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# Representative Policy-Makers? A Behavioral Experiment with French Politicians

Roberto Brunetti, Matthieu Pourieux

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Roberto Brunetti, Matthieu Pourieux. Representative Policy-Makers? A Behavioral Experiment with French Politicians. 2025. hal-04598638v3

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Representative Policy-Makers?
A Behavioral Experiment with French Politicians*

Roberto Brunetti†

Matthieu Pourieux‡

July 1, 2025

Abstract

This study leverages an online behavioral experiment to analyze whether politicians' decisions align with citizens' preferences over the same issue and whether their decisions align with citizens' decisions within the same decision environment. We recruited 760 local politicians and 655 non-politicians in France to participate as policy-makers in a taxation-redistribution game. In the game, two policy-makers compete to choose a flat tax rate for a group of citizens, who are selected from the French general population and state their preferred tax rate. We exogenously manipulate (i) the information provided to policy-makers about citizens' preferred tax rates and (ii) the degree of competition between policy-makers. Finally, we measure policy-makers' beliefs regarding citizens' preferences. We observe that policy-makers positively react to the information, but they often deviate from it, which can be mostly explained by their beliefs. Varying the degree of political competition has no impact on these results. Finally, we find that politicians believe citizens want lower tax rates and are more confident in their beliefs than non-politicians. Once beliefs are accounted for, we observe little differences between the two samples. Our findings highlight the importance of politicians' beliefs and intrinsic motivations as determinants of their decisions.

**Keywords:** Representation, Politicians' Behavior, Online Experiment, Taxation-Redistribution

**JEL codes:** D31, P19, H24, H79, C90

<footnote>*We are grateful to Marius Brülhart, Laurent Denant-Boemont, Alistair Cameron, Etienne Dagorn, Etienne Lehmann, Matthieu Leprince, Benoît Le Maux, Youenn Lohéac, Gabriel Loumeau, Fabien Moizeau, Vincent Pons, Benoît Tarroux, Alain Trannoy, Elisabet Viladecans-Marsal and Thea Zöllner, as well as conference and workshop participants at SIEP (L'Aquila), ESA (Bologna), JMA (Rennes), EAYE (online) and Economics & Politics at Sciences Po (Paris) for insightful comments and discussions. Financial support from CREM CNRS, AMURE CNRS and the POPSU-Brest project is gratefully acknowledged. This research was also partly funded by ANR (Agence Nationale de la Recherche), Grant ANR-22-CE26-0019. A CC-BY public copyright license (https://creativecommons.org/licenses/by/4.0/) has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions.</footnote>

<footnote>†GATE CNRS, Université Lumière Lyon 2, Université Jean-Monnet Saint-Etienne, Emlyon Business School, 69007, Lyon, France. Email: roberto.brunetti@cnrs.fr</footnote>

<footnote>‡Univ Rennes, CNRS, CREM-UMR6211, F-35000 Rennes, France. Email: matthieu.pourieux@univ-rennes.fr</footnote>
# 1 Introduction

Does politicians' behavior align with citizens' preferences? This issue is crucial in representative democracies and has fueled numerous studies. In a well-functioning representative system, politicians implement policies supported by the majority of citizens. This pattern has been widely documented, particularly in studies focusing on changes in policy outputs in reaction to shifts in public opinion (see Beyer and Hänni, 2018, for a review). However, many studies also highlight deviations between politicians' decisions and citizens' preferences (Gerber and Lewis, 2004; Stadelmann et al., 2013; Tausanovitch and Warshaw, 2014). Relatedly, a growing number of studies testify that politicians' personal characteristics matter to policy-making (see e.g., Kuliomina, 2021, and references therein). This is even more likely when institutions fail and politicians retain substantial discretionary power over policies, which they can use to reach personal objectives. In other words, politicians may not only be vote maximizers, but also utility maximizers (Persson and Tabellini, 2002). Yet, despite a growing literature on political selection investigating politicians' specific qualities (Dal Bó et al., 2017; Dal Bó and Finan, 2018), politicians' preferences, beliefs, and decision-making processes as compared to the remainder of the population remains largely unexplored. Overall, the debate about the extent to which politicians' decisions deviate from their constituents' preferences is far from settled.

One critical issue to advance this debate is that providing a causal analysis is complicated by omitted variables and reverse causality issues (Wlezien and Soroka, 2016). Moreover, existing studies provide limited control over politicians' decision environment, which prevents a thorough analysis of politicians' decision-making process and underlying behavioral motives. To complement our understanding of democratic representation, this paper adopts a different approach from existing empirical studies based on an online economic experiment with 760 French local politicians and 655 French non-politicians. Conducting an online economic experiment has several advantages, such as designing exogenous variations in the decision environment, reducing complex multi-dimensional policy spaces into an uni-dimensional space, and abstracting from many political economy constraints. Importantly, our experiment allows us to measure politicians' beliefs about citizens' preferences, a key driver of politicians' decisions identified early in the theoretical literature (e.g., Kingdon, 1967; Calvert, 1985; Coughlin, 1992) but one that remains poorly scrutinized in empirical works (Pereira, 2021; Liaqat, 2023). Finally, we can study behavioral differences between politicians and non-politicians in a controlled environment.

In our experiment, both politicians and non-politicians participate as policy-makers in an online taxation-redistribution experiment (Durante et al., 2014). Pairs of policy-makers compete to choose a tax rate which applies to the initial earning distribution of a 9-citizen group selected from the general French population. One part of the total tax proceeds is lost, while the remainder is equally redistributed among the group members. This setting reproduces the equality-efficiency trade-off (Okun, 1975) in a taxation-redistribution framework (Meltzer and Richard, 1981).

To investigate the alignment between politicians' tax choices and citizens' preferences, we designed four experimental conditions generated from the combination of two treatments. The first treatment varies the information participants receive about the median of citizens' preferred tax rate (hereafter
referred to as the “median rate”). In the *No Information* treatment, participants remain uninformed, whereas in the *Information* treatment they decide with knowledge of each of the possible median rates (strategy method, Selten, 1965). In all conditions, we also elicit participants’ beliefs about the actual median rate. This treatment allows us to compare a situation where policy-makers may decide based on the information about the possible citizens’ preferences with a situation where they rely only on their beliefs about such preferences and personal views about redistribution. It also allows us to assess the within-subject variation in redistribution decisions as a function of the median rate.

The second treatment varies the competition level between pairs of participants. In the *Competition* treatment, the winner is determined by who chooses the tax rate closest to the median rate. In the *No Competition* treatment, the winner is selected at random. In both cases, the winner receives a monetary gain, and the tax rate he chooses is applied to the 9-citizen group. Participants thus compete to obtain a payoff in both treatments, but this payoff is tied to the citizens’ preferences only in the *Competition* treatment. This allows to disentangle policy-makers that would unconditionally converge towards citizens’ preferences (i.e., intrinsically motivated) from those who act upon it only conditional on the existence of incentives to do so. Both types of explanations about politicians’ motivations exist in the literature, although they have very different practical and theoretical implications.

To investigate behavioral differences between politicians and non-politicians, we compare their decisions under identical experimental conditions. Differences in beliefs, preferences, or motivations between politicians and non-politicians, and how these relate to their behavior, have significant implications. For instance, differences in behavior in the same decision environment may be caused by some characteristics of the political environment (e.g., political selection, habits formation, political training, etc.). Importantly, we also evaluate the weight both samples place on their beliefs and the different impact our treatments may have on each sample. Finally, investigating behavioral differences also speaks to the generalizability of experimental findings obtained from convenience or general-population samples to politicians samples.

Our analysis of treatment effects on the politicians sample highlights two main results. First, politicians react to citizens’ preferences. When uninformed about the median rate, politicians primarily rely on their beliefs about it. In the *Information* treatment, the Pearson correlation coefficient between the chosen rate and the median rate is 0.31. Consequently, even in a stylized environment with salient information, politicians deviate from the median rate (by 25.21 p.p., on average). These deviations are mostly related to the distance between the median rate and politicians’ beliefs. Informed politicians, therefore, consider the median rate by reducing the weight placed on their beliefs, albeit imperfectly. Second, varying the degree of political competition bears little impact on the results. Politicians in a competition act more on their beliefs when uninformed and deviate less from the median rate when informed. Yet, both effects are small and neither is statistically significant. Together, both results suggest that local politicians hold an intrinsic preference for fulfilling citizens demands, which is rarely taken into consideration in conventional models of political economy.

Regarding behavioral differences between politicians and politicians, we underline two results. First, we observe that politicians and non-politicians hold different views about citizens’ preferences. Across all
treatments, politicians believe that the median rate is lower than what non-politicians do, and they are more confident in this belief. This difference persists even when controlling for several socio-demographics and translates into tax choices: In the *No Information* treatment, politicians choose a lower tax rate than non-politicians (difference in means of 4.68 p.p.), but this difference disappears when we control for participants' beliefs. We also observe politicians to be more sensitive to the beliefs than non-politicians. Similarly, in the *Information* treatment, where the importance of beliefs is less pronounced in the decision-making process, the behavioral difference between politicians and non-politicians is not statistically significant. Second, we do not observe that politicians significantly react in a different way to our treatments as compared to non-politicians. Yet, competing politicians also act more on their beliefs than non-politicians, especially when uninformed. As a consequence, and although politicians are a select group of the population based on socio-demographic characteristics, we find no evidence of a behavioral difference between politicians and non-politicians, once beliefs are accounted for. Such findings call for a deeper understanding of belief formation among political elites and its impact on daily policies.

**Related literature** This study contributes to different strands of literature. Primarily, it connects to the voluminous empirical literature on political representation, which yields mixed results on the alignment of policy decisions with citizens’ preferences. This literature can be divided into two branches: representation in policy positions and representation in policy outputs (Wlezien and Soroka, 2016). Since we do not scrutinize actual policies, our study relates to the former and, more specifically, to the literature on dyadic representation, that is, the relationship between individual constituencies (here, a group of citizens) and individual representatives (here, competing policy-makers). A daunting task in this literature is finding a good measure of citizens’ preferences to compare with politicians’ actions, while controlling for numerous confounding factors.

There are four empirical strategy to analyze this question. One strategy, especially in political science, relies on survey data to compare the public opinion with politicians' opinions on the same issues (see, e.g., Miller and Stokes, 1963; Ågren et al., 2007) or asking politicians about their future policy behavior after randomly exposing them to survey-collected citizens' preferences (see, e.g. Sevenans, 2021; Soontjens and Sevenans, 2022; Chu and Recchia, 2022). A second strategy uses observed voting behavior to estimate each politician's ideal point on the same uni-dimensional ideological scale (such as the Americans of Democratic Action scale). These studies then correlate politicians' ideology scores with proxies of citizens' preferences (e.g., Levitt, 1996; Ansolabehere et al., 2001; Clinton, 2006) or directly compare such scores with citizens' ideal points obtained from extensive data on voting behavior (Gerber and Lewis, 2004; Bafumi and Herron, 2010; Tausanovitch and Warshaw, 2013, 2014). A third strategy uses referenda to capture both citizens' preferences and politicians' behavior.<fnref n="1" />

A fourth, more recent strategy employs field experiments to study the causal impact of information provision about survey-collected policy preferences of citizens on politicians’ behavior. One seminal

<footnote n="1">¹Although some examples exist in other countries (e.g., Brunner et al., 2013), the majority of studies benefit from the specific Swiss institutional setting, where citizens can demand a popular referendum on political decisions, allowing them to vote on the same laws with identical wording as politicians (Stadelmann et al., 2012; Portmann et al., 2012; Stadelmann et al., 2013, 2014; Portmann and Stadelmann, 2017).</footnote>
contribution is Butler and Nickerson (2011), which analyses how governors in New Mexico respond to
new information about citizens' preferences about a policy issue with highly uncertain public opinion.
They observe that politicians treated with the information vote more in line with it than untreated
politicians. Liaqat (2023) uses the same method with local politicians in Pakistan while scrutinizing a
broader range of policy issues. He observes that Pakistani politicians hold inaccurate beliefs about what
citizens want and change their policy recommendations to party leaders accordingly once informed about
it. Field experiments make two major contributions to the literature: causal interpretation of findings
and external validity. However, they are less suited to provide a refined understanding of the mechanism
at play because the control about the experimental conditions and the data at hand remain limited.
In contrast, our online economic experiment provides full control about the decision environment. This
flexibility allows us to expose participants to a strictly-defined no-information benchmark, which typically
does not occur in naturally-occurring settings, and assess whether their behavior changes when they are
not incentivized to consider the information, as opposed to political competition. Finally, we can fully
evacuate any other confounding factors, such as political economy constraints.

This paper also contributes to the literature relying on large-scale surveys and economic experiments
to study the behavioral characteristics of politicians, such as their economic preferences, personality traits
or cognitive biases (Fatas et al., 2007; Dietrich et al., 2012; LeVeck et al., 2014; Enemark et al., 2016;
Sheffer et al., 2018; Heß et al., 2018; Sheffer and Loewen, 2019; Janezic and Gallego, 2020). Despite
the recent rise in such studies, behavioral experiments involving real-world policy-makers remain scarce
(Kertzer and Renshon, 2022). By comparing redistribution choices between a sample of politicians and
a representative sample of non-politicians in an identical choice environment, this paper documents
the specificity of politicians' characteristics in comparison to the general public. As such, it adds to
the literature on descriptive political selection, which has documented consistent differences between
citizens and politicians, focusing mainly on socio-demographic characteristics (see, e.g: Gulzar, 2021).<fnref n="2" />
To the best of our knowledge, no existing study compares the individual preferences over redistribution of
politicians and non-politicians, with the exception of Helfer et al. (2024). The latter observe that Swiss
MPs are less supportive of redistribution and perceive inequalities to be fairer than a representative sample
from the general population. In addition to differences in the sample composition of politicians (French
and local in our study), there is one major methodological difference with our work: the authors collect
self-declared general opinions whereas we rely on a revealed preferences approach based on an incentivized
experiment. Moreover, we do not only evaluate to what extent politicians and non-politicians differ in
their preferences over redistribution, we also assess whether politicians react to citizens' views about
redistribution and whether it differs from non-politicians facing the same decision.<fnref n="3" />

<footnote n="2">²Our paper is also distantly related to the literature on political selection in general, which focuses on how and why a fraction of the population is selected into political office. Recent reviews of such literature can be found in Dal Bó and Finan (2018) or Gulzar (2021). In this paper, we do not seek to model a political selection process to investigate the mechanisms behind observed behavioral differences between politicians and non-politicians.</footnote>

<footnote n="3">³Our paper also relates to the literature on preferences for redistribution using large-scale surveys as well as economic experiments (for reviews, see Alesina and Giuliano, 2011; Mengel and Weidenholzer, 2022). Such line of inquiry implicitly avoids analyzing the mapping from people's preferences to implemented policies, hence policy-makers' responsiveness to citizens' demands in a redistributive context. Furthermore, our experiments allows to compare the behavior of politicians and non-politicians in the same redistributive context.</footnote>
The remainder of the paper is organized as follows. Section 2 introduces the experimental design and presents our participant pools. Section 3 exposes our empirical strategy and our main behavioral conjectures. Section 4 details our results. In Section 5, we discuss our results and run a battery of robustness checks. Concluding remarks follow in Section 6.

## 2 Experimental design

The experiment is divided into four parts, as illustrated in Figure 1. After reading the preliminary instructions, participants completed a multiple price list risk aversion task (Harrison and Rutström, 2008). Participants made five choices between a fixed payment and a lottery, receiving no feedback on their gains until the end of the experiment. One choice was randomly selected for payment.<fnref n="4" /> The second part of the experiment consisted of a taxation/redistribution task, in which participants selected a flat tax rate to apply to a 9-citizen group. Both tasks were incentivized, and participants were explained that 1 out of 50 participants would be selected to effectively receive their total payoff (sum of gains from the risk aversion task and the taxation redistribution task) at the end of the experiment.<fnref n="5" /> In the third part, participants completed a non-incentivized belief elicitation task. The order of the taxation/redistribution and the belief elicitation tasks was randomized to control for order effects. Finally, participants responded to a socio-demographic questionnaire. An English translation of the instructions is provided in Appendix A.

<figure><img src="image_3.png" /><figcaption>Figure 1: Sequence of the experiment</figcaption></figure>

Note: The dashed lines indicate the randomization in the order of the taxation-redistribution/belief elicitation tasks.

### 2.1 Taxation-redistribution task

**Structure of the task** In the main task, there are two types of participants: policy-makers and citizens. Our sample of French politicians only participated as policy-makers. Non-politicians either participated

<footnote n="4">⁴We control for risk preferences in our analysis because the redistribution task entails uncertainty regarding whether or not the participant earns a payoff (see Section 2.1 below). Following Durante et al. (2014), the risk aversion index is computed as the sum of questions $i \in \{1, 2, 3, 4, 5\}$ in the multiple price list task where participants choose the sure outcome instead of the lottery. Consequently, the index ranges from 0 to 15, with 0 indicating a risk-lover participant who never chooses the sure outcome, and 15 being the highest risk-aversion score (1+2+3+4+5).</footnote>

<footnote n="5">⁵That is, we implement a Between Random Incentive System (BRIS), commonly used in survey experiments. In their literature review, Charness et al. (2016) document that paying a subset of participants generally does not imply a substantial dilution of the incentives to answer truthfully.</footnote>
as policy-makers only or participated first as policy-makers and second as citizens.<fnref n="6" /> In the latter case, they were given the opportunity to earn a payoff for either role, chosen randomly. Crucially, they were told that they would play as citizens only after they submitted their decisions as policy-makers (including the belief elicitation task) and before the questionnaire, so that the decisions made by politicians and non-politicians as policy-makers are readily comparable.

At the beginning of the task, policy-makers are anonymously paired (*stranger matching*) and are informed that no information regarding their competitor's identity of choices will be communicated to them. Each pair is matched with a 9-citizen group and informed that the citizens are drawn from a sample from the general French population. They are also informed that each citizen is randomly assigned one of nine possible initial endowments, and that the distribution of these endowments reflects the income distribution in France in 2017 (see Table A.3.1 in the appendix).<fnref n="7" /> Each policy-maker then chooses a flat tax rate between 0% and 100% in increments of 1 p.p. to apply on the citizen group. Which competing policy-makers' tax rate is applied depends on treatment (see below). Once the tax rate is applied and tax receipts are collected, a fixed fraction of the total tax receipts is lost (*dead-weight loss*) while the remainder is equally redistributed among the nine citizens.<fnref n="8" /> In order to ease comprehension, a graphical representation of the mechanism is displayed to policy-makers (figure A.3.1 in the appendix). Moreover, they can verify the citizens' payoff associated with each tax rate both in the initial instructions and before each decision (figure A.3.2 in the appendix).

After participating as policy-makers, some non-politicians participate as citizens. They are informed that they will be part of a group of 9 anonymous citizens, matched with two anonymous competing policy-makers. This group is distinct from the 9-citizen group for which they made decisions as policy-makers. In other words, their decisions as policy-makers cannot impact their payoff as citizens. Each citizen in the group is then informed about their initial endowment and is asked to express her preference for the tax rate, with options ranging from 0% to 100% in increments of 10 p.p.<fnref n="9" /> The computer then calculates

$$ \pi_j = y_j(1 - \tau) + \tau(1 - e) \frac{1}{9} \sum_{i}^{9} y_i $$

<footnote n="6">⁶Since we use the Strategy Method with policy-makers, we do not need citizens’ decisions for our analysis: they only serve for payoff. To save time and reduce costs, we chose to make non-politicians play first as policy-makers and then as citizens. As a consequence, and although it bears no impact on our analysis, citizens’ decisions are likely influenced by the prior decisions made as policy-makers. Furthermore, in order for the addition of the citizen task to bear little impact on the overall completion time of the experiment (to which we were contractually bounded), we chose to enroll as citizens only non-politicians assigned to the treatment where they make fewer decisions (*No Information*).</footnote>

<footnote n="7">⁷Admittedly, the fact that initial inequality in endowments arises from “brute luck” rather than differences in effort likely increases acceptance of inequality (see, e.g., Almås et al. 2020), which necessarily impacts the generalizability of our results. Furthermore, this design feature may also interact with our treatments: one could argue that policy-makers may be more prone to follow citizens’ preferences if income is earned. Yet, we implement a rainfall endowment because it simplifies the experiment and because it fits the French context. Indeed, French people are more likely than other population to believe that a person’s economic condition is due to circumstances outside her own control, which legitimates redistributive policies (Alesina et al., 2018).</footnote>

<footnote n="8">⁸Formally, a citizen j's payoff is defined as:</footnote>

<footnote>where $y_j$ is the citizen's initial endowment, $\tau$ is the tax rate, 9 is the number of citizens in the group and $e$ is the efficiency loss parameter. The efficiency loss parameter $e = 6.8\%$ has been computed so that the monetary gain of the poorest citizen would have been equal to the total efficiency loss, that is $|\partial\pi_i(y_L)/\partial\tau| = |\partial\sum_{i}^{9} \pi_i/\partial\tau|$. Since the redistributed sum consists of a lump-sum transfer to all citizens, it is equivalent to a *publicly provided private good*.</footnote>

<footnote n="9">⁹The choice of the increment guarantees that the effective median rate be one of the 11 possible median rates that we</footnote>
the median preference within each group (the “median rate”), which may serve to calculate citizens’ final payoff, i.e., to determine the winner of the competition between policy-makers. Assuming citizens care only about maximizing their material payoff, the game’s payoff structure yields a lower bound of 0 and an upper bound of 20% for the equilibrium median rate. The fifth citizen is indeed pivotal and is indifferent between the rates in the [0-20] interval (see Table A.3.2 in the appendix). However, assuming citizens also care about other citizens’ payoffs (e.g., they hold distributional preferences) renders the equilibrium uncertain. Such a possibility is all the more likely as the changes in the fifth citizen’s payoff are very limited (1 centime at most).

**Treatments** Each policy-maker chooses in one out of four experimental conditions, resulting from the combination of two between-subjects treatments.<fnref n="10" />

One treatment varies the information policy-makers receive before choosing the tax rate. In the *No Information* treatment, policy-makers receive no information about the median rate and choose one tax rate only. In the *Information* treatment, policy-makers decide while having the information about the median rate. We employ the strategy method (Selten, 1965), so that participants in the *Information* treatment choose a tax rate 11 times—one per each possible median rate between 0% and 100% in increment of 10 p.p.

The use of the Strategy Method is not only logistically convenient for designing of an online interactive experiment, but it also ensures that all subjects are exposed to the same information. This allows us to assess how tax choices vary with different median rates within the same subjects. In other words, we do not evaluate how policy-makers respond to the true median rate. Instead, we evaluate how they respond to a range of possible median rates, while accounting for their beliefs about the true median rate (see next sub-section). More specifically, participants in all treatments are informed about citizens’ being asked to select a rate and about how the median rate is computed. In the *No Information* treatment, they are further told that such information will remain concealed.<fnref n="11" /> In the *Information* treatment, the exact median rate remains concealed but participants are asked to choose a tax rate for each possible median rate.

The other treatment varies the level of political competition. In the *Competition* treatment, the tax rate closest to the median rate is implemented.<fnref n="12" /> In the *No Competition* treatment, one of the two proposed tax rates is randomly selected with equal probabilities. Therefore, in the *No Competition* treatment, the tax choice does not influence the probability of the tax rate being applied or the participant’s possible payoff.<fnref n="13" /> In both cases, the participant whose decision is implemented earns an additional payoff of 33 Survey Currency Units (SCUs), corresponding to 16.5 euros given the exchange rate of 1 SCU =

<footnote>provide to policy-makers in the *Information* treatment.</footnote>

<footnote n="10">¹⁰For simplicity, non-politicians play in the same experimental conditions when changing from the role of policy-maker to citizen.</footnote>

<footnote n="11">¹¹This feature was implemented to focus on the effect of information *per se*, independently from participants’ awareness that the information exists.</footnote>

<footnote n="12">¹²Participants in the *Competition* treatment were also notified that draws would be resolved by uniform-random selection.</footnote>

<footnote n="13">¹³This setting relates to the spectator condition that is frequently scrutinized in the literature on redistribution (see e.g., Durante et al., 2014; Almås et al., 2020).</footnote>
0.5 euros.<fnref n="14" /> In contrast, the participant whose decision is not implemented earns nothing in this task.

## 2.2 Belief elicitation task

We elicit policy-makers's beliefs about the median rate and about the tax choice of the competitor with whom they are matched. To ease comprehension of this task, we exploit a token allocation task discussed in Delavande et al. (2011) (see Figure A.4.2 for an example). Participants allocate 10 tokens to different events, with each token representing a probability of 10%. To elicit beliefs about the median rate, participants allocate the 10 tokens among the 11 possible median rates. To elicit beliefs about the competitor's tax choice, participants are asked to allocate the 10 tokens to 11 intervals representing the potential range of choices (since policy-makers choose tax rates in 1 p.p. increments, while citizens do so in 10 p.p. increments).<fnref n="15" />

This belief elicitation task provides participants with an intuitive notion of probabilities and, by giving a fixed number of tokens, ensures that the probabilities add up to one (Delavande et al., 2011). Most importantly, we can retrieve the subjective probability distribution of their beliefs and compute its mean, median, mode and standard deviation. In particular, the standard deviation provides a measure of participants' confidence in their beliefs.

Importantly, we chose to rely on introspection-asking people to report their beliefs truthfully-rather than using incentives to elicit beliefs. Indeed, whether and how to incentivize belief-elicitation tasks remains a hotly debated topic in the experimental economics literature. As noted by Charness et al. (2021), complex mechanisms are often recommended because of their adequate theoretical properties, but their practical implementation may be so cognitively demanding that they actually do not outperform simple introspection. Moreover, incentivizing comes with a series of issues that may bias the elicitation while not being easily resolved (e.g., hedging, that is, participants coordinating their beliefs and actions when both are paid in order to maximize expected payoffs). Given that our main target population (politicians) is inexperienced with economic experiments, potentially reluctant to be paid, and more time-constrained than the rest of the population, we deemed incentivizing the belief-elicitation task too risky in terms of both response rate and data quality.

## 2.3 Experimental Settings

At the beginning of the experiment, participants were informed about the expected average duration (25 minutes), guaranteed anonymity, and that 50 cents would be donated to a charity of their choice

<footnote n="14">¹⁴Given the average duration of the survey, this sum corresponds to a payment of about 40 euros per hour.</footnote>

<footnote n="15">¹⁵The intervals are [0%,5%], [6%,15%], [16%,25%], [26%,35%], [36%,45%], [46%,54%], [55%,64%], [65%,74%], [75%,84%], [85%,94%], [95%,100%]. We then assign a value ranging from 0 to 100 in increments of 10 to each interval and treat this transformed variable the same way as the belief about the median rate. Notably, we chose to narrow the range of the two extreme intervals because they correspond to extreme views on redistribution, hence likely candidates for the competitor's tax choice: no redistribution (pure efficiency orientation) and full redistribution (pure equality orientation). In the *No information* treatment, we elicit one belief about the competitor's choice because policy-makers make only one choice. By contrast, in the *Information* treatment, we elicit 11 beliefs about the competitor's choice because policy-makers make 11 choices (strategy method).</footnote>
upon full completion of the survey.<sup>16</sup> Additionally, they were informed that participation could earn them an additional payoff, the amount of which would depend on their decisions during the experiment. Participants could choose to keep this payoff or donate it to a charity, a decision made before knowing if they were selected for payment.<sup>17</sup> Participants knew from the beginning of the experiment that they would make this choice once the experiment was over. However, the charities list was presented only at the end of the experiment to avoid motivation-induced disparities across treatments.

The experiment was conducted on LimeSurvey November 2, 2020, to December 30, 2020. Overall, 1,528 people participated in the experiment, including 801 were French local politicians and 727 were French non-politicians. On average it took 24 minutes for participants to complete the experiment. Participants who opted for payment (14.61% of politicians and 84.89% of non-politicians) and who were randomly selected by the computer received a bank transfer at the end of the survey period (early January). Donations to charity were managed in the same period by a survey institute named Panelabs, which also handled the recruitment of non-politicians. In our analysis, we exclude 71 participants who completed the experiment in less than 4 minutes for short treatments (*No Information*) and less than 9 minutes for long treatments (*Information*). We also exclude one politician from Martinique<sup>18</sup> and 18 participants who logged in using a phone, despite instructions not to do so at the beginning of the experiment.<sup>19</sup> We also removed four politicians who did not answer to the question about their current office. Finally, we removed duplicates based on IP addresses (10 participants). The final sample includes 760 politicians and 655 non-politicians. Figure 2 depicts the geographical distribution of the response rate for politicians and non-politicians.

While the sample of non-politicians was reached through a survey institute (Panelabs), we contacted politicians by sending an official invitation by mail to both associations of local politician (*Associations d'élus*) and to the town halls of all French municipalities.<sup>20</sup> The invitation was accompanied by a formal demand to forward it to politicians. Any local politician could participate in the experiment. We targeted local instead of upper-tiers politicians for several reasons. First, a long-standing tradition considers local governments as the “training grounds for democracy” (John Stuart Mill). Indeed, the proximity between politicians and citizens at the municipal level presumably improves democratic representation through better information, incentives and monitoring of politicians (Mansuri et al., 2023). At the same time,

<footnote n="16"><sup>16</sup>The list of charities included the “*Institut Pasteur*”, a non-profit private organization supporting the study of biology, diseases and vaccines, the “*Restos du Cœur*”, a charity that provides food to people in need, and “*Médecins sans Frontières*”, an organization providing medical support to people in conflict zones.</footnote>

<footnote n="17"><sup>17</sup>Paying politicians has been identified as a potential issue in empirical studies (Kertzer and Renshon, 2022). Indeed, politicians may be reluctant to receive private benefits directly related to their public positions. To maintain high incentives while reducing the risk of attrition and/or adverse publicity, we offered participants the option to donate their payoff to charity as an alternative to the standard individual incentivization scheme. Donations to charity as an incentivization scheme for public officials has already been tested by Butler and Pereira (2018): They find that American state legislators perform equally in a quiz when they receive a personal gain or when their gains are given to a charity. Both incentivization schemes improve performance as opposed to a no-incentive benchmark.</footnote>

<footnote n="18"><sup>18</sup>Our survey was addressed to only people from mainland France (*France métropolitaine*). However, some of the politician associations who agreed upon communicating the link to the experiment had some members abroad, which explains the presence of this Martinican politician.</footnote>

<footnote n="19"><sup>19</sup>Running the experiment on a phone caused display issues in pre-experimental tests, therefore we decided to rule out such an option.</footnote>

<footnote n="20"><sup>20</sup>The details on the recruitment of our politicians sample can be found in appendix A.7.</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 2: Number of participants per department</figcaption></figure>

Note: In the grey shaded departments no answers were given.

holding a local office in France often constitutes the first step toward a political career, so that the targeted group corresponds to the first layer of the political selection process. Consequently, focusing on local politicians appears a valuable starting point to improve our general understanding of politicians’ behavior in representative democracies. Second, local politicians are generally socio-demographically closer to non-politicians than upper-tiers politicians (Gulzar, 2021), which facilitates cross-groups comparisons. Third, existing surveys and economic experiments focusing on politicians’ behavior often merge office holders from different administrative layers or adopt a loose definition of “political elite”, which may result in noisy measures and cause interpretation issues (Kertzer and Renshon, 2022). Contrary to such practices, this paper focuses on a well-defined group of politicians. Fourth, the underlying population from which to sample is larger and presumably less time-constrained than the population from higher administrative layers, which reduces the risk of non-responses. Fifth, increasingly due to the decentralization movement, local politicians have many competencies (such as urban planning, property taxation, schooling, housing, etc.) that have a substantial impact on citizens’ daily well-being.

Of all invitations that were sent and accessed, 13% were completed in the politicians sample and 64% in the non-politician sample. Such a difference is not surprising considering that our politician sample was constructed from scratch whereas our non-politician sample was selected from *Panelabs's* database, so that part of the selection is already accounted for in the latter group. For the sake of comparison, Vis and Stolwijk (2021) compile 342 large, multi-wave survey samples of political elites and observe an average response rate of about 23% for online-only surveys. Further, they observe important variations in response rates and identify elite level (e.g., subnational politicians participate more) as well as countries' survey tradition (e.g., France shows among the lowest response rates) as important drivers for such variations. As a consequence, the response rate we observe corresponds to a lower bound with
respect to existing findings on politicians' surveys, but does not fall out of range. Notably, it is likely that the secretaries who received our mail accessed the questionnaire to check its validity, which means that 13% is a very conservative lower bound for the actual completion rate. Indeed, Figure B.3.1 plots the cumulative distribution of dropouts and shows that most politicians' dropouts occur on the first page. Upon excluding those dropouts, the completion rate rises to 27%.

<table><caption>Table 1: Summary statistics</caption><thead><tr><th rowspan="2"></th><th colspan="6">Politicians (N=760)</th><th colspan="6">Non-politicians (N=655)</th></tr><tr><th>NI-NC</th><th>NI-C</th><th>I-NC</th><th>I-C</th><th>Pooled</th><th>Gen. pop.</th><th>NI-NC</th><th>NI-C</th><th>I-NC</th><th>I-C</th><th>Pooled</th><th>Gen. pop.</th></tr></thead><tbody><tr><td>Age</td><td>51.42</td><td>51.57</td><td>53.36</td><td>51.93</td><td>51.91</td><td>52.16</td><td>48.21</td><td>48.75</td><td>46.69</td><td>47.92</td><td>47.98</td><td>50.77</td></tr><tr><td>Gender</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>0.40</td><td>0.36</td><td>0.40</td><td>0.37</td><td>0.38</td><td>0.42</td><td>0.56</td><td>0.64</td><td>0.61</td><td>0.61</td><td>0.61</td><td>0.52</td></tr><tr><td>Male</td><td>0.60</td><td>0.64</td><td>0.60</td><td>0.63</td><td>0.62</td><td>0.58</td><td>0.44</td><td>0.36</td><td>0.39</td><td>0.39</td><td>0.39</td><td>0.48</td></tr><tr><td>Occupation</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Artisans, Entrepreneurs</td><td>0.10</td><td>0.08</td><td>0.05</td><td>0.10</td><td>0.08</td><td>0.07</td><td>0.05</td><td>0.05</td><td>0.04</td><td>0.04</td><td>0.04</td><td>0.04</td></tr><tr><td>Employees</td><td>0.15</td><td>0.18</td><td>0.13</td><td>0.17</td><td>0.16</td><td>0.14</td><td>0.26</td><td>0.27</td><td>0.28</td><td>0.27</td><td>0.27</td><td>0.16</td></tr><tr><td>Executive</td><td>0.30</td><td>0.36</td><td>0.34</td><td>0.31</td><td>0.33</td><td>0.20</td><td>0.08</td><td>0.12</td><td>0.10</td><td>0.12</td><td>0.10</td><td>0.10</td></tr><tr><td>Farmers</td><td>0.08</td><td>0.02</td><td>0.01</td><td>0.07</td><td>0.05</td><td>0.08</td><td>0.00</td><td>0.01</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.01</td></tr><tr><td>Inactive</td><td>0.04</td><td>0.05</td><td>0.04</td><td>0.03</td><td>0.04</td><td>0.04</td><td>0.11</td><td>0.08</td><td>0.12</td><td>0.09</td><td>0.10</td><td>0.14</td></tr><tr><td>Intermediate professions</td><td>0.12</td><td>0.09</td><td>0.14</td><td>0.07</td><td>0.10</td><td>0.17</td><td>0.12</td><td>0.19</td><td>0.13</td><td>0.17</td><td>0.15</td><td>0.15</td></tr><tr><td>Laborers</td><td>0.02</td><td>0.05</td><td>0.01</td><td>0.02</td><td>0.03</td><td>0.07</td><td>0.06</td><td>0.04</td><td>0.07</td><td>0.05</td><td>0.05</td><td>0.12</td></tr><tr><td>Retired</td><td>0.17</td><td>0.16</td><td>0.25</td><td>0.20</td><td>0.19</td><td>0.23</td><td>0.31</td><td>0.24</td><td>0.24</td><td>0.26</td><td>0.26</td><td>0.28</td></tr><tr><td>No answer</td><td>0.03</td><td>0.02</td><td>0.01</td><td>0.03</td><td>0.02</td><td>0.00</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.00</td></tr><tr><td>Geographical area</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>North</td><td>0.11</td><td>0.15</td><td>0.16</td><td>0.13</td><td>0.13</td><td>0.24</td><td>0.30</td><td>0.38</td><td>0.36</td><td>0.39</td><td>0.35</td><td>0.33</td></tr><tr><td>East</td><td>0.15</td><td>0.16</td><td>0.16</td><td>0.12</td><td>0.15</td><td>0.22</td><td>0.10</td><td>0.10</td><td>0.07</td><td>0.10</td><td>0.09</td><td>0.13</td></tr><tr><td>West</td><td>0.36</td><td>0.28</td><td>0.37</td><td>0.42</td><td>0.35</td><td>0.26</td><td>0.32</td><td>0.25</td><td>0.27</td><td>0.22</td><td>0.26</td><td>0.25</td></tr><tr><td>South</td><td>0.38</td><td>0.40</td><td>0.31</td><td>0.33</td><td>0.36</td><td>0.28</td><td>0.29</td><td>0.27</td><td>0.30</td><td>0.29</td><td>0.29</td><td>0.30</td></tr><tr><td>Education</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>High school diploma</td><td>0.35</td><td>0.35</td><td>0.39</td><td>0.38</td><td>0.36</td><td></td><td>0.51</td><td>0.47</td><td>0.46</td><td>0.46</td><td>0.48</td><td>0.68</td></tr><tr><td>Higher education</td><td>0.65</td><td>0.65</td><td>0.61</td><td>0.62</td><td>0.64</td><td></td><td>0.49</td><td>0.53</td><td>0.54</td><td>0.54</td><td>0.52</td><td>0.32</td></tr><tr><td>Gross personal income</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 20k</td><td>0.21</td><td>0.20</td><td>0.14</td><td>0.17</td><td>0.19</td><td></td><td>0.32</td><td>0.23</td><td>0.24</td><td>0.24</td><td>0.26</td><td></td></tr><tr><td>Between 20k and 30k</td><td>0.25</td><td>0.31</td><td>0.22</td><td>0.29</td><td>0.27</td><td></td><td>0.26</td><td>0.29</td><td>0.29</td><td>0.27</td><td>0.28</td><td></td></tr><tr><td>Between 30k and 40k</td><td>0.20</td><td>0.18</td><td>0.22</td><td>0.15</td><td>0.19</td><td></td><td>0.12</td><td>0.21</td><td>0.24</td><td>0.20</td><td>0.19</td><td></td></tr><tr><td>More than 40k</td><td>0.28</td><td>0.25</td><td>0.34</td><td>0.31</td><td>0.29</td><td></td><td>0.20</td><td>0.21</td><td>0.16</td><td>0.21</td><td>0.20</td><td></td></tr><tr><td>No answer</td><td>0.06</td><td>0.06</td><td>0.07</td><td>0.08</td><td>0.07</td><td></td><td>0.10</td><td>0.07</td><td>0.07</td><td>0.07</td><td>0.08</td><td></td></tr><tr><td>Party Preferences</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Left</td><td>0.36</td><td>0.32</td><td>0.32</td><td>0.39</td><td>0.35</td><td></td><td>0.29</td><td>0.27</td><td>0.25</td><td>0.28</td><td>0.28</td><td></td></tr><tr><td>Center</td><td>0.15</td><td>0.18</td><td>0.13</td><td>0.14</td><td>0.15</td><td></td><td>0.15</td><td>0.15</td><td>0.16</td><td>0.17</td><td>0.16</td><td></td></tr><tr><td>Right</td><td>0.18</td><td>0.17</td><td>0.15</td><td>0.14</td><td>0.16</td><td></td><td>0.21</td><td>0.24</td><td>0.29</td><td>0.18</td><td>0.23</td><td></td></tr><tr><td>None</td><td>0.31</td><td>0.32</td><td>0.41</td><td>0.33</td><td>0.34</td><td></td><td>0.34</td><td>0.34</td><td>0.30</td><td>0.37</td><td>0.34</td><td></td></tr><tr><td>Ideology</td><td>6.58</td><td>6.70</td><td>7.01</td><td>6.83</td><td>6.74</td><td></td><td>6.73</td><td>7.16</td><td>7.26</td><td>6.90</td><td>7.01</td><td></td></tr><tr><td>Risk aversion index</td><td>8.25</td><td>7.22</td><td>8.41</td><td>7.54</td><td>7.84</td><td></td><td>10.49</td><td>9.16</td><td>8.80</td><td>9.89</td><td>9.62</td><td></td></tr><tr><td>Current office</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mayor</td><td>0.23</td><td>0.18</td><td>0.26</td><td>0.17</td><td>0.21</td><td>0.07</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Vice-mayor</td><td>0.23</td><td>0.22</td><td>0.21</td><td>0.27</td><td>0.23</td><td>0.23</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Local councillor</td><td>0.54</td><td>0.60</td><td>0.53</td><td>0.56</td><td>0.56</td><td>0.70</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Municipality's population</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 1000</td><td>0.47</td><td>0.52</td><td>0.45</td><td>0.38</td><td>0.46</td><td>0.56</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Between 1000 and 3500</td><td>0.34</td><td>0.21</td><td>0.33</td><td>0.35</td><td>0.30</td><td>0.24</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>More than 3500</td><td>0.19</td><td>0.27</td><td>0.22</td><td>0.27</td><td>0.24</td><td>0.19</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>255</td><td>220</td><td>135</td><td>150</td><td>760</td><td>493900</td><td>177</td><td>190</td><td>135</td><td>153</td><td>655</td><td>14929239</td></tr><tr><td>Share</td><td>33.55%</td><td>28.95%</td><td>17.76%</td><td>19.74%</td><td>100%</td><td></td><td>23.29%</td><td>25%</td><td>17.76%</td><td>20.13%</td><td>100%</td><td></td></tr></tbody></table>

Note: NI refers to *No Information* treatment, I to *Information* treatment, C to *Competition* treatment, and NC to *No Competition* treatment. For the correspondence between French regions and geographical areas, see Table B.1.1. For the classification of party preferences, see Table B.1.2. “Ideology” represents a numerical variable between 0 (extreme left) and 10 (extreme right) based on a likert scale question. The dataset on all French politicians in the 2020-2026 mandate is collected by the French Interior Ministry and available online.

Table 1 presents the summary statistics of the individual characteristics obtained from the end-of-
experiment questionnaire for both politicians and non-politicians for all treatments, as well as summary statistics from the corresponding general populations.

Regarding treatment differences, we first observe some variations in the number of participants, especially between the *Information* (I) and *No Information* treatments (NI), especially for politicians.<fnref n="21" /> This difference might stem from the higher number of choices in treatment I, extending the experiment duration. Indeed, on average, treatment I took 32 minutes to complete, while treatment NI took 20 minutes. Further, we observe in Figure B.3.1 that the difference in dropouts between treatments for both treatments is relatively steady except for a peak when participants are either asked to select a tax rate or to state their beliefs, depending on the order of tasks. In both cases, treatment NI involves making 10 decisions less than treatment I. However, we also observe limited differences in observables across treatments. We perform balance checks across treatment using pairwise Standardized Difference in Means (SDM) and plot the results in Figure B.2.1. The large majority of comparisons falls below the 0.25 threshold often referred to as indicative of worrisome imbalance (see e.g. Imbens and Rubin, 2015), and those that fall above remain relatively close to this threshold. Also, we do not detect any particular pattern where some comparisons would be involved in systematically more frequent imbalances than others overall.<fnref n="22" /> Moreover, we make use of the fact that the risk elicitation task occurred at the beginning of the experiment to check whether participants who dropped out behaved differently in this task than participants who stayed. We find no statically significant difference in risk aversion. Consequently, we believe that selection into treatment is a rather limited risk in our data for cross-treatments comparisons, which can be dealt with using standard regression adjustments.

We also document differences in observables between politicians and non-politicians: politicians are slightly older (two-sided t-test: *p* < 0.01), more educated (Pearson chi-test: *p* < 0.01) and richer than non-politicians (Pearson chi-test: *p* < 0.01). Moreover, the proportion of male politicians exceeds that of female politicians, while the reverse holds true for non-politicians (Pearson chi-test: *p* < 0.01). All these differences have been documented in the literature on political selection, both in developed and developing countries (Gulzar, 2021). Our sample of politicians also shows fewer respondents from the northern regions of France and more from the western regions (Pearson chi-test: *p* < 0.01). In terms of political preferences, the non-politician sample leans more toward the right-wing compared to politicians, both in terms of party preferences (Pearson chi-test: *p* < 0.01) and self-declared ideology on a 0-10 scale (two-sided t-test: *p* < 0.01). Lastly, politicians display a higher propensity for risk-taking than non-politicians (two-sided t-test: *p* < 0.01). This last finding aligns with Heß et al. (2018) and Sheffer et al. (2018), who find that political representatives are significantly more risk loving than citizens.

Finally, with respect to differences between our samples and their respective general populations,

<footnote n="21">²¹The one-proportion test rejects the null hypothesis that the share of participants in the *Information* treatment (40%) is equal to 50% (*p* < 0.01). This is true both for the share of politicians (37.5%) and non-politicians (43.97%) in the *Information* treatment (in both cases, *p* < 0.01).</footnote>

<footnote n="22">²²We acknowledge two notable exceptions: occupation for politicians and income for non-politicians. Also, the NI-NC v. I-NC comparison is involved in several imbalances for non-politicians. As an additional check, we estimated the propensity scores for all treatment by adapting the stepwise method described in Imbens and Rubin (2015) to a multivalued treatment. We consider all covariates as well as quadratic and interaction terms in the multinomial logistic regression on treatment assignment. We plot the SDMs for the linearized scores in Figure B.2.2. We find that all SDMs falls below 0.5 (which is deemed acceptable for a composite measure).</footnote>
we do observe non-negligible differences. Although we do not observe significant differences in terms of age and gender between our politician sample and the whole population of French local politicians, our sample contains a higher proportion of politicians holding executive positions (that is mostly offset by a lower proportion of intermediate professions), coming from western or southern regions, mayors, and intermunicipal councilors, and is skewed towards more populous municipalities. Concerning non-politicians, our sample appears younger and less-educated than the general French population, with a higher proportion of women and employees (that is mostly offset by a lower proportion of laborers). To account for such differences with the two general populations, we will use normalized sample-specific post-stratification weights in our analysis.<fnref n="23" />

## 3 Empirical strategy

We address our first research question—the alignment of politicians’ decisions with citizens’ preferences—by analyzing treatment effects within the politician sample. Next, we address our second research question—behavioral differences between politicians and non-politicians—by conducting a similar analysis on the full sample and comparing politicians with non-politicians. Regarding treatment effects, we consider two separate analyses: one on the chosen tax rate (the decision variable) and another on the deviation from the median rate.

First, we conduct OLS regressions with politicians’ chosen tax rate as the dependent variable. Due to the differing number of observations per participant in the *No Information* and *Information* treatments (1 and 11, respectively), the two treatments are not directly comparable. Therefore, we consider two different specifications, one for each treatment.

For the *No Information* treatment:

$$ \tau_i = \beta_0 + \beta_1 C_i + \beta_2 \text{Belief Median}_i + \beta_3 C_i \times \text{Belief Median}_i + \gamma X_i + \epsilon_i, \quad (1) $$

For the *Information* treatment:

$$ \tau_{it} = \beta_0 + \beta_1 C_i + \beta_2 \text{Belief Median}_i + \beta_3 C_i \times \text{Belief Median}_i + \beta_4 \text{Info}_{it} + \beta_5 C_i \times \text{Info}_{it} + \gamma X_i + \epsilon_{it}, \quad (2) $$

where, in (1), $\tau_i$ is the tax rate chosen by participant $i$, whereas in (2), $\tau_{it}$ is the tax rate chosen by participant $i$ when the information given is $\text{Info} = [0, 10, 20...100]$ for each choice $t = [0, 1, ..., 10]$. $C_i$ is an indicator for the *Competition* treatment, $\text{Belief Median}_i$ is the mean of the subjective probability distribution about the median rate, $X_i$ is a vector of personal characteristics and experiment-specific controls (e.g., the risk aversion index), and $\epsilon_i/\epsilon_{it}$ are error terms assumed to be normally distributed.

This analysis of the chosen tax rate addresses the alignment between policymakers’ decisions and

<footnote n="23">²³We apply post-stratification weights using the iterative proportional fitting (or raking) method (Kolenikov, 2014) to account for differences observables between our samples and their respective general populations. For politicians, we consider age, gender, geographical area, current office and municipality’s population for constructing the weights. For non-politicians we consider age, gender, geographical area and education.</footnote>
citizens' preferences by examining the within-subject correlations between the chosen tax rate and the 11 possible median rates (Information treatment) and/or policymakers' beliefs (both treatments). It also captures the causal impact of the Competition treatment on the correlations with beliefs and the median rate, as well as the direct impact on the chosen tax rate. However, it does not allow for the study of the causal effect of the Information treatment on the alignment with the median rate. As a consequence, we complement the previous analysis by running OLS regressions on deviations from the median rate. Specifically, we draw a median rate $\tau_i^M$ at random from [0, 10, 20...100] for each policy-maker. The deviation is then computed as the absolute difference between the chosen rate and this simulated median rate ($Dev_i = |\tau_i - \tau_i^M|$). In the No Information treatment, the chosen rate corresponds to the decision made by the policy-maker: in this treatment, since policy-makers are completely uninformed, they decide as if any rate could be the actual median rate. In the Information treatment, we select the chosen rate for which the possible median rate is equal to the simulated median rate. We then consider the following specifications (with $I_i$ an indicator for the Information treatment and $DevBelief_i$ the absolute difference between the belief about the median rate and the simulated median rate):

$$ 
\begin{aligned}
Dev_i = & \beta_0 + \beta_1 I_i + \beta_2 C_i + \beta_3 I_i \times C_i + \beta_4 DevBelief_i \\ 
& + \beta_5 I_i \times DevBelief_i + \beta_6 C_i \times DevBelief_i + \beta_7 I_i \times C_i \times DevBelief_i \\ 
& + \gamma X_i + \epsilon_i
\end{aligned} 
\quad (3) 
$$

Given the sensitivity of this analysis to the results of random draws on the median rate, we repeat the process 1,001 times to obtain a distribution for each of the $\beta$ coefficients. For each $\beta$, we summarize its distribution using the model from which the median coefficient is estimated.

**Behavioral conjectures.** Based on all specifications, we can derive hypotheses for the estimated coefficients. Theoretical models in political economy assume that political candidates' self-interest and electoral concerns steer them toward citizens' preferences (Mueller, 2003). Conversely, without proper democratic institutions, the same self-interest may drive them away from citizens' preferences. Assuming policy-makers do not care about citizens' preferences when not incentivized to do so (i.e., in the No Competition treatment), we should expect their behavior to be purely driven by distributional concerns.<fnref n="24" /> Such behavior would be captured by $\beta_2 = \beta_4 = 0$ in the context of specifications (1) and (2), and by $\beta_1 = \beta_4 = \beta_5 = 0$ in specification (3). That is, policy-makers would not take into account citizens' preferences, no matter whether or not they are informed about it. Furthermore, we would expect the Competition treatment to steer payoff-maximizing policy-makers toward citizens' preferences. When uninformed about such preferences (No Information treatment), they would align with their beliefs: $\beta_3 > 0$ in (1) and $\beta_6 > 0$ in specification (3). When informed about such preferences (Information treatment), they would align with them: $\beta_5 > 0$ in (2) and $\beta_3 < 0$ in specification (3). We would also expect beliefs to matter less in the Information treatment than in the No Information treatment, that

<footnote n="24">²⁴In the extreme, distributional preferences would lead to either implementing a 100% tax (minimizing inequalities) or not implementing any tax (maximizing the social surplus). In our analysis, these preferences are captured either by some of the control variables contained in $X_i$ (such as political preferences) or as residuals.</footnote>
is $\beta_3$ to be lower in (2) than in (1),$^{25}$ as well as $\beta_7 < 0$ in (3).

Alternatively, the convergence toward citizens’ preferences may originate from an intrinsic concern about the preferences of the people for whom the policy-maker decides. This second explanation is consistent with the conceptualization of politicians as socially-motivated agents whose mission is to fulfill the interests of the people (Miller and Stokes, 1963; Besley, 2005; Besley and Ghatak, 2018). Assuming policy-makers hold pro-social motivations, we may still observe a positive relationship between citizens’ preferences and the chosen tax rates even in the absence of political competition. Indeed, granting citizens what they want may seem fair, even without extrinsic incentives. In such case, policy-makers would we willing to take into account citizens’ preferences in all scenarios, which would be captured by $\beta_2 > 0$ and $\beta_4 > 0$ in (1) and (2), and by $\beta_4 > 0$ in (3). In (3), $\beta_5$ would then represent the effect of being informed on the impact of beliefs, which we expect to be negative as policy-makers would weigh more the information as compared to their personal beliefs. In the extreme, we may also observe some crowding-out effect of intrinsic motivations in the *Competition* treatment: extrinsic incentives (the political competition) could deplete policy-makers’ intrinsic willingness to abide by citizens’ preferences. In such case fostering political competition may not have any effect ($\beta_3 = \beta_5 = 0$ in (1) and (2), as well as $\beta_3 = \beta_6 = \beta_7 = 0$ in (3)).

The second objective of this study is to compare the behavior of politicians and non-politicians. We thus run the same models on the combined politicians/non-politicians sample while adding a *Politician*<sub>*i*</sub> dummy, which indicates whether participant *i* is a politician (=1) or not (=0). Given the scarcity of studies focusing on behavioral differences between politicians and the rest of the population, we could not make any literature-based conjectures regarding the effect of this dummy variable. Additionally, we are interested in behavioral differences in treatment effects, which we examine by adding interaction terms with the *Politician*<sub>*i*</sub> dummy. Since part of their daily activities implies being attentive to citizens’ demands, we expect politicians to be more inclined to implement citizens’ preferences in our experiment. However, the exact consequence such statement has on estimated coefficients ultimately relies on the underlying behavioral assumptions we make for each sample. Therefore, we make no specific hypotheses in this regard and adhere to an exploratory analysis.

# 4 Results

## 4.1 Descriptive analysis

Figure 3 reports the differences in the tax choices between the *No Competition* and the *Competition* treatments for politicians, non-politicians, and both samples pooled, in the *No Information* treatment. First, there is no difference between the two treatments for either politicians and non-politicians (in all cases, $p > 0.1$ in a two-sided t-test for differences in mean). Second, politicians choose a lower tax rate compared to non-politicians. The difference in mean between politicians’ and non-politicians’ choices is 2.38 p.p. in the *Competition* treatment and 6.74 p.p. in the *No Competition* treatment. However, the

<footnote>$^{25}$We do not expect $\beta_3$ to be null in (2) due to the uncertainty about the actual median rate resulting from the Strategy Method. For instance, policy-makers could pay less attention to values that are far away from their beliefs.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 3: Tax choices - *No Information* treatment</figcaption></figure>

**Notes:** Mean and 95% confidence interval of the tax rate choice by sample and by treatment. The p-values are derived from two-sided t-tests comparing means: across treatments within the politician, non-politician, and full samples, and between politicians and non-politicians within each treatment.

difference is statistically significant only in the latter case (two-sided t-test: *p* < 0.01).<fnref n="26" /> Third, extreme strategies, which involve either maximizing efficiency (no redistribution) or minimizing inequalities (full redistribution), make up only a minority (7.6%) of observations (see Figure B.4.1).

Turning to the *Information* treatment, Figure 4 shows the correlation between the information about the median rate and the chosen tax rate. This correlation is positive for both politicians and non-politicians in both the *No Competition* and *Competition* treatments. However, such correlation is hardly perfect: ρ = 0.33 (resp. ρ = 0.35) in the *No Competition* (resp. *Competition*) treatment. Participants thus respond to the median rate but also substantially deviate from it, especially when the information represents an extreme view on taxation (tax rate close to 0% and to 100%). The average absolute difference between tax choice and the information is 24.54 p.p. when considering both samples. Moreover, politicians deviate more when they do not play in a competition compared to non-politicians: The Pearson's correlation coefficient between participants' tax choice and the information in the *No Competition* treatment is 0.28 for politicians and 0.38 for non-politicians. By contrast, in the *Competition* treatment the same correlation is 0.33 for politicians and 0.37 for non-politicians. Finally, we observe no differences between the *Competition* and the *No Competition* treatments.<fnref n="27" />

To provide additional insights on the difference between our treatments and samples, we now focus on the elicited beliefs about the median rate. Figure 5 shows the distribution of the mean of politicians' and non-politicians' subjective probability distribution in all conditions.<fnref n="28" /> In all cases, beliefs exhibit

<footnote n="26">²⁶We find similar results when we consider the entire distribution of tax choices, as in Figure B.4.1.</footnote>

<footnote n="27">²⁷For each level of the median rate, we do not observe a significant difference in the tax distribution between the two treatments, based on Kolmogorov-Smirnov (KS) tests (*p* > 0.1 in all cases), except for non-politicians when the median rate is 50% (*p* = 0.067).</footnote>

<footnote n="28">²⁸In Figures B.5.1 and B.5.2, we plot the distribution of both the median and the mode of participants’ subjective probability distribution. We observe consistent pattern across metrics overall, which is unsurprising considering all metrics are highly correlated: The correlation between the mean and the median (resp. mode) is 0.97 (resp. 0.92). Furthermore, in Figure B.5.3, we depict the correlation between beliefs about the median rate and about the competitor’s choice. The</footnote>
<figure><img src="image_6.png" /><figcaption>Figure 4: Tax choices - *Information* treatment</figcaption></figure>

**Notes:** The red line represents the hypothetical case in which participants' tax choice is always equal to the median rate. That is, it corresponds to the behavior we would expect of policy-makers that are fully responsive to the median rate. In each boxplot, the lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles). The upper (lower) whisker extends from the hinge to the largest (smallest) value no further than 1.5*inter-quartile range from the hinge. The remaining points represent outliers. $\rho$ represents the Pearson correlation coefficient in the *Competition* and *No Competition* treatments. The p-values are derived from Kolmogorov-Smirnov tests comparing the distribution of tax choices in the *Competition* and *Non-competition* treatments for each median rate.

a relatively normal distribution. Moreover, we find evidence that politicians' beliefs are more skewed toward the right (KS test pooling all treatments: $p < 0.01$), implying that they believe the median rate to be lower than what non-politicians believe. Breaking down the analysis across treatments, we observe the difference to be significant in the NI-NC and NI-C conditions ($p < 0.01$), but not in the I-NC ($p = 0.76$) and I-C ($p = 0.18$) conditions. Finally, another difference involves the standard deviation of the beliefs about the median rate: The average standard deviation of the belief about the median rate distribution is 15.17 (14.31) for non-politicians and 12.52 (12.28) for politicians in the *No Information* (*Information*) treatment, with the difference being statistically significant (two-sided t-test: $p < 0.01$ in the *No Information* treatment and $p = 0.0175$ in the *Information* treatment).

<footnote>correlation is strong and positive, with a Pearson's correlation coefficient of 0.78, which is highly significant ($p < 0.01$).</footnote>
<figure><img src="image_7.png" /><figcaption>Figure 5: Beliefs about the median rate</figcaption></figure>

**Notes:** NI refers to *No Information* treatment, I to *Information* treatment, C to *Competition* treatment, and NC to *No Competition* treatment. This graph reports the distribution of the mean of participants’ subjective probability distribution about the median rate. The Kolmogorov-Smirnov test indicates significant differences between politicians and non-politicians in the NI-NC and NI-C conditions ($p < 0.01$), but not in I-NC ($p = 0.76$) and I-C conditions ($p = 0.18$). Within both the *No Information* and *Information* treatments, differences between *No Competition* and *Competition* are not significant ($p = 0.53$ ($p = 0.56$) for politicians (non-politicians) in the *No Information* treatment, $p = 0.66$ ($p = 0.34$) for politicians (non-politicians) in the *Information* treatment). Similarly, within both *No Competition* and *Competition* treatments, differences between *No Information* and *Information* are not significant ($p = 0.27$ ($p = 0.24$) for politicians (non-politicians) in the *No Competition* treatment, $p = 0.88$ ($p = 0.56$) for politicians (non-politicians) in the *Competition* treatment).

## 4.2 Regression analysis - Politicians

We now turn to the regression analysis presented in Section 3. Table 2 reports the results on the chosen tax rate (specifications (1) and (2)) for the politicians sample, distinguishing the *No Information* (column 1, without controls, and column 2, with controls) and the *Information* treatments (column 3, without controls, and column 4, with controls).<fnref n="29" /> In the *No Information* treatment, we observe a correlation between the beliefs about the median rate and the tax choice ($p < 0.01$) when politicians are not in competition (i.e., $C = 0$). As expected, the interaction term $C \times \text{Belief about Median}$ is positive, indicating a higher influence of beliefs in the competition treatment. However, the coefficient is not statistically significant ($p = 0.41$).

In the *Information* treatment, we observe a strong significant correlation between the median rate and the tax choice ($p < 0.01$), both in the *Competition* and *No Competition* treatments. Yet, this correlation is far from perfect,<fnref n="30" /> and lower than the correlation between tax choices and participants’

<footnote n="29">²⁹We focus our discussion on the specifications without controls, yet all results are virtually identical with controls.</footnote>

<footnote n="30">³⁰Testing for $\beta_4 = 1$ and $\beta_4 + \beta_5 = 1$ in column (4), that is, a perfect correlation between tax choices and the median rate in the *No Competition* and *Competition* treatments, we find a significant difference ($p < 0.01$).</footnote>
<table><caption>Table 2: OLS regressions - Tax choice (Politicians)</caption><thead><tr><th rowspan="2"></th><th colspan="2">Treat:NI</th><th colspan="2">Treat:I</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>C</td><td>1.847<br>(2.229)</td><td>3.533<br>(2.236)</td><td>0.124<br>(3.769)</td><td>-0.762<br>(3.547)</td></tr><tr><td>Belief about Median</td><td>0.741***<br>(0.0964)</td><td>0.690***<br>(0.100)</td><td>0.405***<br>(0.131)</td><td>0.455***<br>(0.118)</td></tr><tr><td>C × Belief about Median</td><td>0.0600<br>(0.117)</td><td>0.101<br>(0.123)</td><td>0.151<br>(0.168)</td><td>0.105<br>(0.155)</td></tr><tr><td>Median rate</td><td></td><td></td><td>0.252***<br>(0.0438)</td><td>0.252***<br>(0.0440)</td></tr><tr><td>C × Median rate</td><td></td><td></td><td>0.0851<br>(0.0605)</td><td>0.0851<br>(0.0607)</td></tr><tr><td>Controls</td><td>None</td><td>All</td><td>None</td><td>All</td></tr><tr><td>R<sup>2</sup></td><td>0.388</td><td>0.428</td><td>0.222</td><td>0.301</td></tr><tr><td>Observations</td><td>475</td><td>475</td><td>3135</td><td>3135</td></tr><tr><td>Clusters</td><td>475</td><td>475</td><td>285</td><td>285</td></tr></tbody></table>

**Notes:** NI refers to *No Information* treatment and I to *Information* treatment. C is an indicator for the *Competition* treatment. The Belief about Median variable is normalized by subtracting its average value. We apply post-stratification weights to account for differences in observables between our politicians sample and the sample of all French politicians. Controls include: age, gender, income, education level, occupation, geographical area of residence, party preferences, a risk aversion index, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the survey. Robust standard errors in parentheses in columns 1-2 and clustered at the individual level in columns 3-4. * significant at 10%, ** significant at 5%, *** significant at 1%.

beliefs about the median rate. Furthermore, we observe a stronger, although insignificant, effect of both beliefs ($p = 0.5$) and the median rate ($p = 0.16$) in the *Competition* treatment.

In Table 3, we show the results of the analysis where the dependent variable is the deviation between the chosen tax rate and the median rate and we pool data from the *No Information* and the *Information* treatments (see Section 3). For this analysis, we simulate 1001 median rates for each participant and run specification (3) for each of these rates, which provides a distribution for each of the $\beta$ coefficients (see Figures B.7.1 to B.7.4). We summarize the distributions by the median coefficients, which we report in Table 3.31 We find that the *Information* treatment decreases the deviation from the median rate ($p = 0.01$, column 1), whereas the *Competition* treatment has no effect ($p = 0.937$, column 1). Also, the interaction between both treatments show the expected negative sign, but the effect is small and not statistically different from 0 ($p = 0.6$, column 1). Furthermore, we observe that participants deviate more when their beliefs about the median are far from the median rate ($p < 0.01$, column 2)). This effect is lower in the *Information* treatment ($p = 0.03$ on the interaction coefficient, column 2). By contrast, the *Competition* treatment has little impact on this effect, although it shows the expected positive sign ($p = 0.77$, column 2). Finally, we observe that the interaction of both treatments has the expected negative effect on the correlation with the deviation from beliefs, but the coefficient is not statistically

<footnote n="31">31 In Tables B.8.1 to B.8.4, we show the results for the regressions from which each coefficient is estimated. Looking at the results from the off-diagonal coefficients, we observe globally consistent results with the one reported here.</footnote>
<table><caption>Table 3: Summary table for 1001 OLS regressions - Distance to median rate (Politicians)</caption><thead><tr><th></th><th colspan="4">Dep. var.: |Tax rate - Median rate|</th></tr></thead><tbody><tr><td>I</td><td>-7.43**<br>(2.95)</td><td>1.51<br>(3.45)</td><td>0.53<br>(4.35)</td><td>1.29<br>(4.94)</td></tr><tr><td>C</td><td>0.21<br>(2.70)</td><td>-0.93<br>(3.24)</td><td>-1.66<br>(3.46)</td><td>-1.21<br>(3.53)</td></tr><tr><td>I × C</td><td>-2.34<br>(4.41)</td><td>-2.02<br>(3.38)</td><td>-0.08<br>(5.80)</td><td>-1.18<br>(5.65)</td></tr><tr><td>|Belief about Median - Median rate|</td><td colspan="2">0.74***<br>(0.07)</td><td>0.73***<br>(0.08)</td><td>0.73***<br>(0.07)</td></tr><tr><td>I × |Belief about Median - Median rate|</td><td colspan="2">-0.28**<br>(0.12)</td><td>-0.25**<br>(0.12)</td><td>-0.24**<br>(0.11)</td></tr><tr><td>C × |Belief about Median - Median rate|</td><td colspan="2">0.02<br>(0.08)</td><td>0.05<br>(0.08)</td><td>0.05<br>(0.09)</td></tr><tr><td>I × C × |Belief about Median - Median rate|</td><td colspan="2">-0.06<br>(0.23)</td><td>-0.06<br>(0.24)</td><td>-0.06<br>(0.24)</td></tr><tr><td>Mean deviation (NI-NC)</td><td>33.69</td><td>33.69</td><td>33.69</td><td>33.69</td></tr><tr><td>Mean deviation (NI-C)</td><td>33.22</td><td>33.22</td><td>33.22</td><td>33.22</td></tr><tr><td>Mean deviation (I-NC)</td><td>25.81</td><td>25.81</td><td>25.81</td><td>25.81</td></tr><tr><td>Mean deviation (I-C)</td><td>24.73</td><td>24.73</td><td>24.73</td><td>24.73</td></tr><tr><td>Observations</td><td>760</td><td>760</td><td>760</td><td>760</td></tr><tr><td>Controls</td><td>None</td><td>None</td><td>None</td><td>All</td></tr></tbody></table>

**Notes:** I is an indicator for the *Information* treatment and C for the *Competition* treatment. This table is not to be confused with a standard regression table. Each column summarizes the results from 1001 similar regressions based on 1001 random draws of the median rate for each participant (see Section 3). Each coefficient corresponds to the median coefficient over all regressions for the corresponding variable. Coefficients for different variables are thus likely to originate from different regressions. Standard errors and p-values associated with one coefficient are computed from the regression from which this coefficient originates. Similarly, the “Mean deviations” at the bottom of the table are computed by averaging the deviation in each condition for each random draw and taking the median of such average over the 1001 random draws. See Figures B.7.1 to B.7.4 for the distributions of the 1001 coefficients for each variable, along with confidence intervals. Also see Tables B.8.1 to B.8.4 for full display of actual regressions corresponding to each median coefficient. For each regression, we apply post-stratification weights to account for differences observables between our politicians sample and the sample of all French politicians. Controls include: age, gender, income, education level, occupation, geographical area of residence, party preferences, a risk aversion index, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the survey. Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%.

significant ($p = 0.36$, column 3).<fnref n="32" />

We summarize these results as:

**Result 1.** Politicians respond to citizens' preferences. In the *No Information* treatment, they follow their beliefs about the median rate. In the *Information* treatment, the information about the median rate reduces the weight they place on their beliefs and steer their behavior towards this rate.

**Result 2.** The *Competition* treatment increases the convergence to citizens' preferences, but the effect is not statistically significant.

<footnote n="32">³²We also check whether there is substantial change when we account for policy-makers’s beliefs about the competitor. Despite a very high correlation between both types of beliefs, which likely blurs the findings’ interpretation, we observe qualitatively similar results on our main effects. See Table B.10.1 for tax choices. To economize on space, the analysis on the deviations is available upon request.</footnote>
<table><caption>Table 4: OLS regressions - Tax choice (Politicians and Non-politicians)</caption><thead><tr><th rowspan="2"></th><th colspan="5">Treat:NI</th><th colspan="5">Treat:I</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>Politician</td><td>-4.843**<br>(1.979)</td><td>-8.657***<br>(2.708)</td><td>-1.480<br>(1.693)</td><td>-3.418<br>(2.446)</td><td>-2.878<br>(2.511)</td><td>-0.986<br>(1.885)</td><td>-3.946<br>(2.801)</td><td>-0.0663<br>(2.397)</td><td>-0.115<br>(3.555)</td><td>-2.362<br>(3.527)</td></tr><tr><td>C</td><td>2.013<br>(1.981)</td><td>-2.014<br>(2.791)</td><td>-0.119<br>(1.665)</td><td>-1.860<br>(2.624)</td><td>-1.520<br>(2.571)</td><td>0.995<br>(1.851)</td><td>-1.583<br>(2.192)</td><td>0.875<br>(1.718)</td><td>0.0700<br>(3.021)</td><td>-0.300<br>(2.982)</td></tr><tr><td>Politician × C</td><td></td><td>7.661*<br>(3.936)</td><td></td><td>3.707<br>(3.444)</td><td>3.720<br>(3.467)</td><td></td><td>5.590<br>(3.769)</td><td></td><td>0.0540<br>(4.828)</td><td>-0.0264<br>(4.652)</td></tr><tr><td>Belief about Median</td><td></td><td></td><td>0.567***<br>(0.0698)</td><td>0.605***<br>(0.0890)</td><td>0.658***<br>(0.0881)</td><td></td><td></td><td>0.354***<br>(0.0554)</td><td>0.443***<br>(0.104)</td><td>0.445***<br>(0.0966)</td></tr><tr><td>Politician × Belief about Median</td><td></td><td></td><td>0.215**<br>(0.0897)</td><td>0.137<br>(0.131)</td><td>0.0540<br>(0.133)</td><td></td><td></td><td>0.128<br>(0.101)</td><td>-0.0382<br>(0.167)</td><td>-0.00784<br>(0.147)</td></tr><tr><td>C × Belief about Median</td><td></td><td></td><td></td><td>-0.0746<br>(0.139)</td><td>-0.128<br>(0.138)</td><td></td><td></td><td></td><td>-0.142<br>(0.123)</td><td>-0.167<br>(0.117)</td></tr><tr><td>Politician × C × Belief about Median</td><td></td><td></td><td></td><td>0.135<br>(0.182)</td><td>0.215<br>(0.183)</td><td></td><td></td><td></td><td>0.293<br>(0.208)</td><td>0.272<br>(0.185)</td></tr><tr><td>Median rate</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.307***<br>(0.0262)</td><td>0.329***<br>(0.0418)</td><td>0.329***<br>(0.0419)</td></tr><tr><td>Politician × Median rate</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.00938<br>(0.0401)</td><td>-0.0763<br>(0.0605)</td><td>-0.0763<br>(0.0607)</td></tr><tr><td>C × Median rate</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.0415<br>(0.0530)</td><td>-0.0415<br>(0.0531)</td></tr><tr><td>Politician × C × Median rate</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.127<br>(0.0804)</td><td>0.127<br>(0.0805)</td></tr><tr><td>Controls</td><td>None</td><td>None</td><td>None</td><td>None</td><td>All</td><td>None</td><td>None</td><td>None</td><td>None</td><td>All</td></tr><tr><td>R<sup>2</sup></td><td>0.0113</td><td>0.0173</td><td>0.325</td><td>0.327</td><td>0.362</td><td>0.000678</td><td>0.00335</td><td>0.218</td><td>0.226</td><td>0.264</td></tr><tr><td>Observations</td><td>842</td><td>842</td><td>842</td><td>842</td><td>842</td><td>6303</td><td>6303</td><td>6303</td><td>6303</td><td>6303</td></tr><tr><td>Clusters</td><td>842</td><td>842</td><td>842</td><td>842</td><td>842</td><td>573</td><td>573</td><td>573</td><td>573</td><td>573</td></tr></tbody></table>

Notes: NI refers to *No Information* treatment and I to *Information* treatment. C is an indicator for the *Competition* treatment. We apply post-stratification weights to account for differences in observables between our samples and their respective general populations. To account for differences in sampling across Politicians and Non-politicians, we compute and normalize the weights separately for both samples. Controls include: age, gender, income, education level, occupation, geographical area of residence, party preferences, a risk aversion index, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the survey. Robust standard errors in parentheses in column (1) and (3). Standard errors clustered at the individual level in parentheses in column (2) and (4). \* significant at 10%, \*\* significant at 5%, \*\*\* significant at 1%.

## 4.3 Regression analysis - Politicians v. non-politicians

We now present the analysis of the difference between politicians and non-politicians. For exposition purpose, we first regress participants' tax choice on the *Politician* and the *Competition* dummies, and we incrementally add the other interactions of interest (see Table 4).

In the *No information* treatment (column 1), the coefficient on *Politician* is negative and significant ($p = 0.015$), while the coefficient on *Competition* is positive and not significant ($p = 0.59$). When we interact the *Politician* and *Competition* dummy, we find that the difference between politicians and non-politicians is strongest in the *No Competition* treatment ($\beta = -8.657$, $p < 0.01$). However, the difference in tax choice between politicians and non-politicians disappears when, in column 3, we consider their beliefs about the median rate. Also, politicians' tax choice is more correlated to their beliefs
than non-politicians (p = 0.043 on the interaction term). Finally, when we include all the interactions
in column 4, we obtain no difference between the two samples. Yet, a linear combination of Politi-
cian×Belief about Median and C×Politician×Belief about Median, results in a positive and significant
difference (p = 0.032) between politicians and non-politicians in the Competition treatment. In other
words, uninformed politicians appear more sensitive to their beliefs than non-politicians, especially in
the Competition treatment.

In the *Information* treatment, we observe no difference in tax choices between politicians and non-
politicians (p = 0.6, column 6). Moreover, there is no difference across samples in the impact of either the
*Competition* treatment (p = 0.14, column 7), or in the correlation with median rate (p = 0.82, column
8). When we include all the interactions, we find no difference in the impact of beliefs between politicians
and non-politicians in the *No Competition* treatment (p = 0.81). However, this difference is significant
in the *Competition* treatment (p = 0.029). All results hold when we include the controls (columns 5 and
10).

Our analysis on the deviation from the median rate confirms the above patterns between politicians and non-politicians (see Table B.9.1) and show similar findings as in Table 3. Moreover, we find that politicians seem to deviate slightly more from the information when they are not in a competition and react more to the competition when informed than non-politicians. Yet, the interactions between our treatments and the *Politician* dummy are not statistically significant.

We summarize our results as follows:

**Result 3:** When no information is given, politicians choose a lower tax rate than non-politicians, but this difference is driven by differences in their beliefs about citizens' preferences. When the information is given, the same difference does not hold.

**Result 4:** There is no significant differential impact of the *Information* and *Competition* treatments on the tax choice between politicians and non-politicians. However, beliefs are stronger predictors of politicians behavior in the *Competition* treatment.

# 5 Discussion

Studying democratic representation with an economic experiment offers many advantages, such as im-
proved control, qualitative data collection, financial incentives, and a reproducible context. However,
there are some limitations, both general to the experimental methodology and specific to our implemen-
tation. In this section, we emphasize some of the most prominent caveats and discuss them in relation
to the robustness checks we implement.<fnref n="33" />

**Absence of effect of competition.** A striking result is that the *Competition* treatment bears no significant impact on the results. This might stem from the fact that the treatment effect depends on the

<footnote n="33"><sup>33</sup>We report all tables exposing robustness analyses with respect to tax choices in the appendices. Running similar robustness checks for the analysis on deviations yield similar results: the corresponding tables are available upon request.</footnote>
saliency of the monetary gains: If participants regard the amounts at stake as negligible, they may not be sufficiently motivated to consider political competition, hence a low treatment effect. Such concern may be even more significant for politicians who are used to manage important financial amounts. To investigate this issue, we restrict our attention to the participants who choose to keep their payoff, which, a priori, entails a higher interest in the monetary gains (columns 1 and 2 in Table B.10.2). We find consistent results with respect to Table 4. In addition, we find a stronger effect of beliefs for informed politicians in the Competition treatment. However, we should not over-interpret such an effect as there are only very few politicians that decided to keep their payoffs (14.61%), so that they may represent a very specific sub-sample.

More generally, the smallness of stakes may have laid participants to pay low attention to their answers, which could undermine the data quality. Consequently, we re-run the main regressions by excluding those participants who are not consistent in the risk aversion task (7.92%), that is, participants who choose the lottery when the expected gain is low but switch to the fixed payment when the expected gain of the lottery increases, and those participants who took less than 10 minutes to complete the survey (24.31%). The results remain robust even with these more stringent sample selection criteria (columns 3 to 6 in Table B.10.2). Moreover, we do not observe that politicians are less consistent and more hasty than non-politicians: if anything, we observe the opposite.<fnref n="34" /> Ultimately, thus, we observe that the Competition treatment has a limited impact in our experiment, which may originate from an already high sensitivity to citizens’ preferences in the No Competition treatment.

**Correlation with median rate.** We find that policy-makers’s decisions correlate positively with the median rate in the *Information* treatment. Arguably, this correlation may be mechanical and not related to participants taking the information into account: the repetitive nature of the task (sequential choice of 11 tax rates) and potential biases, such as the experimenter demand effect, may indeed drive this result. In other words, we can expect the observed response gradient to be steeper than for a real-life “informational shock”. However, we note that the number of participants that strictly stick to the median rate remains very limited. Out of 573 participants in the *Information* treatment, only 14 (2.44%) participants always choose a tax rate equal to the information provided. Similarly, out of 6,303 tax choices made in the *Information* treatment, only 694 (11.01%) corresponded to the median rate. Removing both types of observations we obtain virtually the same results on both tax choices and deviations from the median rate (columns 7 and 8 in Table B.10.2). Additionally, observing substantial deviations from the median rate in a stylized setting, which may inflate the correlation with the median rate, provides, in our view, a lower-bound estimate of such deviations and underscores the importance of studying their drivers.

**Beliefs.** Our analysis underscores the fundamental importance of politicians’ beliefs about citizens’ preferences in explaining both their tax choices and the differences we observe between politicians and

<footnote n="34">³⁴Only 49 politicians exhibited inconsistency in the risk aversion task, as opposed to 63 non-politicians (Chi-squared: *p* = 0.028). Politicians, on average, took 7.9 minutes more to complete the survey in the *No Information* treatment and 14.5 minutes more in the *Information* treatment (in both cases, t-test: *p* < 0.01).</footnote>
non-politicians. Specifically, we find that deviations from the median rate are primarily related to the distance between this rate and politicians' beliefs about it. Additionally, such beliefs significantly influence participants' behavior when they are uninformed.<fnref n="35" /> In other words, policymakers seem to follow what they believe citizens want and imperfectly adjust their behavior when given the opportunity to decide based on possible values of the median rate. This is consistent with a belief updating mechanism, by which policy-makers may still rely on their priors even when informed. Indeed, policy-makers may not equally value all possible median rates and dismiss some as unrealistic, especially when they deviate from their priors. If politicians do value fulfilling citizens' demands, this may create a tension between acting on the median rate and their priors, which could explain the importance of beliefs despite the presence of the information. Figure 4 indeed shows that decisions are more heterogeneous and deviations are higher when the median rate is at the extremes (close to 0% and 100%).

Alternatively, the significance of the belief variable in all our regressions may stem from the possibility that our belief measure captures participants' idiosyncratic preferences rather than their priors. To investigate this possibility, we regress participants' beliefs on our treatments, a politician dummy, and the control variables in Table B.6.1. Intuitively, if beliefs were capturing preferences, we would expect a significant correlation between beliefs and the main predictors of preferences for redistribution identified in the literature (Alesina and Giuliano, 2011; Mengel and Weidenholzer, 2022). Most strikingly, we find little correlation between participants' beliefs and their political ideology.<fnref n="36" /> Moreover, we only observe income to be (negatively) significantly correlated with beliefs. However, this effect may result from an exposure mechanism and not necessarily from participants' own preferences: wealthier individuals are more likely to interact with other wealthy individuals and, consequently, may believe that the median citizen prefers a lower tax rate because their acquaintances do (Pereira, 2021).

Additionally, if beliefs were mainly capturing preferences, we would not expect participants to be uncertain about such preferences, i.e., to provide a wide range of “beliefs”. However, the standard deviation of beliefs observed in our data is substantial, with averages of 12.43 for politicians and 14.79 for non-politicians. We also check whether our results are driven by a specific order of the tasks by running separate regressions (see Table B.10.3). We find consistent results overall on our main effects. We also observe some weakly significant differences across sub-groups but we do not find a consistent pattern in these differences overall. Thus we do not find evidence that one particular sub-group is driving our results.<fnref n="37" /> Finally, we observe that the beliefs about the median are not impacted by whether the belief elicitation task is completed before or after the redistribution task (KS test: *p* = 0.88 for politicians

<footnote n="35">³⁵We acknowledge that targeting the median rate, which aligns with the usual practice in theoretical models of political economy (Meltzer and Richard, 1981), may not provide a comprehensive assessment on the link between politicians' behavior and citizens' preferences. For instance, it is still debated whether politicians focus on citizens' mean or median preferences (Mueller, 2003), and alternative statistics could also be considered. However, the incentives in our experiment specifically align with the median rate by design, which should mitigate such concerns.</footnote>

<footnote n="36">³⁶In our experiment, political ideology as measured either on a Likert-scale or through party preferences is also highly correlated with policy-makers choices in the expected direction: right-wing participants opt for lower redistribution than left-wing participants (see Table B.12.1).</footnote>

<footnote n="37">³⁷We also repeated such exercise on the analysis on deviations (see Table B.10.4). All signs and magnitudes are consistent across samples, although we observe that when beliefs are elicited before they have slightly more impact on the results. Also, we observe that the order differences are slightly more pronounced for non-politicians than for politicians.</footnote>
$p = 0.78$ for non-politicians). All together, we take such additional analyses as indicative that our beliefs variable is likely not capturing participants idiosyncratic preferences.

**Politicians and non-politicians.** One important finding in our experiment is that our politician sample believes that the 9-citizen group demanded a lower tax rate on average than non-politicians. Additionally, we find evidence that politicians are more confident in their beliefs and adhere to them more strongly. This finding echoes recent research indicating that politicians, and “the elite” in general, hold biased beliefs about citizens’ preferences (Pereira, 2021; Liaqat, 2023). One explanation for such differences could be an exposure mechanism (Pereira, 2021), whereby politicians are more exposed to groups or lobbies that emphasize the need for more efficient public policies and a balanced government intervention in the economy.<fnref n="38" /> Once beliefs are accounted for, we find little behavioral differences between politicians and non-politicians.

Such finding speaks against the view that politicians are a select group of the population in terms of preferences for taxation/redistribution or motivations to implement citizens’ demands, despite being selected on socio-demographic characteristics. As such, it aligns with existing meta-analytic evidence emphasizing that public-elite gaps in decision-making are less pronounced than what is commonly believed (Kertzer and Renshon, 2022). Yet, our sample is also highly specific in this respect since we targeted local politicians, that is, the politicians that are most likely to resemble the general population. It is also possible that bigger differences would have arisen with a different sample of politicians. Some exploratory findings using the politician-relevant variables at our disposal indeed point at some heterogeneity among politicians concerning both beliefs and behavior (Table B.11.1). In particular, we observe that politicians from more populous municipalities believe citizens’ want lower tax rates and are more confident about it. In addition, mayors and vice-mayors choose a higher tax rate than local councilors, while more experienced politicians (higher number of mandates) choose a lower tax rate in the *Information* treatment. Such exploratory results are thus consistent with the view that bigger differences in preferences, beliefs and motivations may be found with upper-tier politicians.

**Balance across treatments.** In order to account for the small imbalances we observe across treatments in both our samples (as documented in Section 2.3), we also implement an inverse-probability weighting regression. We observe virtually the same results for both the analysis on the politicians sample and the analysis on the full sample (see Table B.10.5). Further, since ideology and income have been identified as key drivers of redistribution preferences (Alesina and Giuliano, 2011; Mengel and Weidenholzer, 2022) and they are correlated with tax choices in our experiment, and since those relationships are differently significant across treatments and samples (see Table B.12.1), we run additional exploratory analyses by dividing our sample with respect to both variables. We do not find any meaningful differences across

<footnote n="38">³⁸Alternatively, it could also be a combination of higher reasoning abilities among politicians, which is consistent with existing findings (Dal Bó et al., 2017), and the belief that citizens are particularly selfish. Assuming strictly self-interested citizens, the voting equilibrium in our game indeed lies in the [0-20] interval. Yet, given the minimal payoff differences for the median voter in our game, it would require a very strict selfishness assumption. Moreover, the mass of politicians’ belief is not strictly higher than that of non-politicians in most treatment conditions over the [0,15] interval. Consequently, we believe an interpretation in terms of different visions on the ideological stands of citizens is more likely.</footnote>
income categories (Table B.11.3). Concerning ideology, we observe similar results with respect to our main findings when dividing our sample between Leftwing and Rightwing participants (based on the Likert-scale score). Yet, we also find some interesting patterns. In particular, rightwing politicians follow less the median rate than both non-politicians and leftwing politicians in the *Information* treatment without competition. Yet, the *Competition* treatment partly mitigates this effect. Also, leftwing politicians follow slightly less their beliefs than leftwing non-politicians in the *Information* treatment without competition. In other words, politicians, and particularly rightwing politicians, seem to adopt a behavior that is marginally more consistent with the predictions from political economy models.

As a final discussion point, caution should be exercised regarding the generalizability of our results and their extrapolation to other contexts. Our findings were derived from a highly specific decision-making environment that intentionally abstracts many contextual elements of real-world policy decisions. Specifically, we cannot directly compare our treatment, which varies the degree of political competition and has little impact on the results, with policies designed to enhance political accountability in practice. Such policies may be effective precisely because of the political economy constraints that this study abstracts. Furthermore, we should stress that our design does not, *stricto sensu*, implement an electoral competition, meaning no voting occurs. As a consequence, we do not strictly test the median citizen theorem according to which political candidate converge toward the median preference in the electorate because they are vote maximizers. Rather, we enforce competition by tying the personal incentives to the median rate. This feature allows for an interpretation in terms of unconditional pro-social motivations to implement citizens' preferences, as compared to a situation where reciprocity concerns (i.e. conditional social preferences) would have played a role. Yet, the absence of reciprocal relationship between citizens and politicians may also partly explain the small effect of the *Competition* treatment in aligning tax choices with citizens' preferences. In addition, the stakes in our experiment are nothing to be compared with the stakes involved in a real-world election. As such, our results provide insights on the behavior of politicians when faced with a modest tightening of the political constraint, as compared to a situation when they are free to implement whatever taxation choice that suits them. More generally, our results should be interpreted as informative about politicians' individual-level behavior in a revealed preference context, and especially how they react to both varying information about the preference of the people for whom the decide and varying political competition, as compared to non-politicians.

# 6 Conclusion

This paper adopts a complementary stance to the existing empirical literature on democratic representation by analyzing the behavior of 760 local French politicians in an incentivized behavioral experiment. We study how politicians react to citizens’ preferences in a taxation-redistribution context by exogenously manipulating the information set and the degree of political competition, as well as measuring beliefs about citizens’ preferences. Additionally, we recruit a sample of 655 French non-politicians to investigate whether politicians’ decisions differ from non-politicians’ decisions on the same issue.
Our findings highlight that politicians consider the preferences of the people for whom they decide. Without any information about the median rate, politicians' beliefs about the median rate are highly correlated with their tax choice. When provided with the information about the median rate, politicians adjust their tax choice. Such finding relate to *Butler and Nickerson (2011)* and *Liaqat (2023)'s result* that informing politicians about citizens' preferences in various domains lead to policy changes in the field. However, we also observe substantial deviations of the chosen rate from the median rate. Such deviations appear mainly related to decisions-makers' beliefs about such preferences, so that politicians adjust their beliefs when they receive an information, albeit imperfectly.

Studying politicians' beliefs appears of even greater importance considering that they differ from those of non-politicians which translates into less redistributive taxation choices. Such finding may for instance hint at a possible explanation for the conundrum posed by the simultaneity of increasing inequalities and lack of implemented redistribution policies in democratic countries (Causa and Hermansen, 2020; Chancel et al., 2022). Consequently, it underscores the importance of incorporating politicians' beliefs into our understanding of policy-making, as well as into the design of the incentives and institutions that shape political behavior.

In addition, we find that modifying the degree of political competition bears no impact on the results: participants select similar tax rates in the *Competition* and *No Competition* treatments. All together our findings are indicative of an intrinsic motivation to implement citizens' preferences. As a consequence, politicians' behavior in our experiment lies in contradiction with political economy models under which self-serving policy-makers are policed by political competition into implementing citizens' preferences. Rather, our results are consistent with models assuming pro-socially motivated policy-makers that would strive to implement citizens' demands even without any extrinsic motivation to do so. Such result is important insofar as the literature has largely focused on improving politicians' accountability to mitigate government failures (Besley and Burgess, 2002; Olken, 2007; Ferraz and Finan, 2011; Fujiwara, 2015), but far less on motivations-based interventions (Mansuri et al., 2023). A recent field experiment indeed shows the potential of emphasizing pro-social motivations for public office as a mean to attract politicians that effectively implement policies aligned with citizens' preferences (Gulzar and Khan, 2025). Our experiment highlights the need for future research to address this gap, while also acknowledging that conducting large-scale behavioral experiments with such a specific population may be challenging—but ultimately feasible.
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# A Experiment details

This section contains a French-to-English translation of the experimental instructions. We highlight changes from one treatment to another by bold characters at the beginning of a chunk of text. For instance, the mention [*Competition + No Information treatment*] indicates that the next chunk of text exposes the instructions for the combination of *Competition* and *No Information* treatments.

## A.1 Introduction

This survey is part of a scientific research project aimed at gaining a better understanding of decision-making motives in certain economic contexts. It was developed by a team from the CREM laboratory (Centre de Recherche en Economie et en Management, CNRS, Université de Rennes I and Université de Caen Normandie).

The survey takes around 25 minutes to complete. If you complete the survey, a 50-centime donation will be made to a charity of your choice from a list presented to you at the end of the survey.

Please note: this survey is not compatible with the use of a cell phone. To complete the survey, you must use a standard-sized computer or tablet, otherwise some questions will be cut in half and it will not be possible to process the results.

The survey is divided into three parts. In the first 2 parts, you will make a series of decisions in the form of questions. Each of these two parts will begin with instructions to help you understand how to make your decisions. Then you will make your decisions. Finally, you will move on to the last part, which consists of a short questionnaire that closes the survey. Unless otherwise stated, the decisions you make in each section will generate a reward for you and possibly for other survey participants. This reward will be expressed in Survey Currency Units (SCUs). At the end of the survey, the rewards you earn in each section will add up to a total reward. When all the data has been collected, the computer will draw 1 out of 50 participants. All participants drawn will receive the total reward generated by their participation in the survey. Similarly, any rewards generated by the decisions of these randomly selected participants for other participants will be paid out. The conversion rate of SCUs into euros is as follows: 1 SCU = 0.5 euros. By participating, you have the opportunity to receive a reward in euros, the amount of which depends on your decisions and possibly on the decisions of other participants. All rewards will be paid out shortly after the end of data collection, but no later than December 30, 2020.

At the end of the survey, you will be asked to choose between two options regarding the reward you will receive if the computer selects you: you can choose to keep the reward or donate it in full to a charity of your choice from a list.

There is no right or wrong decision: You are free to decide as you wish. All we ask is that you read the instructions carefully and answer as truthfully as possible: Your reward at the end of the experiment, and that of other participants, depends on it!

In accordance with the French Data Protection Act (Loi Informatique et Libertés), all data collected is strictly anonymous and confidential. In particular, you will never know the identity or reward of other participants, and they will never know your identity or reward.
You can start the survey by clicking “Next”. Starting the survey indicates that you wish to volunteer to take part. You may stop responding at any time. However, for the purposes of the study, we will only be able to use fully completed questionnaires. What is more, if you don’t complete the survey, the above-mentioned 50 cents will not be donated to charity, and you forfeit any reward you may have received.

Thank you for your valuable participation!

## A.2 Risk aversion task

In this task, you will make 5 decisions. Each decision consists in choosing one out of two options:

1. 100% chance of receiving 7 SCUs.

2. 50% chance of receiving 0 SCUs and 50% chance of receiving another amount of SCUs.

To determine your gratification for this part at the end of the survey, the computer will draw one of 5 decisions. If for this decision you chose option 1, you will receive 7 SCUs. If you chose option 2, the computer will draw randomly the amount that you will obtain: either 0 SCUs or the amount shown for this decision.

You can now make the 5 decisions below.

1. Please indicate if you wish to receive 7 SCUs with a probability of 100% or if you wish to have 50% chances to receive 0 and 50% to receive 12 SCUs.

2. Please indicate if you wish to receive 7 SCUs with a probability of 100% or if you wish to have 50% chances to receive 0 and 50% to receive 14 SCUs.

3. Please indicate if you wish to receive 7 SCUs with a probability of 100% or if you wish to have 50% chances to receive 0 and 50% to receive 16 SCUs.

4. Please indicate if you wish to receive 7 SCUs with a probability of 100% or if you wish to have 50% chances to receive 0 and 50% to receive 18 SCUs.

5. Please indicate if you wish to receive 7 SCUs with a probability of 100% or if you wish to have 50% chances to receive 0 and 50% to receive 20 SCUs.
## A.3 Taxation-Redistribution task

During this game, you will make decisions about different people. You will never get to know the identity of these people. Similarly, they will never know your identity.

You will make decisions about a group of 9 people. These people have been selected within a sample of the French population by Panelabs, the leading provider of quantitative data for French researchers. These people are real and can be any resident of Metropolitan France. At the end of the survey and according to the decisions you will take, these 9 people will receive a certain reward expressed in euros. This reward will be paid to them by bank transfer at the end of the survey.

At the beginning of this game, a total of 473 SCUs will be distributed among these 9 people (identified by letters from A to I). Each of these individuals will therefore receive an initial amount in SCUs. The 9 initial amounts have been calculated to reflect the distribution of annual pre-tax income of French households (INSEE 2017 data).

Thus, the first decile indicates that 10% of French households earned less than 7,310 euros per year in 2017. The equivalent of this amount in the survey is 17 SCUs. The fifth decile indicates that half (50%) of French households earned less than 21,120 euros per year in 2017 and the other half of French households earned more than 21,120 euros per year in 2017. The equivalent of this sum in the survey is 49 SCUs. The ninth decile indicates that 90% of French people earned less than 42,370 euros per year in 2017. The equivalent of this sum in the survey is 100 SCUs.

The table below shows all the income deciles of French households and the possible initial amounts in SCUs.

<table><caption>Table A.3.1: French income distribution and correspondent distribution of initial endowments</caption><tr><td>Household income decile (euros)</td><td>7310</td><td>12,050</td><td>15,480</td><td>18,360</td><td>21,120</td><td>24,100</td><td>27,720</td><td>32,810</td><td>42,370</td></tr><tr><td>Initial possible endowments (SCU)</td><td>17</td><td>28</td><td>37</td><td>43</td><td>49</td><td>57</td><td>65</td><td>77</td><td>100</td></tr></table>

Each initial amount will be allocated to one person at random. Thus, one person will receive 17 SCUs, another will receive 28 SCUs, a third will receive 37 SCUs, etc.

You will be able to choose a tax rate expressed in percentages. The rate can range from 0% (no tax) to 100% (maximum tax).

* The rate chosen will be applied to the initial amount for each of the 9 people. A portion of each initial amount will be deducted. Each person will keep the amount not deducted;

* The amounts deducted will add up to a total amount;

* Then, a part of this total amount will be removed and will not be given to anyone. This part is fixed and amounts to 6.8% of the total amount;

* The rest of the total amount (93.2%) will then be divided equally among the 9 people.

Here is a schematic representation of the decision:
<figure><img src="image_8.png" /><figcaption>Figure A.3.1: Schema for comprehension of main task</figcaption></figure>

The table below shows the amounts obtained by each of the 9 individuals for several possible rates. The first line corresponds to the initial situation. This situation is therefore identical to the final situation with a rate of 0% (second line). The final amounts obtained for different rates are presented in the following rows. The last column of the table shows the sum of the amounts. Thus, for a rate of 10% this corresponds to $20.2 + 30.1 + 38.2 + 43.6 + 49 + 56.2 + 63.4 + 74.2 + 94.9 = 469.8$ SCUs.

<table><caption>Figure A.3.2: Payoff table</caption><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>D</th><th>E</th><th>F</th><th>G</th><th>H</th><th>I</th><th>Total</th></tr></thead><tbody><tr><td>Montant initial</td><td>17</td><td>28</td><td>37</td><td>43</td><td>49</td><td>57</td><td>65</td><td>77</td><td>100</td><td>473</td></tr><tr><td>Taux = 0%</td><td>17</td><td>28</td><td>37</td><td>43</td><td>49</td><td>57</td><td>65</td><td>77</td><td>100</td><td>473</td></tr><tr><td>Taux=10%</td><td>20.2</td><td>30.1</td><td>38.2</td><td>43.6</td><td>49</td><td>56.2</td><td>63.4</td><td>74.2</td><td>94.9</td><td>469.8</td></tr><tr><td>Taux=20%</td><td>23.4</td><td>32.2</td><td>39.4</td><td>44.2</td><td>49</td><td>55.4</td><td>61.8</td><td>71.4</td><td>89.8</td><td>466.6</td></tr><tr><td>Taux=30%</td><td>26.59</td><td>34.29</td><td>40.59</td><td>44.79</td><td>48.99</td><td>54.59</td><td>60.19</td><td>68.59</td><td>84.69</td><td>463.31</td></tr><tr><td>Taux=40%</td><td>29.79</td><td>36.39</td><td>41.79</td><td>45.39</td><td>48.99</td><td>53.79</td><td>58.59</td><td>65.79</td><td>79.59</td><td>460.11</td></tr><tr><td>Taux=50%</td><td>32.99</td><td>38.49</td><td>42.99</td><td>45.99</td><td>48.99</td><td>52.99</td><td>56.99</td><td>62.99</td><td>74.49</td><td>456.91</td></tr><tr><td>Taux=60%</td><td>36.19</td><td>40.59</td><td>44.19</td><td>46.59</td><td>48.99</td><td>52.19</td><td>55.39</td><td>60.19</td><td>69.39</td><td>453.71</td></tr><tr><td>Taux=70%</td><td>39.39</td><td>42.69</td><td>45.39</td><td>47.19</td><td>48.99</td><td>51.39</td><td>53.79</td><td>57.39</td><td>64.29</td><td>450.51</td></tr><tr><td>Taux=80%</td><td>42.59</td><td>44.79</td><td>46.59</td><td>47.79</td><td>48.99</td><td>50.59</td><td>52.19</td><td>54.59</td><td>59.19</td><td>447.31</td></tr><tr><td>Taux=90%</td><td>45.78</td><td>46.88</td><td>47.78</td><td>48.38</td><td>48.98</td><td>49.78</td><td>50.58</td><td>51.78</td><td>54.08</td><td>444.02</td></tr><tr><td>Taux=100%</td><td>48.98</td><td>48.98</td><td>48.98</td><td>48.98</td><td>48.98</td><td>48.98</td><td>48.98</td><td>48.98</td><td>48.98</td><td>440.82</td></tr></tbody></table>
To select a rate, position a cursor along a straight line from 0% to 100%. When you first position the cursor, a table will appear. This table will show the initial amount (first line) and the final amount (second line) for each of the 9 people. The totals for each line will be shown in the last column of the table.

Here's an example of a slider. You can now try out different configurations.

Please note: you don't have to decide anything right now; this is just a trial run to familiarize yourself with the decision process.

<figure><img src="image_9.png" /><figcaption>Figure A.3.3: Slider</figcaption></figure>

In addition, each of the 9 people was asked what rate they would like to see implemented. Each of the 9 people then positioned a slider to reflect this wish. Unlike the slider shown above, the slider presented to them ranged in 10 p.p. increments. Each person had the choice of 0%, 10%, 20%, etc. up to 100%.

The computer then calculated the “median wish”, i.e. the rate at which half the people wanted a higher or equal rate, and the other half wanted a lower or equal rate.

*[No Competition + No Information treatment]* You will not be informed of this median wish.

Finally, you will be randomly grouped with another participant in this experiment. This participant will also make the same type of decisions as you for the same 9 people. This participant is not one of these 9 people. At the end of the game, the computer will randomly select one of the two participants (you or the other participant). It will then apply the drawn participant's decision and award him 33 SCUs. The participant who is not drawn will receive 0 SCUs and his decision will not be applied.

Example: if you are drawn, you will receive 33 SCUs, the other participant will receive 0 SCUs and the computer will apply the decision you have made. Each of the 9 people will therefore receive the final amount according to your decision. Conversely, if the other participant is drawn at random, you will receive 0 SCUs, the other participant will receive 33 SCUs and the computer will apply the decision the other participant has made. Each of the 9 people will therefore receive the final amount according to the other participant's decision.

*[Competition + No Information treatment]* You will not be informed of this median wish.

Finally, you will be randomly grouped with another participant in this experiment. This participant will also make the same type of decisions as you for the same 9 people. This participant is not one of these 9 people. At the end of this part, the computer will look at the median wish of the 9 people. Then, it will apply the decision of the participant who chose the rate closest to this median wish, and award 33 SCUs to this participant. The other participant will receive 0 SCUs and his decision will not be applied.

Example: if the rate you have chosen is closer to the median wish than the rate chosen by the other participant, you will receive 33 SCUs, the other participant will receive 0 SCUs and the computer will
apply the decision you have made. Each of the 9 people will therefore receive the final amount according to your decision. Conversely, if the rate chosen by the other participant is closer to the median wish than the rate you have chosen, you will receive 0 SCUs, the other participant will receive 33 SCUs and the computer will apply the decision the other participant has made. Each of the 9 people will therefore receive the final amount according to the other participant's decision.

Note: if the rate you choose and the rate chosen by the other participant are identical, the computer will draw one of the two participants (you or the other participant) at random. It will then apply the drawn participant's decision and award him or her 33 SCUs. The participant who is not drawn will receive 0 SCUs and his decision will not be applied.

[No Competition + Information treatment] You will not be informed of this median wish. However, you will be asked to enter a rate for the 11 possible median wishes, from 0% to 100%. For each possible wish, you will have to position a cursor to choose a rate to apply in this situation. There will therefore be 11 decisions, each with a heading like "If the median wish is ...%, please choose a rate".

Finally, you will be randomly grouped with another participant in this experiment. This participant will also make the same type of decisions as you. At the end of this part, the computer will draw one of the two participants (you or the other participant) at random. Then, it will look at the median wish of the 9 people. For this median wish, it will apply the decision of the drawn participant and award him 33 SCUs. The participant who is not drawn will receive 0 SCUs and none of his decisions will be applied.

Example: Let us imagine that the median wish of the 9 people is 50%. If you are drawn, you will receive 33 SCUs, the other participant will receive 0 SCUs and the computer will apply the decision you made under the heading "If the median wish is 50%". Each of the 9 people will therefore receive the final amount according to your decision. Conversely, if the other participant is drawn at random, you will receive 0 SCUs, the other participant will receive 33 SCUs and the computer will apply the decision the other participant made under the heading "If the median wish is 50%". Each of the 9 people will therefore receive the final amount according to the other participant's decision.

[Competition + Information treatment] You will not be informed of this median wish. However, you will be asked to enter a rate for the 11 possible median wishes, from 0% to 100%. For each possible wish, you will have to position a cursor to choose a rate to apply in this situation. There will therefore be 11 decisions, each with a heading like "If the median wish is ...%, please choose a rate".

Finally, you will be randomly grouped with another participant in this experiment. This participant will also make the same type of decisions as you for the same 9 people. This participant is not one of these 9 people. At the end of this part, the computer will look at the median wish of the 9 people. Then, it will apply the decision of the participant who chose the rate closest to this median wish, and award 33 SCUs to this participant. The other participant will receive 0 SCUs and his decision will not be applied.

Example: if the rate you have chosen is closer to the median wish than the rate chosen by the other participant, you will receive 33 SCUs, the other participant will receive 0 SCUs and the computer will apply the decision you have made. Each of the 9 people will therefore receive the final amount according
to your decision. Conversely, if the rate chosen by the other participant is closer to the median wish
than the rate you have chosen, you will receive 0 SCUs, the other participant will receive 33 SCUs and
the computer will apply the decision the other participant has made. Each of the 9 people will therefore
receive the final amount according to the other participant's decision.

Note: if the rate you choose and the rate chosen by the other participant are identical, the computer
will draw one of the two participants (you or the other participant) at random. It will then apply the
drawn participant's decision and award him or her 33 SCUs. The participant who is not drawn will
receive 0 SCUs and his decision will not be applied.
## A.4 Belief elicitation task

[Before the redistribution task] Before making your decisions, you will now be asked to assess the likelihood of a series of events.

[After the redistribution task] You will now be asked to assess the likelihood of a series of events.

You will receive 10 tokens. You will have to distribute all 10 tokens between the different possible events. Each token represents 1 chance in 10 (a probability of 10%). If you place a lot of tokens on an event, it means you think it is very likely that this event is true. If, on the other hand, you place few tokens on an event, it means you think it is unlikely that this event is true. Finally, if you place half your tokens on an event, it means that you think there's a 1-in-2 chance of that event being true.

Dividing up these tokens will not result in a reward. The aim is simply to indicate what you think. We do, however, ask you to be as precise as possible when placing your tokens.

[Before the redistribution task] In Part II, you will be deciding on a rate that will affect the amount obtained by a group of 9 people.

[After the redistribution task] In Part II, you decided on a rate affecting the amount obtained by a group of 9 people.

Each of the 9 people has been asked individually what rate they would like to see implemented. Each of the 9 people has therefore positioned a slider similar to yours to reflect this wish. Unlike your slider, the slider presented to them went from 10 to 10, so each person had the choice between 0%, 10%, 20%, etc. up to 100%. The computer then calculated the “median wish”, i.e., the rate such that half the people wanted a higher or equal rate and the other half wanted a lower or equal rate.

The events you are now going to evaluate as more or less probable concern this “median wish” and will be of the form: “According to you, the median wish is ...%”.

Example: if you place all your 10 tokens on the event “According to you, the median wish is 50%”, this means that you are certain that the median wish is 50%. On the other hand, if you place none of your 10 tokens on the event “According to you, the median wish is 50%”, this means that you are certain that the median wish is not 50%. Finally, if you place 5 tokens on the event “According to you, the median wish is 50%”, it means you think there’s as much chance of the median wish being 50% as there is of it not being 50%.

[Before the redistribution task - No Information treatment] During Part II, you will also be grouped with another (anonymous) participant who will make the same type of decisions as you. This participant will choose a rate from 0 to 100%.
<figure><img src="image_10.png" /><figcaption>Figure A.4.1: Example of belief elicitation task - Median citizen</figcaption></figure>

[Before the redistribution task - *Information treatment*] During Part II, you will also be grouped with another (anonymous) participant who will make the same type of decisions as you. This participant will choose several rates from 0% to 100%, one for each of the 11 possible median wishes.

[After the redistribution task - *No Information treatment*] In Part II, you were also grouped with another (anonymous) participant who made the same type of decision as you. This participant chose a rate from 0 to 100%.

[After the redistribution task - *Information treatment*] In Part II, you were also grouped with another (anonymous) participant who made the same type of decision as you. This participant chose several rates from 0% to 100%, one for each of the 11 possible median wishes.

The events whose probability you are now going to evaluate concern this participant's decisions. Each event is of the following form: "The other participant's rate is between ...% and ...%". For example, if you place all your 10 tokens on the event "The other participant's rate is between 46% and 54%", this means you think it is certain that the other participant will choose a rate between 46% and 54%. On the contrary, if you place none of your 10 tokens on the event "The other participant's rate is between 46% and 54%", this means you think it is certain that the other participant will not choose a rate between 46% and 54%. Finally, if you place 5 tokens on the event "The other participant's rate is between 46% and 54%", this means that you think there is as much chance that the other participant will choose a rate between 46% and 54% as there is that he will choose a rate lower than 45% or higher than 55%.

[No Information treatment] Now divide your 10 tokens between the various possible events.

[Before the redistribution task - *Information treatment*] You will distribute a total of 10 chips 11 times. The other participant will make 11 decisions, one for each possible median wish. You are therefore asked to assess the likelihood of the same events occurring in the other participant's decisions for each of the possible median wishes. So, for example, you will have to determine how many tokens you will place on the event "In your opinion, the other participant's rate is between 46% and 54%" if the median wish is 0%, if it is 10%, if it is 20%, and so on. Note: for each median wish, you will have 10 tokens to divide between the different events. Each time, you must allocate all 10 tokens.
[After the redistribution task - Information treatment] You will distribute a total of 10 chips 11 times. The other participant made 11 decisions, one for each possible median wish. You are therefore asked to assess the likelihood of the same events occurring in the other participant's decisions for each of the possible median wishes. So, for example, you will have to determine how many tokens you will place on the event "In your opinion, the other participant's rate is between 46% and 54%" if the median wish is 0%, if it is 10%, if it is 20%, and so on. Note: for each median wish, you will have 10 tokens to divide between the different events. Each time, you must allocate all 10 tokens.

Now divide your 10 tokens between the various possible events.

<figure><img src="image_11.png" /><figcaption>Figure A.4.2: Example of belief elicitation task - Competitor</figcaption></figure>

## A.5 Voting decision [only non-politicians]

To complete Part II, you will now take the position of one of the 9 people. You will be grouped with 8 other people from the French population taking part in this survey. The computer will assign each person an initial amount. Then, you will choose the rate you would like to see implemented. The other 8 people will do the same. The computer will then calculate the "median wish", i.e., the rate at which half the people (including you) would like to see a higher or equal rate, and the other half (including you) would like to see a lower or equal rate.

Finally, 2 other participants in this survey will be selected to decide for your group of 9 people. These 2 participants will not be informed of the median wish.

[No Competition treatment] The computer will select at random which of the two participants' decisions will be implemented.

[Competition treatment] The computer will apply the decision of the participant who chose the rate closest to the median wish.

Please note: these 2 participants are not part of the 9 people. Consequently, the decision you made earlier in Part II cannot count for your group of 9 (and therefore for you). On the other hand, it can count for another group of 9 people. The same applies to each of the participants in the survey: the decision taken by each of them does not apply to their group of 9 people, but to another group of 9 people.
To calculate your reward at the end of the experiment for this part II, the computer will conduct a random drawing. Either you will be one of the 2 participants deciding for a group of 9 people, or you will be part of a group of 9 people. In the first case, your previous decision can count toward your reward and that of the 9 people involved. In the second case, two other participants will decide for your group of 9, and your reward will be determined by their decisions.

Your initial amount is 37 SCUs. You are therefore person C in the table above.

Please choose the rate you would like to see applied (the amounts that will appear in the table correspond to what would happen if the rate applied were equal to your wish): *Slider between 0% and 100%, similar to Figure A.3.3.*

## A.6 Exit survey

Answer options are in italic, separated by a semicolon.

* You answered this survey *At your home*; *At work*; *On public transportation*; *In a café, library or other public space*; *Other*

* Did other people help you to answer this survey? *Yes*; *No*

* Did you answer on *Your laptop*; *A tablet*; *Other*

* Are you *A man*; *A woman*; *Other*

* What is your year of birth?

* Which département do you live in?

* What is your highest level of education? *CAP/BEP*; *High school degree*; *Higher education degree*; *No qualification*; *Other*

* **[If High school diploma is chosen in the previous question]** What High school degree was it? *General, Technological; Professional*. If higher education degree: Was it a: *1-year college or equivalent*; *2-year college or equivalent*; *3-year college or equivalent*; *1-year Master degree or equivalent*; *2-year master or equivalent*; *Ph.D or equivalent*; *Other*

* In which field did you mainly study? *Art*; *Biology/Geology*; *Chemistry*; *Law*; *Geography*; *History*; *Informatics*; *Engineering*; *Languages*; *Literature*; *Mathematics*; *Medicine*; *Physics*; *Economics*; *Political science*; *Sociology*; *Other*

* To which socio-professional category do you belong? *Farmers*; *Craftsmen, shopkeepers and company managers*; *Executives and higher intellectual professions*; *Intermediate professions*; *Employees*; *Manual workers*; *Retired*; *Other people not in employment*; *Do not wish to answer*

* In which gross annual income bracket do you fall? *Less than 10k*; *Between 10k and 20k*; *Between 20k and 30k*; *Between 30k and 40k*; *Between 40k and 50k*; *Between 50k and 60k*; *Between 60k and 70k*; *Between 70k and 80k*; *Between 80k and 90k*; *Between 90k and 100k*; *More than 100k*
* Without thinking only of the elections, please indicate which political party you feel closest to, or least distant from: Debout la France; Europe Écologie - Les Verts (EELV); La France Insoumise (LFI); La République En Marche (LREM); Le MoDem; Les Républicans (LR); Lutte Ouvrière (LO) ou Nouveau Parti Anticapitaliste (NPA); Nouvelle Donne (ND); Parti Communiste (PC); Parti Socialiste (PS); Rassemblement National (RN); Union des Démocrates et des Indépendants (UDI); Aucun parti; Other

* In politics, people talk about the left and the right. On a scale of 0 to 10, where would you rank yourself? 0-10; Does not wish answer

* And on the same 0 to 10 scale, how would you rank the following parties? La France Insoumise (LFI), Parti Socialiste (PS), La République En Marche (LREM), Les Républicains (LR), Rassemblement National (RN) 0-10; Does not wish answer

* Once all the data has been collected, 1 in 50 participants will be selected by the computer to receive the reward generated during the survey. If you are selected, please indicate whether you would like to keep the reward for yourself or donate it in full to charity: Keep the reward; Donate the reward to a charity

* [If Donate the reward to a charity is chosen in the previous question] Please select the association to which you would like to donate your gratification. This association will also receive an additional 50 cents as a thank you for your participation in this survey. Institut Pasteur; Restos du Coeur; Médecins sans Frontières

* [If Keep the reward is chosen in the previous question] Please choose the association that will receive 50 cents to thank you for your participation in this survey.

For politicians only:

* The population of your municipality is: Less than 1,000 inhabitants; Between 1,000 and 3,500 inhabitants; More than 3,500 inhabitants

* At the local level, this mandate is your: First; Second; Third; Fourth; Fifth; Sixth; Seventh or more; Not concerned

* You are: Mayor; Vice-Mayor; Town councilor

* In addition, for the period 2020-2026, are you: Intermunicipal councilor; Department's councilor; Region councilor; Member of Parliament (Deputy or Senator)

* Previously, have you been: Mayor; Vice-Mayor; Town councilor; Intermunicipal councilor; Department's councilor; Region's councilor; Member of Parliament (Deputy or Senator)

* In total how many mandates did you have as Mayor, Deputy Mayor etc. (depending on the answers to previous questions)
## A.7 Details on politicians recruitment

First, we sent official invitations by mail to French associations of local politicians. Our primary target was the Association des Maires de France (AMF), which is the most prominent French national association of mayors. The AMF is subdivided into several sections, also referred to as department associations.<fnref n="39" /> To gain the consent of an AMF section for sharing a link to our experiment, we employed a two-stage procedure. Initially, we contacted each section via phone to introduce ourselves and obtain the contact email of its president or president's secretary. Subsequently, we reached out to the section's president through email, outlining the project's purpose without revealing the exact experiment details, and requested consent.

Following the follow-up approach recommended by Vis and Stolwijk (2021) for elite studies, we regularly repeated either of these steps for two months to minimize attrition. Of the 89 AMF sections (out of 93) that responded positively to the initial call, 51 replied to our email, and 32 ultimately granted consent. Thereafter, the experiment was disseminated in various forms depending on the section, such as through mailing lists, newsletters, etc. Some sections even directly provided us with email addresses of city halls or politicians for our use. To ensure a more comprehensive representation, we also contacted 9 other national associations, of which 4 eventually granted permission. The list of the national associations we contacted is detailed in Table A.7.1 while the map A.7.1 presents the AMF department sections that participated in the invitation-forward.

Second, we sent mails with the invitations directly to French town halls. The mails of all municipalities were collected by web-scrapping using Python from a public service directory (https://lannuaire.service-public.fr/navigation/mairie).

<table><caption>Table A.7.1: List of national associations of local politicians</caption><thead><tr><td>Association</td><td>Accepted</td></tr></thead><tbody><tr><td>Association des Maires Ruraux de France</td><td>Yes</td></tr><tr><td>Villes de France</td><td>No</td></tr><tr><td>Fédération Nationale des Communes Forestières</td><td>No</td></tr><tr><td>Association Nationale des Elus du Littoral</td><td>Yes</td></tr><tr><td>Association Nationale des Elus des Territoires Touristiques</td><td>Yes</td></tr><tr><td>Association Nationale des Elus de la Montagne</td><td>No</td></tr><tr><td>Association des Petites Villes de France</td><td>Yes</td></tr><tr><td>Intercommunalités de France</td><td>No</td></tr><tr><td>France Urbaine</td><td>No</td></tr></tbody></table>

<footnote n="39">³⁹The list of all department associations can be found on https://www.amf.asso.fr/m/page/ad.php.</footnote>
<figure><img src="image_12.png" /><figcaption>Figure A.7.1: Responses from AMF department sections</figcaption></figure>
# B Additional figures and tables

## B.1 Geographical areas and ideological classification of political parties

<table><caption>Table B.1.1: Geographical areas and French regions</caption><thead><tr><td>Geographical Area</td><td>Regions</td></tr></thead><tbody><tr><td>North</td><td>Hauts-de-France, Île-de-France, Normandie</td></tr><tr><td>East</td><td>Grand Est, Bourgogne-Franche-Comté</td></tr><tr><td>West</td><td>Bretagne, Centre-Val de Loire, Nouvelle-Aquitaine, Pays de la Loire</td></tr><tr><td>South</td><td>Provence-Alpes-Côte d'Azur, Occitanie, Auvergne-Rhône-Alpes</td></tr></tbody></table>

<table><caption>Table B.1.2: Political parties on the left, center and right</caption><thead><tr><td>Ideology</td><td>Political party</td></tr></thead><tbody><tr><td>Left</td><td>Parti Communiste, La France Insoumise, Lutte Ouvrière/Nouveau Parti Anticapitaliste, Parti Socialiste, Europe-Ecologie les Verts, Nouvelle Donne, Générations Parti Radical de Gauche/Mouvement Radical Social-Libéral, Place Publique</td></tr><tr><td>Center</td><td>La République en Marche, Mouvement démocrate</td></tr><tr><td>Right</td><td>Les Républicains, Union des démocrates et indépendants, Rassemblement National, Action Française, Debout la France</td></tr></tbody></table>
## B.2 Treatment balance

<figure><img src="image_13.png" /><figcaption>Figure B.2.1: Love plot on covariates balance</figcaption></figure>

**Notes:** NI refers to *No Information* treatment, I to *Information* treatment, C to *Competition* treatment, and NC to *No Competition* treatment. Each dot corresponds to the (absolute) Standardized Difference in Mean (SDM) between two conditions for a given socio-demographic variable. We compute SDM for politicians (left panel) and non-politicians (right panel) separately. For continuous variables (age, risk aversion index and ideology), the SDM between conditions A and B is given by $(\bar{X}_A - \bar{X}_B)/(\sqrt{(\sigma_A + \sigma_B)/2})$, with $\bar{X}_G$ and $\sigma_G$ the variable's average and standard deviation in condition $G = \{A, B\}$ (respectively). For dummy variables (gender and higher education), the SDM is given by $(P_A - P_B)/(\sqrt{(P_A(1-P_A) + P_B(1-P_B))/2})$, with $P_G$ the variable's proportion of ones in condition $G = \{A, B\}$. For polytomous variables (income, occupation, geographical area and party preferences), a multivariate Mahalanobis distance method is used to generalize the SDM for binary variables (for details on that method, see e.g. Yang and Dalton, 2012). The vertical purple dotted line correspond to the 0.25 threshold that is often used in the literature (e.g. advocated by Imbens and Rubin, 2015) to indicate significant imbalances.
<figure><img src="image_14.png" /><figcaption>Figure B.2.2: Love plot on balance in the propensity scores</figcaption></figure>

**Notes:** NI refers to *No Information* treatment, I to *Information* treatment, C to *Competition* treatment, and NC to *No Competition* treatment. Each dot corresponds to the (absolute) Standardized Difference in Mean (SDM) between two conditions for the linearized propensity score for a given treatment ($lp = \ln(\hat{p}/(1 - \hat{p}))$, with $\hat{p}$ the estimated propensity score). The SDM between conditions A and B is given by ($\overline{lp_A} - \overline{lp_B})/\sqrt{(\sigma_A + \sigma_B)/2$), with $\overline{lp_G}$ and $\sigma_G$ the average and standard deviation of the linearized propensity score in condition $G = \{A, B\}$ (respectively). We compute SDM for politicians (left panel) and non-politicians (right panel) separately. The vertical purple dotted line correspond to the 0.25 threshold that is often used in the literature to indicate significant imbalances, although for a composite measure such as the propensity score, 0.5 is often considered acceptable (see e.g. Imbens and Rubin, 2015). To estimate the propensity scores for each condition (NI-NC, NI-C, I-NC and I-C), we adapted the stepwise procedure from Imbens and Rubin (2015) based on estimating a multimonial logit model for treatment assignment. We start with income to initialize the model because it likely affects both the outcome variable and treatment assignment (e.g. due to the opportunity cost of answering the survey). Then we iteratively incorporate the variable among the others that improves the most the fit of the model (using Wald tests). We repeat this step until all variables are either included or the test does not pass improve the model enough ($p$-value at most 0.3173, following Imbens and Rubin, 2015). We complement the model with testing improvements of the fit for quadratic and interaction terms from the selected variables (with $p$-value at most 0.1). Finally, we predict the propensity scores from the finalized model of treatment assignment.
## B.3 Attrition analysis

<figure><img src="image_15.png" /><figcaption>Figure B.3.1: Cumulative distribution - Last page accessed</figcaption></figure>

**Notes:** Page 4 and 5 correspond to the tax choices or the belief elicitation task, depending on the randomization of the tasks order.

## B.4 Distribution of tax choices

<figure><img src="image_16.png" /><figcaption>Figure B.4.1: Tax choice distribution - *No Information* treatment</figcaption></figure>

**Note:** The Kolmogorov-Smirnov test for differences in distributions between politicians' and non-politicians' tax choices reports *p* < 0.01 in the *No Competition* treatment and *p* = 0.21 in the *Competition* treatment. The same test for differences in distributions between the *No Competition* and *Competition* treatments reports *p* = 0.31 for politicians and *p* = 0.95 for non-politicians.
## B.5 Additional figures on beliefs

<figure><img src="image_17.png" /><figcaption>Figure B.5.1: Median of subjective probability distribution about the median rate</figcaption></figure>

**Note:** This graph reports the distribution of the median of participants' subjective probability distribution about the median rate. The Kolmogorov-Smirnov test for differences in distributions of politicians' and non-politicians' beliefs reports *p* < 0.01 in figure (a), *p* = 0.077 in figure (b), *p* = 0.99 in figure (c), and *p* = 0.69. In all cases, the difference in distributions between the *No Competition* and the *Competition* and between the *No Information* and the *Information* treatment is not significant (*p* > 0.1).

<figure><img src="image_18.png" /><figcaption>Figure B.5.2: Mode of subjective probability distribution about the median rate</figcaption></figure>

**Note:** This graph reports the distribution of the mode of participants' subjective probability distribution about the median rate. The Kolmogorov-Smirnov test for differences in distributions between politicians' and non-politicians' beliefs reports *p* = 0.045 in figure (a), *p* = 0.76 in figure (b), 0.99 in figure (c), *p* = 0.57 in figure (d). In all cases, the difference in distributions between the *No Competition* and the *Competition* and between the *No Information* and the *Information* treatment is not significant (*p* > 0.1).
<figure><img src="image_19.png" /><figcaption>Figure B.5.3: Correlation between beliefs (median rate v. competitor's chosen rate)</figcaption></figure>

**Note:** Scatterplot including a regression line where the dependent variable is *Belief about competitor* and the independent variable is *Belief about Median*. In the *Information* treatment, we gathered participants' beliefs about the competitor's tax choice 11 times. Here, we calculate the mean of these 11 values for participants in the *Information* treatment to study the correlation with the beliefs about the median rate. *ρ* represents the Pearson correlation coefficient in the *Competition* and *Dictator* treatments separately.
## B.6 OLS regressions on beliefs

<table><caption>Table B.6.1: OLS regression - Beliefs</caption><thead><tr><th></th><th>Belief about Median<br>(1)</th><th>Sd Belief about Median<br>(2)</th><th>Belief about Median<br>(3)</th><th>Sd Belief about Median<br>(4)</th></tr></thead><tbody><tr><td>Politician</td><td>-2.779*<br>(1.488)</td><td>-2.492***<br>(0.688)</td><td>-3.140**<br>(1.462)</td><td>-2.664***<br>(0.680)</td></tr><tr><td><em>Ideology - Base: Left</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Center</td><td></td><td></td><td>-0.883<br>(1.952)</td><td>0.683<br>(0.919)</td></tr><tr><td>Right</td><td></td><td></td><td>-3.273*<br>(1.786)</td><td>-1.169<br>(0.873)</td></tr><tr><td>None</td><td></td><td></td><td>1.529<br>(1.560)</td><td>0.316<br>(0.794)</td></tr><tr><td>Ideology (Likert scale)</td><td>-0.0537<br>(0.248)</td><td>0.0743<br>(0.117)</td><td></td><td></td></tr><tr><td><em>Income - Base: &lt;20k</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Between 20k and 30k</td><td>-4.616**<br>(1.886)</td><td>-0.626<br>(0.916)</td><td>-4.564**<br>(1.875)</td><td>-0.680<br>(0.909)</td></tr><tr><td>Between 30k and 40k</td><td>-4.968**<br>(2.124)</td><td>0.219<br>(1.010)</td><td>-4.933**<br>(2.110)</td><td>0.184<br>(1.001)</td></tr><tr><td>More than 40k</td><td>-5.952***<br>(2.069)</td><td>-0.0641<br>(0.980)</td><td>-5.519***<br>(2.078)</td><td>-0.130<br>(0.976)</td></tr><tr><td>Age</td><td>0.00519<br>(0.0610)</td><td>-0.0706**<br>(0.0289)</td><td>0.0130<br>(0.0605)</td><td>-0.0690**<br>(0.0289)</td></tr><tr><td>Female</td><td>-1.263<br>(1.348)</td><td>-0.718<br>(0.647)</td><td>-1.348<br>(1.345)</td><td>-0.733<br>(0.650)</td></tr><tr><td>Risk aversion index</td><td>-0.0350<br>(0.102)</td><td>0.0134<br>(0.0492)</td><td>-0.0526<br>(0.101)</td><td>0.0116<br>(0.0488)</td></tr><tr><td><em>Education - Base: No higher education</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Higher education</td><td>-1.116<br>(1.352)</td><td>-0.862<br>(0.661)</td><td>-1.089<br>(1.345)</td><td>-0.974<br>(0.651)</td></tr><tr><td>Other controls</td><td>All</td><td>All</td><td>All</td><td>All</td></tr><tr><td>R<sup>2</sup></td><td>0.0339</td><td>0.0431</td><td>0.0406</td><td>0.0462</td></tr><tr><td>Observations</td><td>1415</td><td>1415</td><td>1415</td><td>1415</td></tr></tbody></table>

**Notes:** We do not report the coefficient of the treatment variables, which are not significant. Other controls include occupation, geographical area or residence, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the questionnaire. Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%.
## B.7 Distributions of coefficients from analysis on deviations (Politicians)

<figure><img src="image_20.png" /><figcaption>Figure B.7.1</figcaption></figure>

**Note:** I is an indicator for the Information treatment. C is an indicator for the Competition treatment. Each graph plots the point estimates and confidence intervals for the corresponding variable from 1001 similar regressions based on 1001 random draws of the median rate for each participant (see Section 3). The estimates are sorted by ascending order for each variable. The specification of the regression model is $Dev_i = \beta_0 + \beta_1 I_i + \beta_2 C_i + \beta_3 I_i \times C_i + \epsilon_i$. We plot here the $\beta$ and their confidence intervals (95% confidence in dark grey and 90% in light grey) for each regression. All the 1001 regression models appear on the x-axis, and they are sorted according to the value of the corresponding point estimate. E.g: the top-left graph corresponds to the effect of the Information treatment and the models are sorted according to the estimated coefficient for the I indicator. The dotted orange vertical line corresponds to the median coefficient (which is reported in Table 3). For each regression, we apply post-stratification weights to account for differences in observables between our politicians sample and the sample of all French politicians.

<figure><img src="image_21.png" /><figcaption>Figure B.7.2</figcaption></figure>

**Note:** I is an indicator for the Information treatment. C is an indicator for the Competition treatment. Each graph plots the point estimates and confidence intervals for the corresponding variable from 1001 similar regressions based on 1001 random draws of the median rate for each participant (see Section 3). The estimates are sorted by ascending order for each variable. The specification of the regression model is $Dev_i = \beta_0 + \beta_1 I_i + \beta_2 C_i + \beta_3 I_i \times C_i + \beta_4 DevBelief_i + \beta_5 I_i \times DevBelief_i + \beta_6 C_i \times DevBelief_i + \epsilon_i$. We plot here the $\beta$ and their confidence intervals (95% confidence in dark grey and 90% in light grey) for each regression. All the 1001 regression models appear on the x-axis, and they are sorted according to the value of the corresponding point estimate. E.g: the top-left graph corresponds to the effect of the Information treatment and the models are sorted according to the estimated coefficient for the I indicator. The dotted orange vertical line corresponds to the median coefficient (which is reported in Table 3). For each regression, we apply post-stratification weights to account for differences in observables between our politicians sample and the sample of all French politicians.
<figure><img src="image_22.png" /><figcaption>Figure B.7.3</figcaption></figure>

**Note:** I is an indicator for the *Information* treatment. C is an indicator for the *Competition* treatment. Each graph plots the point estimates and confidence intervals for the corresponding variable from 1001 similar regressions based on 1001 random draws of the median rate for each participant (see Section 3). The estimates are sorted by ascending order for each variable. The specification of the regression model is $Dev_i = \beta_0 + \beta_1 I_i + \beta_2 C_i + \beta_3 I_i \times C_i + \beta_4 DevBelief_i + \beta_5 I_i \times DevBelief_i + \beta_6 C_i \times DevBelief_i + \beta_7 I_i \times C_i \times DevBelief_i + \epsilon_i$. We plot here the $\beta$ and their confidence intervals (95% confidence in dark grey and 90% in light grey) for each regression. All the 1001 regression models appear on the x-axis, and they are sorted according to the value of the corresponding point estimate. E.g: the top-left graph corresponds to the effect of the *Information* treatment and the models are sorted according to the estimated coefficient for the I indicator. The dotted orange vertical line corresponds to the median coefficient (which is reported in Table 3). For each regression, we apply post-stratification weights to account for differences in observables between our politicians sample and the sample of all French politicians.
<figure><img src="image_23.png" /><figcaption>Figure B.7.4</figcaption></figure>

**Note:** I is an indicator for the *Information* treatment. C is an indicator for the *Competition* treatment. Each graph plots the point estimates and confidence intervals for the corresponding variable from 1001 similar regressions based on 1001 random draws of the median rate for each participant (see Section 3). The estimates are sorted by ascending order for each variable. The specification of the regression model is $Dev_i = \beta_0 + \beta_1 I_i + \beta_2 C_i + \beta_3 I_i \times C_i + \beta_4 DevBelief_i + \beta_5 I_i \times DevBelief_i + \beta_6 C_i \times DevBelief_i + \beta_7 I_i \times C_i \times DevBelief_i + \gamma X_i + \epsilon_i$. We plot here the $\beta$ and their confidence intervals (95% confidence in dark grey and 90% in light grey) for each regression. All the 1001 regression models appear on the x-axis, and they are sorted according to the value of the corresponding point estimate. E.g: the top-left graph corresponds to the effect of the *Information* treatment and the models are sorted according to the estimated coefficient for the I indicator. The dotted orange vertical line corresponds to the median coefficient (which is reported in Table 3). For each regression, we apply post-stratification weights to account for differences in observables between our politicians sample and the sample of all French politicians. Controls include: age, gender, income, education level, occupation, geographical area of residence, party preferences, a risk aversion index, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the survey.
## B.8 Original regression models from analysis on deviations

<table><caption>Table B.8.1: Original models for Table 3 (column 1)</caption><thead><tr><th></th><th colspan="3">Dep. var.: |Tax rate - Median rate|</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>I</td><td>-7.43**<br>(2.95)</td><td>-11.32***<br>(3.21)</td><td>-7.90**<br>(3.21)</td></tr><tr><td>C</td><td>-2.65<br>(2.34)</td><td>0.21<br>(2.70)</td><td>1.32<br>(2.67)</td></tr><tr><td>I × C</td><td>1.47<br>(4.27)</td><td>-1.12<br>(4.50)</td><td>-2.34<br>(4.41)</td></tr><tr><td>Mean deviation (NI-NC)</td><td>34.78</td><td>37.17</td><td>32.67</td></tr><tr><td>Mean deviation (NI-C)</td><td>31.93</td><td>35.12</td><td>32.04</td></tr><tr><td>Mean deviation (I-NC)</td><td>26.13</td><td>23.27</td><td>23.62</td></tr><tr><td>Mean deviation (I-C)</td><td>24.53</td><td>24.77</td><td>23.96</td></tr><tr><td>R<sup>2</sup></td><td>0.02</td><td>0.05</td><td>0.03</td></tr><tr><td>Observations</td><td>760</td><td>760</td><td>760</td></tr><tr><td>Controls</td><td>None</td><td>None</td><td>None</td></tr></tbody></table>

**Notes:** I is an indicator for the *Information* treatment and C for the *Competition* treatment. Each coefficient on the diagonal of this table is the median coefficient for the corresponding variable as obtained from 1001 regression coefficients, each of which being run for 1 of 1001 random draws of the median rate for each participant (see section 3). That is, e.g., column 1 corresponds to the regression model where the coefficient for the first variable is the median coefficient over all the 1001 coefficients for this variable. All off-diagonal coefficients are the estimated coefficients from the model where the in-diagonal coefficient is estimated. Column 1 in Table 3 is constructed from the diagonal of this table. Similarly, the "Mean deviations" at the bottom of the table are computed by averaging the deviation in each condition for the random draw which correspond to the regression model that is exposed in the corresponding column. E.g.: for the random draw from which we estimated the model in column 1, we computed the mean deviation in each condition and such deviations are displayed at the bottom of column 1. For each regression, we apply post-stratification weights using the iterative proportional fitting (or raking) method (Kolenikov, 2014) to account for differences in gender, age, and geographical area of residence between our politicians sample and the sample of all French politicians. Controls include: age, gender, income, education level, occupation, geographical area of residence, party preferences, a risk aversion index, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the survey. Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%.
<table><caption>Table B.8.2: Original models for Table 3 (column 2)</caption><thead><tr><th></th><th colspan="6">Dep. var.: |Tax rate - Median rate|</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>I</td><td>1.51<br>(3.45)</td><td>0.79<br>(4.07)</td><td>1.54<br>(3.95)</td><td>5.15<br>(3.64)</td><td>5.36<br>(4.44)</td><td>-0.30<br>(3.50)</td></tr><tr><td>C</td><td>-0.97<br>(3.25)</td><td>-0.93<br>(3.24)</td><td>-0.23<br>(2.86)</td><td>1.30<br>(3.05)</td><td>1.83<br>(3.15)</td><td>-3.39<br>(3.20)</td></tr><tr><td>I × C</td><td>-3.39<br>(3.91)</td><td>0.28<br>(4.00)</td><td>-2.02<br>(3.38)</td><td>-5.20<br>(3.65)</td><td>-5.65<br>(4.23)</td><td>-1.43<br>(3.55)</td></tr><tr><td>|Belief about Median - Median rate|</td><td>0.68***<br>(0.06)</td><td>0.69***<br>(0.06)</td><td>0.71***<br>(0.06)</td><td>0.74***<br>(0.07)</td><td>0.87***<br>(0.06)</td><td>0.71***<br>(0.07)</td></tr><tr><td>I × |Belief about Median - Median rate|</td><td>-0.28***<br>(0.11)</td><td>-0.23*<br>(0.12)</td><td>-0.19*<br>(0.10)</td><td>-0.38***<br>(0.11)</td><td>-0.28**<br>(0.12)</td><td>-0.25**<br>(0.10)</td></tr><tr><td>C × |Belief about Median - Median rate|</td><td>0.04<br>(0.09)</td><td>0.03<br>(0.11)</td><td>0.02<br>(0.08)</td><td>0.00<br>(0.09)</td><td>-0.10<br>(0.10)</td><td>0.02<br>(0.08)</td></tr><tr><td>Mean deviation (NI-NC)</td><td>35.28</td><td>32.85</td><td>33.00</td><td>34.16</td><td>33.94</td><td>35.02</td></tr><tr><td>Mean deviation (NI-C)</td><td>31.97</td><td>30.43</td><td>30.18</td><td>32.85</td><td>33.15</td><td>31.35</td></tr><tr><td>Mean deviation (I-NC)</td><td>26.21</td><td>26.09</td><td>23.36</td><td>28.14</td><td>29.21</td><td>26.36</td></tr><tr><td>Mean deviation (I-C)</td><td>26.19</td><td>26.26</td><td>25.62</td><td>24.39</td><td>24.38</td><td>22.56</td></tr><tr><td>R<sup>2</sup></td><td>0.35</td><td>0.33</td><td>0.38</td><td>0.34</td><td>0.40</td><td>0.39</td></tr><tr><td>Observations</td><td>760</td><td>760</td><td>760</td><td>760</td><td>760</td><td>760</td></tr><tr><td>Controls</td><td>None</td><td>None</td><td>None</td><td>None</td><td>None</td><td>None</td></tr></tbody></table>

**Notes:** I is an indicator for the *Information* treatment and C for the *Competition* treatment. Each coefficient on the diagonal of this table is the median coefficient for the corresponding variable as obtained from 1001 regression coefficients, each of which being run for 1 of 1001 random draws of the median rate for each participant (see section 3). That is, e.g., column 1 corresponds to the regression model where the coefficient for the first variable is the median coefficient over all the 1001 coefficients for this variable. All off-diagonal coefficients are the estimated coefficients from the model where the in-diagonal coefficient is estimated. Column 2 in Table 3 is constructed from the diagonal of this table. Similarly, the "Mean deviations" at the bottom of the table are computed by averaging the deviation in each condition for the random draw which correspond to the regression model that is exposed in the corresponding column. E.g.: for the random draw from which we estimated the model in column 1, we computed the mean deviation in each condition and such deviations are displayed at the bottom of column 1. For each regression, we apply post-stratification weights using the iterative proportional fitting (or raking) method (Kolenikov, 2014) to account for differences in gender, age, and geographical area of residence between our politicians sample and the sample of all French politicians. Controls include: age, gender, income, education level, occupation, geographical area of residence, party preferences, a risk aversion index, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the survey. Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%.
<table><caption>Table B.8.3: Original models for Table 3 (column 3)</caption><thead><tr><th></th><th colspan="7">Dep. var.: |Tax rate - Median rate|</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>I</td><td>0.53<br>(4.35)</td><td>-6.18<br>(4.88)</td><td>0.85<br>(4.30)</td><td>-0.75<br>(5.58)</td><td>-1.06<br>(3.54)</td><td>5.05<br>(3.82)</td><td>5.00<br>(6.47)</td></tr><tr><td>C</td><td>-2.94<br>(2.98)</td><td>-1.66<br>(3.46)</td><td>-0.73<br>(2.98)</td><td>0.27<br>(3.91)</td><td>-3.12<br>(2.60)</td><td>-1.58<br>(2.78)</td><td>-1.57<br>(3.20)</td></tr><tr><td>I × C</td><td>-2.62<br>(5.68)</td><td>3.64<br>(6.09)</td><td>-0.08<br>(5.80)</td><td>0.13<br>(7.36)</td><td>2.85<br>(5.28)</td><td>-12.33**<br>(5.13)</td><td>-1.16<br>(7.68)</td></tr><tr><td>|Belief about Median - Median rate|</td><td>0.67***<br>(0.06)</td><td>0.63***<br>(0.07)</td><td>0.81***<br>(0.06)</td><td>0.73***<br>(0.08)</td><td>0.70***<br>(0.06)</td><td>0.75***<br>(0.05)</td><td>0.77***<br>(0.07)</td></tr><tr><td>I × |Belief about Median - Median rate|</td><td>-0.30<br>(0.18)</td><td>0.09<br>(0.16)</td><td>-0.19<br>(0.14)</td><td>-0.23<br>(0.18)</td><td>-0.25**<br>(0.12)</td><td>-0.51***<br>(0.13)</td><td>-0.34**<br>(0.17)</td></tr><tr><td>C × |Belief about Median - Median rate|</td><td>0.12<br>(0.08)</td><td>0.10<br>(0.10)</td><td>-0.04<br>(0.08)</td><td>0.05<br>(0.11)</td><td>0.09<br>(0.08)</td><td>0.05<br>(0.08)</td><td>0.02<br>(0.10)</td></tr><tr><td>I × C × |Belief about Median - Median rate|</td><td>0.02<br>(0.22)</td><td>-0.35<br>(0.21)</td><td>-0.01<br>(0.20)</td><td>-0.10<br>(0.24)</td><td>-0.11<br>(0.19)</td><td>0.35*<br>(0.18)</td><td>-0.06<br>(0.23)</td></tr><tr><td>Mean deviation (NI-NC)</td><td>31.89</td><td>32.22</td><td>35.94</td><td>33.76</td><td>31.62</td><td>33.28</td><td>33.92</td></tr><tr><td>Mean deviation (NI-C)</td><td>34.24</td><td>33.06</td><td>30.90</td><td>36.52</td><td>32.35</td><td>33.50</td><td>33.80</td></tr><tr><td>Mean deviation (I-NC)</td><td>22.38</td><td>30.98</td><td>26.01</td><td>26.75</td><td>25.34</td><td>24.04</td><td>30.39</td></tr><tr><td>Mean deviation (I-C)</td><td>23.24</td><td>22.48</td><td>28.00</td><td>24.79</td><td>24.51</td><td>24.12</td><td>24.28</td></tr><tr><td>R<sup>2</sup></td><td>0.39</td><td>0.38</td><td>0.40</td><td>0.36</td><td>0.38</td><td>0.43</td><td>0.36</td></tr><tr><td>Observations</td><td>760</td><td>760</td><td>760</td><td>760</td><td>760</td><td>760</td><td>760</td></tr><tr><td>Controls</td><td>None</td><td>None</td><td>None</td><td>None</td><td>None</td><td>None</td><td>None</td></tr></tbody></table>

**Notes:** I is an indicator for the *Information* treatment and C for the *Competition* treatment. Each coefficient on the diagonal of this table is the median coefficient for the corresponding variable as obtained from 1001 regression coefficients, each of which being run for 1 of 1001 random draws of the median rate for each participant (see section 3). That is, e.g., column 1 corresponds to the regression model where the coefficient for the first variable is the median coefficient over all the 1001 coefficients for this variable. All off-diagonal coefficients are the estimated coefficients from the model where the in-diagonal coefficient is estimated. Column 3 in Table 3 is constructed from the diagonal of this table. Similarly, the "Mean deviations" at the bottom of the table are computed by averaging the deviation in each condition for the random draw which correspond to the regression model that is exposed in the corresponding column. E.g: for the random draw from which we estimated the model in column 1, we computed the mean deviation in each condition and such deviations are displayed at the bottom of column 1. For each regression, we apply post-stratification weights using the iterative proportional fitting (or raking) method (Kolenikov, 2014) to account for differences in gender, age, and geographical area of residence between our politicians sample and the sample of all French politicians. Controls include: age, gender, income, education level, occupation, geographical area of residence, party preferences, a risk aversion index, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the survey. Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%.
<table><caption>Table B.8.4: Original models for Table 3 (column 4)</caption><thead><tr><th rowspan="2"></th><th colspan="7">Dep. var.: |Tax rate - Median rate|</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>I</td><td>1.29<br>(4.94)</td><td>-3.46<br>(4.36)</td><td>1.79<br>(4.54)</td><td>-2.52<br>(4.41)</td><td>-1.48<br>(4.13)</td><td>4.95<br>(6.05)</td><td>3.29<br>(5.09)</td></tr><tr><td>C</td><td>0.16<br>(3.57)</td><td>-1.21<br>(3.53)</td><td>-3.42<br>(3.28)</td><td>-3.26<br>(3.12)</td><td>-1.81<br>(2.83)</td><td>-1.22<br>(3.14)</td><td>3.56<br>(3.15)</td></tr><tr><td>I × C</td><td>-0.33<br>(6.71)</td><td>-1.69<br>(5.99)</td><td>-1.18<br>(5.65)</td><td>3.37<br>(6.28)</td><td>-3.04<br>(5.56)</td><td>-4.25<br>(7.47)</td><td>-4.69<br>(6.88)</td></tr><tr><td>|Belief about Median - Median rate|</td><td>0.81***<br>(0.07)</td><td>0.71***<br>(0.06)</td><td>0.75***<br>(0.07)</td><td>0.73***<br>(0.07)</td><td>0.72***<br>(0.07)</td><td>0.69***<br>(0.07)</td><td>0.74***<br>(0.07)</td></tr><tr><td>I × |Belief about Median - Median rate|</td><td>-0.28<br>(0.18)</td><td>-0.02<br>(0.12)</td><td>-0.34**<br>(0.16)</td><td>-0.06<br>(0.14)</td><td>-0.24**<br>(0.11)</td><td>-0.29*<br>(0.16)</td><td>-0.11<br>(0.16)</td></tr><tr><td>C × |Belief about Median - Median rate|</td><td>0.05<br>(0.09)</td><td>0.02<br>(0.10)</td><td>0.06<br>(0.10)</td><td>0.10<br>(0.09)</td><td>0.10<br>(0.08)</td><td>0.05<br>(0.09)</td><td>-0.09<br>(0.12)</td></tr><tr><td>I × C × |Belief about Median - Median rate|</td><td>-0.08<br>(0.22)</td><td>-0.13<br>(0.18)</td><td>0.05<br>(0.20)</td><td>-0.26<br>(0.20)</td><td>-0.04<br>(0.18)</td><td>-0.02<br>(0.22)</td><td>-0.06<br>(0.24)</td></tr><tr><td>Mean deviation (NI-NC)</td><td>35.08</td><td>34.39</td><td>34.71</td><td>33.45</td><td>33.79</td><td>32.53</td><td>32.26</td></tr><tr><td>Mean deviation (NI-C)</td><td>34.82</td><td>33.23</td><td>31.87</td><td>33.21</td><td>35.80</td><td>32.55</td><td>30.19</td></tr><tr><td>Mean deviation (I-NC)</td><td>26.19</td><td>28.70</td><td>25.59</td><td>26.36</td><td>26.50</td><td>28.26</td><td>27.31</td></tr><tr><td>Mean deviation (I-C)</td><td>25.93</td><td>21.97</td><td>25.43</td><td>25.21</td><td>24.38</td><td>26.75</td><td>25.33</td></tr><tr><td>R<sup>2</sup></td><td>0.44</td><td>0.43</td><td>0.43</td><td>0.44</td><td>0.49</td><td>0.38</td><td>0.37</td></tr><tr><td>Observations</td><td>760</td><td>760</td><td>760</td><td>760</td><td>760</td><td>760</td><td>760</td></tr><tr><td>Controls</td><td>All</td><td>All</td><td>All</td><td>All</td><td>All</td><td>All</td><td>All</td></tr></tbody></table>

**Notes:** I is an indicator for the *Information* treatment and C for the *Competition* treatment. Each coefficient on the diagonal of this table is the median coefficient for the corresponding variable as obtained from 1001 regression coefficients, each of which being run for 1 of 1001 random draws of the median rate for each participant (see section 3). That is, e.g., column 1 corresponds to the regression model where the coefficient for the first variable is the median coefficient over all the 1001 coefficients for this variable. All off-diagonal coefficients are the estimated coefficients from the model where the in-diagonal coefficient is estimated. Column 4 in Table 3 is constructed from the diagonal of this table. Similarly, the "Mean deviations" at the bottom of the table are computed by averaging the deviation in each condition for the random draw which correspond to the regression model that is exposed in the corresponding column. E.g: for the random draw from which we estimated the model in column 1, we computed the mean deviation in each condition and such deviations are displayed at the bottom of column 1. For each regression, we apply post-stratification weights using the iterative proportional fitting (or raking) method (Kolenikov, 2014) to account for differences in gender, age, and geographical area of residence between our politicians sample and the sample of all French politicians. Controls include: age, gender, income, education level, occupation, geographical area of residence, party preferences, a risk aversion index, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the survey. Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%.
## B.9 Deviation from the median rate - Politicians v. non-politicians

<table><caption>Table B.9.1: Summary table for 1001 OLS regressions - Distance to median rate (Politicians and Non-politicians)</caption><thead><tr><th></th><th colspan="8">Dep. var.: |Tax rate - Median rate|</th></tr></thead><tbody><tr><td>Politician</td><td>0.87<br>(1.38)</td><td>1.44<br>(2.24)</td><td>0.59<br>(2.47)</td><td>-3.53<br>(2.73)</td><td>-2.63<br>(3.48)</td><td>-2.66<br>(3.41)</td><td>-2.62<br>(3.40)</td><td></td></tr><tr><td>I</td><td>-8.78***<br>(2.22)</td><td>-8.91***<br>(2.56)</td><td>-10.07***<br>(2.97)</td><td>-9.92***<br>(2.51)</td><td>-2.85<br>(3.71)</td><td>-4.08<br>(4.98)</td><td>-4.01<br>(4.15)</td><td></td></tr><tr><td>C</td><td>-0.25<br>(1.94)</td><td>0.42<br>(2.36)</td><td>-0.48<br>(2.53)</td><td>-0.40<br>(2.19)</td><td>0.67<br>(3.30)</td><td>-0.29<br>(4.66)</td><td>-0.14<br>(3.31)</td><td></td></tr><tr><td>I × C</td><td>0.21<br>(2.93)</td><td>0.11<br>(2.97)</td><td>2.33<br>(3.70)</td><td>1.58<br>(3.38)</td><td>1.70<br>(3.11)</td><td>4.13<br>(5.85)</td><td>4.08<br>(5.58)</td><td></td></tr><tr><td>Politician × I</td><td></td><td>0.32<br>(2.84)</td><td>2.66<br>(4.35)</td><td>2.83<br>(3.87)</td><td>4.32<br>(5.26)</td><td>4.31<br>(5.22)</td><td>4.43<br>(5.30)</td><td></td></tr><tr><td>Politician × C</td><td></td><td></td><td>-1.43<br>(2.94)</td><td>0.38<br>(3.57)</td><td>0.08<br>(2.88)</td><td>-1.71<br>(4.48)</td><td>-1.49<br>(4.68)</td><td>-1.34<br>(5.17)</td></tr><tr><td>Politician × I × C</td><td></td><td></td><td></td><td>-4.73<br>(5.96)</td><td>-3.64<br>(5.21)</td><td>-3.73<br>(5.06)</td><td>-3.62<br>(8.46)</td><td>-4.15<br>(9.03)</td></tr><tr><td>|Belief about Median - Median rate|</td><td></td><td></td><td></td><td></td><td>0.54***<br>(0.05)</td><td>0.65***<br>(0.07)</td><td>0.64***<br>(0.08)</td><td>0.64***<br>(0.07)</td></tr><tr><td>Politician × |Belief about Median - Median rate|</td><td></td><td></td><td></td><td></td><td>0.12*<br>(0.06)</td><td>0.09<br>(0.10)</td><td>0.10<br>(0.11)</td><td>0.10<br>(0.09)</td></tr><tr><td>I × |Belief about Median - Median rate|</td><td></td><td></td><td></td><td></td><td></td><td>-0.23**<br>(0.10)</td><td>-0.19<br>(0.17)</td><td>-0.19<br>(0.13)</td></tr><tr><td>Politician × I × |Belief about Median - Median rate|</td><td></td><td></td><td></td><td></td><td></td><td>-0.05<br>(0.14)</td><td>-0.04<br>(0.19)</td><td>-0.04<br>(0.19)</td></tr><tr><td>C × |Belief about Median - Median rate|</td><td></td><td></td><td></td><td></td><td></td><td>-0.04<br>(0.09)</td><td>0.00<br>(0.11)</td><td>0.00<br>(0.10)</td></tr><tr><td>Politician × C × |Belief about Median - Median rate|</td><td></td><td></td><td></td><td></td><td></td><td>0.05<br>(0.12)</td><td>0.05<br>(0.14)</td><td>0.05<br>(0.14)</td></tr><tr><td>I × C × |Belief about Median - Median rate|</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.07<br>(0.19)</td><td>-0.08<br>(0.18)</td></tr><tr><td>Politician × I × C × |Belief about Median - Median rate|</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.00<br>(0.26)</td><td>-0.01<br>(0.28)</td></tr><tr><td>Mean deviation (NI-NC)</td><td>33.25</td><td>33.25</td><td>33.25</td><td>33.25</td><td>33.25</td><td>33.25</td><td>33.25</td><td></td></tr><tr><td>Mean deviation (NI-C)</td><td>32.66</td><td>32.66</td><td>32.66</td><td>32.66</td><td>32.66</td><td>32.66</td><td>32.66</td><td></td></tr><tr><td>Mean deviation (I-NC)</td><td>24.63</td><td>24.63</td><td>24.63</td><td>24.63</td><td>24.63</td><td>24.63</td><td>24.63</td><td></td></tr><tr><td>Mean deviation (I-C)</td><td>24.49</td><td>24.49</td><td>24.49</td><td>24.49</td><td>24.49</td><td>24.49</td><td>24.49</td><td></td></tr><tr><td>Observations</td><td>1415</td><td>1415</td><td>1415</td><td>1415</td><td>1415</td><td>1415</td><td>1415</td><td></td></tr><tr><td>Controls</td><td>None</td><td>None</td><td>None</td><td>None</td><td>None</td><td>None</td><td>All</td><td></td></tr></tbody></table>

**Notes:** I is an indicator for the *Information* treatment. C is an indicator for the *Competition* treatment. This table is not to be confused with a standard regression table. Each column summarizes the results from 1001 similar regressions based on 1001 random draws of the median rate for each participant (see Section 3). Each coefficient corresponds to the median coefficient over all regressions for the corresponding variable. Coefficients for different variables are thus likely to originate from different regressions. Standard errors and p-values associated with one coefficient are computed from the regression from which this coefficient originates. Similarly, the "Mean deviations" at the bottom of the table are computed by averaging the deviation in each condition for each random draw and taking the median of such average over the 1001 random draws. For each regression, we apply post-stratification weights to account for differences in observables between our samples and their respective general populations. To account for differences in sampling across Politicians and Non-politicians, we compute and normalize the weights separately for both samples. Controls include: age, gender, income, education level, occupation, geographical area of residence, party preferences, a risk aversion index, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the survey. Robust standard errors in parentheses. \* significant at 10%, \*\* significant at 5%, \*\*\* significant at 1%.
## B.10 Robustness checks

We present here all robustness checks that relate to the analysis on tax choices. Additional tables for the analysis on deviations are available upon request (or supplied as part of the replication package).

### B.10.1 Belief about the Competitor

<table><caption>Table B.10.1: OLS regression - Tax choice (Politicians)</caption><thead><tr><th></th><th colspan="2">No Information treatment</th><th colspan="2">Information treatment</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>C</td><td>1.486<br>(1.894)</td><td>-6.094<br>(5.305)</td><td>1.903<br>(2.012)</td><td>-1.096<br>(6.913)</td></tr><tr><td>Belief about Median</td><td>0.458***<br>(0.107)</td><td>0.505***<br>(0.138)</td><td>0.329***<br>(0.0718)</td><td>0.356***<br>(0.104)</td></tr><tr><td>Belief about Competitor</td><td>0.407***<br>(0.0953)</td><td>0.266**<br>(0.119)</td><td>0.368***<br>(0.0462)</td><td>0.314***<br>(0.0687)</td></tr><tr><td>C × Belief about Median</td><td></td><td>-0.225<br>(0.238)</td><td></td><td>-0.0529<br>(0.147)</td></tr><tr><td>C × Belief about Competitor</td><td></td><td>0.407*<br>(0.215)</td><td></td><td>0.101<br>(0.0907)</td></tr><tr><td>Median rate</td><td></td><td></td><td>0.138***<br>(0.0291)</td><td>0.131***<br>(0.0413)</td></tr><tr><td>C × Median rate</td><td></td><td></td><td></td><td>0.0126<br>(0.0575)</td></tr><tr><td>Controls</td><td>All</td><td>All</td><td>All</td><td>All</td></tr><tr><td>R<sup>2</sup></td><td>0.415</td><td>0.424</td><td>0.352</td><td>0.355</td></tr><tr><td>Observations</td><td>475</td><td>475</td><td>3135</td><td>3135</td></tr><tr><td>Clusters</td><td>475</td><td>475</td><td>285</td><td>285</td></tr></tbody></table>

**Notes:** C is an indicator for the Competition treatment. The Belief about Median and Belief about Competitor variables are normalized by subtracting their average value. We apply post-stratification weights to account for differences in observables between our politicians sample and the sample of all French politicians. Controls include: age, gender, income, education level, occupation, geographical area of residence, party preferences, a risk aversion index, a dummy for whether the belief elicitation task was played before/after the redistribution game, and the time taken completing the survey. Robust standard errors in parentheses in columns 1-2 and clustered at the individual level in columns 3-4. * significant at 10%, ** significant at 5%, *** significant at 1%.