’ll roll the die. That’s it.
[Classification: promise; Decision: Don’t roll]
15. Well, I know it’s hard to trust someone, especially when you don’t know them. But thisgame is like in real life, it’s a kind of trusting and not trusting. So if you’re used to having. . .
to trust people, well, bet on me. [Classification: non-promise; Decision: Don’t roll]
16. So, I think. . . just like me, you have one chance in two to act as a player. And you have
either. . . the individual choice, therefore. . . in the image of our society, if we put ourselves
forward, we gain more at the expense of the other. And the other choice, which was to put usboth, so player A and player B, on the same line, with positive effects for these two players.
So I’ve made that choice. And others may have made the opposite choice. But, in any
case, that’s it, I’m in. . . I’m not introducing myself because we’re not allowed to introduce
ourselves, if I remember correctly. But here it is. [Classification: promise; Decision: Roll]
17. Well, hello to all of you. So you have 6 choices to make, so 6 chances to win. It’s sure that. . .
choosing “Left” is safety, but choosing “Right” can make you earn twice as much. So, it’s up
to you whether you’re a gambler or not. [Classification: non-promise; Decision: Don’t roll]
18. Hello to all player As. In fact, we’re all here to increase our earnings, so we should help eachother. So I propose you to choose the “Right” so that I. . . of course, I will choose “Roll”, sothat, actually, we will have a chance of 5 out of 6 to have: you 12e more, and me 10e on
top. So choose carefully, and above all think about how much you can earn. That’s it.
[Classification: promise; Decision: Roll]

51

19. Listen, I won’t take long. If you choose “Left”, then we’ll have, no matter what I do, we’ll
have 10e to gain for two, meaning 5e each. But if one takes “Right”. . . I see you asassociates, and as associates we can get 22e for two, 12 for you, and 10 for me. We havea 80% chance. . . if we. . . and for the 20%, we have, uh, only 10e for me, I guess, and you,
you get nothing. We have a greater chance to gain together, well, individually, to gain more.
Full stop. [Classification: promise; Decision: Roll]
20. Uh, hello. Actually, there is. . . I’m player B, I’m going to show you that. . . uh, you. . . you
choose the. . . uh. . . Actually. . . because I forgot the thing, I don’t know. . . Well, uh, I suggest
you choose, choose “Don’t roll”. Because we, uh, it’s not too beneficial compared to the. . . I
don’t know. [Classification: non-promise; Decision: Roll]
21. So as you know, you have the opportunity to. . .. two possibilities, I meant. If you choose“Right”, you have the possibility. . . your expected earnings can reach up to 12e. So you
have more chances to win than by choosing “Left”. If, on the other hand, you choose “Left”,
your expectation of gain is set at 5e. So your decision depends on whether or not you
want to earn more money. Think carefully before making a decision because if you choose“Right”, you can win up to 12e. If, on the other hand, you choose “Left”, it is set at 5e.
[Classification: non-promise; Decision: Roll]
22. Hello. In fact, in. . . we’re in a game. And like in a game, we may or may not take risk. If
you choose to take risk, you can earn even more, while if you do not take risk, the gains arecertain, but. . . but they are low. OK. [Classification: non-promise; Decision: Don’t roll]
23. Hello. Dear shareholders, I can reassure you that this action is a win-win situation. So don’t
think much and, uh, I suggest you go for it. [Classification: non-promise; Decision: Roll]
24. So, actually, for this experiment, I think that it’s in the common interest that player B makes
a decision, and I think rolling the die is a good decision. And player As, by choosing “Right”,
I think it’s a good decision too, given that. . .. By choosing “Left”, there is a minimum gain,
but by choosing “Right”, there is more chance of getting a maximum gain, and it is more52

interesting and wiser to. . . to choose. . . uh. . . the possibility of earning a profit higher thanwhat you could get at a minimum. So I think. . . it’s wiser for player As to. . . to decide tochoose the action “Right”, and player B, well, to. . . to roll the die. [Classification: promise;
Decision: Roll]
25. Well, choose “Right”. That way, you’ll make 12e and I’ll make 10. It’s already better than
5e each. If you played the lottery earlier, uh. . . trust me, you have a 5 in 6 chance ofgetting, uh, more than the double, while the lottery earlier was just double. And also like inthe questionnaire, you have to trust others. [Classification: promise; Decision: Don’t roll]
26. Hello to everyone, uh. . . we’re all here to make money. And above all, to win as much as
possible. So, uh, I’m telling you, if you’re a player A, uh, you might as well choose “Right”,
and if you’re a player B, you might as well choose to roll the die. That way, we’ll win between
10 and 12e and. . . whereas if player A chooses “Left”, then we’ll only win 5e. That’s it.
[Classification: non-promise; Decision: Roll]
27. Well, let me first say hi to everyone. I hope you’re envoying yourselves. I guess you’ve allread the instructions, and that we all care about making some money. So, I think it’s betterfor you to choose “Right”, and for me to choose to roll the die. Because we have, well, we
have 5 chances out of 6 that you get 12, and me only 10. I don’t mind. But if I sense you
lack confidence in me, and that you’re tempted not to roll the die, then. . . no, to say “Left”,
I will not roll the die. Which means we will all lose a lot. So make good choices. You knowwhat to do. Choose “Right”, I will choose “Roll”. [Classification: promise; Decision: Roll]
28. Well, hello. What I wanted to tell you is: in this game, one shouldn’t, one shouldn’t beselfish or too "worried" because, as they say, too much thinking kills the game. One should
know how to share the cake. Because, because in the end we’ll all get our. . . our pack ofcigarettes. It’s just when you’re smoking your cigarette, you have to tell yourself that. . .
how to say. You need to have a good conscience. Do you understand? So we have. . ... You
know what the right decision to be made. And. . .. All I have to say to you is: trust, follow
your heart. Okay? Thank you. [Classification: non-promise; Decision: Roll]
5329. Hello. In fact, from what I’ve seen, you have to, uh, actually, you choose the action thatis most profitable for both of us, for both parties, and that will actually allow you to earnmore than me. It’s okay. It’s okay. [Classification: non-promise; Decision: Roll]
30. Good morning, everyone. So, very simple: by choosing “Right”, you have one chance insix to lose 5e. But by choosing “Left”, you are sure to lose 7e. That’s it. [Classification:
non-promise; Decision: Don’t roll]
31. Anyways, let me explain. What is my option? It’s that you choose “Right” and I choose“Roll”. Anyways, we have. . ... 5 out of 6 chances that you will win more than me, and I – 1
out of 6 chances that I will win more than you, and you will lose. Because if it falls. . . if it
falls on 6, I’ll win 14 and you 0. And in the other cases, you 12 and I 10. So it’s better to. . ..
than to take 5e / 5e. That’s my option. [Classification: promise; Decision: Don’t roll]
32. So. . . well. . . now, I’m starting to bug here. Actually, I have a choice between rolling and not
rolling, actually. In fact, I’m already going to. . . in fact, I’m already going to roll, it’s 100%.
And you, you have a choice, you have a choice between “Right” and “Left”. “Right” and “Left”.
Basically, if you choose “Right”. . . and then there will be. . . if I remember correctly. . . a diethat will be rolled. If it falls between 1 and 5, you will. . . basically, you’re going to win 12
clams, I think. And if it falls. . . basically, on the number 6, you’ll lose everything. That’s it,
it’s your choice. [Classification: promise; Decision: Roll]
33. Hello. Well, then we’ve all read the rules. We all know what it’s like, what is happening.
All I have to say to you is that I’m going to cooperate anyway, so I’m going to roll the die
because I don’t really care if I win 10 or 14. So if you can win 12, it’s good for everyone
and everyone’s happy. No, I meant it! Believe me or not, you can do whatever you want.
If you want to go home with five, that’s your problem. That’s it. [Classification: promise;
Decision: Don’t roll]34. Hello player As, I’m player B. So I invite you all to put “Right” each time, since you willlose a lot if you put something else. I’ll give you, I’ll give you. . .. I commit myself to put

54

“Roll” each time so that you can have a 1 in 5 chance of winning at least 12e. I hope I wasconvincing. [Classification: promise; Decision: Roll]
35. Hello to everyone. I have to make a speech. I don’t know what to say, but. . . there are
two choices. There is “Left” or “Right”. If one takes “Left”, we’ll win 5e each. If one takes“Right” and I roll the die, we win. . . you win 12e and I win 10e. I don’t want to makemoney that much, so if you want, you. . . you choose “Right” and I’ll roll the die, and. . . we’llsee, huh. I hope we win. [Classification: promise; Don’t roll]
36. The goal of this game is to earn as much as possible. You have two choices. There is either
the wise choice or. . . a safe choice. I would advise you on the wise choice, the second one.
[Classification: non-promise; Decision: Don’t roll]
37. Good morning everyone. Well, I’m getting into this funny speech, with my face floured
and. . . besides, I’m a little sick. I almost have a runny nose too, so it’s just great. Mmm. . .
yes, well, go ahead, be creative, go for it, or rather let me go for it and “Right”. That’s
it. What else can I say? Mmm. . . yes. . . go ahead, be creative, that’s it. [Classification:
non-promise; Decision: Don’t roll]38. Hello. Well, it’s going to be very simple. So I plan to choose to roll constantly. So I hope
you will do the same, so that we can get the most out of it, that is, that you choose “Right”,
in fact. That’s it. Thank you. [Classification: promise; Decision: Roll]
39. Hello everyone. So, in order for the roll to be taken into account and for us all have earnings,
I advise you to do. . . to choose the option “Left”. That’s it. Thank you all. [Classification:
non-promise; Decision: Roll]
40. Well, hello. . . Well, I’m going to choose to roll, so. . . if you’re a player. . . you have a 5 outof 6 chance of winning. That’s it. [Classification: promise; Decision: Roll]
41. Hello. Well. . . I’m not very good at speeches, and I didn’t really prepare anything, so. . .
I’ll tell you that if we’re already here, let’s have fun! Have a good day. [Classification:
non-promise; Decision: Don’t roll]55

Table 7: Rolling and promise-making: logistic regression analysis

1[P romise]β, (SE)
2.535(1.182)p
0.032

Session FE’s

AM E, (SE)
0.394
(0.122)
YES

p
0.001

Note. N = 41. The dependent variable is player B’s decision to Roll or not. The main explanatory variable is 1[P romise] = 1
if player B makes a promise to Roll, and set to 0 otherwise. The table provides the coefficient (β) and the average marginal
effect (AM E), along with the respective standard errors (SE). Session fixed effects are also included; testing for their joint
significance yields: p = 0.287. Pseudo-R2 = 0.233.

F

Who makes a promise?

In Section 4.1, we have provided evidence that a promise to Roll is a credible strategic signal,
since it is strongly and significantly predictive of the actual behavior (see Table 7). In this part,
we take a step back and address the following questions: Who makes a promise? Which individual
characteristics can predict a player B making a promise to Roll in front of player As? To address
this question, we follow the lead of a recent experimental study by Ismayilov and Potters (2016)
who report (p. 390) that: promises do not cause trustworthiness, they are just more likely to besent by trustworthy players than by untrustworthy ones.38 Our experimental design allows us toscreen the prospective promise-makers based on a rich set of individual characteristics elicited in
the preliminary stage of the experiment and summarized in Table 5.39
Below, we show that pro-social distributional preferences (measured through the Social Value
Orientation slider task by Murphy et al., 2011) are highly predictive of promise-making. Importantly, such preferences have been previously shown to explain trustworthy behavior in the trust
game (Cox, 2004; Ashraf et al., 2006). Our result suggests that they also explain one’s willingness
to make a promise which corroborates the conjecture made by Ismayilov and Potters (2016).
The econometric estimates of this screening exercise are reported in Table 8. For reasons
38
This conjecture differs at face value from the perspective commonly adapted by modern behavioral theories, likethe lying aversion theory (Ellingsen and Johannesson, 2004), or the guilt aversion theory (Charness and Dufwenberg,
2006), that assume a causal link from promises to trustworthy behavior and explain its behavioral underpinnings.
39
The distribution of the CRT scores is strongly left-skewed (see Appendix D for details) and thus the numberof observations with a positive score is relatively small and quickly decreasing in the score. For this reason, in
the regression analysis we use a binary variable 1[CRT > 0] that splits the sample into two categories: those whocorrectly answered at least one question (1[CRT > 0] = 1), and those who did not (1[CRT > 0] = 0).

56

stated in footnote 13, our econometric model accounts for session fixed effects. In our dataset, thisprecludes fitting a model using standard maximum likelihood method because of quasi-complete
separation of observations.40 Rather, we use two alternative specifications. First, we reportcoefficients of a linear probability model (LPM) with robust standard errors estimated throughstandard OLS procedure. As a robustness check, we report the outcomes of Firth’s logit model
estimated through penalized maximum likelihood (ML) procedure.41
The main finding is that player Bs with a stronger pro-social orientation (as measured by their
SVO angle) are more likely to make a promise to Roll. The positive effect of pro-sociality on
promise-making is highly significant and robust to the econometric specification (pLP M = 0.001;
pF irth = 0.010).42

G

Robustness analysis for Figure 2

Table 9 re-assesses the comparisons reported in 1b after including player B fixed effects (41 indicator variables). Note that such robustness check is not meaningful for 1d, i.e. after including thepromise/non-promise indicators which (unlike the treatment indicator variables) do vary within agiven recording.

H

Trust and direct communication

Here, we compare player As’ trust under direct (F2F) and indirect communication (VIDEO, AUDIO, and TEXT). Given that the experimental design of F2F differs from the remaining treatments
40
In session 6, all player Bs made a promise to Roll, so that the corresponding indicator variable is a perfectpredictor of “success”. This, in turn, makes it impossible to fit a binary response model through a standard ML
exercise, since the value of the fixed effect for session 6 that maximizes the likelihood function is unbounded andtends to infinity.
41
The Firth (1993) method has been proposed as a remedy for data separation problems (Heinze and Schemper,
2002). For Firth’s logit, it is recommended to use likelihood ratio (LR) test rather than the Wald test for significancetesting (given that the estimated Hessian matrix does not allow for a reliable estimation of standard errors); forthe same reason, odds ratios (OR) should be used instead of marginal effects.
42
Firth’s logit provides some evidence that promise-making may also depend on emotional intelligence (ORincreasing in the RMET score) and risk preferences (OR decreasing in the investment in the Gneezy-Potters task).
However, these results are only (marginally) significant at the 10% level (pF irth = 0.096 and 0.061, respectively),
and they are not robust to the econometric specification of the model (respective coefficients are insignificant in the
LPM with pLP M = 0.182 and 0.185).

57

Table 8: Individual predictors of promise-making

Intercept
SVO angle
1[CRT > 0]
RMET score
GP investment
SOEP trust 1
SOEP trust 2
SOEP trust 3
Political views
Session FE’s

Session FE’s
β’s
β’s and FE’s

LPM
βOLS , (SE)
0.214
(0.705)0.014(0.004)0.152(0.172)0.026(0.019)-0.004(0.003)0.064(0.092)-0.072(0.082)-0.154(0.144)0.007(0.028)
YES

p
0.764

Firth’s logit
βF irthORp
0.178
1.195 0.976

0.001

0.120

1.128

0.010

0.384

1.162

3.197

0.377

0.182

0.302

1.353

0.096

0.185

-0.041

0.960

0.061

0.494

2.201

9.035

0.443

0.389

-2.625

0.072

0.318

0.294

-2.973

0.051

0.194

0.810

0.203

1.225

0.570

YES

Joint insignificance tests
0.001<0.001<0.001

0.029
0.057
0.060

Note. N = 41. The first two columns provide OLS estimates (βOLS ) of a linear probability model (heteroscedasticity-robust
standard errors are given in parentheses) along with p-values from t-test for statistical significance. R2 = 0.586. The second
part of the table provides the results from an alternative specification, Firth’s logit: estimated coefficients (βF irth ), odds
ratios (OR) and p-value from LR test for statistical significance. Joint significance tests in the lower part of the table are:F -test (LR test) for the left-hand (right-hand) side model.

Table 9: Robustness check: treatment comparisons controlling for player Bs fixed effects

VIDEO vs. AUDIO(H0 : α1 − α2 = 0)
VIDEO vs. TEXT
(H0 : α1 − α3 = 0)
AUDIO vs. TEXT
(H0 : α2 − α3 = 0)

58

Diff.
(SE)
0.036
(0.042)0.104(0.041)
0.067(0.039)

p
0.388
0.012
0.087

in several ways, we would like to start by discussing the most important caveats for such comparison. Although we respond to each of them below, the results presented in the remaining part thissection should be taken with a grain of salt.
One important concern is that the game is more “interactive” in F2F then elsewhere. In F2F,
player A’s decision affects both players’ payoffs, while in the remaining treatments it does notaffect player B’s gains (only the vice versa holds). As a result, as the role distributional concerns(“kindness”) may be more pronounced in player As’ decisions in F2F that elsewhere. Based on somethe previous evidence on distributive preferences and trust (see, e.g., Cox, 2004), one may expect
this design asymmetry per se to stimulate trust in F2F relative to other treatments. We respondto (and allay) this concern in two ways. First, we do not observed such a pattern in our data: F2F
does not generate more trust than other conditions. We also note that so far, the experimental
evidence is mixed regarding the role of distributional concerns in explaining trust. Cox (2004)
estimates that distribututional concerns are as important as the expected reciprocity. However,in a subsequent study Ashraf et al. (2006) find that kindness is not a primary driver of trust –most of the variance in trust is explained by expectations of return (which is a common factor in
all our treatments). Second, in a study closely related to ours (in which players in a laboratory
trust game communicate through pre-recorded, standardized video messages), Centorrino et al.
(2015) collect data in two waves of experimental sessions. In the first wave (N = 84), both players’
choices affected both players’ payoffs. The second wave (N = 114) uses the same set of recordings,
and this time player As’ decisions have no impact on player Bs’ gains. They do not find evidence
that this design affects trust, that is, player As’ trust does not depend on the presence of monetaryconsequences for player Bs. 43
Another design feature that distinguishes F2F from the other treatments is the number of decisions made by each player A (6 decisions in F2F and 10 decisions elsewhere). When parametrizing
the VIDEO treatment, we also carried out a separate experimental session with 6 videos instead of
10. Clearly, the advantage of having 10 recordings per subject in an increase in the amount of experimental data. We did not find any evidence (either descriptive or statistical) that this increase
43

Importantly, their experimental sample comes from a subject pool (university students in a large French city)that is quite similar to ours (university students in another large French city).

59

Table 10: Comparing F2F to other treatments: regression analysis

Frequency of:Intercept (λ0 )1[V IDEO] (λ1 )1[AU DIO] (λ2 )1[T EXT ] (λ3 )1[P romise] (λ4 )1[P romise] × 1[V IDEO] (λ5 )1[P romise] × 1[AU DIO] (λ6 )1[P romise] × 1[T EXT ] (λ7 )F2F (H0 : λ4 = 0)
VIDEO (H0 : λ4 + λ5 = 0)
AUDIO (H0 : λ4 + λ6 = 0)
TEXT (H0 : λ4 + λ7 = 0)
Joint F -test (H0 : λ5 = λ6 = λ7 = 0)InInλ, (SE)p0.350< 0.001(0.055)
0.128
0.070
0.309
(0.068)0.451-0.0080.908(0.065)0.488-0.0490.434(0.063)0.102
0.099(0.062)0.029
0.686(0.071)0.090
0.214(0.072)0.031
0.671(0.072)
Effect of promise on trust rates across treatments
0.099< 0.001< 0.001< 0.001
0.514
λ, (SE)
0.402(0.047)0.088(0.058)0.042(0.055)-0.038(0.054)p< 0.001

Note. Estimates of linear probability models (LPM). Dependent variable: indicator of player A’s decision to go In. In thefirst model, the dependent variable is regressed on the all-experiment set of treatment dummies (= 1 for a given treatment,= 0 otherwise). Each of the estimated coefficients corresponds to the average effect of the underlying experimental conditionon the probability of observing a given outcome, relative to the reference F2F treatment (the outcome of which is capturedby the intercept). The second model also include a dummy variable 1[P romise] (= 1 if a player A received a promise
to Roll from player B, = 0 otherwise), and its interactions with each of the treatment indicator variables. Models use
cluster-robust standard errors (with session-level clustering for the F2F treatment and individual-level clustering in theremaining treatments) to account for the potential unobserved correlation between individual observations, as well as theheteroscedasticity of residuals in the LPM. There are 246 observations (7 clusters) in F2F, 620 (62) in VIDEO, 700 (70) in
AUDIO, and 850 (85) in TEXT.

60

in the number of observations per subject comes at a cost of affecting player As’ behavior.44
The overall trust rate in F2F is 40.2%. Descriptively, it is higher than in TEXT (36.5%),and lower than in AUDIO (44.4%) and VIDEO (49.0%); however, none of those differences isstatistically significant (for statistical support, see Table 10). Like elsewhere, in F2F there is also
a positive effect (equal to 10.2 percentage points, p = 0.099) of player B’s promise to Roll on
player A’s trust. Pairwise comparisons suggest that this effect is not significantly different fromthose observed in any of the remaining treatments; a joint F -test leads us to the same conclusion(p = 0.514).
Altogether, these results suggest that making communication direct – i.e. moving from video
messages to face-to-face interaction – does not improve trust. If anything, it may be counterproductive (although the differences between those two conditions are not statistically significant),
and the outcomes from the face-to-face communication treatment are very close to those in thebaseline treatment with plain text messages. This is in line with the previous results from public
goods games and trust games reported in Bochet et al. (2006); Bicchieri et al. (2010), but goesagainst the findings in Balliet (2010) who suggests that face-to-face communication is a far more
powerful device for inducing cooperation than written messages.

44

We observed similar trust rates in the 6-round game, and in rounds 1-6 and 7-10 of VIDEO.

61

I

Player As’ perceptions of player Bs’ characteristics

Table 11 summarizes Player A’s’ perception of player Bs in our experiments.
Table 11: Player As’ perceptions of player Bs on a 1-8 scale

Average score on:

beautyintelligence

No promise
PromiseRanksum p
Overall mean score

4.817
4.921
0.754
4.870

5.150
5.421
0.289
5.289

No promise
PromiseRanksum p
Overall mean score

4.661
4.704
0.876
4.684

5.112
5.611
0.015
5.381

No promise
PromiseRanksum p
Overall mean score

4.706
4.878
0.256
4.797

4.921
5.476
0.020
5.214

No promise
PromiseRanksum p
Overall mean score

4.971
5.407
0.124
5.180

trustworthiness
F2F
4.367
4.484
0.556
4.427
VIDEO
4.476
4.760
0.167
4.629
AUDIO
4.291
4.557
0.175
4.431
TEXT
4.145
4.412
0.179
4.273

self-confidence
4.608
5.071
0.327
4.846
4.493
5.198
0.074
4.873
4.973
5.757
0.037
5.387
5.290
5.623
0.322
5.449

Note. p-values come from ranksum test using a player B’s average score in player As’ evaluations as a single observation.
Comparisons are made between promise-makers (21 observations) and others (20 observations).

62