# Upset but (almost) correct: a conceptual replication of Di Tella, Perez-Truglia, Babino and Sigman (2015)

Eugenio Verrina

► To cite this version:

Eugenio Verrina. Upset but (almost) correct: a conceptual replication of Di Tella, Perez-Truglia, Babino and Sigman (2015). Journal of the Economic Science Association, 2023, 9 (2), pp.327-336. 10.1007/s40881-023-00136-4 . hal-04829178

HAL Id: hal-04829178

https://hal.science/hal-04829178v1

Submitted on 3 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Upset but (almost) correct: A conceptual replication of di Tella, Perez-Truglia, Babino and Sigman (2015)

Eugenio Verrina*

June 5, 2023

## Abstract

This paper provides a close conceptual replication of the study by Di Tella et al. (2015) on self-serving beliefs. The design differs in some aspects from the original study, but maintains its fundamental structure and uses a larger sample size. The main findings of the original study are not replicated. If anything, beliefs seem to be biased in the opposite direction. These results are discussed jointly with two other replication efforts by Ging-Jehli et al. (2020) and Ahumada et al. (2022). The main conclusion is that self-serving beliefs about others in strategic settings seem to be quite sensitive and hard to capture.

Keywords: self-serving beliefs; conceptual replication; altruism

JEL Classification: C72, C91, D63, D64, D83

<footnote>*Sciences Po Paris — CNRS, France; E-mail: eugenio.verrina@sciencespo.fr; ORCID: 0000-0002-4442-4601. Financial support for this project by the Max Planck Society and the Deutsche Forschungsgemeinschaft (DFG) is gratefully acknowledged. The author also received support from the French National Research Agency (ANR, DECISION project, ANR-19-CE26-0019) and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement n°850996 – MOREV). The replication material for the study is available at OSF under DOI https://doi.org/10.17605/OSF.IO/UD5G7 or URL https://osf.io/ud5g7/.</footnote>
# 1 Introduction

In recent years, a large body of literature has researched the conditions under which people display moral behavior and those under which they act in their own self-interest. It has become clear that people often develop biased views about the world around them to resolve the tension between being or appearing moral and pure self-interest (Miller and Ross, 1975; Kahan et al., 2012; Gino et al., 2016). Examples of such self-serving beliefs include self-serving manipulation of fairness arguments (Konow, 2000), of ambiguity (Haisley and Weber, 2010) and of risk (Exley, 2015).

Another way in which people may justify their selfishness is by biasing their beliefs about the behavior of the people they interact with. The idea is simple: if the other is unfair or bad, then I can be unfair or bad in return.<fnref n="1" /> Di Tella et al. (2015) investigate the formation of such self-serving beliefs justifying unfair behavior in a modified Dictator Game, the “corruption game”. In the “basic” version of the game, an Allocator decides how to split 20 tokens between herself and a Seller. The Seller decides how to convert the tokens into money. She can decide to convert them at a high conversion rate (1 token = 2 AR$ (Argentinian Pesos)) or a lower one (1 token = 1 AR$), in which case she receives a side-payment or “bribe” (10 AR$). Importantly, the Allocator is asked to state her beliefs about the behavior of the Seller she is paired with and about the behavior of the Sellers in her session. A self-serving bias in beliefs can be identified by comparing two between-subjects treatments. In both treatments, 10 tokens are assigned to the Allocator and 10 to the Seller. In the *Able=8* treatment, an Allocator can give or take up to 8 tokens. In the *Able=2* treatment, the ability to take is restricted, and an Allocator can give or take only up to 2 tokens.

Crucially, Sellers do not know about these restrictions and believe that Allocators can give or take any amount they want in both treatments. In turn, Allocators know that the Sellers are not aware of any restrictions. An Allocator who cares about her self-image (Bénabou and Tirole, 2006) and wants to allocate more tokens to herself in the *Able=8* might self-servingly

<footnote n="1">¹The behavioral motive behind this reasoning is the well-known principle of reciprocity (Falk and Fischbacher, 2006).</footnote>
believe that the Seller chose to take the bribe. The authors find that, indeed, Allocators in the *Able=8* treatment think that more Sellers chose the unfair option compared to the *Able=2* treatment.

This paper presents a close conceptual replication (Crandall and Sherman, 2016; DellaVigna and Pope, 2019; Derksen and Morawski, 2022) of the basic corruption game of Di Tella et al. (2015). This means that some details of the design were different (as described in Section 2), but the main spirit of the experiment remained intact. As such, the present experiment can be seen as a robustness or sensitivity check of the theoretical construct rather than a direct replication.<fnref n="2" /> The experiment was well powered (80%) based on the original effect size and also in comparison to the original study, but fails to reproduce the main findings.<fnref n="3" /> Allocators’ beliefs, in this experiment, are not self-servingly biased. If anything, Allocators in the *Able=8* treatment have less negative beliefs about Sellers, i.e., were less upset, than Allocators in the *Able=2* treatment.

## 2 Experimental Design

The basic corruption game of Di Tella et al. (2015) was run with only some changes in the design to maintain the basic structure of the experiment. The changes are described in the following and a summary can be found in Table 1. The main task of the experiment remained substantively unchanged. Allocators and Sellers earned 10 tokens each through a real effort

<footnote n="2">²Note that direct and conceptual replications can be seen as complementary (Derksen and Morawski, 2022). While direct replications provide insights into whether the original procedure reliably generates a result, conceptual replications are fundamental to test the robustness of a phenomenon and advance theory. In fact, some even maintain that the latter are “superior” for scientific progress (Crandall and Sherman, 2016). Others argue that the distinction is not really relevant and that one should focus more on whether the underlying theory would predict the same result (Nosek and Errington, 2020; Machinery, 2020; Nosek et al., 2022). Also note that a direct replication of the “modified game” of Di Tella et al. (2015) has been performed by Ging-Jehli et al. (2020) and Ahumada et al. (2022), as discussed in Section 4.</footnote>

<footnote n="3">³Given the original effect size, the original study had only 45% power, making it underpowered. Hence, it is likely that its effect size was biased upward, which would inflate the claimed power of the present study (Simonsohn, 2015). However, the effect in this study goes in the opposite direction compared to the original one. Also, the original effect lies far away from the 95% confidence interval of the effect found here. This suggests that the present study was not underpowered to find a potentially smaller, positive effect compared to the one of the original study.</footnote>
task. Then, each Allocator could choose how to distribute the 20 tokens
between herself and the Seller matched with her. Her choice was restricted
depending on the treatment. In the *Able=8* (*Able=2*) treatment, she could
give or take up to 8 (2) tokens. The Seller simultaneously decided how to
convert the tokens into Euros. Each token was worth 0.50 € if she chose
the high conversion rate (Option 1). If she chose the low conversion rate,
instead, each token was only worth 0.25 €, but she got an additional pay-
ment of 5 € just for herself (Option 2). A first difference concerns the fact
that this side-payment is higher than in the original study. however, this
does not change the strategic nature of the game and, in some respects,
it may even increase the potential for holding a negative belief about the
Seller.<fnref n="4" /> A second difference regards the way in which the restriction in the
choice set of Allocators was communicated to them. In this study, Allo-
cators were told from the beginning that they could give or take either 2
or 8 tokens, depending on the treatment they were in. On the contrary, in
the original design of the basic game, Allocators were led to believe they
could give or take any amount and learned about the restriction only upon
making their allocation decision, without an explanation as to why this
was the case. The restriction was communicated in yet a different way in
the “modified game” that Di Tella et al. (2015) use as a robustness check,
where Allocators were told about the existence of both treatments and that
they had been assigned to one of the two. The most important difference
between the basic and the modified game in the original study is that, while
the corrupt option is a weakly dominant strategy in the basic corruption
game (provided Allocators only take from and do not give to the Seller), in
its modified version the optimal action of the Seller depends on how much
the Allocator takes from her.

After the main task and before knowing the actual outcome of the in-
teraction, the beliefs of the Allocator were elicited in two ways. First,
Allocators were asked which option they thought the Seller matched with
them had chosen. This question was not incentivized, as in the original

<footnote n="4">⁴Note that, if the Seller chose Option 2 and took the bribe, the Allocator could never earn more than her. Also, the payoff of the Allocator would have been lower compared to the case in which the Seller did not take the bribe and the Allocator split the tokens equally. This could give Allocators even more reasons to think negatively about the Sellers and, hence, make it easier for them to bias their beliefs.</footnote>
study. Second, Allocators made an incentivized guess of the proportion of
Sellers who took Option 2 in their session. A third difference between this
study and the original one concerns the way in which these beliefs were
incentivized. Allocators in the original study were asked to estimate the
percentage of Sellers who chose the unfair option in their session and were
given 10 predefined brackets going from 0-10% to 90-100%. Subjects were
rewarded if the actual percentage of Sellers choosing the unfair option fell
in the bracket they had chosen. While easy to understand, this method has
one major drawback. Depending on the number of subjects in a session, a
single bracket can contain no correct response at all or more than one, mak-
ing the brackets unequally likely and potential distorting incentives. This
introduces noise in the elicitation procedure. To avoid this, in this study,
Allocators were told the actual number of Sellers present in their session
and were asked to estimate the exact number of Sellers who chose the unfair
option. The shift from percentages to natural numbers should not interfere
with the formation of self-servingly biased beliefs, which Allocators were
likely to have formed already before they reach the beliefs elicitation stage.
Correct beliefs in the incentivized elicitation were rewarded with 3 €.

The instructions were translated to German and the use of loaded lan-
guage was avoided (see Appendix B), which constitutes a fourth change to
the design of the original study.<fnref n="5" /> The Allocator and the Seller, as they were
called in the original instructions, were simply referred to as participant A
and B. The two options the Seller could choose from were described neu-
trally. On the contrary, in the original instructions, Allocators were told
that the experimenter would have preferred the Seller to take Option 2.
Note that this was not the way the options were described to the Seller, to
whom they were presented neutrally, and also note that Allocators saw the
Seller’s instructions. Option 2 was also not described as the experimenter’s
preferred option in the modified game of the original study. A last minor
difference compared to the original study is that a different real effort task
was used at the beginning of the experiment. In the original study, partici-
pants had to find a given hidden sequence of 0s and 1s in a series of 0s and

<footnote n="5">⁵Of course, Germany and Argentina differ in many cultural aspects, which could potentially affect the results. However, it is not a priori clear which cultural aspects one should focus on and how they could affect results.</footnote>
1s. In this study, participants, instead, had to complete a computerized slider task (see Appendix B). As in the original experiment the task lasted for around 1 minute and participants earned 10 tokens by completing it.

<table><caption>Table 1: Summary of main changes to original design</caption><tr><td>Higher side-payment (bribe)</td><td>The side-payment the Seller received by choosing Option 2 was higher than in the original study.</td></tr><tr><td>Allocators knew about restrictions from the beginning</td><td>Allocators were told from the beginning that they could give or take either 2 or 8 tokens, depending on the treatment they were in.<br/>In the original design of the basic game, Allocators learned about the restriction only upon making their allocation decision.<br/>In the modified game, Allocators were told about the existence of both treatments and that they had been assigned to one of the two.</td></tr><tr><td>Belief elicitation</td><td>Allocators were told the actual number of Sellers present in their session and were asked to estimate the exact number of Sellers who chose the unfair option.<br/>In the original study, they were asked to estimate the percentage of Sellers who chose the unfair option in their session and were given 10 predefined brackets going from 0-10% to 90-100%.</td></tr><tr><td>No use of loaded language</td><td>The Allocator and the Seller were simply called participant A and B and Allocators were not told that the experimenter would have preferred the Seller to choose Option 2.</td></tr></table>

**Procedure.** The experiment was run at the Cologne Laboratory for Economic Research (CLER) in June and July 2018. The entire experiment was programmed using z-Tree (Fischbacher, 2007). Participants were recruited via ORSEE (Greiner, 2015). A total of 118 subjects (55% female, average age 24.9 years) took part in the experiment across 8 sessions.

Subjects received on-screen instructions. After reading the instructions they went through a series of control questions. Once they answered all questions subjects received personalized, on-screen feedback about their answers. They were told whether their answers were correct or not, and, in the latter case, what the correct answer was. The experiment started only after all participants had finished this phase and lasted between 30 and 40 minutes overall. Subjects earned 9.25 € on average, including a show-up fee of 4 €.
# 3 Results

<table><caption>Table 2: Comparison of Allocators’ behavior and beliefs</caption><thead><tr><th rowspan="2"></th><th colspan="3">Basic Game<br>(di Tella et al., 2015)</th><th colspan="3">This study</th></tr><tr><th>Able=2</th><th>Able=8</th><th>p-value</th><th>Able=2</th><th>Able=8</th><th>p-value</th></tr></thead><tbody><tr><td>Tokens taken</td><td>1.53<br>(0.22)</td><td>6.00<br>(0.72)</td><td>&lt;0.01</td><td>1.14<br>(0.18)</td><td>4.07<br>(0.76)</td><td>&lt;0.01</td></tr><tr><td>Is Corrupt</td><td>0.47<br>(0.13)</td><td>0.87<br>(0.09)</td><td>0.02</td><td>0.86<br>(0.07)</td><td>0.50<br>(0.09)</td><td>&lt;0.01</td></tr><tr><td>%-Corrupt</td><td>0.49<br>(0.09)</td><td>0.69<br>(0.06)</td><td>0.08</td><td>0.76<br>(0.04)</td><td>0.65<br>(0.05)</td><td>0.11</td></tr><tr><td>N</td><td>15</td><td>15</td><td></td><td>29</td><td>30</td><td></td></tr></tbody></table>

Average values with standard errors in parentheses. P-values of standard mean difference test with null hypothesis that means are equal in Able=2 and Able=8. The measure of %-Corrupt for this study is constructed by transforming the results from the incentivized belief elicitation in percentages. Based on the effect size of the basic corruption game in Di Tella et al. (2015), 30 observations per treatment were needed to achieve a power of 80%.

Table 2 offers a comparison between the original study and the present one, reproducing the main analysis presented by the authors in their paper. It compares Allocators’ average behavior and beliefs in the *Able=2* and *Able=8* treatment. Allocators in the present study took slightly fewer tokens in both treatments compared to the original study. More strikingly, there is a failure to replicate the pattern of results concerning Allocators’ beliefs about Sellers. The average difference between the two treatment conditions for the unincentivized measure (*Is Corrupt*) goes in the opposite direction and is statistically significant. 86% of Allocators in the *Able=2* treatment thought that the Seller they were paired with chose the unfair option, while only 57% of Allocators in the *Able=8* treatment believe so. The same pattern is also present in the incentivized measure (%-Corrupt), although the difference between the two averages is not statistically significant. However, a comparison between the distributions of the incentivized beliefs measure (%-Corrupt) in the *Able=2* and *Able=8* treatment yields a significant difference (see Appendix A).
# 4 Discussion and Conclusion

This close conceptual replication was more highly powered than the original experiment, but failed to replicate its findings. If anything, the unincen- tivized beliefs measure (Is Corrupt) suggests that Allocators who were able to take only little (*Able=2* treatment) were more upset than those who could take more (*Able=8* treatment). In fact, beliefs in the *Able=2* treat- ment are objectively less biased, since they are closer to the actual share of Sellers who choose the unfair option (80 %). It is also interesting to note that beliefs about Sellers are in general more negative in the present study.

<table><caption>Table 3: Comparison of Allocators’ beliefs (%-Corrupt) for Equalizers and Takers</caption><thead><tr><th rowspan="2"></th><th colspan="3">Basic Game<br>(di Tella et al., 2015)</th><th colspan="3">This study</th></tr><tr><th>Able=2</th><th>Able=8</th><th>p-value</th><th>Able=2</th><th>Able=8</th><th>p-value</th></tr></thead><tbody><tr><td>Equalizers</td><td>0.23<br>(0.15)</td><td>0.7<br>-</td><td>-</td><td>0.64<br>(0.07)</td><td>0.41<br>(0.06)</td><td>0.01</td></tr><tr><td>N</td><td>3</td><td>1</td><td></td><td>11</td><td>11</td><td></td></tr><tr><td>Takers</td><td>0.56<br>(0.1)</td><td>0.69<br>(0.07)</td><td>0.28</td><td>0.83<br>(0.05)</td><td>0.79<br>(0.05)</td><td>0.61</td></tr><tr><td>N</td><td>12</td><td>14</td><td></td><td>18</td><td>19</td><td></td></tr></tbody></table>

Average values with standard errors in parentheses. P-values of standard mean dif- ference test with null hypothesis that means are equal in Able=2 and Able=8. The measure of %-Corrupt for this study is constructed by transforming the results from the incentivized belief elicitation in percentages. An Allocator is categorized as an Equalizer if she split the tokens equally between herself and the Seller and as a Takers if she took one or more tokens from the Seller.

Digging deeper in the data of the present experiment, the right part of Table 3 shows a comparison of Allocators who decided to split the tokens equally (Equalizers) with those who took at least one token from the Seller (Takers).<fnref n="6" /> As one would expect, Takers hold more negative beliefs com- pared to Equalizers. If beliefs are used in a self-serving way, they should be more negative in the *Able=8* treatment compared to the *Able=2* treat- ment and this difference should come from Takers who use their beliefs to justify taking more. However, Takers hold almost the same beliefs across

<footnote n="6">⁶All Allocators can be categorized in one of the two groups, because no Allocator gave any of her tokens to her Seller across the two treatments. This is true also in the original study.</footnote>
the two treatments ($p = 0.61$). Interestingly, the percentage of Sellers who actually choose the unfair option in the present experiment is 80%, which almost perfectly coincides with the average belief of Takers across the two treatments (81%). The difference between the two treatments comes from Equalizers, who hold much more negative beliefs than their peers in the *Able=2* treatment compared to the *Able=8* treatment ($p = 0.01$). This evidence seems to suggest that Allocators who equalize the tokens are rather “optimistic” in their beliefs about Sellers and become more “realistic” when they take.

The left part Table 3 reconstructs the same comparison with the original data of the basic game.<fnref n="7" /> As in the present study, there is no significant difference in Takers’ beliefs ($p = 0.28$). A similar test for Equalizers cannot be performed, since there is only one observation in the *Able=8* treatment. However, it seems that while Equalizers in the present study had more negative beliefs in the *Able=2* compared to the *Able=8* treatment, the data in Di Tella et al. (2015) go in the opposite direction.<fnref n="8" />

A potential explanation for the pattern observed in this study could be that beliefs are not used self-servingly, but rather that subjects simply trade off the possibility to increase their payoff against the loss (gain) in image that comes from (not) taking. This would lead to a different composition of the groups of Equalizers and Takers in the two treatments. In line with what is reported above, beliefs of Equalizers in the *Able=2* treatment would then be more negative compared to those of Equalizers in the *Able=8* treatment, in which only Allocators who have very positive beliefs would still split the tokens equally. Beliefs of Takers, instead, would not necessarily change. However, there are two points that speak against such an explanation. First, the proportion of Equalizers in the *Able=8* treatment should be lower compared to the *Able=2* treatment, which is not the case in the data. Second, average beliefs should not change across the two treatments, which there is also some weak evidence against.

To provide a broader view on the phenomenon of self-serving beliefs, the results of two further studies based on the paper by Di Tella et al.

<footnote n="7">⁷The same comparison cannot be performed for the modified game, as there are no Equalizers in the *Able=8* treatment.</footnote>

<footnote n="8">⁸This evidence is to be taken with a grain salt, since the tests are run with few observations when splitting the sample in this way, especially in the original study.</footnote>
(2015) are discussed. In a paper by Ging-Jehli et al. (2020), the authors study the role of self-serving strategic beliefs in a “pre-emptive taking game” and also replicate the modified corruption game of Di Tella et al. (2015).<fnref n="9" /> While they find no evidence of self-serving beliefs in the former game, they are able to replicate the same results of the original study for the latter. In addition, Ging-Jehli et al. (2020) also elicit beliefs about the Sellers’ behavior from third parties, who are not involved in the game, and find no difference between the beliefs of Allocators in the *Able=8* treatment which are supposed to be biased self-servingly and those of third parties. Moreover, these beliefs are closer than those of Allocators in the *Able=2* treatment to the true share of corrupt Sellers. The authors interpret this as evidence against the presence of an absolute self-serving bias and in favor of a “positivity” bias. Hence, it is not subjects in the *Able=8* treatment who are upset, but rather those in the *Able=2* treatment who have too rosy expectations about the Sellers.

Ahumada et al. (2022) also replicate the modified corruption game by Di Tella et al. (2015) alongside two other experiments on excuse-driven behavior (i.e., Exley (2015) and Dana et al. (2007)). The authors only find a marginally significant effect in the same direction as in the original study for the belief about the Seller an Allocator is matched with, which they incentivize. They find no significant effect on the main beliefs measure about the share of Sellers choosing the corrupt option at the session-level. Ahumada et al. (2022) also investigate whether excuse-driven behavior across the three experiments is related and find no positive association. They conclude that this may be driven by the sensitivity of such behavior to the type of measurement used.

In conclusion, while it is not in doubt that self-serving beliefs arise in certain settings (see, Gino et al., 2016), these phenomena seem to be quite sensitive and not very easy to capture. Future research should investigate the formation of self-serving beliefs to understand the precise mechanisms that cause them and their bounds.

<footnote n="9">⁹In the pre-emptive taking game, two subjects start off with the same endowment and a first-mover can decide to take from the second-mover, which limits the second-mover’s ability to take from her.</footnote>
References

Ahumada, B., Y. Chen, N. Gupta, K. Hyde, M. Lepper, W. Mathews, S. Neil, L. Vesterlund, T. Weidman, A. Wilson, et al. (2022). Well excuse me! replicating and connecting excuse-seeking behaviors.

Bénabou, R. and J. Tirole (2006). Incentives and prosocial behavior. *American Economic Review* 96(5), 1652–1678.

Crandall, C. S. and J. W. Sherman (2016). On the scientific superiority of conceptual replications for scientific progress. *Journal of Experimental Social Psychology* 66, 93–99.

Dana, J., R. A. Weber, and J. X. Kuang (2007). Exploiting moral wiggle room: experiments demonstrating an illusory preference for fairness. *Economic Theory* 33(1), 67–80.

DellaVigna, S. and D. Pope (2019). Stability of experimental results: Forecasts and evidence. Technical report, National Bureau of Economic Research.

Derksen, M. and J. Morawski (2022). Kinds of replication: Examining the meanings of “conceptual replication” and “direct replication”. *Perspectives on Psychological Science*, 17456916211041116.

Di Tella, R., R. Perez-Truglia, A. Babino, and M. Sigman (2015). Conveniently upset: Avoiding altruism by distorting beliefs about others’ altruism. *American Economic Review* 105(11), 3416–42.

Exley, C. L. (2015). Excusing selfishness in charitable giving: The role of risk. *The Review of Economic Studies* 83(2), 587–628.

Falk, A. and U. Fischbacher (2006). A theory of reciprocity. *Games and Economic Behavior* 54(2), 293–315.

Fischbacher, U. (2007). z-tree: Zurich toolbox for ready-made economic experiments. *Experimental Economics* 10(2), 171–178.

Ging-Jehli, N. R., F. H. Schneider, and R. A. Weber (2020). On self-serving strategic beliefs. *Games and Economic Behavior*.
Gino, F., M. I. Norton, and R. A. Weber (2016). Motivated bayesians: Feeling moral while acting egoistically. *Journal of Economic Perspectives* 30(3), 189–212.

Greiner, B. (2015). Subject pool recruitment procedures: Organizing experiments with or see. *Journal of the Economic Science Association* 1(1), 114–125.

Haisley, E. C. and R. A. Weber (2010). Self-serving interpretations of ambiguity in other-regarding behavior. *Games and Economic Behavior* 68(2), 614–625.

Kahan, D. M., E. Peters, M. Wittlin, P. Slovic, L. L. Ouellette, D. Braman, and G. Mandel (2012). The polarizing impact of science literacy and numeracy on perceived climate change risks. *Nature Climate Change* 2(10), 732.

Konow, J. (2000). Fair shares: Accountability and cognitive dissonance in allocation decisions. *American Economic Review* 90(4), 1072–1091.

Machery, E. (2020). What is a replication? *Philosophy of Science* 87(4), 545–567.

Miller, D. T. and M. Ross (1975). Self-serving biases in the attribution of causality: Fact or fiction? *Psychological Bulletin* 82(2), 213.

Nosek, B. A. and T. M. Errington (2020). What is replication? *PLoS Biology* 18(3), e3000691.

Nosek, B. A., T. E. Hardwicke, H. Moshontz, A. Allard, K. S. Corker, A. Dreber, F. Fidler, J. Hilgard, M. K. Struhl, M. B. Nuijten, et al. (2022). Replicability, robustness, and reproducibility in psychological science. *Annual Review of Psychology* 73(1), 719–748.

Simonsohn, U. (2015). Small telescopes: Detectability and the evaluation of replication results. *Psychological Science* 26(5), 559–569.
Supplementary material to the paper “Upset but (almost) correct: A conceptual replication of di Tella, Perez-Truglia, Babino and Sigman (2015)”, by Eugenio Verrina

Table of content:

• Appendix A: Further analysis

• Appendix B: Instructions

# A Appendix A: Further analysis

Figure 1 displays the cumulative density function of %-Corrupt. The solid line corresponding to the *Able=2* treatment lies always to the right of the dotted line of the *Able=8* treatment, meaning that the whole distribution of the incentivized beliefs measure is shifted to the right in the *Able=2* treatment compared to the *Able=8* treatment (Chi-squared test with simulated p-value, p = 0.01). This confirms the observations made for Table 2 and shows that the shift in beliefs is substantial and goes in the opposite direction compared to the original study.

<figure><img src="image_2.png" /><figcaption>Figure 1: Ecdf plot of %-Corrupt</figcaption></figure>
# B Appendix B: Instructions

*Instructions are translated from German.*

Welcome to this experiment!

Thank you for your participation in this experiment. Please read the instructions carefully. For your participation today you will receive **4 Euro**. During the experiment you will have the possibility to earn further money. Your additional payment will depend on your choices, the choices of other participants, as well as random events.

Please switch off your mobile phone and avoid any communication with your neighbors. We kindly ask you to read the following instruction carefully. If you have any questions, please raise your hand and we will come to answer your questions at your seat.

This experiment has two parts. You will first work on Part 1. When you will be finished with Part 1, you will receive the instruction of Part 2.

**Part 1** In this part you have to work on a given number of tasks just as all other participants in today's experiment.

These tasks consist of multiple sliders that will appear on your screen. Each slider has to be moved from its initial location to the “target” location. When the target location is reached, the value will be displayed in green.

All participants have to complete 12 such sliders. Each participant will get **10 tokens** for the completion of these tasks.

*Instructions of Allocators:*

**Part 2** In this part of the experiment, you have been paired with another participant. You have been randomly assigned to the role of **Participant A**. The other participant has been randomly assigned to the role of **Participant B**.

**Your task:** Both participants have earned 10 tokens in Part 1. Hence, together you obtained 20 tokens. As Participant A you have to distribute these **20 tokens** between yourself and Participants B. You can take up to
8(2) tokens from Participant B and you can give up to 8(2) of your own
tokens to Participant B.

**Task of Participant B:** Participant B decides at which price the 20 tokens are sold. He can set a price of 0.5€ per token (20 tokens = 10€) or a price of 0.25€ per token (20 tokens = 5€), whereby he then will get an additional compensation of 5€ just for himself.

On the next screen you will see the options of Participant B in detail.

**These are not your instructions! These are the complete in-
structions of Participant B.**

Participant A is going to distribute the 20 tokens between himself and
you. He will, hence, decide how many tokens he wants to keep and how
many he wants to give to you. As Participant B you have to decide what
is the value of the tokens:

**Option A:** Each token will be worth 0,50€ (20 tokens = 10€).

**Option B:** Each token will be worth 0,25€ (20 tokens = 5€), and as compensation you will receive 5€ just for yourself.

Participant A will know only after his decision which of the two op-
tions you have chosen. Likewise, you will get to know how Participant A
distributed the tokens only after your decision.

**Please note that the instructions to the seller do not specify**
**how many tokens you can distribute as Participant A.**

You can now go back to your own instructions by clicking on
the box on the right (“Part2”). Comprehension questions will
now follow.

*Instructions of Sellers:*

**Part 2** In this part of the experiment, you have been paired with another participant. You have been randomly assigned to the role of Participant B. The other participant has been randomly assigned to the role of Participant A.

Both participants have earned 10 tokens in Part 1. Hence, together you
obtained 20 tokens.
Participant A is going to distribute the 20 tokens between himself and you. He will, hence, decide how many tokens he wants to keep and how many he wants to give to you. As Participant B you have to decide what is the value of the tokens:

**Option A:** Each token will be worth 0,50€ (20 tokens = 10€).

**Option B:** Each token will be worth 0,25€ (20 tokens = 5€), and as compensation you will receive 5€ just for yourself.

Participant A will know only after his decision which of the two options you have chosen. Likewise, you will get to know how Participant A distributed the tokens only after your decision.

<figure><img src="image_3.png" /><figcaption>Figure 2: Slider Task</figcaption></figure>