# Trickle-Down Economics, Merit, and Redistribution: An Experiment with the Poorest and Richest US Americans

Roberto Brunetti, Gianluca Grimalda, Maria Marino

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Roberto Brunetti, Gianluca Grimalda, Maria Marino. Trickle-Down Economics, Merit, and Redistribution: An Experiment with the Poorest and Richest US Americans. 2025. hal-05112815

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# Trickle-Down Economics, Merit, and Redistribution: An Experiment with the Poorest and Richest US Americans

Roberto Brunetti*

Gianluca Grimalda†

Maria Marino‡§

June 14, 2025

Latest version

## Abstract

Despite growing income inequality, demand for redistribution has remained stagnant, which is particularly puzzling for the poor. We investigate whether attitudes toward “trickle-down” economics and preferences for fairness affect demand for redistribution. We involve US residents in the bottom ($N = 1,200$) and top ($N = 1,146$) 20% of the income distribution in experimental redistributive decisions from high-income real-life entrepreneurs to low-income recipients. We find that entrepreneurs’ activities with potential for trickle-down, such as high employment or innovation rates, are largely irrelevant to redistribution. Philanthropic donations, however, reduce redistribution demand among the poor. The most important factor for redistribution is the desire to sanction the “undeserving poor” and, to a lesser extent, to reward the “deserving rich”, measured by daily working hours and the founding of the firm. Decisions by high-income and low-income participants generally follow the same patterns, are quantitatively similar, and are mediated by economic and political identity.

**Keywords:** Trickle-down, Fairness, Merit, Redistribution

**JEL codes:** D72, D91, H2, H23, H41

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

<footnote>†Passau University, DE. gianluca.grimalda@uni-passau.de</footnote>

<footnote>‡Universitat de Barcelona, ES. maria.marino@ub.edu</footnote>

<footnote>§We are grateful to Marius Brülhart, Marc Fleurbaey, Lasse Jessen, Etienne Lehmann, Benoît Le Maux, Johanna Mollerstrom, Adam Przeworski, Paul Smeet, Alain Trannoy, Joël van der Weele, Elisabet Viladecans-Marsal, Thea Zöllner, participants in seminars at CREM (Rennes), Universitat de Barcelona, and in the conferences In equality (Konstanz), ASFEE (Grenoble), ADRES (Paris-Daphine), and CISEPS (Milano-Bicocca) for helpful comments. A special thanks goes to Louis Putterman for a careful reading of a first draft of the paper. We thank Nancy Buchan for revising the survey. All errors are our sole responsibility. We acknowledge funding from the University of Rennes, the Kiel Institute for the World Economy, the university of Barcelona (SGR-2021-261), and grant H2020-MSCA-IF-2019-891256.</footnote>
# 1 Introduction

According to the median voter theorem, income redistribution rates in democratic industrialized countries should be high and increase with rising inequality (Meltzer and Richard, 1981). Since the elasticity of labor supply to the income tax rate is low (Roemer et al., 2007; Piketty et al., 2014)<fnref n="1" /> and given the rightward skew in income distribution (Chancel et al., 2021), this theory predicts what Dworkin (2018) called the “slavery of the rich”. That is, highly productive individuals will be heavily taxed to maximize the incomes of lowly productive individuals. The reality stands in stark contrast to this prediction, as rising inequality in most Western countries (Chancel et al., 2021) coexists with stagnant or declining demand for redistribution (Kenworthy and McCall, 2008; Ashok et al., 2015; Roth and Wohlfart, 2018; Stantcheva, 2024). Moreover, tax rates are substantially far from the exploitative rates predicted by the theory. Although several accounts have been proposed to amend the theory,<fnref n="2" /> the determinants of redistribution remain poorly understood.

This paper examines the relevance of two theories to explain this puzzle. One theory, commonly referred to as “Trickle-Down” (TD) economics, posits that an increase in the incomes of the rich, for example through tax cuts, will benefit the rest of society and the poor in particular (Stiglitz, 2016). The reason is that the rich will use the additional income from the tax cuts to expand economic activities, with positive multiplier effects on all strata of society. Despite the widespread use of TD in the political debate (Stiglitz, 2016),<fnref n="3" /> evidence of its influence on the demand for redistribution is scarce. To the best of our knowledge, this study is the first to systematically assess the relevance of TD for the demand for redistribution, examining various channels and mechanisms.

The second theory why the poor might accept an income tax rate lower than the one that maximizes their after-tax income is that, in addition to self-interest, they are motivated by social and fairness preferences (Fong, 2001; Corneo and Grüner, 2002; Konow et al., 2020; Almås et al., 2020). In the survey conducted by Stantcheva (2021), this factor is one of the most important determinants of preferences for redistribution. In particular, the poor may wish to reward the perceived merit of the rich or sanction the perceived demerit of the poor (Gilens, 1999; Alesina et al., 2018). Although the relevance of “meritocracy” for the demand for redistribution has already been established (Gilens and Page, 2014), our understanding of its ramifications has not been systematically analyzed. In particular, we do not know whether the desire to reward the “deserving rich” is stronger than the desire to sanction the

<footnote n="1">¹In Piketty et al. (2014), the central estimate for this elasticity is 0.08, and its upper bound is 0.2. The maximum associated with the Laffer curve is found at a tax rate of 83%. Roemer et al. (2007) obtains similar estimates.</footnote>

<footnote n="2">²Among these, the most common are the Prospect of Upward Mobility (POUM) hypothesis (Benabou and Ok, 2001), individuals’ misperceptions about their actual position in the income distribution (Cruces et al., 2013; Karadja et al., 2017; Hoy and Mager, 2021), and criticisms of the median voter theorem (Roemer et al., 2007; Karabarbounis, 2011; Groot and van der Linde, 2016).</footnote>

<footnote n="3">³TD has been at the center of political discourse for decades and has been the primary justification for tax cuts by politicians. See, e.g., Ronald Reagan’s justification for tax cuts in 1981: https://www.reaganlibrary.gov/archives/speech/address-nation-economy-february-1981</footnote>
“undeserving poor”. We believe that ours is the first study to address this issue.

We developed a choice/conjoint experiment in which survey participants were asked to make several decisions about redistributing a fixed sum of money ($50) from a real-life entrepreneur to a recipient. This sum was provided by us researchers. The entrepreneur earned more than $100,000 per year in real life, while the recipient earned less than $10,000. Consistent with the literature (Konow et al., 2020; Almås et al., 2020), we label the allocators impartial spectators to emphasize their lack of any material interest in the decision, which could instead possibly influence the payoffs of the two stakeholders - the entrepreneur and the recipient. For our participants’ choices to actually determine redistributive transfers in real life, we recruited several high-income entrepreneurs and low-income earners whose individual characteristics matched those portrayed in the experiment. One choice was randomly selected from among all those made by the participants and determined the division of the $50 between an entrepreneur and a recipient. We paid all the other recruited stakeholders the standard show-up fee (see section 2 for more details on the experimental protocol).

We recruited participants from only the bottom 20% and the top 20% of the US income distribution, whom we refer to as “poor” and “rich” spectators, respectively. Our main target group consists of US residents belonging to the poorest 20% of the income distribution. These are the individuals who should have the strongest interest in redistribution, so their deviation from self-interested rationality is largest. By understanding the determinants of their behavior, we can shed light on the reasons for the stagnation of demand for redistribution. Furthermore, recent empirical evidence shows that the best predictor of country-level income redistribution in a cross-section of 93 countries is the preferences of the lowest economic strata (Maréchal et al., 2025). Focusing on bottom-income earners, therefore, seems crucial for determining the overall rate of redistribution in a country. As a “control”, we chose the symmetrical group to this, comprising the top 20% of the income distribution. This group should have no interest in redistributing according to self-interested rational choice. Therefore, comparing these two groups can illuminate differences in redistributive behavior between economic groups. Moreover, many theoretical and empirical contributions posit the hypothesis of a “government capture” by the élites, who end up playing a disproportionate role in the political process (Karabarbounis, 2011; Acemoglu and Robinson, 2013; Groot and van der Linde, 2016; Ghiglino et al., 2021). Therefore, focusing on the élites is also important for understanding the determinants of redistribution at the societal level. Our sample of n = 2,346 allows for an in-depth analysis of these groups.

We understand TD in a broad sense, which encompasses various mechanisms in which increased income accruing to the rich will also lead to an increase in the income of the poor.<fnref n="4" /> Many theoretical contributions emphasize the key role of employment in making TD benefi-

<footnote n="4">⁴We can distinguish a “weak” version of TD, according to which economic growth for the entire economy will benefit the poor. This approach is often summarized by the metaphor “A rising tide lifts all boats.” (Stiglitz, 2016). A “strong” version of TD posits that not only will tax cuts on the income of the rich benefit the poor, but also that the additional tax revenues resulting from the increase in GDP will exceed the initial budget deficit (Stantcheva, 2021).</footnote>
cial to the poor, either directly through jobs created by entrepreneurs who reinvest the tax
relief (Stiglitz, 2016; Young, 2019; Risch, 2023) or indirectly through easier access to credit
for low-income entrepreneurs (Aghion and Bolton, 1997; Ravallion, 2009). Although less ex-
plored theoretically, charitable donations can also be seen as an important channel through
which the rich benefit the poor. This is particularly true in the US, where philanthropic
activities can be seen as a *de facto* substitute for income support for the poor by the welfare
state (Clotfelter, 1985; Esping-Andersen, 1990; List, 2011). Finally, it can also be argued
that tax cuts promote innovations that create prosperity for the entire economy and for the
poor in particular (Antonelli and Gehringer, 2017; Sampson, 2023; Lichter et al., 2025).

Our novel experimental design combines these three channels through which the wealth
of the affluent can trickle down to the poor with two additional characteristics related to
individual merit. In our conjoint analysis, the entrepreneur could either: (a1) employ more
than 1,000 employees or fewer than 5; (a2) donate more than $3,600 per year or less than
$20; (a3) obtain more than 180 innovation patents per year or none. These specific values
were determined based on standard firm performance and the behavior of top-income earners
in real life (see section 2.1). According to our post-experiment questionnaire, participants
considered these three factors more relevant than two other factors–consumption and invest-
ment by the rich– for the economy. Regarding factors pertaining to individual merit, we
varied (b) whether the entrepreneur founded their enterprise or inherited it and (c) whether
a stakeholder normally works more than 10 hours per day or less than 6 hours. The latter at-
tribute was manipulated for both the rich entrepreneur and the poor recipient. While other
third-party allocation experiments use workers whose real-life personal characteristics are
irrelevant for the redistribution decision (e.g., Andre (2024); Almås et al. (2020); Cappelen
et al. (2007)), in our approach such characteristics become essential to vary the theoretical
aspects on which we focus.

We find a remarkable similarity in redistribution decisions between rich and poor spec-
tators, both in terms of magnitude–although poor spectators redistribute marginally more
than rich spectators–and in terms of their patterns. The most important factor in redistribu-
tion, for both rich and poor spectators, is the number of hours worked by the poor recipient
in real life, followed by the number of hours worked by the entrepreneur. Attributes re-
lated to TD seem to be largely irrelevant, with the notable exception of the entrepreneur’s
philanthropic activities. This attribute significantly reduces poor spectators’ redistribution,
without altering the decisions of rich spectators. We find some evidence that this result can
be explained by the belief that philanthropic donations are beneficial to the economy, rather
than by a desire to reward the moral standing of the donor. This finding emphasizes that
the effect is primarily driven by participants’ belief in the TD.

**Literature** Our study relates to several strands of literature. Despite its political impor-
tance, the empirical evidence on the redistributive impact of beliefs in TD is limited to two
studies and yields partially conflicting results. Stantcheva (2021) finds that 31% of US re-
spondents believe that raising taxes on the wealthy would hurt the economy.<fnref n="5" /> Those who believe in TD also have less strong preferences for tax progressivity and redistribution than others, although with a moderately low effect size. However, an information experiment in which participants were randomly exposed to a video showing the distortionary costs of increasing taxation on economic activities did not significantly change their demand for redistribution relative to the baseline. In another information experiment, Hope et al. (2023) provided participants with evidence of the weak relationship between top-income tax rates and economic growth. The information provided increased respondents’ support for raising taxes on the rich.<fnref n="6" /> Our study is the first to systematically investigate the impact of TD on incentivized redistributive decisions, considering three different channels through which TD can be effective and further analyzing the underlying psychological mechanisms.

Another strand of literature related to our study concerns preferences to reward individual merit. Numerous studies show that individuals are not motivated solely by self-interest (Henrich et al., 2001) or by outcome-oriented social preferences (Fehr and Schmidt, 1999; Bolton and Ockenfels, 2000), but also by procedural fairness (Karni and Safra, 2002; Bolton et al., 2005; Trautmann, 2009; Krawczyk, 2011). Individuals whose earnings are due to luck rather than hard work are generally deemed less deserving of their wealth, which reduces acceptance of inequality (Konow, 2003; Durante et al., 2014; Almås et al., 2020; Andre, 2024). Beliefs about opportunities for economic mobility and individual responsibility in determining the economic outcomes of both the rich and the poor are considered a crucial determinant of the demand for redistribution (Gilens, 1999; Alesina and Glaeser, 2004; McCall, 2013; Atkinson, 2015, 2016; Kim, 2021). Indeed, it has been observed that countries where people believe that social mobility is high, thus making the “playing field” more level, also show greater tolerance for inequality (Corneo and Grüner, 2002; Alesina and Glaeser, 2004; Alesina and La Ferrara, 2005; Alesina et al., 2018). Similarly, the stronger the belief that the rich deserve their larger share of income and that the poor are responsible for their condition, the lower the support for the welfare state and redistribution (Petersen et al. (2011); Aarøe and Petersen (2014); Brown-Iannuzzi et al. (2017); see also Hauser et al.

<footnote n="5">⁵Previous surveys investigating support for tax cuts had found stronger support for the TD hypothesis. A Harris poll conducted in June 2003 found that 50% of respondents viewed the 2003 tax cut implemented by US President G.W. Bush, which mainly favored high-income earners, to be “a good thing” (Bartels, 2005). Nevertheless, another poll conducted by CBS News in April 2001 showed that the US public believed that these tax cuts would mainly benefit the wealthy, with 55%, 26%, and 4% of respondents saying that the rich, the middle class, and the poor, respectively, would benefit *the most* from George W. Bush’s tax cut plan.</footnote>

<footnote n="6">⁶Theoretically, the trickle-down of income to the poor can be hindered if the process of growth involves labor-saving technological innovations, as has probably been the case in recent decades (Acemoglu, 2024), or if the initial level of economic inequality is relatively high (Ravallion, 1997). Empirically, evidence for the TD effect is, at best, mixed. While some studies do find evidence of TD in a “weak sense” (see footnote 4 and Dollar and Kraay, 2002; Ravallion, 2009), other studies reject the relevance of TD, arguing that income tax cuts are transferred almost entirely into CEOs’ income with no appreciable effect on economic growth, let alone the income of the poor (Piketty et al., 2014; Piketty, 2022; Zidar, 2019; Gechert and Heimberger, 2022). Other studies find no evidence of TD in individual low-income countries (Young, 2019; Basu and Mallick, 2008). Akinci (2018) does find evidence supporting TD even in a strong sense in a cross-section of countries, but also notes that the “trickle up”, i.e., the benefit to the entire economy from transferring money *to the poor*, far exceeds the benefit from transferring money to the rich.</footnote>
(2021)).

We contribute to this literature by manipulating information about the hours worked by the rich and poor stakeholders in our conjoint analysis. We also include another attribute arguably relevant to belief about the merit of the rich (Arndt, 2020; Cohn et al., 2023), namely whether the entrepreneur founded their enterprise or acquired it through inheritance. This allows us to directly measure, using the same metric, preferences to reward the “deserving rich” vs. sanction the “undeserving poor”. Despite the relevance of this topic, our study is, to the best of our knowledge, the first to do so.<fnref n="7" />

The rest of the paper is organized as follows. Section 2 presents the experimental design, data collection methods, and sample. Section 3 describes the theoretical and empirical framework and sets out our hypotheses. Section 4 reports results, while section 5 performs robustness checks. Section 6 discusses the results, and Section 7 concludes.

# 2 Experimental design

## 2.1 The redistribution interaction

The stage game Experimental instructions and the full questionnaire are reported in Appendix C.1.<fnref n="8" /> Our basic interaction builds on the third-party allocator game first developed by Konow (2000). It involved two “stakeholders” and one “spectator”, who were recruited by an opinion poll agency. The first stakeholder was recruited from US residents whose individual income was higher than $100,000⁹ and who owned a business at the time of research. In the experiment, we referred to this player as “Person 1”. In this paper, we label them the “entrepreneur”. The second stakeholder was a US resident whose income was lower than $10,000. We called this player “Person 2” in the experiment and label them the “recipient” in this paper.<fnref n="10" /> The entrepreneur was assigned $50 upon completion of a task. The recipient was not given the opportunity to complete any task and was assigned $1. These two sums were allocated to Person 1 and Person 2 on top of the standard show-up fee. The ratio between $50 and $1 was in line with the ratio of the actual average income accruing earners above $100,000 and earners below $10,000, which is estimated to be 71, according to the US Census (see Durante et al. (2014) for a similar approach).

<footnote n="7">⁷Alesina et al. (2018) also measure beliefs about the merit of the rich and the demerit of the poor on the same scale. However, they only determine that these beliefs are relevant predictors of social mobility but do not analyze their impact on the demand for redistribution.</footnote>

<footnote n="8">⁸The text was proofread by a US native speaker, who also checked that the instructions were easily accessible to people with low-education. We also sought to simplify the language using an AI software.</footnote>

<footnote n="9">⁹Even though the income attribution was self-reported, the opinion poll company which carried out the recruitment ensured us that the income profiling was correct.</footnote>

<footnote n="10">¹⁰According to the US Census data, in 2021 the threshold for the first quintile of the U.S. personal income distribution was $10,054, while the threshold for the top quintile was $75,004. Since the information on the stakeholders’ income was presented to participants in the conjoint experiment, we preferred to adjust these numbers to $10,000 and $100,000, in order to make the income difference more salient and present rounded numbers to participants.</footnote>
The stakeholders were not asked to make any decisions in the experiment. The spectator was instead given the above information (see Appendix: C.2.1 for the text that was used) and was asked to decide how much money he or she wanted to transfer from the entrepreneur to the recipient. The spectator could transfer any amount from $0 to $50 up to the second decimal digit.

Our stage game aimed to incorporate in the experimental setting the basic characteristics of a real-life decision on how much taxation to levy on rich income earners to benefit low income earners. In order to increase the salience of the merit of the entrepreneur vis-à-vis the recipient, we decided to assign a task to the entrepreneur but not to the recipient. (see the discussion of alternative designs in section 6).

**Stakeholders.** We gave each spectator 16 different redistributive choices based on the stage game described above. The key characteristic of our design is that we varied several attributes of the stakeholders to test different aspects of theories of TD and individual merit. In order to avoid deception (see below), we recruited several stakeholders whose characteristics matched the attributes presented in the 16 choices. The following attributes were varied across decisions and communicated to spectators when they made their redistributive decisions (see Table 1).

• First, entrepreneurs and recipients earned incomes above $100,000 or below $10,000, respectively.

• Second, both stakeholders were presented as working either more than 10 hours or less than six hours per day. This attribute taps into rewarding merit related to effort, as, arguably, people working longer hours are perceived as more deserving than those working less.

• Third, Person 1 was always presented as the owner of a firm. However, we varied whether Person 1 founded or inherited their firm. This manipulation taps into the assessment of rewarding the entrepreneur's effort in setting up their own business, and it often contributes to the narrative of the “deserving rich”.

• Fourth, we varied, across three different treatments attributes pertaining to three different channels that we considered relevant for TD (see section 1). These attributes were: (a) the number of employees hired by the entrepreneur's firm; (b) the money donated to charity by the entrepreneur; (c) the number of patents obtained by the entrepreneur's firm (see Table 1 for the actual values that were used).<fnref n="11" /> Depending on the specific TD attribute that was assigned to the entrepreneur, we identify the *Employment*, *Donation*, and *Innovation* treatments. Higher values of donations,

<footnote n="11">¹¹Since we thought that some participants may have not been familiar with the notion of patent, we gave the following explanation in the relevant decisions: “A patent is an exclusive right granted for an invention, which is a product or a process that provides, in general, a new way of doing something, or offers a new technical solution to a problem.” In a subsample of survey answers (N=179), we tested the clarity of this definition. Only 2% of participants stated that it was unclear.</footnote>
employees, and patents arguably characterize more deserving stakeholders, since they provide more benefits to the economy.<fnref n="12" />

<table><caption>Table 1: Attribute's Values for Entrepreneurs and Recipients</caption><thead><tr><th></th><th>The entrepreneur:<br>Income > $100,000</th><th>The recipient:<br>Income < $10,000</th></tr></thead><tbody><tr><th>Daily working hours</th><td>&lt; 6 hours / &gt; 10 hours</td><td>&lt; 6 hours / &gt; 10 hours</td></tr><tr><th>Source of firm ownership</th><td>Founder / Inheritor</td><td>Does not own a firm</td></tr><tr><th>Charity donations (Donation treatment only)</th><td>&lt; $20 last year / &gt; $3600<br>last year</td><td>Did not donate any money</td></tr><tr><th>Firm's employees (Employment treatment only)</th><td>&lt; 5 employees / &gt; 1000<br>employees</td><td>–</td></tr><tr><th>Firm's patents (Innovation treatment only)</th><td>0 last year / &gt; 180 last<br>year</td><td>–</td></tr></tbody></table>

Notes: The table shows the entire set of attributes for entrepreneurs and recipients. Some of the attributes (number of hours worked and source of firm ownership) are common to all treatments, but the TD attributes change according to the treatment to which the spectators are assigned (Employment, Donation, or Innovation treatment).

The task actually performed by the entrepreneur consisted of answering four general questions about the role of merit and luck in people's economic success, the evolution of inequalities, and preferences for redistribution. The details of the tasks were not revealed to the spectator.

**Spectators.** As motivated in the introduction, we recruited samples from the bottom and top 20% of the US distribution, according to the 2021 US Census. We refer to these two groups as “rich spectators” and “poor spectators”. Income thresholds were set to be above $150,000 for the *household* income of rich spectators and below $30,000 for poor spectators.<fnref n="13" />

Differently from Almås et al. (2020), we employed a within-subject framework (List, 2025). The spectator was given 16 different redistributive choices matching eight different profiles of Person 1 (high/low effort, inherited/founded firm, low/high TD attribute) and two different profiles for Person 2 (high/low effort). The TD attributes depended on which of the three treatments the spectators was assigned to, namely the *Donation*, *Employment*, and *Innovation* treatments. Figure 1 provides an example - from the employment

<footnote n="12">¹²The values for the donation in the Donation treatment were drawn from the actual average donations made by US residents earning more than $100,000 (List, 2011), which roughly corresponds to 3% of their average income. For the Employment treatment, we chose 1,000 employees, the threshold generally employed to identify a large business (2018 County Business Patterns - Census Bureau). Finally, for the Innovation treatment, we computed the average number of patents of firms with more than 1,000 employees (ORBIS data).</footnote>

<footnote n="13">¹³The thresholds we used were an approximation of the real quintiles: the bottom quintile of the US *household* income distribution was $35,856, while the top quintile was $162,666. Using rounded quintiles allows us to reduce the cognitive burden of participants answering the income question and conforms to the selection criterion used by the survey company to pre-select the participants.</footnote>
treatment - of how one of the 16 redistribution choices looked like. In the experiment, we randomized the order of the attributes and the order of the 16 choices. Spectators were informed that one decision by one spectator would be randomly selected and implemented to determine the payoffs of one Person 1 and one Person 2 in real life.

<figure><img src="image_3.png" /><figcaption>Figure 1: Example of redistribution choice</figcaption></figure>

**Notes:** The Figure represents one of the 16 conjoint tables that spectators are shown in the employment treatment. The order of the attributes is always randomized.

**Deception avoidance.** In order to avoid deception, we wanted the attributes assigned to the stakeholders in the experiment to be possessed by people in real life. Considering all the different attributes that were used in the experiment, we have 24 different profiles for the entrepreneurs and 2 for the recipients. The opinion poll agency that we contracted ensured that it would have been possible to find a person in their panel corresponding to each of the profiles. We conducted a targeted stakeholder recruitment, in which potential participants were selected based on their attributes and were turned down if their profile did not meet the basic characteristics or if that profile had already been filled. Due to budget and time constraints, we stopped our selection after we recruited two recipients and four entrepreneurs with a different set of attributes. The real-life characteristics of these stakeholders could generate ten different profiles, no less than three for each treatment. Therefore, each spectator had at least six choices in the experiment that could determine the payoffs of two stakeholders in real life. Our instructions clearly stated that only one randomly selected decision among all decisions made by the participants would have determined the payoffs of two stakeholders, something that was done in fact at the end of the fieldwork. Since we did not state that each decision had the same probability of being selected, nor did
we mention any probability for a decision to be implemented for the actual determination
of payoffs, we can truthfully say that deception was avoided.<fnref n="14" />

## 2.2 Data collection and sample

We conducted two data collection waves between July 2022 and August 2023 to survey spec-
tators. In the first wave, we gathered responses from 1,500 participants, randomly assigned
to either the donation treatment (N=900, with 450 rich and 450 poor) or the employment
treatment (N=600, with 300 rich and 300 poor). In the second wave, we focused only on
the innovation treatment, collecting 846 observations (450 poor and 396 rich spectators).
Overall, our sample consists of 2,346 individuals, comprising 1,200 poor and 1,146 rich spec-
tators.<fnref n="15" />

We designed the survey using Limesurvey and the survey links were distributed by an
international data collection agency, CINT (https://www.cint.com/). Respondents who de-
cided to take the survey were first administered a screening question on their household
income. This ensured that the final sample includes the two economic groups of spectators
we targeted. Respondents were paid upon completion of the survey. The median time to
complete the survey was 23.11 minutes.

Table 2 compares the background characteristics of our sample of rich and poor spectators with the U.S. population (in Table A.1.1 we show the same statistics by treatment). Since there exist significant deviations of our sample demographic characteristics from the US population, especially for the gender dimension of the poor, we show that our results are robust to re-weighting of the sample in the Appendix: section B.2. Moreover, the null of equality of distribution for some demographic characteristics was rejected across treatments.<fnref n="16" /> For this reason, and to ensure that our results are not driven by compositional differences between the rich and the poor (e.g., rich are more likely to be educated than the poor), we control for socio-demographic variables in all our regressions.

<footnote n="14">¹⁴Andre (2024) followed an approach similar to ours, in that only a subset of decisions was actually implemented, with the concrete possibility that none of a participant’s decisions had any bearing on real-life payoffs. Andre (2024) explicitly mentioned in the instructions that some decisions were hypothetical, while only one decision was potentially relevant to determine real-life payoffs. We opted to not follow this approach, as it may have been difficult for the participants to understand what a “hypothetical scenario” meant, especially for participants with low educational attainment. This approach might also have given the false impression that all choices were, in fact, hypothetical. In any case, Andre (2024) shows the robustness of his results to removing this contingency in the selection of relevant decisions.</footnote>

<footnote n="15">¹⁵We also conducted a pilot collecting data for 300 individuals equally divided between rich and poor spectators and all assigned to the employment treatment. While in the main analysis, we use only the data from the two waves, in Table B.7.1 in the appendix, we run the main analyses including the pilot data, and show that the results for the employment treatment are robust and consistent with the other treatments.</footnote>

<footnote n="16">¹⁶That was in particular the case for age for the low-income group ($chi2(2) = 24.1, p = 0.0001$), and gender ($chi2(2) = 39.1, p = 0.0001$) and liberals ($chi2(2) = 4.9, p = 0.088$) for the high-income group.</footnote>
<table><caption>Table 2: Summary statistics by treatment</caption><thead><tr><th rowspan="2"></th><th colspan="2">Rich</th><th colspan="2">Poor</th></tr><tr><th>Our sample (N=1,146)</th><th>Population</th><th>Our sample (N=1,200)</th><th>Population</th></tr></thead><tbody><tr><td><em>Gender</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td>0.50</td><td>0.51</td><td>0.28</td><td>0.41</td></tr><tr><td>Female</td><td>0.50</td><td>0.49</td><td>0.72</td><td>0.59</td></tr><tr><td><em>Age</em></td><td></td><td></td><td></td><td></td></tr><tr><td>[18-35]</td><td>0.33</td><td>0.29</td><td>0.34</td><td>0.23</td></tr><tr><td>[36-55]</td><td>0.53</td><td>0.40</td><td>0.35</td><td>0.22</td></tr><tr><td>[56+]</td><td>0.15</td><td>0.31</td><td>0.31</td><td>0.55</td></tr><tr><td><em>Region</em></td><td></td><td></td><td></td><td></td></tr><tr><td>South</td><td>0.40</td><td>0.32</td><td>0.48</td><td>0.44</td></tr><tr><td>Midwest</td><td>0.15</td><td>0.20</td><td>0.22</td><td>0.21</td></tr><tr><td>Northeast</td><td>0.22</td><td>0.22</td><td>0.16</td><td>0.15</td></tr><tr><td>West</td><td>0.23</td><td>0.26</td><td>0.14</td><td>0.19</td></tr><tr><td><em>Ideology</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Conservative</td><td>0.24</td><td>0.38</td><td>0.25</td><td>0.33</td></tr><tr><td>Moderate</td><td>0.29</td><td>0.20</td><td>0.48</td><td>0.33</td></tr><tr><td>Liberal</td><td>0.47</td><td>0.41</td><td>0.27</td><td>0.34</td></tr></tbody></table>

Notes: The Table displays summary statistics from our sample alongside population statistics. For the latter, the statistics on Gender, Age, and Region are computed from the 2020 U.S. Census dataset. Regarding Ideology, we leverage the data provided by the American National Election Studies to compute the share of liberals, conservatives, and moderates by income group.

# 3 Theoretical and empirical framework

## 3.1 Individual Preferences

While self-interested spectators should be indifferent to others' well-being, we assume that individuals are sensitive to the relative well-being of the stakeholders. Individual preferences are described by the following Cobb-Douglas utility function:

$$U(y_R, y_P) = (y_R)^{(\gamma_R + \mu_R + \mu_{TD} + \mu_F)} (y_P)^{(\gamma_P + \mu_P)} \quad (1)$$

$y_R$ ($y_P$) is the income received by the rich entrepreneur (the poor recipient). $\gamma_R$ ($\gamma_P$) is the basic elasticity of the utility function to the income accruing to the entrepreneur (the recipient) in the absence of merit characteristics. Hence, the higher $\gamma_R$ relative to $\gamma_P$, the higher the propensity to attribute larger shares of income to the entrepreneur relative to the recipient, and vice versa, in the absence of merit characteristics. The $\mu$ parameters represent the extra weights assigned to the stakeholders when they hold merit characteristics. We have three sets of such merit characteristics for the rich, those related to trickle-down and those related to effort and to having founded the enterprise, and one for the poor, related to effort. $\mu_R$ ($\mu_P$) capture the elasticities of the utility function to individual effort by the rich (the poor) in terms of their daily working hours in comparison to baseline. $\mu_{TD}$ is a vector of parameters that captures the importance of TD activities exerted by the rich. Since different TD activities may be differently appreciated by spectators, we have one $\mu_{TD}$ parameter for
each TD activity we consider. $\mu_F$ is the elasticity of the utility function to the entrepreneurs having founded their enterprise. By definition, the poor cannot exert TD activities and, we assume, have not founded any enterprise. We define $\Pi = [\gamma_R, \mu_R, \mu_{TD}, \gamma_P, \mu_P]$ as the vector of parameters. We impose $\Pi_i \ge 0$.

If self-interest was relevant for decision-makers, a self-interested component would be added to (1). This is normally done additively, as in Cappelen et al. (2007). Nevertheless, since self-interest is absent by construction in the experimental choices, this component has been omitted and only the fairness component is modeled in the utility function.

Individuals maximize the utility function with respect to the constraints: $y_R + y_P = 51$ and $y_P \ge 1$. This yields the following optimal condition for an internal solution:

$$y_P^* = \left( \frac{\gamma_P + \mu_P}{\gamma_P + \mu_P + \gamma_R + \mu_R + \mu_F + \mu_{TD}} \right) 51 \quad (2)$$

The variable redistribution - labeled $R^*$ - is defined as:

$$R^* = \frac{y_P^* - 1}{50}. \quad (3)$$

Note that by construction $R^* \in [0, 1]$.

## 3.2 Econometric model

Our aim is to estimate the effect of each stakeholder's attributes on spectators' redistribution choices. To do this, we analyze the within-subject variation of rich and poor spectators' choices in each treatment separately to gauge the importance of each TD attribute relative to the other merit attributes. Hence, we pool the samples of rich and poor spectators, and we estimate the following model:

$$R_{ict} = \alpha_1 TD_{ict} + \alpha_2 F_{ict} + \alpha_3 HEE_{ict} + \alpha_4 LER_{ict} + \alpha_5 Rich_i + \alpha_6 TD_{ict} \cdot Rich_i + \\ \alpha_7 F_{ict} \cdot Rich_i + \alpha_8 HEE_{ict} \cdot Rich_i + \alpha_9 LER_{ict} \cdot Rich_i + \gamma \mathbf{X}_i + \kappa + \epsilon_{ict}, \quad (4)$$

where $R_{ict}$ is the percentage of income transferred from the entrepreneur to the recipients by spectator $i$, as derived in (3). The participant is shown a conjoint table $c = \{1, \dots, 16\}$, and is assigned to a treatment $t = \{\text{Employment, Donation, Innovation}\}$.

$TD_{ict}$ is a vector of dummy variables identifying high TD activities by the entrepreneur, that is, high donations, employment and innovation (see Table 1 for the exact values).

Considering the other merit attributes, $F_{ict}$ (Founder) is also a dummy, which equals 1 if the rich spectator founded the firm she currently owns and 0 if she inherited it; $HEE_{ict} = 1$ (High effort entrepreneur) if the entrepreneur works more than 10 hours during a normal working day, and 0 if she works less than 6 hours daily. Finally, $LER_{ict}$ (Low effort recipient) is a dummy variable equal to 1 if the recipient works less than 6 hours during a normal working day, and 0 if she works more than 10 hours daily.
We also include the interactions between the attribute dummies ($TD_{ict}$, $F_{ict}$, $HEE_{ict}$, $LER_{ict}$) and the income group dummy $Rich_i = 1$, which equals 1 if the spectator is in the top 20% of the income distribution and 0 if the spectator belongs to the bottom 20%.

$X_{it}$ is a vector of control variables, including gender, age, region of residence, level of education, political ideology,<fnref n="17" /> and a dummy for the order in which the tables were presented. Finally, $\kappa$ is the constant and $\epsilon_{ict}$ is the error term. In the estimation, we cluster standard errors at the individual level to take into account the serial correlation of the decisions.

The aim is to study how the marginal effect of each attribute varies with the spectators' income group. The $\alpha$ parameters in (4) identify the merit premia $\mu$ in (1), which allows rich and poor spectators to hold a different set of $\mu$. Specifically, coefficients $\alpha_1, \alpha_2, \alpha_3$, and $\alpha_4$ represent the effects of each attribute value on poor spectators' redistribution choices (i.e., for $Rich_i = 0$) against the alternative value of the same attribute while keeping the joint distribution of the other attributes equal. For rich spectators (i.e., $Rich_i = 1$), the coefficients of interest are the linear combination of $\alpha_1, \alpha_2, \alpha_3$ and $\alpha_4$ and the coefficient on the interaction terms. We are interested in the values of the coefficients for both rich and poor spectators and in their differences between the two subgroups. The tobit model estimates the coefficients on the latent variable defined on $(-\infty, +\infty)$. As is customary, we report the coefficients for such unconstrained variables in the regression tables, which we refer to as $\beta$ in the text. To derive an economically meaningful estimation of the regression coefficients for key results, we also report effect sizes computed as the average marginal effect on $E(\hat{R}_{ict}|x)$, where $\hat{R}_{ict}$ is the observed (censored) redistribution level rather than the latent variable $R_{ict}$. We name these values the average marginal effect (AME).

For some of our analyses, we consider model (4) pooled over the three treatments and split between poor and rich spectators.

## 3.3 Hypotheses

Our general identification strategy rests on the idea that spectators are willing to reward individuals holding characteristics considered beneficial to society or who are intrinsically worthy. This is particularly true for individual characteristics related to work effort, such as the parameters $\mu_R, \mu_F, \mu_P$, but also for the characteristics pertaining to TD, measured by the parameters $\mu_{TD}$. Extensive experimental evidence shows that, indeed, when individuals act as third-party allocators, or even as “dictators”, they transfer more resources to individuals or organizations that they deem deserving.<fnref n="18" /> More specifically, reciprocal altruism, i.e.,

<footnote n="17">¹⁷The questions from which we retrieved these variables can be found in C.2.</footnote>

<footnote n="18">¹⁸Eckel and Grossman (1996) found that experimental subjects were more likely to donate to a charity than a fellow student, arguably because the former is deemed morally worthier. Fong and Luttmer (2011) also found that manipulating the “neediness” of the recipients led to larger donations. Konow (2010) found that larger donations create “good feelings” in experimental donors when recipients are charities and “bad feelings” when they are fellow students. Fong and Oberholzer-Gee (2011) showed that a third of their experimental donors were willing to purchase information about their recipient’s characteristics, although acquiring information resulted in lower transfers to poor individuals deemed undeserving. Kappes et al. (2018) showed that, while people generally use uncertainty about the outcome of their donation as “moral</footnote>
the willingness to reward recipients conditionally on the expectation that they also behave prosocially, seems to be a fundamental motivation in the social sphere, both in experiments (Fong, 2007; Mollerstrom et al., 2015; Grimalda et al., 2021) and natural settings (Heclo, 1985; Gilens, 1999; Benz and Meier, 2008; Burgoon and Rooduijn, 2021). Implicit in the concept of reciprocity is the idea that an individual who behaves prosocially in the present may also receive some personal benefits in the future from others’ prosociality (Nowak and Sigmund, 2005; Engelmann and Fischbacher, 2009).

Konow (2000) and Croson and Konow (2009) argued forcefully against measuring social preferences jointly with self-interest, because of the moral biases that self-interest causes on the expression of social preferences. For this reason, an extensive literature measuring social preferences has adopted the “spectator” paradigm, in which self-interest is, by construction, silenced (Cappelen et al., 2007, 2013; Almås et al., 2020). Based on this discussion, in our design, we measure attitudes toward TD characteristics within the framework of third-party decisions, even though the TD hypothesis emphasizes the benefit to the self arising from the activity of the rich. In light of the evidence reviewed above, according to which reciprocity is a fundamental motivation even in unilateral prosocial acts, our identifying assumption is that individuals who believe in TD will reward the rich stakeholder with TD characteristics in our allocation game, even in the absence of self-interest. We discuss this aspect further in section 6.

More specifically, we are primarily interested in analyzing the AME of the TD attributes. We conjecture that spectators will reward entrepreneurs’ ability to positively impact the economy through employment creation, charitable donations, and technological innovation.

**Hypothesis 1.** When the entrepreneur has (i) a higher number of employees, or (ii) a higher amount of donations to charity, or (iii) a higher number of patents, the level of redistribution to the recipient decreases. Therefore, we expect $\alpha_1$ and $\alpha_1 + \alpha_6$ to be negative across all treatments.

Second, we expect that spectators’ redistribution choices will be affected by attributes related to a stakeholder’s effort. To start with, an entrepreneur who has founded an enterprise is likely to be viewed as more hard-working than someone who inherited it.

**Hypothesis 2.** When the entrepreneur is presented as a founder, the level of redistribution to the recipient decreases. We expect, therefore, $\alpha_2$ and $\alpha_2 + \alpha_7$ to be negative.

Another crucial dimension of personal merit is the effort people put into their work. In the present study, we proxied this dimension by using the attribute related to the number

<footnote>wiggle room” to donate less (Dana et al., 2007), other types of uncertainty, such as uncertainty about the well-being of the recipients of one’s action, can stimulate greater prosociality. In a framed field experiment, Heeb et al. (2023) demonstrated that investors have a substantial willingness to pay for sustainable investments.</footnote>
of hours worked on a normal weekday.

**Hypothesis 3.** When the entrepreneur is presented as working more than 10 hours on a weekday, the level of redistribution to the recipient decreases. Similarly, when the recipient is presented as working less than 6 hours on a weekday, the level of redistribution to the recipient decreases. We expect, therefore, $α_3, α_4, α_3 + α_8$, and $α_4 + α_9$ to be negative.

Finally, we are interested in the differences between poor and rich spectators. The rich may justify their unwillingness to redistribute to the poor, by claiming that the poor are responsible for their own condition, while the rich earned their privileged position. This is possibly due to self-serving bias, but also to the fact that rich individuals' upbringing emphasizes individual achievements through effort (Khan, 2011; Markovits, 2019; Piketty, 2020; Batruch et al., 2023). Evidence shows that the rich tend to have a stronger belief in the importance of merit for economic success compared to the general population (Page et al., 2013; Fisman et al., 2017; Kuusela, 2020; Atria et al., 2020; Suhay et al., 2021; Hecht, 2021; Cohn et al., 2023; Krozer, 2020), especially if they *became* rich, rather than being born rich (Cohn et al., 2023). Given this, we expect the impact of the entrepreneurs and recipients' attributes on redistribution choices to be stronger for rich spectators.

**Hypothesis 4.** All attributes highlighting stakeholders' merit impact rich spectators' redistributive choices more than poor spectators'. Hence, we expect $α_6, α_7, α_8$, and $α_9$ to be negative.

Given the lack of previous research on TD, we remain agnostic on the relative size of the TD and merit coefficients.

The above hypotheses have been pre-registered at https://osf.io/45n8x. All additional information on our pre-registration is provided in Appendix D.

# 4 Results

First, we provide a descriptive analysis of spectators' redistributive choices. Next, we carry out regression analysis to estimate the AMEs of stakeholders' attributes on spectators' choices. We then explore the potential mechanisms driving our main results. Finally, we examine how the AMEs vary across relevant subgroups.

## 4.1 Descriptive analysis

After pooling the data from the three treatments, the average redistribution choice for rich and poor spectators differs by a mere 0.33% of the transferable amount ($R = 49.13\%$, s.d. = 31.14 for the poor; $R = 48.8\%$, s.d. = 29.82 for the rich). A non-parametric Mann-Whitney
(MW henceforth) test on the redistribution choices averaged over each participant fails to reject the null that the distribution for rich and poor spectators is the same ($z = 0.46, p = 0.645, n = 2,346$, of which $n_R = 1,146$ rich spectators and $n_P = 1,200$ poor spectators). Cohen's *d*, a measure of the size of the difference of the means between rich and poor spectators, is extremely small ($d = 0.01$) and the null that $d = 0$ is not rejected ($z = 0.30, p = 0.77$; bootstrapped standard errors with 1,000 repetitions).

In line with the literature (Conte and Moffatt, 2014; Almås et al., 2020; Grimalda et al., 2023), the distribution shows three peaks in both groups (see Figure 2 and Figure A.2.1 for the distributions by treatment). The mode is at $R = 1$ for both groups of rich and poor spectators, where $R$ is the observed redistribution rate, theoretically determined in (3). It is noticeable that full redistribution is chosen more frequently by poor spectators (12.09% of all choices) than rich spectators (9.07%). After computing the share of fully redistributive choices out of 16 choices for each individual, a MW test strongly rejects the null that this variable is generated by the same distribution for both rich and poor spectators ($z = 4.08, p < 0.01$). The second peak is at $R = 0$. Interestingly, this option is also chosen more frequently by poor spectators (9.35% of all choices) than rich spectators (8.10%). A MW test strongly rejects the null that the share of no redistribution choices is generated by the same distribution for both rich and poor spectators ($z = 2.78, p < 0.01$). Hence, overall, poor spectators tend to choose at the extremes of the action space more frequently than rich spectators. The third peak corresponds to the equal split of the transferable amount.

<figure><img src="image_4.png" /><figcaption>Figure 2: Distribution of spectators' choices, by income group</figcaption></figure>
## 4.2 Regression analysis

To gain a general overview of our findings, we first report the results of the regression analysis of model (4) (section 3.2) after pooling the three treatments. Figure 3a provides a graphical representation of the Tobit regression coefficients of this model. The p-values reported in the present section are not adjusted for multiple hypothesis testing (MHT). The complete set of unadjusted and adjusted p-values using the sharpened q-value approach (Anderson, 2008) can be found in the Appendix: Table A.4.1.<fnref n="19" /> Significant results in this section are robust to MHT unless otherwise stated.

TD:Donation is the only TD attribute that has a significant impact on redistribution. Spectators take away 1.29 p.p. less<fnref n="20" /> ($p < 0.01$) from entrepreneurs who donated more than $3,600 in the previous year compared to those who donated less than $20. In contrast, spectators do not seem to reward more entrepreneurs providing more employment or creating more innovation patents, as the null hypothesis that a coefficient is equal to zero is not rejected for both TD:Employment ($p = 0.768$) and TD:Innovation ($p = 0.839$).

The number of hours worked appears to command larger redistributive differences than what was observed for TD attributes or for having founded one's firm. We find the largest effect for the number of hours worked by the recipient. When recipients work less than 6 hours a day, redistribution is 3.04 p.p. lower than when they work more than 10 hours ($p < 0.01$). In contrast, when the entrepreneur works less than 6 hours a day, redistribution is 1.76 p.p. higher than when she works more than 10 hours ($p < 0.01$). The impact on redistribution of the hours worked by the recipient is significantly higher than that of the hours worked by the entrepreneur ($p < 0.01$). Finally, entrepreneurs who founded their enterprise are rewarded with a 0.48 p.p. decrease in redistribution ($p < 0.01$).

Interestingly, stakeholders' attributes tend to have similar effects on rich and poor spectators, with the exception of those related to donations ($p < 0.01$ on a test of difference of coefficients) and innovation ($p = 0.059$) (see Figure 3b). There is no significant difference between rich and poor spectators in rewarding an entrepreneur who employed many people ($p = 0.51$) and who founded her firm ($p=0.58$).<fnref n="21" /> Likewise, we find no significant difference between rich and poor spectators with respect to the number of hours worked by the entrepreneur ($p = 0.74$) and the poor recipient ($p = 0.40$), with both rich and poor spectators reacting significantly to either attribute.

In order to better identify treatment effects by income group, we analyze each treatment separately in Table 3. The amount donated by the entrepreneur significantly affects the choices of poor spectators ($AME = -0.03, p < 0.01$), but not the choices of rich spectators ($AME = -0.000014, p = 1$), the difference between the two coefficients being strongly sig-

<footnote n="19">¹⁹We also provide p-values adjusted with the Bonferroni and Holm-Bonferroni methods to provide a more conservative reference point.</footnote>

<footnote n="20">²⁰As clarified in section 3.2, we report effect sizes as the average marginal effect of the AMEs over the censored variable $E(\hat{R}_{ict}|x)$ rather than over the latent variable $E(R_{ict}|x)$.</footnote>

<footnote n="21">²¹Rich spectators ($AME = -0.01, p < 0.01$) but not poor spectators ($AME = -0.004, p = 0.114$) respond significantly to the Founder attribute, but the effect is small.</footnote>
nificant ($p$ < 0.01 on the interaction coefficient; see Table 3, column 1). In contrast, only rich spectators responded to the entrepreneur's innovation activity, albeit with a small effect ($AME$ =-0.01, $p$= 0.04 and $p$ = 0.081 by adjusting for MHT). Poor spectators are instead indifferent to the entrepreneur's innovation activity ($AME$ =0.005, $p$ = 0.36) and the difference with rich spectators is weekly significant ($p$ = 0.059; Table 3, column 3). In contrast, both poor ($AME$ = -0.0013, $p$= 0.76) and rich ($AME$ = 0.003, $p$= 0.5) spectators are indifferent to whether the entrepreneur employs lots or few workers. Finally, the number of hours worked by either the entrepreneur or the recipient has a significant effect in each treatment for both rich and poor spectators ($p$ < 0.01 for all coefficients).

<figure><img src="image_5.png" /><figcaption>Figure 3: Effects of stakeholders’ attributes on amount redistributed</figcaption></figure>

**Notes:** Coefficients from a Tobit regression with 95% confidence intervals in which the dependent variable is the percentage of the entrepreneurs' money transferred to recipients. Table A.3.1 reports the coefficients and standard errors. *TD:Donation* is a dummy = 1 if the entrepreneur donated more than $3,600 in the last 12 months. *TD:Employment* equals 1 if the entrepreneur's firm has over 1,000 employees. *TD:Innovation* equals 1 if the entrepreneur's firm obtained over 180 patents. *Founder* is a dummy = 1 if the entrepreneur founded the firm they own. *High effort entrepreneur* equals 1 if the entrepreneur works more than 10 hours daily. *Low effort recipient* equals 1 if the recipient works less than 6 hours daily. Controls include age, gender, region of residence, education level, political ideology, a dummy for the order the tables were presented, and a treatment dummy. Standard errors clustered at the individual level.

We conclude:

**Result 1:** The TD attribute related to donation (low/high level of donation) reduces poor spectators' propensity to redistribute but does not significantly affect the rich spectator's redistribution, while the TD attribute related to entrepreneurs' firms' innovation (low/high number of patents of the firm) slightly reduces rich spectators' redistribution choices. By contrast, TD attributes related to employment (low/high number of employees of the firm) do not impact the redistribution choices either of the rich or the poor spectators.

**Result 2:** The attribute relative to entrepreneurs’ firm’s ownership (founded/inherited) has a comparatively small negative effect on spectators’ choices.
<table><caption>Table 3: Tobit Regression results on amount transferred</caption><thead><tr><th></th><th>Treat:Donation<br>(1)</th><th>Treat:Employment<br>(2)</th><th>Treat:Innovation<br>(3)</th></tr></thead><tbody><tr><td>TD:Donation</td><td>-0.031***<br>(0.007)</td><td></td><td></td></tr><tr><td>TD:Donation × Rich</td><td>0.031***<br>(0.008)</td><td></td><td></td></tr><tr><td>TD:Employment</td><td></td><td>-0.0016<br>(0.005)</td><td></td></tr><tr><td>TD:Employment × Rich</td><td></td><td>0.0057<br>(0.008)</td><td></td></tr><tr><td>TD:Innovation</td><td></td><td></td><td>0.0060<br>(0.006)</td></tr><tr><td>TD:Innovation × Rich</td><td></td><td></td><td>-0.015*<br>(0.008)</td></tr><tr><td>Founder</td><td>-0.0032<br>(0.005)</td><td>-0.011**<br>(0.006)</td><td>-0.0011<br>(0.005)</td></tr><tr><td>Founder × Rich</td><td>-0.0023<br>(0.006)</td><td>0.0031<br>(0.008)</td><td>-0.0064<br>(0.007)</td></tr><tr><td>High effort entrepeneur</td><td>-0.019***<br>(0.005)</td><td>-0.022***<br>(0.006)</td><td>-0.024***<br>(0.006)</td></tr><tr><td>High effort entrepeneur × Rich</td><td>0.0091<br>(0.007)</td><td>-0.000067<br>(0.009)</td><td>-0.0084<br>(0.008)</td></tr><tr><td>Low effort recipient</td><td>-0.029***<br>(0.006)</td><td>-0.037***<br>(0.009)</td><td>-0.037***<br>(0.007)</td></tr><tr><td>Low effort recipient × Rich</td><td>0.0078<br>(0.008)</td><td>-0.013<br>(0.01)</td><td>-0.017<br>(0.01)</td></tr><tr><td>Rich</td><td>-0.020<br>(0.03)</td><td>-0.042<br>(0.03)</td><td>-0.044*<br>(0.03)</td></tr><tr><td>Constant</td><td>0.53***<br>(0.05)</td><td>0.53***<br>(0.06)</td><td>0.66***<br>(0.05)</td></tr><tr><td>Observations</td><td>14400</td><td>9600</td><td>13536</td></tr><tr><td>Pseudo R-squared</td><td>0.015</td><td>0.028</td><td>0.043</td></tr><tr><td>TDE:Donation (Rich)</td><td>-0.000<br>(0.005)</td><td></td><td></td></tr><tr><td>TDE:Employment (Rich)</td><td></td><td>0.004<br>(0.006)</td><td></td></tr><tr><td>TDE:Innovation (Rich)</td><td></td><td></td><td>-0.009**<br>(0.004)</td></tr><tr><td>Founder (Rich)</td><td>-0.005<br>(0.004)</td><td>-0.008<br>(0.006)</td><td>-0.008*<br>(0.004)</td></tr><tr><td>High effort entrepreneur (Rich)</td><td>-0.010**<br>(0.004)</td><td>-0.022***<br>(0.006)</td><td>-0.033***<br>(0.005)</td></tr><tr><td>Low effort recipient (Rich)</td><td>-0.021***<br>(0.006)</td><td>-0.050***<br>(0.009)</td><td>-0.053***<br>(0.008)</td></tr></tbody></table>

Notes: The dependent variable is the percentage of money transferred from the entrepreneur to the recipient. Rich is a dummy = 1 if the spectator's household income exceeds $150,000. See the note to Figure 3 for the definition of the other variables. The last section of the table reports results of t-tests on the null hypotheses that a variable coefficient for the rich sub-group is significantly different from 0. This coefficient is obtained as the sum of the relative coefficient for the poor and the interaction term. Controls include age, gender, region of residence, education level, political ideology, and a dummy for the order the tables were presented. Standard errors clustered at the individual level in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%.
**Result 3:** The attribute relative to effort (low/high number of hours worked) has a significant effect on spectators' choices, with redistribution decreasing when entrepreneurs work many hours and recipients work few hours.

**Result 4:** Rich and poor spectators behave similarly across all attributes, except for the TD attributes related to donation and innovation, with the rich being more impacted by the innovation attribute and the poor by the donation attribute.

## 4.3 Mechanisms

### 4.3.1 Beliefs about Trickle-down and the moral status of rich entrepreneurs

There may be two reasons why spectators are sensitive to a TD attribute. The first is the belief that the attribute is advantageous for the economy, and therefore for the individual. The other is the belief that the person having that attribute possesses a high moral standard and is therefore worthy of reward. This is particularly the case for donations, because people may think that a person who donates large amounts of money is a “good person”, so their philanthropic activities deserve to be rewarded regardless of their impact on the economy. We use some questions from our post-experiment questionnaire to disentangle these two mechanisms. First of all, about 28% of poor spectators and 45% of rich spectators believe that the rich give to charities more than $3,600 per year (see questionnaire in Appendix C.2.2: Q16), so this TD channel is economically meaningful.

To probe into the first mechanism, we use a set of three questions asking participants the extent to which they believe that the TD activities of rich individuals are important to the economy. We created three dummy variables that we call *Importance Donation*, *Importance Employment*, and *Importance Innovation*, which equal 1 if respondents consider the rich's charitable donations, employment creation, or technological innovations to be “Very important” or “Extremely important” to benefit the economy (Appendix C.2.3: Q29). Respectively, 27.83% (51.40%), 30.08% (56.81%), and 28.58% (57.42%) of poor (rich) spectators believe that donations, employment, and innovations are important channels through which rich individuals benefit the economy. A McNemar's test does not reject the null hypothesis of no difference in the relevant proportions among poor spectators. In contrast, rich spectators believe that donations are less important than employment and innovation (McNemar's test: *p* < 0.01), while their beliefs about employment and innovation are similar (McNemar's test: *p* = 0.63). Rich spectators display a more positive view than poor spectators on the impact of the rich's activities on the economy in all three domains (Chi-squared test: *p* < 0.01 for each of the three comparisons).

To probe into the second mechanism, we use a dummy variable, *Selfish Rich*, identifying respondents who “Agree” or “Strongly agree” that one of the primary reasons for the rich being wealthy in the U.S. is their selfishness (Appendix C.2.3: Q26). 45.67% of poor spec-
tators and 41.88% of rich spectators fall into this category (Chi-Squared test for differences between rich and poor: *p* < 0.01).<fnref n="22" /> Admittedly, this question inquires about the moral standing of the rich in general rather than specifically for each of the TD attributes and it does so with respect to the process of becoming rich rather than about the general moral standing of the rich. Even so, we believe that this variable is a useful proxy for the attitude we want to capture.

We insert both variables in our main econometric model, interacting them with each TD attribute in turn and separating the analysis for rich and poor spectators. The interaction between *Importance Donation* and *TD:Donation* is significant for poor spectators (*β* = -0.049; *p* = 0.018; Table 4, column 1) but not for the rich (*β* = 0.015; *p* = 0.123; Table 4, column 2). The sign of the interaction term is the expected one: The more poor spectators believe that donations are important for the economy, the lower *R* when the entrepreneur is a donor.

If the moral standing of the donor were relevant, we would expect a positive sign for the interaction between *Importance Donation* and *Selfish Rich*. On the contrary, the interaction term is insignificantly different from 0 (*p* = 0.56 for poor spectators, *p* = 0.51 for rich spectators). All the same, poor respondents who believe that the rich derive their wealth from selfishness are overall significantly more likely to redistribute (*β* = 0.13, *p* < 0.01) but, as just noted, this does not occur at a significantly different rate when entrepreneurs donate compared to when they do not. The latter result also holds for rich spectators at virtually the same rate as for poor spectators (*β* = 0.13, *p* < 0.01). It is worth noting that the coefficient for *TD:Donation* for poor spectators is negative and significantly different from 0 (*β* = -0.023, *p* = 0.010; Table 4, column 1). This means that redistribution for high donors is significantly lower than for low donors, even for respondents who believe that entrepreneurs are non-selfish and who do not think that donations are important for the economy. This suggests that other mechanisms are at play to explain the lower redistribution for high donors than the two singled out in this section.

We do not detect any significant effect on redistribution for the beliefs about the impor-
tance for the economy of employing a large number of people or creating many innovations.
This is the case for both rich and poor spectators. Therefore, none of these mechanisms can
account for the small effect of innovation on redistribution by the rich (section 4.2, Result
1). As seen above, believing that the rich derived their wealth from selfishness has no impact
on *R* in the Employment and Innovation treatments. Nevertheless, believing that the rich
had to be selfish to acquire their wealth is associated with significantly lower redistribu-
tion in these two treatments, with the only exception of rich spectators in the Employment
treatment (Table 4, columns 3-6).

To further analyze the relevance of beliefs on the moral standing of the entrepreneur,

<footnote n="22">²²Almås et al. (2022) reports strong support for the rich selfishness hypothesis at a global level, with the mode being “Strongly agree” in the majority of countries.</footnote>
we asked participants in the second wave<fnref n="23" /> a supplementary question as to the reasons why they thought rich people donated to charities (Appendix C.2.3, Q66). Only 6.13% of the poor spectators responded “Because they are altruist”, the mode of the answers being the option “For tax purposes” (53.70%).<fnref n="24" /> Therefore, the share of poor people believing that rich donors have a merit-worthy moral standing seems very low in the first place. For these reasons, we conclude that the belief that donations are important for the economy is seemingly an important reason why participants redistributed.<fnref n="25" /> In contrast, the mechanism based on the moral standing of the rich entrepreneur did not seem to play a large role in accounting for the significant effect of the Donation treatment.<fnref n="26" />

### 4.3.2 Beliefs about effort

To investigate the psychological mechanisms concerning the manipulation of the attribute regarding hours of work, we study the interaction of that attribute with the standard survey questions that are asked to measure beliefs about the merit of the poor and the rich in real life. These questions ask if people agree more with the idea that the poor (the rich) are poor (rich) because of factors under their control rather than factors outside their control (Appendix C.2.3: Questions 23 and 24). 27.08% of poor spectators and 47.64% of rich spectators in our sample believe that the poor are poor due to factors under their control (e.g., poor people work less hard than others) (Chi-squared test: *p* < 0.01). Similarly, 29.92% of poor spectators and 63.70% of rich spectators believe that the rich are rich because of factors under their control (e.g., rich people work harder than others, or they are more talented than others, etc.) (Chi-squared test: *p* < 0.01).

We find evidence for the relevance of this mechanism for poor spectators and significantly less so for rich spectators (Appendix: Table A.5.2). Two dummies indicating whether spectators believe that factors under people's control determine economic success (labeled *Factors under control rich* and *Factors under control poor*) are strongly significant predictors of redistribution in the experiment for the poor and, less so, for the rich. Poor spectators believing that the rich and poor economic status is determined by factors under people's con-

<footnote n="23">²³The Donation treatment was conducted in the first wave, hence we cannot use this variable in the analysis of the Donation treatment.</footnote>

<footnote n="24">²⁴The proportion of rich people believing that the rich donate to charities out of altruism is significantly higher - 21.74%. Even for them, the mode of the distribution (38.41%) is the answer “For tax purposes”.</footnote>

<footnote n="25">²⁵We obtain similar results using a scale based on three items inquiring about the potential for trickle-down of “allowing business to make good profits”, or “setting low tax rates on rich people”, and even “the existence of rich people”. (Appendix C.2.3: Question 28).</footnote>

<footnote n="26">²⁶However, we obtain different results using a different measure of attitudes toward the rich, which asks whether respondents have a favorable or an unfavorable opinion of those who own businesses - labeled *Positive View of Business*, see Question 27 in the questionnaire (Section A.5.1). A dummy variable identifying respondents who have a very favorable opinion of business owners is slightly significantly different from 0 when interacted with *TD:Donation* (*p* = 0.071, Table A.5.1, column 1). The *Importance Donation* variable continues having a significant effect when interacted with the Donation treatment, the coefficient being very similar in size to that found in Table 4, column 1. However, we believe that the variable *Selfish Rich* offers a better proxy for the construct we want to investigate than *Positive View on Business* because the former, and not the latter, explicitly refers to rich people.</footnote>
<table><caption>Table 4: Tobit regression - Mechanism - Beliefs in TD and moral status of entrepreneurs</caption><thead><tr><th rowspan="2"></th><th colspan="2">Treat:Donation</th><th colspan="2">Treat:Employment</th><th colspan="2">Treat:Innovation</th></tr><tr><th>(1)<br>Poor</th><th>(2)<br>Rich</th><th>(3)<br>Poor</th><th>(4)<br>Rich</th><th>(5)<br>Poor</th><th>(6)<br>Rich</th></tr></thead><tbody><tr><td>Selfish rich</td><td>0.13***<br>(0.03)</td><td>0.13***<br>(0.03)</td><td>0.081**<br>(0.04)</td><td>0.033<br>(0.04)</td><td>0.13***<br>(0.03)</td><td>0.22***<br>(0.03)</td></tr><tr><td>Importance donation</td><td>0.034<br>(0.04)</td><td>0.035<br>(0.03)</td><td></td><td></td><td></td><td></td></tr><tr><td>TD:Donation</td><td>-0.023**<br>(0.009)</td><td>-0.011<br>(0.008)</td><td></td><td></td><td></td><td></td></tr><tr><td>TD:Donation × Importance donation</td><td>-0.049**<br>(0.02)</td><td>0.016<br>(0.010)</td><td></td><td></td><td></td><td></td></tr><tr><td>TD:Donation × Selfish rich</td><td>0.0069<br>(0.01)</td><td>0.0062<br>(0.010)</td><td></td><td></td><td></td><td></td></tr><tr><td>Importance employment</td><td></td><td></td><td>-0.039<br>(0.04)</td><td>0.044<br>(0.04)</td><td></td><td></td></tr><tr><td>TD:Employment</td><td></td><td></td><td>-0.0088<br>(0.008)</td><td>0.012<br>(0.01)</td><td></td><td></td></tr><tr><td>TD:Employment × Importance employment</td><td></td><td></td><td>0.0041<br>(0.01)</td><td>-0.017<br>(0.01)</td><td></td><td></td></tr><tr><td>TD:Employment × Selfish rich</td><td></td><td></td><td>0.013<br>(0.01)</td><td>0.0064<br>(0.01)</td><td></td><td></td></tr><tr><td>TD:Innovation</td><td></td><td></td><td></td><td></td><td>0.0080<br>(0.009)</td><td>-0.0027<br>(0.007)</td></tr><tr><td>Importance innovation</td><td></td><td></td><td></td><td></td><td>-0.013<br>(0.03)</td><td>-0.0052<br>(0.03)</td></tr><tr><td>TD:Innovation × Importance innovation</td><td></td><td></td><td></td><td></td><td>0.014<br>(0.01)</td><td>-0.010<br>(0.009)</td></tr><tr><td>TD:Innovation × Selfish rich</td><td></td><td></td><td></td><td></td><td>-0.013<br>(0.01)</td><td>-0.0025<br>(0.009)</td></tr><tr><td>Founder</td><td>-0.0035<br>(0.005)</td><td>-0.0054<br>(0.004)</td><td>-0.012**<br>(0.006)</td><td>-0.0083<br>(0.006)</td><td>-0.0016<br>(0.005)</td><td>-0.0073*<br>(0.004)</td></tr><tr><td>High effort entrepeneur</td><td>-0.019***<br>(0.005)</td><td>-0.0094**<br>(0.004)</td><td>-0.022***<br>(0.006)</td><td>-0.022***<br>(0.006)</td><td>-0.025***<br>(0.006)</td><td>-0.033***<br>(0.005)</td></tr><tr><td>Low effort recipient</td><td>-0.030***<br>(0.006)</td><td>-0.021***<br>(0.006)</td><td>-0.037***<br>(0.009)</td><td>-0.050***<br>(0.009)</td><td>-0.037***<br>(0.007)</td><td>-0.052***<br>(0.008)</td></tr><tr><td>Observations</td><td>7200</td><td>7200</td><td>4800</td><td>4800</td><td>7200</td><td>6336</td></tr><tr><td>Pseudo R-squared</td><td>0.040</td><td>0.072</td><td>0.032</td><td>0.053</td><td>0.041</td><td>0.21</td></tr></tbody></table>

Notes: See Table 3. *Importance donation*, *Importance employment* and *Importance innovation* are dummies taking value 1 if the respondents believe that the rich's donations to charity, the number of employees they have, and their technological innovations are “Very important” or “Extremely important” to benefit the economy. *Selfish rich* is a dummy equal to one whether respondents agree or strongly agree that in the US, one of the main reasons the rich are wealthy is their selfishness.
trol redistribute significantly less (Table A.5.2; column 1: *p* < 0.01 for both variables). For rich spectators (Table A.5.2; column 2), this negative effect is less strong and not significant for *Factors under control poor* (*p* = 0.538) and only marginally significant for *Factors under control rich* (*p* = 0.074). We conjecture that these variables may have a role in accounting for one of our main results, that is, that the poor recipient's and the rich entrepreneur's effort (in terms of daily hours of work) are the most relevant attributes determining redistribution. To explore this mechanism, we interacted the dummies *Factors under control rich* and *Factors under control poor* with the attributes *Low effort recipient* and *High effort entrepreneur*, respectively. We find a strongly significant effect only for the interaction relative to the poor recipient effort but not for the rich entrepreneur effort. The former interaction is strongly significant for both the poor and the rich (*p* < 0.01). This result suggests that spectators who hold the poor responsible for their condition are less prone to redistribute toward poor recipients *and* reward their daily effort, thus corroborating the importance of the poor merit in redistribution.

### 4.3.3 Political ideology

Among spectators, 26.76% of low-income and 48.42% of high-income individuals classify themselves as liberals, while 24.67% of low-income and 21.84% of high-income spectators identify themselves as conservatives. The remaining participants consider themselves moderates. In the following, we create a dummy equal to one if spectators are liberal, and zero if they are either moderates or conservatives. Nothing substantial would change if the groups of moderates and conservatives were separated.

Liberal spectators redistribute significantly more than non-liberals (*p* < 0.01, Table A.6.1, columns 1 and 2), with poor spectators tending to respond more to this trait than rich spectators, although the difference between the two coefficients is not significant (*p* = 0.11). Interestingly, when we regress *R* on the *Rich* dummy without controlling for Liberal, *Rich* turns out weakly significant ($\beta$ = -0.0285, *p* = 0.08). After controlling for *Liberal*, *Rich* turns out more statistically significant and the coefficient increases by a factor of 1.4 ($\beta$ = -0.04, *p* = 0.015). This result can be accounted for by the high and positive correlation between the *Liberal* dummy and the *Rich* category (Pearson's $\rho$ = 0.22, *p* < 0.01). Since liberals are more redistributive and the rich are generally less redistributive, the fact that there are more liberals among rich spectators than poor ones entails that, without controlling for liberals, the positive effect of *Liberal* on *R* is offset by the negative effect of *Rich*. Once the effect of *Liberal* is accounted for in the regression, the net effect of being rich emerges.<fnref n="27" />

Being liberal does not influence the impact of the stakeholders' attributes for poor spectators (Table A.6.2, column 1). Conversely, the effect of *Low effort recipient*, and *High effort*

<footnote n="27">²⁷This analysis points to the importance of controlling for the actual proportion of liberals and non-liberals in the *Rich* and *Poor* groups. In a weighted regression that uses the actual shares of the US population for each relevant attribute, the magnitude of the coefficient for *Rich* is virtually unchanged ($\beta$ = -0.04), although the significance level is now at *p* = 0.085.</footnote>
*entrepreneur* is mediated by political ideology. The two attributes have a lower yet signifi-
cant impact on rich liberals' choices than rich conservatives'. This suggests that rich liberals
are less meritocratic than rich conservatives, consistent with other studies focusing on the
economic élite (Page et al., 2013; Suhay et al., 2021).

4.3.4 Economic and political identity

A well-established tradition in social psychology posits that individuals tend to interpret
reality in terms of ingroup / outgroup relations, where the ingroup (outgroup) is the group
providing source of identity and attachment to individuals (Brewer, 1999; Tajfel and Turner,
2004). The tendency to favor the ingroup over the outgroup has been extensively cor-
roborated (Chen and Li, 2009; Balliet et al., 2014; Romano et al., 2021). Consequently,
identification with social groups has been posited to be an important explanatory factor for
preferences for redistribution (Fong et al., 2006; Shayo, 2009; Ghiglino et al., 2021). We con-
jected that two types of groups were important in redistributive decisions. The first group
is obviously the group of rich and poor people in the US. It is expected that the higher the
attachment to the group of the rich (the poor), the lower (higher) the redistribution observed
in the experiment. However, the literature on political polarization posits that attachment
to political groups may have overtaken attachment to economic groups as the main source of
identity in the political domain. This could be the case because issues such as immigration
or racial animosity are more important to people than issues related to economic inequality
(Roemer et al., 2007; Akkerman et al., 2014). Since the Republican (Democratic) party
typically presents policy platforms oriented to less (more) economic redistribution, we tenta-
tively conjectured that people who identify more with Republicans (Democrats) demand less
(more) redistribution. In this section, we report results from attachment scales constructed
with respect to the groups of people who supported either Donald Trump or Joe Biden in the
previous presidential election. We framed questions in terms of attachment to supporters of
Trump or Biden because we considered this framing to be more salient than that given by
supporters of the Republican or the Democratic party.

We probe into these mechanisms using four four-item scales of social attachment to the
two economic groups, *i.e.*, poor and rich people, and the two political groups, *i.e.*, Trump
and Biden voters. The scales were adapted from Buchan et al. (2011).<fnref n="28" /> Higher values of
the four scales denote higher attachment to the relevant group.

Figure 4 depicts the average score of the scales over the four groups for rich and poor spectators. First of all, we note that the poor tend to have an overall *lower* level of attachment to any group. This is reminiscent of Putnam (2007), who finds high correlation of individual trust across different groups, with socially marginalized individuals being less trusting toward others in general. The fact that attachment by poor spectators appears to be

<footnote n="28">²⁸The four questions inquired as to how strongly participants were attached/close/trusted/felt favorable toward people from each of the four groups. See q45-q52 in Appendix C.2.3 for economic identity, and q37-44 for political identity.</footnote>
particularly lower than rich spectators in the political dimension may be construed in terms of greater disaffection with politics by the poor than the rich, consistently with decreasing turnout rates at elections by low-income US citizens (Gethin et al., 2022).

The highest source of attachment for poor spectators is to the group of the poor. The associated ingroup bias for the poor vis-à-vis economic groups, that is, the difference in the attachment score between ingroup (the poor) and outgroup (the rich), is significant (paired t-test for attachment to poor = attachment to rich: $p < 0.01$). Instead, it is not significant for political identity (paired t-test for attachment to Trump = attachment to Biden $p = 0.50$). In contrast, rich spectators show a lower but all the same significant ingroup bias (paired t-test: $p < 0.01$). Likewise, no ingroup bias appears relevant for rich spectators with respect to Trump and Biden voters ($p = 0.14$).

We compare the relative importance of economic and political identity using the following ratios of the scales:

$$ATT_{POOR}^{REL} = \frac{ATT_{POOR}}{1 + ATT_{POOR} + ATT_{RICH}} \qquad (5)$$

$$ATT_{TRUMP}^{REL} = \frac{ATT_{TRUMP}}{1 + ATT_{TRUMP} + ATT_{BIDEN}} \qquad (6)$$

where $ATT_g$ is the sum of the scores of the attachment scale over the four items for each of the four items for each $g = \{POOR, RICH, TRUMP, BIDEN\}$.<fnref n="29" /> We label these variables *Attachment to poor* (*ratio*) and *Attachment to Trump* (*ratio*). These two indexes capture the propensity to be more attached to the poor (Trump) relative to the rich (Biden).

We first ascertain whether economic identity is more relevant than political identity in general. Hence, we run a model merging all observations (Table A.5.3, column 1-2). Without including the identity measures, rich spectators are found to redistribute less than poor spectators by a factor of 4 p.p. ($p = 0.015$; Table A.5.3, column 1). Nevertheless, the two identity scales completely moderate this effect (Table A.5.3, column 2). Although both economic and political identity are significant in predicting redistribution ($p < 0.01$ for both), the coefficient for the former is three times as large as that for the latter. Even if the ingroup/outgroup identification in the redistributive task is more directly construable in terms of economic groups than political groups, we believe that this result suggests that economic identity is at least as relevant to individuals for redistribution as political identity. The signs are those we would expect, with greater relative attachment to the poor (Trump) leading to more (less) redistribution. Economic identity appears to be more relevant for the poor than for the rich (Table A.5.3, columns 3-4), while political identity seems to be equally important (Table A.5.3, columns 5-6). Interestingly, the coefficients for economic and political identity are nearly unchanged when entering the regressions jointly (Table A.5.3, columns 7-8) compared to when entering separately. This suggests that the two constructs are roughly orthogonal to each other. When examining each of the four scales individually

<footnote n="29">²⁹We augment the denominator by 1 to avoid cases in which the denominator equals 0.</footnote>
<figure><img src="image_6.png" /><figcaption>Figure 4: Economic and political identity</figcaption></figure>

(columns 9-10), we note that while attachment to the poor and attachment to the rich matter for both rich and poor spectators, attachment to Trump supporters is insignificant. The relevance of political identity is entirely driven by attachment to Biden supporters, which is associated with more redistribution and is statistically significant.

Finally, we tested the moderating effect of economic and political identity on the TD and effort attributes (Table A.5.4). We do not find any moderating effect, except for a significant effect of economic identity on the attribute of low effort by the poor for poor spectators. The more poor spectators are attached to the poor (relative to the rich), the lower the redistribution toward the poor exerting low effort. Hence, it seems that meritocratic attitudes are more relevant for poor spectators who have a strong attachment to the poor.

We report pre-registered analysis on beliefs misalignment with the TD treatment in Appendix: section A.5, and pre-registered heterogeneity analysis on gender, Prospect of Upward Mobility, and some other dimensions in Appendix: section A.6.

# 5 Robustness

## 5.1 Experimental decisions and real-life pro-social decision

To test whether redistributive choices in the experiment are externally valid, we analyze their correlation with a monetarily incentivized redistributive decision that participants faced at the end of the survey. After completing the post-experiment questionnaire, participants were told that ten randomly selected participants in the survey would be assigned $50. They were then asked to make a choice on how to allocate this sum, should they be selected. They could keep this sum or donate part or all of it to the charity “Feeding America”, which helps the poor in the US. Hence, this “natural” donation choice reproduces the experimental decision in that the recipients of the donation are poor US residents.
The histogram of the donation decision appears similar to that of the experimental decision (see Figure B.1.1). The distribution has three peaks with donation of the full $50 amount being the mode (21.7%), followed by splitting the money evenly (12.3%) and keeping all the money (11.2%). Participants donated on average 52% of the available amount (s.d.=17.4), again in line with the experimental choices (section 4.1). Interestingly, the amount donated by the rich (63.0%) was significantly higher than that donated by the poor (43.2%), the difference being strongly significant ($z = -10.2, p < 0.01, n = 1,321$).

The individual-level mean of the experimental choices is highly positively correlated with the amount donated to charity (Pearson's $\rho = 0.21, p < 0.01$). Moreover, the experimental redistribution choices are a strongly significant predictor of the amount donated to charity in an OLS econometric model having the same set of controls deployed in the analyzes so far ($p < 0.01$, Appendix: Table B.1.1, column 1).

We also use the same econometric model to predict a set of variables capturing different aspects of redistributive preferences, i.e., voting for Joe Biden in the 2016 presidential election (Table B.1.1, column 2), agreement with the statement that reducing inequality is important (column 3), and that progressive taxation, education policies, government regulations, and transfers are important tools to reduce inequality in the US (columns 4-7). Once again, the experimental redistributive choices are a strongly significant predictor of all the above variables (for all coefficients, $p < 0.01$). These results further corroborate the view that our experimental choices are indeed correlated with participants' preferences to benefit poor people and their attitudes towards redistributive policies in general.

## 5.2 Additional TD beliefs

To test the external validity of how TD was modeled in the experiment, we use a scale derived from three survey questions inquiring about how businesses' profits, low taxes on wealthy individuals, and the existence of wealthy individuals benefit society (Appendix C.2.3: Questions 28). We applied principal component analysis (PCA) to these responses to extract the first principal component, which summarizes the shared variance in the answers. This first principal component is used as an independent variable to explain spectators' views on redistribution. This measure of beliefs in TD is negatively correlated with the level of redistribution among poor spectators ($p = 0.012$), while it is insignificant for rich spectators ($p = 0.113$, Table B.1.2, columns 1-2). Beliefs in TD weakly significantly predict the lower level of redistribution by the poor for high-donor entrepreneurs (Table B.1.2, column 3). They are insignificant, albeit with the expected negative sign, in predicting the other two TD attributes. This may be due to the fact that redistribution on such TD attributes varied little in the first place (Table B.1.2, columns 5, 7). TD beliefs do not predict the choices of rich spectators vis-à-vis deserving entrepreneurs with regard to TD attributes (Table B.1.2, columns 4, 6, 8). Overall, we conclude that this analysis supports the view that beliefs in the capacity of the rich to benefit the economy played a role in motivating poor spectators
in their redistributive choices.

## 5.3 Sample representativeness

Recruiting a relatively high number of respondents from two specific and hard-to-reach subgroups came at the cost of limited representativeness in their demographic and attitudinal characteristics. In particular, liberals are under-represented (over-represented) in the poor (rich) groups in our sample and poor men are under-represented (Table 2). To address this issue, we created post-stratification weights applying the iterative proportional fitting (or raking) method (Kolenikov, 2014). Specifically, we built survey weights to make our sample of rich and poor spectators within each treatment representative of the population of rich and poor with respect to gender, age, region of residence, education level, and political orientation. We can infer from Table B.2.1 that all results reviewed above hold even in this specification.

## 5.4 Focusing on the top and bottom 10%

The limited differences in redistribution between the poor and the rich in our experiment could be attributed to our relatively broad definition of these groups, which encompass the top and bottom quintiles of the US income distribution rather than the top and bottom deciles. The inclusion of the second and fourth decile in the group of rich and poor, respectively, may have “watered down” the difference between the “super-rich” and the “very poor”. In fact, 66.08% of our sample of poor spectators includes people from the bottom 10% (with household income lower than $20,000), while 45.29% of our sample of rich spectators includes people from the top 10% (with household income higher than $200,000).

However, the main results of our analysis are robust to restricting the sample to the bottom and top 10% of the income distribution, as can be inferred by comparing Table 3 and Appendix: Table A.3.1 and Table B.3.1. In particular, the coefficient for the variable *Rich* is virtually unchanged in the whole sample and the restricted sample (Table A.3.1, column 1 vs. Table B.3.1, column 4). The only difference lies in that the TD:Innovation attribute has the expected negative effect for both spectators from the bottom 10% and top 10% (B.3.1, column 3) rather than (weakly) only the top 20% (Table A.3.1, column 3).

## 5.5 Experimenter Demand effect

Another possible concern is that respondents’ choices might be biased by their beliefs about what the researchers expect them to do (Zizzo, 2010). Even though recent empirical evidence indicates limited support for experimenter demand effects in online surveys (Haaland et al., 2020), we sought to gauge its magnitude. Specifically, we assured participants they would never be identified and adopted a neutral framing in the main task (Haaland et al., 2020). We also sought to measure the possible impact of experimenter demand effects in our study. In
the last part of the questionnaire, we asked participants whether they thought the survey was biased (Appendix C.2.3: Questions 60). The majority (80%) of participants did not perceive any bias in the survey, with 13% believing that the researchers wanted the participants to transfer a large amount of money to recipients and 7% claiming the opposite. In B.4.1, we replicate the key econometric model dropping participants who believed the survey was biased. Comparing this Table with Table 3 we can conclude that all results are robust to the exclusion of this sub-sample.

## 5.6 Data quality

We also verify the quality of our data following standard survey practices. First, according to the exclusion criterion in our preregistration, we check if the completion time was less than a quarter of the sample median and find that no participant was as fast. Second, 80% of the low-income sample and 85.95% of the high-income sample answered a standard attention check question correctly (Appendix C.2.3: Question 13). Moreover, most participants stated that the survey was very clear (60.58% of poor spectators and 69.02% of rich spectators) or quite clear (35.00% of poor spectators and 28.53% of rich spectators). After expunging both inattentive participants and those answering that the survey was not clear, our main results do not change (Table B.5.1).

## 5.7 Alternative econometric models

Our main analysis deploys a Tobit estimator. However, given that participants choose to re-distribute 16 times, we can exploit our data panel dimension and use fixed effects regressions to control for any individual unobserved characteristics. Moreover, we also run a random effects regression, given that we can safely assume that the individuals’ random effects are orthogonal to the independent variables of interest (stakeholders’ attributes, which are manipulated through the conjoint tables). Results, shown in table B.6.1, show that coefficients and standard errors from these analyses are remarkably close to the main estimation in Table 3.

# 6 Discussion

Experiments are generally evaluated in terms of internal and external validity.

**Internal validity.** Our strategy for simultaneously identifying attitudes toward TD and individual merit hinged upon using the choices of impartial spectators to evaluate the relevance of attributes related to TD. It could be argued that, by relying on decisions in which self-interest is not relevant, we neglected an aspect that is particularly important for TD. Our identification strategy was based on the auxiliary assumption that, if a person believes in the
benefits of TD for themselves, they would also be willing to reward a real-life entrepreneur who embodies some of the beneficial properties of TD.

To be sure, we cannot prove that our finding that TD does not appear to be a key determinant of redistributive choices is not due to the violation of this auxiliary assumption but rather to the actual violation of the main TD hypothesis. However, we already argued in section 3.3 that removing self-interest from the experimental decision leads to better experimental identification. Moreover, previous research (Cappelen et al., 2013) unequivocally demonstrates that the spectators' choices in distribution interactions similar to ours are remarkably similar to the fairness component of decision-makers whose self-interest is at stake (see equation 1 in section 3.1). In other words, even if the self-interested component is by construction “silenced” for spectators, decisions guided by their social preferences component are very close to those made by individuals for whom self-interest is relevant. Clearly, when self-interest matters, individuals' decisions are a combination of the action maximizing their self-interest and the action they believe to be fair. Nonetheless, only a minority of participants' choices (41%) are consistent with full earnings maximization in the experiment by Grimalda et al. (2023), which confirms the relevance of social preferences in individual decision-making. The same is true also in real life (Fong, 2001; Stantcheva, 2021; Cohn et al., 2023). This is why spectator allocation choices are increasingly used as a method for measuring individual fairness (Konow, 2000; Charness and Rabin, 2002; Engelmann and Strobel, 2004; Cappelen et al., 2013; Almås et al., 2020). We also note that the robustness analysis conducted in section 5.2 does show that the experimental choices were correlated with real-life beliefs about the relevance of TD. The fact that the real-life relevance of TD was more relevant for poor rather than rich spectators further corroborates the validity of our approach.

One could argue that our design did not include some relevant dimensions in which TD can exert its effects, such as consumption and investment by the rich. Nevertheless, the dimensions included in the experiment were the three respondents deemed most important for TD (Appendix: Figure B.7.1). It is also possible that some more specific aspects, such as the quality of the jobs created or the nature of the innovations produced by rich entrepreneurs, might play a significant role in the attitudes held toward the rich. It is difficult to imagine, though, that such more specific aspects could significantly alter the nature of our results. Ultimately, our results align with Stantcheva (2021), who also finds a secondary role for TD in how the US public conceives redistribution, using only one item in her survey.

Another possible limitation of our design is that the assignment of the high stake of $50 to the entrepreneur was never manipulated. An alternative design could have included a treatment where the high stake was assigned to the poor stakeholder instead of the rich stakeholder. Alternatively, we could have given the recipient the possibility of carrying out a job that was paid 50 times less than the job carried out by the entrepreneur. Or we could have given no task to either stakeholder at all. Admittedly, a potential confound exists between the “pure” preference for redistribution and the fact that the entrepreneur was assigned the
high stake because they had previously worked for us. However, this limitation can only
affect the amount of redistribution in the baseline but cannot compromise the validity of the
treatment effects. We opted not to introduce this additional manipulation into the design
to explore the relevance of multiple TD factors for redistribution. Since the situation in
which the entrepreneur is endowed with a high stake is undoubtedly more realistic than the
opposite situation in which the real-life poor stakeholder is endowed with the high stake, we
believe that our research design captures the most important aspects of redistribution, also
making income inequality salient to spectators.

On some more technical aspects, one could argue that our results are affected by the relatively high number of decisions the respondents had to make. The fact that the data collection took place in two waves, rather than one, was admittedly suboptimal. As a result, the Innovation treatment was only assigned to the second wave. Although 16 decisions lie at the high end of the number of decisions typically made in conjoint analysis, it is not unusual to have respondents complete more than a dozen decisions in this approach. (Bansak et al., 2018) finds that even when respondents are exposed to 30 conjoint tables, there is no substantial “survey satisficing”; that is, respondents process the conjoint profiles in similar ways and provide internally consistent results. The relatively large number of choices may perhaps drive the relatively small effect sizes we found. However, our results are robust to controlling for the order in which decisions were made (section 4.2). Similarly, analyzing the two waves jointly permits the control of the sample imbalances in demographic characteristics. This robustness check does not affect the results.

Another reason for concern is that the decisions had barely any monetary real-life im-
pact, as only one of the many choices made by the participants was implemented. Budget
constraints prevented us from increasing the number of decisions that actually determined
real-life payoffs. Even if the probability that a respondent's choice would determine payoffs
in real life was negligible, research shows that experiments in which monetary incentives are
low do not differ significantly from those where they are higher (Charness et al., 2016; Ro-
mano et al., 2021). Andre (2024) also shows the robustness of his results to the contingent
strategy method that he used, which is similar to ours. More generally, even if the use of very
low monetary incentives (in expected value) may have affected decisions, it would be difficult
to imagine that this aspect distorted the impact of some of the stakeholders' attributes more
than others. If anything, low incentives likely lead to more noise in the experimental choices,
so that the effects we observed are a lower bound of the real ones.

**External validity** With regard to the external validity of our study, limiting data collection to one country raises obvious questions of generalizability. Nonetheless, the fact that previous comparative experimental research (Rey-Biel et al., 2018; Almås et al., 2020; Buser et al., 2020; Grimalda et al., 2023) included the US alongside other countries allows for a rough assessment of the extent to which results in other countries might differ. In particular, since extensive research considers the US “the land of opportunities” (Alesina and La Fer-
rara, 2005; Alesina et al., 2018), it is plausible that the US provides the upper bound of the strength of TD attitudes around the world. Additionally, the sample used may not fully represent the diverse experiences of the poor, particularly those who do not have access to the Internet or are marginalized. This potential selection bias could affect the validity of our conclusions about the poor and highlights the need for further research that includes a broader and more representative range of participants.

The factor that turned out to have the largest effect on the redistribution decisions of both rich and poor spectators was the demerit of the poor. Given the self-serving bias affecting most individuals, it is unsurprising that this belief is important for rich spectators, as explained in section 3.3. However, it is remarkable that the proportion of poor people holding the belief that other poor are “undeserving” and that the rich are “deserving” is substantial (see section 4.3.2 and Gilens, 1999). These beliefs may be the result of ethnic fragmentation (Ghiglino et al., 2021), or of a desire to see the world as just (Benabou and Tirole, 2006; Alesina et al., 2018) or to defend and rationalize existing social, economic, and political systems (Jost et al., 2003; Goudarzi et al., 2020; Caricati and Owuamalam, 2020). It is well known that excessive optimism about the chances of upward mobility in the US is, *ceteris paribus*, widespread among the poorest (Chetty et al., 2014a; Alesina et al., 2018). Therefore, our finding that the demerits of the poor impact substantially also poor spectators appears consistent with existing evidence.

# 7 Conclusions

Not taxing the rich is sometimes portrayed as the equivalent of “not killing the goose that lays the golden eggs”. As in Aesop’s fable, not taking too much from the rich would allow some of their wealth to trickle down to the rest of society. In this paper, we tested the impact of the TD attributes of rich entrepreneurs versus the impact of hard work or lack thereof by rich entrepreneurs and poor potential recipients on the desire to redistribute by rich third parties, and by poor third parties. We find that the effects of TD attributes are mainly modest and not too different for rich vs. poor third parties. Therefore, it does not seem that the ability of the rich to “lay golden eggs” is too relevant in our sample. In fact, only a minority of our poor sample believes that TD is “very important” for the economy. In contrast, hard work by the poor, especially, and also by the rich, has much larger effects on redistribution choices by both rich and poor third parties. Overall, the redistributive choices of the rich and the poor are surprisingly similar, despite their holding sharply different beliefs about whether economic success is due to hard work versus luck.

Even if the impact of the TD attributes on redistribution is generally modest, we do find some effects. Entrepreneurs who donate large amounts of money to charity are rewarded by low-income participants, but not by high-income participants. Conversely, entrepreneurs with high innovation rates are rewarded by high-income participants, but not by low-income participants. Those who founded their firms are also rewarded relative to the entrepreneurs
who inherited them. Nevertheless, the effect sizes of these factors are small and do not always
reach statistical significance. Moreover, entrepreneurs who employ large numbers of workers
in their firms are not rewarded at all by either rich or poor participants. This result is
suprising, as this is likely the most relevant channel often attributed to TD. In comparison,
individual merit, as measured by the number of hours worked per day, is a stronger and
more important determinant of preferences for redistribution. The merit of the poor is the
strongest predictor of redistribution and outstrips the merit of the rich by a factor of 1.72.

Drawing on the post-experiment questionnaire, we can also shed light on the psychological
mechanisms underlying our findings. The main reason why participants want to reward rich
donors seems to be their belief that donations are beneficial for the economy, rather than
their willingness to compensate donors for their moral standing. The willingness to reward
the merit of the poor and the rich in the experiment correlates with beliefs about the personal
responsibility of the rich and the poor in determining such outcomes in real life. Two newly
developed indexes of social identity vis-à-vis the poor/rich and Trump/Biden supporters
explain almost all of the difference in the experimental choices of rich and poor spectators,
with the former index having higher predictive power than the latter.

Our study contributes to the ongoing debate on rising inequality, stagnating demand for redistribution, and the role of individual fairness preferences in shaping economic policies. Our answer to the question of why the poor do not “exploit” the rich is that, ultimately, the preferences of these two groups appear to be relatively close to one other, both quantitatively and in terms of their patterns. The merit of the poor appears to be, for both groups, the most important factor driving preferences for redistribution in our sample. In a rapidly changing political landscape, the observation that attachment to the group of Trump supporters is irrelevant for redistribution, unlike attachment to the group of Biden supporters, suggests tentatively that addressing income inequality may not be part of the US political agenda, at least in the short term.
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# Appendices

## Table of Contents

<table><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>A</strong> Additional tables and figures</td><td><strong>A-1</strong></td></tr><tr><td>A.1 Summary statistics by treatment</td><td>A-1</td></tr><tr><td>A.2 Redistribution distributions by treatment</td><td>A-2</td></tr><tr><td>A.3 Regression table by pooling treatments plotted in Figure 3</td><td>A-3</td></tr><tr><td>A.4 Multiple hypothesis testing correction</td><td>A-4</td></tr><tr><td>A.5 Mechanisms</td><td>A-5</td></tr><tr><td>A.6 Heterogeneity analysis</td><td>A-13</td></tr><tr><td>A.7 Social mobility transition matrices</td><td>A-17</td></tr><tr><td><strong>B</strong> Additional analysis and robustness checks</td><td><strong>B-18</strong></td></tr><tr><td>B.1 Incentivized behaviors and preferences</td><td>B-18</td></tr><tr><td>B.2 Representativity</td><td>B-21</td></tr><tr><td>B.3 Bottom 10% vs Top 10%</td><td>B-22</td></tr><tr><td>B.4 Experimenter demand effect</td><td>B-23</td></tr><tr><td>B.5 Attention of participants</td><td>B-24</td></tr><tr><td>B.6 Different models</td><td>B-25</td></tr><tr><td>B.7 Regression including pilot data</td><td>B-26</td></tr><tr><td><strong>C</strong> Survey details</td><td><strong>C-27</strong></td></tr><tr><td>C.1 Survey - STAKEHOLDERS</td><td>C-27</td></tr><tr><td>C.2 Survey - SPECTATORS</td><td>C-29</td></tr><tr><td><strong>D</strong> Research transparency</td><td><strong>38</strong></td></tr></tbody></table>
# A Additional tables and figures

## A.1 Summary statistics by treatment

<table><caption>Table A.1.1: Summary statistics by treatment</caption><thead><tr><th rowspan="2"></th><th colspan="4">Rich</th><th colspan="4">Poor</th></tr><tr><th>Treat:Donation</th><th>Treat:Employment</th><th>Treat:Innovation</th><th>US Population</th><th>Treat:Donation</th><th>Treat:Employment</th><th>Treat:Innovation</th><th>US Population</th></tr></thead><tbody><tr><td><em>Gender</em></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td>0.56</td><td>0.57</td><td>0.36</td><td>0.51</td><td>0.25</td><td>0.30</td><td>0.28</td><td>0.41</td></tr><tr><td>Female</td><td>0.44</td><td>0.43</td><td>0.64</td><td>0.49</td><td>0.74</td><td>0.70</td><td>0.72</td><td>0.59</td></tr><tr><td><em>Age</em></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[18-35]</td><td>0.33</td><td>0.29</td><td>0.37</td><td>0.29</td><td>0.34</td><td>0.34</td><td>0.33</td><td>0.23</td></tr><tr><td>[36-55]</td><td>0.53</td><td>0.51</td><td>0.54</td><td>0.40</td><td>0.29</td><td>0.31</td><td>0.46</td><td>0.22</td></tr><tr><td>[56+]</td><td>0.14</td><td>0.20</td><td>0.093</td><td>0.31</td><td>0.37</td><td>0.35</td><td>0.21</td><td>0.55</td></tr><tr><td><em>Region</em></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>South</td><td>0.42</td><td>0.36</td><td>0.42</td><td>0.32</td><td>0.48</td><td>0.44</td><td>0.52</td><td>0.44</td></tr><tr><td>Midwest</td><td>0.14</td><td>0.18</td><td>0.14</td><td>0.20</td><td>0.23</td><td>0.22</td><td>0.20</td><td>0.21</td></tr><tr><td>Northeast</td><td>0.21</td><td>0.22</td><td>0.24</td><td>0.22</td><td>0.16</td><td>0.17</td><td>0.16</td><td>0.15</td></tr><tr><td>West</td><td>0.23</td><td>0.24</td><td>0.20</td><td>0.26</td><td>0.14</td><td>0.16</td><td>0.12</td><td>0.19</td></tr><tr><td><em>Ideology</em></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Conservative</td><td>0.23</td><td>0.27</td><td>0.22</td><td>0.38</td><td>0.25</td><td>0.26</td><td>0.23</td><td>0.33</td></tr><tr><td>Moderate</td><td>0.26</td><td>0.28</td><td>0.34</td><td>0.20</td><td>0.47</td><td>0.45</td><td>0.51</td><td>0.33</td></tr><tr><td>Liberal</td><td>0.51</td><td>0.45</td><td>0.44</td><td>0.41</td><td>0.27</td><td>0.30</td><td>0.25</td><td>0.34</td></tr></tbody></table>

Notes: The Table displays summary statistics by treatment group from our sample alongside population statistics. For the latter, the statistics on Gender, Age, and Region are computed from the 2020 U.S. Census dataset. Regarding Ideology, we leverage the data provided by the American National Election Studies to compute the share of liberals, conservatives, and moderates by income group.
## A.2 Redistribution distributions by treatment

<figure><img src="image_7.png" /><figcaption>Figure A.2.1: Distribution of spectators' choices, by income group</figcaption></figure>
A.3 Regression table by pooling treatments plotted in Figure 3

<table><caption>Table A.3.1: Tobit regression on amount transferred</caption><thead><tr><th rowspan="3"></th><th colspan="3">Treat:Pooled</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th>Rich & Poor</th><th>Poor</th><th>Rich</th></tr></thead><tbody><tr><td>TD:Donation</td><td>-0.016***<br>(0.004)</td><td>-0.032***<br>(0.007)</td><td>-0.000054<br>(0.005)</td></tr><tr><td>TD:Employment</td><td>0.0012<br>(0.004)</td><td>-0.0016<br>(0.005)</td><td>0.0037<br>(0.006)</td></tr><tr><td>TD:Innovation</td><td>-0.00082<br>(0.004)</td><td>0.0061<br>(0.007)</td><td>-0.0087**<br>(0.004)</td></tr><tr><td>Founder</td><td>-0.0058***<br>(0.002)</td><td>-0.0048<br>(0.003)</td><td>-0.0070***<br>(0.003)</td></tr><tr><td>High effort entrepreneur</td><td>-0.021***<br>(0.002)</td><td>-0.022***<br>(0.003)</td><td>-0.021***<br>(0.003)</td></tr><tr><td>Rich</td><td>-0.040**<br>(0.02)</td><td></td><td></td></tr><tr><td>Constant</td><td>0.59***<br>(0.03)</td><td>0.51***<br>(0.04)</td><td>0.65***<br>(0.08)</td></tr><tr><td>Observations</td><td>37536</td><td>19200</td><td>18336</td></tr><tr><td>Pseudo R-squared</td><td>0.025</td><td>0.016</td><td>0.053</td></tr></tbody></table>

Notes: The dependent variable is the percentage of money transferred from the entrepreneur to the recipient. *TD:Donation* is a dummy = 1 if the entrepreneur donated more than $3600 in the last 12 months. *TD:Employment* equals 1 if the entrepreneur's firm has over 1000 employees. *TD:Innovation* equals 1 if the entrepreneur's firm obtained over 180 patents. *Founder* is a dummy = 1 if the entrepreneur founded the firm they own. *High effort entrepreneur* equals 1 if the entrepreneur works more than 10 hours daily. *Low effort recipient* equals 1 if the recipient works less than 6 hours daily. *Rich* is a dummy = 1 if the spectator's household income exceeds $150,000. Controls include age, gender, region of residence, education level, political ideology, and a dummy for the order the tables were presented. Standard errors clustered at the individual level in parentheses.
* significant at 10%, ** significant at 5%, *** significant at 1%.
## A.4 Multiple hypothesis testing correction

The sharpened q-value approach applies the method exposed in Anderson (2008). The Holm-adjusted p-values are defined as $p_i = \min(1, k_i p_i)$, where $k_i$ is the number of p-values at least as large as the unadjusted $p_i$. The Bonferroni-adjusted p-values are $p_i = \min(1, k p_i)$, where $k$ is the number of unadjusted p-values.

<table><caption>Table A.4.1: MHT correction</caption><thead><tr><th></th><th>pval</th><th>sharpened.qvalue</th><th>p.holm</th><th>p.bonferroni</th></tr></thead><tbody><tr><td colspan="5"><strong>(a) Treatments pooled - Without interactions</strong></td></tr><tr><td>Founder</td><td>0.004</td><td>0.004</td><td>0.012</td><td>0.024</td></tr><tr><td>High effort entrepreneur</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td></tr><tr><td>Low effort recipient</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td></tr><tr><td>Trickle-down:Donation</td><td>0.000</td><td>0.001</td><td>0.001</td><td>0.001</td></tr><tr><td>Trickle-down:Employment</td><td>0.768</td><td>0.389</td><td>1.000</td><td>1.000</td></tr><tr><td>Trickle-down:Innovation</td><td>0.839</td><td>0.389</td><td>0.839</td><td>1.000</td></tr><tr><td colspan="5"><strong>(c) Treatment employment</strong></td></tr><tr><td>Founder</td><td>0.044</td><td>0.077</td><td>0.354</td><td>0.530</td></tr><tr><td>Founder (Rich)</td><td>0.150</td><td>0.213</td><td>1.000</td><td>1.000</td></tr><tr><td>Founder X Rich</td><td>0.697</td><td>0.719</td><td>1.000</td><td>1.000</td></tr><tr><td>High effort entrepreneur</td><td>0.000</td><td>0.002</td><td>0.004</td><td>0.005</td></tr><tr><td>High effort entrepreneur (Rich)</td><td>0.000</td><td>0.002</td><td>0.004</td><td>0.005</td></tr><tr><td>High effort entrepreneur X Rich</td><td>0.993</td><td>0.859</td><td>0.993</td><td>1.000</td></tr><tr><td>Low effort poor</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td></tr><tr><td>Low effort poor X Rich</td><td>0.269</td><td>0.369</td><td>1.000</td><td>1.000</td></tr><tr><td>Low effort recipient (Rich)</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td></tr><tr><td>TDE:Employment</td><td>0.769</td><td>0.722</td><td>1.000</td><td>1.000</td></tr><tr><td>TDE:Employment (Rich)</td><td>0.529</td><td>0.545</td><td>1.000</td><td>1.000</td></tr><tr><td>TDE:Employment X Rich</td><td>0.505</td><td>0.545</td><td>1.000</td><td>1.000</td></tr><tr><td colspan="5"><strong>(b) Treatment donation</strong></td></tr><tr><td>Founder</td><td>0.527</td><td>0.361</td><td>1.000</td><td>1.000</td></tr><tr><td>Founder (Rich)</td><td>0.177</td><td>0.153</td><td>0.883</td><td>1.000</td></tr><tr><td>Founder X Rich</td><td>0.733</td><td>0.364</td><td>1.000</td><td>1.000</td></tr><tr><td>High effort entrepreneur</td><td>0.000</td><td>0.001</td><td>0.002</td><td>0.002</td></tr><tr><td>High effort entrepreneur (Rich)</td><td>0.032</td><td>0.040</td><td>0.226</td><td>0.387</td></tr><tr><td>High effort entrepreneur X Rich</td><td>0.158</td><td>0.153</td><td>0.949</td><td>1.000</td></tr><tr><td>Low effort poor</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td></tr><tr><td>Low effort poor (Rich)</td><td>0.000</td><td>0.001</td><td>0.002</td><td>0.003</td></tr><tr><td>Low effort poor X Rich</td><td>0.341</td><td>0.295</td><td>1.000</td><td>1.000</td></tr><tr><td>TDE:Donation</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td></tr><tr><td>TDE:Donation (Rich)</td><td>1.000</td><td>0.500</td><td>1.000</td><td>1.000</td></tr><tr><td>TDE:Donation X Rich</td><td>0.000</td><td>0.001</td><td>0.002</td><td>0.003</td></tr><tr><td colspan="5"><strong>(d) Treatment innovation</strong></td></tr><tr><td>Founder</td><td>0.751</td><td>0.335</td><td>0.751</td><td>1.000</td></tr><tr><td>Founder (Rich)</td><td>0.089</td><td>0.114</td><td>0.536</td><td>1.000</td></tr><tr><td>Founder X Rich</td><td>0.353</td><td>0.192</td><td>0.706</td><td>1.000</td></tr><tr><td>High effort entrepreneur</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td></tr><tr><td>High effort entrepreneur (Rich)</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td></tr><tr><td>High effort entrepreneur X Rich</td><td>0.313</td><td>0.192</td><td>1.000</td><td>1.000</td></tr><tr><td>Low effort poor</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td></tr><tr><td>Low effort poor (Rich)</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td></tr><tr><td>Low effort poor X Rich</td><td>0.126</td><td>0.126</td><td>0.632</td><td>1.000</td></tr><tr><td>TDE:Innovation</td><td>0.344</td><td>0.192</td><td>1.000</td><td>1.000</td></tr><tr><td>TDE:Innovation (Rich)</td><td>0.046</td><td>0.081</td><td>0.372</td><td>0.558</td></tr><tr><td>TDE:Innovation X Rich</td><td>0.059</td><td>0.086</td><td>0.412</td><td>0.707</td></tr></tbody></table>

Notes: These tables display the p-values associated with three multiple hypothesis testing corrections from the coefficients plotted in Figure 3 for table (a) and from the coefficients of the regression results displayed Table 3 for table (b), (c), and (d).
## A.5 Mechanisms

<table><caption>Table A.5.1: Tobit regression results - Beliefs in TD and view of business owners</caption><thead><tr><th rowspan="2"></th><th colspan="2">Treat:Donation</th><th colspan="2">Treat:Employment</th><th colspan="2">Treat:Innovation</th></tr><tr><th>(1)<br>Poor</th><th>(2)<br>Rich</th><th>(3)<br>Poor</th><th>(4)<br>Rich</th><th>(5)<br>Poor</th><th>(6)<br>Rich</th></tr></thead><tbody><tr><td>Positive view business</td><td>-0.044<br>(0.04)</td><td>0.012<br>(0.03)</td><td>0.0079<br>(0.04)</td><td>0.060<br>(0.04)</td><td>-0.028<br>(0.03)</td><td>0.057*<br>(0.03)</td></tr><tr><td>Importance donation</td><td>0.052<br>(0.04)</td><td>0.056*<br>(0.03)</td><td></td><td></td><td></td><td></td></tr><tr><td>TD:Donation</td><td>-0.013*<br>(0.007)</td><td>-0.013<br>(0.008)</td><td></td><td></td><td></td><td></td></tr><tr><td>TD:Donation × Importance donation</td><td>-0.043**<br>(0.02)</td><td>0.013<br>(0.01)</td><td></td><td></td><td></td><td></td></tr><tr><td>TD:Donation × Positive view business</td><td>-0.037*<br>(0.02)</td><td>0.012<br>(0.01)</td><td></td><td></td><td></td><td></td></tr><tr><td>Importance employment</td><td></td><td></td><td>-0.039<br>(0.04)</td><td>0.030<br>(0.04)</td><td></td><td></td></tr><tr><td>TD:Employment</td><td></td><td></td><td>-0.0042<br>(0.007)</td><td>0.012<br>(0.01)</td><td></td><td></td></tr><tr><td>TD:Employment × Importance employment</td><td></td><td></td><td>0.0029<br>(0.01)</td><td>-0.019<br>(0.01)</td><td></td><td></td></tr><tr><td>TD:Employment × Positive view business</td><td></td><td></td><td>0.0065<br>(0.01)</td><td>0.0088<br>(0.01)</td><td></td><td></td></tr><tr><td>TD:Innovation</td><td></td><td></td><td></td><td></td><td>0.0013<br>(0.008)</td><td>-0.0046<br>(0.007)</td></tr><tr><td>Importance innovation</td><td></td><td></td><td></td><td></td><td>0.0042<br>(0.03)</td><td>0.0055<br>(0.03)</td></tr><tr><td>TD:Innovation × Importance innovation</td><td></td><td></td><td></td><td></td><td>0.011<br>(0.01)</td><td>-0.012<br>(0.009)</td></tr><tr><td>TD:Innovation × Positive view business</td><td></td><td></td><td></td><td></td><td>0.0048<br>(0.02)</td><td>0.0044<br>(0.009)</td></tr><tr><td>Founder</td><td>-0.0035<br>(0.005)</td><td>-0.0054<br>(0.004)</td><td>-0.012**<br>(0.006)</td><td>-0.0082<br>(0.006)</td><td>-0.0017<br>(0.005)</td><td>-0.0075*<br>(0.004)</td></tr><tr><td>High effort entrepreneur</td><td>-0.019***<br>(0.005)</td><td>-0.0094**<br>(0.004)</td><td>-0.022***<br>(0.006)</td><td>-0.022***<br>(0.006)</td><td>-0.025***<br>(0.006)</td><td>-0.033***<br>(0.005)</td></tr><tr><td>Low effort recipient</td><td>-0.030***<br>(0.006)</td><td>-0.021***<br>(0.006)</td><td>-0.037***<br>(0.009)</td><td>-0.050***<br>(0.009)</td><td>-0.037***<br>(0.007)</td><td>-0.052***<br>(0.008)</td></tr><tr><td>Observations</td><td colspan="2">7200</td><td colspan="2">7200</td><td colspan="2">7200</td></tr><tr><td>Pseudo R-squared</td><td colspan="2">0.024</td><td colspan="2">0.034</td><td colspan="2">0.022</td></tr></tbody></table>

**Notes:** The dependent variable is the percentage of money transferred from the entrepreneur to the recipient. *TD:Donation* is a dummy = 1 if the entrepreneur donated more than $3600 in the last 12 months. *TD:Employment* equals 1 if the entrepreneur's firm has over 1000 employees. *TD:Innovation* equals 1 if the entrepreneur's firm obtained over 180 patents. *Founder* is a dummy = 1 if the entrepreneur founded the firm they own. *High effort entrepreneur* equals 1 if the entrepreneur works more than 10 hours daily. *Low effort recipient* equals 1 if the recipient works more than 10 hours daily. *Importance donation*, *Importance employment* and *Importance innovation* are dummies taking value 1 if the respondents believe that the rich's donations to charity, the number of employees they have, and their technological innovations are “Very important” or “Extremely important” to benefit the economy. *Positive view business* is a dummy taking value 1 if the respondents have a “very favorable” opinion of business owners. Controls include age, gender, region of residence, education level, political ideology, and a dummy capturing the order in which the tables have been presented. Standard errors clustered at the individual level in parentheses. \* significant at 10%, \*\* significant at 5%, \*\*\* significant at 1%.
<table><caption>Table A.5.2: Tobit regression results - Beliefs about effort and luck</caption><thead><tr><th rowspan="2"></th><th colspan="4">Treat:Pooled</th></tr><tr><th>(1)<br>Poor</th><th>(2)<br>Rich</th><th>(3)<br>Poor</th><th>(4)<br>Rich</th></tr></thead><tbody><tr><td>TD:Donation</td><td>-0.032***<br>(0.007)</td><td>-0.000035<br>(0.005)</td><td>-0.032***<br>(0.007)</td><td>-0.000044<br>(0.005)</td></tr><tr><td>TD:Employment</td><td>-0.0016<br>(0.005)</td><td>0.0037<br>(0.006)</td><td>-0.0016<br>(0.005)</td><td>0.0037<br>(0.006)</td></tr><tr><td>TD:Innovation</td><td>0.0061<br>(0.007)</td><td>-0.0087**<br>(0.004)</td><td>0.0061<br>(0.007)</td><td>-0.0087**<br>(0.004)</td></tr><tr><td>Founder</td><td>-0.0048<br>(0.003)</td><td>-0.0070***<br>(0.003)</td><td>-0.0048<br>(0.003)</td><td>-0.0070***<br>(0.003)</td></tr><tr><td>High effort entrepeneur</td><td>-0.022***<br>(0.003)</td><td>-0.021***<br>(0.003)</td><td>-0.022***<br>(0.004)</td><td>-0.024***<br>(0.005)</td></tr><tr><td>Low effort recipient</td><td>-0.034***<br>(0.004)</td><td>-0.039***<br>(0.004)</td><td>-0.042***<br>(0.005)</td><td>-0.050***<br>(0.007)</td></tr><tr><td>Factors under control poor</td><td>-0.078***<br>(0.02)</td><td>-0.011<br>(0.02)</td><td>-0.092***<br>(0.02)</td><td>-0.022<br>(0.02)</td></tr><tr><td>Factors under control rich</td><td>-0.092***<br>(0.02)</td><td>-0.034*<br>(0.02)</td><td>-0.091***<br>(0.02)</td><td>-0.036*<br>(0.02)</td></tr><tr><td>Low effort recipient × Factors under control poor</td><td></td><td></td><td>0.029***<br>(0.009)</td><td>0.022**<br>(0.009)</td></tr><tr><td>High effort entrepeneur × Factors under control rich</td><td></td><td></td><td>-0.0019<br>(0.007)</td><td>0.0052<br>(0.006)</td></tr><tr><td>Observations</td><td>19200</td><td>18336</td><td>19200</td><td>18336</td></tr><tr><td>Pseudo R-squared</td><td>0.035</td><td>0.056</td><td>0.035</td><td>0.056</td></tr></tbody></table>

**Notes:** The dependent variable is the percentage of money transferred from the entrepreneur to the recipient. *TD:Donation* is a dummy = 1 if the entrepreneur donated more than $3600 in the last 12 months. *TD:Employment* equals 1 if the entrepreneur's firm has over 1000 employees. *TD:Innovation* equals 1 if the entrepreneur's firm obtained over 180 patents. *Founder* is a dummy = 1 if the entrepreneur founded the firm they own. *High effort entrepeneur* equals 1 if the entrepreneur works more than 10 hours daily. *Low effort recipient* equals 1 if the recipient works more than 10 hours daily. *Factors under control poor* and *Factors under control rich* are dummies taking value 1 if, respectively, the respondents believe that the poor is poor due to the factors under their control and the rich is rich due to factors under their control. Controls include age, gender, region of residence, education level, political ideology, a dummy for the order in which the tables have been presented, and a dummy for the treatment. Standard errors clustered at the individual level in parentheses. \* significant at 10%, \*\* significant at 5%, \*\*\* significant at 1%
<table><caption>Table A.5.3: Tobit regression results - Economic and political identity</caption><thead><tr><th rowspan="2"></th><th colspan="10">Treat:Pooled</th></tr><tr><th>(1)<br>Poor & Rich</th><th>(2)<br>Poor & Rich</th><th>(3)<br>Poor</th><th>(4)<br>Rich</th><th>(5)<br>Poor</th><th>(6)<br>Rich</th><th>(7)<br>Poor</th><th>(8)<br>Rich</th><th>(9)<br>Poor</th><th>(10)<br>Rich</th></tr></thead><tbody><tr><td>Rich</td><td>-0.040**<br>(0.02)</td><td>0.00096<br>(0.02)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Attachment to poor (ratio)</td><td></td><td>0.54***<br>(0.08)</td><td>0.67***<br>(0.1)</td><td>0.26*<br>(0.2)</td><td></td><td></td><td>0.64***<br>(0.1)</td><td>0.26*<br>(0.2)</td><td></td><td></td></tr><tr><td>Attachment to Trump (ratio)</td><td></td><td>-0.18***<br>(0.04)</td><td></td><td></td><td>-0.22***<br>(0.05)</td><td>-0.16**<br>(0.07)</td><td>-0.19***<br>(0.05)</td><td>-0.17**<br>(0.07)</td><td></td><td></td></tr><tr><td>Attachment to poor</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.014***<br>(0.003)</td><td>0.0097**<br>(0.004)</td></tr><tr><td>Attachment to rich</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.019***<br>(0.004)</td><td>0.0016<br>(0.004)</td></tr><tr><td>Attachment to Trump</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.00026<br>(0.003)</td><td>0.0019<br>(0.003)</td></tr><tr><td>Attachment to Biden</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.011***<br>(0.003)</td><td>0.012***<br>(0.003)</td></tr><tr><td>TD:Donation</td><td>-0.016***<br>(0.004)</td><td>-0.016***<br>(0.004)</td><td>-0.032***<br>(0.007)</td><td>-0.000079<br>(0.005)</td><td>-0.032***<br>(0.007)</td><td>-0.000036<br>(0.005)</td><td>-0.032***<br>(0.007)</td><td>-0.000063<br>(0.005)</td><td>-0.032***<br>(0.007)</td><td>-0.00018<br>(0.005)</td></tr><tr><td>TD:Employment</td><td>0.0012<br>(0.004)</td><td>0.0012<br>(0.004)</td><td>-0.0015<br>(0.005)</td><td>0.0037<br>(0.006)</td><td>-0.0016<br>(0.005)</td><td>0.0037<br>(0.006)</td><td>-0.0016<br>(0.005)</td><td>0.0037<br>(0.006)</td><td>-0.0016<br>(0.005)</td><td>0.0037<br>(0.006)</td></tr><tr><td>TD:Innovation</td><td>-0.00082<br>(0.004)</td><td>-0.00073<br>(0.004)</td><td>0.0063<br>(0.007)</td><td>-0.0087**<br>(0.004)</td><td>0.0061<br>(0.007)</td><td>-0.0087**<br>(0.004)</td><td>0.0063<br>(0.007)</td><td>-0.0087**<br>(0.004)</td><td>0.0064<br>(0.007)</td><td>-0.0089**<br>(0.004)</td></tr><tr><td>Founder</td><td>-0.0058***<br>(0.002)</td><td>-0.0058***<br>(0.002)</td><td>-0.0047<br>(0.003)</td><td>-0.0070***<br>(0.003)</td><td>-0.0048<br>(0.003)</td><td>-0.0070***<br>(0.003)</td><td>-0.0048<br>(0.003)</td><td>-0.0070***<br>(0.003)</td><td>-0.0048<br>(0.003)</td><td>-0.0069***<br>(0.003)</td></tr><tr><td>High effort entrepreneur</td><td>-0.021***<br>(0.002)</td><td>-0.021***<br>(0.002)</td><td>-0.022***<br>(0.003)</td><td>-0.021***<br>(0.003)</td><td>-0.022***<br>(0.003)</td><td>-0.021***<br>(0.003)</td><td>-0.022***<br>(0.003)</td><td>-0.021***<br>(0.003)</td><td>-0.022***<br>(0.003)</td><td>-0.021***<br>(0.003)</td></tr><tr><td>Low effort recipient</td><td>-0.037***<br>(0.003)</td><td>-0.037***<br>(0.003)</td><td>-0.034***<br>(0.004)</td><td>-0.039***<br>(0.004)</td><td>-0.034***<br>(0.004)</td><td>-0.039***<br>(0.004)</td><td>-0.034***<br>(0.004)</td><td>-0.039***<br>(0.004)</td><td>-0.034***<br>(0.004)</td><td>-0.039***<br>(0.004)</td></tr><tr><td>Observations</td><td>37536</td><td>37536</td><td>19200</td><td>18336</td><td>19200</td><td>18336</td><td>19200</td><td>18336</td><td>19200</td><td>18336</td></tr><tr><td>Pseudo R-squared</td><td>0.025</td><td>0.043</td><td>0.036</td><td>0.055</td><td>0.024</td><td>0.057</td><td>0.042</td><td>0.059</td><td>0.043</td><td>0.098</td></tr></tbody></table>

Notes: The dependent variable is the percentage of money transferred from the entrepreneur to the recipient *TD:Donation* is a dummy = 1 if the entrepreneur donated more than $3,600 in the last 12 months. *TD:Employment* equals 1 if the entrepreneur's firm has over 1000 employees. *TD:Innovation* equals 1 if the entrepreneur's firm obtained over 180 patents. *Founder* is a dummy = 1 if the entrepreneur founded the firm they own. *High effort entrepreneur* equals 1 if the entrepreneur works more than 10 hours daily. *Low effort recipient* equals 1 if the recipient works more than 10 hours daily. *Rich* is a dummy = 1 if the spectator's household income exceeds $150,000. *Attachment to rich* (*Attachment to poor*) is a variable between 1 and 16 measuring how spectators feel attached to rich (poor) individuals. Controls include: age, gender, region of residence, education level and a dummy capturing the order in which the tables were presented. Standard errors clustered at the individual level in parentheses. \* significant at 10%, \*\* significant at 5%, \*\*\* significant at 1%.
<table><caption>Table A.5.4: Tobit regression results - Economic and political identity</caption><thead><tr><th rowspan="3"></th><th colspan="4">Treat:Pooled</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>Poor</th><th>Rich</th><th>Poor</th><th>Rich</th></tr></thead><tbody><tr><td>Attachment to poor (ratio)</td><td>0.71***<br>(0.1)</td><td>0.26<br>(0.2)</td><td></td><td></td></tr><tr><td>Attachment to Trump (ratio)</td><td></td><td></td><td>-0.22***<br>(0.06)</td><td>-0.16**<br>(0.07)</td></tr><tr><td>TD:Donation</td><td>-0.13*<br>(0.08)</td><td>-0.013<br>(0.10)</td><td>-0.075**<br>(0.04)</td><td>0.00040<br>(0.04)</td></tr><tr><td>TD:Donation × Attachment to poor (ratio)</td><td>0.18<br>(0.1)</td><td>0.029<br>(0.2)</td><td></td><td></td></tr><tr><td>TD:Donation × Attachment to Trump (ratio)</td><td></td><td></td><td>0.092<br>(0.07)</td><td>-0.00093<br>(0.09)</td></tr><tr><td>TD:Employment</td><td>0.14<br>(0.10)</td><td>0.15<br>(0.1)</td><td>0.019<br>(0.05)</td><td>-0.015<br>(0.06)</td></tr><tr><td>TD:Employment × Attachment to poor (ratio)</td><td>-0.27<br>(0.2)</td><td>-0.32<br>(0.2)</td><td></td><td></td></tr><tr><td>TD:Employment × Attachment to Trump (ratio)</td><td></td><td></td><td>-0.044<br>(0.1)</td><td>0.042<br>(0.1)</td></tr><tr><td>TD:Innovation</td><td>0.0081<br>(0.07)</td><td>-0.19*<br>(0.1)</td><td>0.044<br>(0.03)</td><td>-0.0057<br>(0.04)</td></tr><tr><td>TD:Innovation × Attachment to poor (ratio)</td><td>-0.0035<br>(0.1)</td><td>0.40*<br>(0.2)</td><td></td><td></td></tr><tr><td>TD:Innovation × Attachment to Trump (ratio)</td><td></td><td></td><td>-0.081<br>(0.07)</td><td>-0.0064<br>(0.08)</td></tr><tr><td>Founder</td><td>-0.032*<br>(0.02)</td><td>-0.015<br>(0.02)</td><td>-0.017**<br>(0.008)</td><td>-0.011<br>(0.010)</td></tr><tr><td>Founder × Attachment to poor (ratio)</td><td>0.051<br>(0.03)</td><td>0.016<br>(0.05)</td><td></td><td></td></tr><tr><td>Founder × Attachment to Trump (ratio)</td><td></td><td></td><td>0.027<br>(0.02)</td><td>0.0095<br>(0.02)</td></tr><tr><td>High effort entrepeneur</td><td>-0.016<br>(0.02)</td><td>-0.014<br>(0.03)</td><td>-0.024***<br>(0.009)</td><td>-0.010<br>(0.01)</td></tr><tr><td>High effort entrepeneur × Attachment to poor (ratio)</td><td>-0.012<br>(0.04)</td><td>-0.014<br>(0.06)</td><td></td><td></td></tr><tr><td>High effort entrepeneur × Attachment to Trump (ratio)</td><td></td><td></td><td>0.0036<br>(0.02)</td><td>-0.022<br>(0.02)</td></tr><tr><td>Low effort recipient</td><td>0.040<br>(0.03)</td><td>-0.014<br>(0.04)</td><td>-0.028**<br>(0.01)</td><td>-0.039**<br>(0.02)</td></tr><tr><td>Low effort recipient × Attachment to poor (ratio)</td><td>-0.14***<br>(0.05)</td><td>-0.055<br>(0.09)</td><td></td><td></td></tr><tr><td>Low effort recipient × Attachment to Trump (ratio)</td><td></td><td></td><td>-0.014<br>(0.02)</td><td>-0.00093<br>(0.03)</td></tr><tr><td>Observations</td><td colspan="2">19200</td><td>18336</td><td>19200</td><td>18336</td></tr><tr><td>Pseudo R-squared</td><td colspan="2">0.037</td><td>0.057</td><td>0.024</td><td>0.057</td></tr></tbody></table>

**Notes:** The dependent variable is the percentage of money transferred from the entrepreneur to the recipient *TD:Donation* is a dummy = 1 if the entrepreneur donated more than $3,600 in the last 12 months. *TD:Employment* equals 1 if the entrepreneur's firm has over 1000 employees. *TD:Innovation* equals 1 if the entrepreneur's firm obtained over 180 patents. *Founder* is a dummy = 1 if the entrepreneur founded the firm they own. *High effort entrepreneur* equals 1 if the entrepreneur works more than 10 hours daily. *Low effort recipient* equals 1 if the recipient works more than 10 hours daily. *Rich* is a dummy = 1 if the spectator's household income exceeds $150,000. *Attachment to rich* (*Attachment to poor*) is a variable between 1 and 16 measuring how spectators feel attached to rich (poor) individuals. Controls include: age, gender, region of residence, education level and a dummy capturing the order in which the tables were presented. Standard errors clustered at the individual level in parentheses. \* significant at 10%, \*\* significant at 5%, \*\*\* significant at 1%.
**Beliefs on characteristics of entrepreneurs and recipients**
The information we pro-
vide through the conjoint analysis (e.g., number of hours worked, firm ownership and TD
attributes) could either coincide with respondents' prior beliefs about rich and poor indi-
viduals on these same attributes or not. Arguably, participants' response to the same piece
of information may be different depending on whether their prior was in line with the in-
formation. Accordingly, in our pre-registration we hypothesized that the effect of a certain
attribute on redistribution should be larger the less the attribute is in line with prior beliefs.
For instance, a spectator having a prior belief that entrepreneurs donate nothing to charities
might reward an entrepreneur donating large amounts significantly more than a spectator
already believing that entrepreneurs donate a lot. In other words, if the information con-
tained in the profile counters a spectator's pessimistic prior on the diffusion of that trait, we
should expect a larger response in terms of redistribution.

To investigate this hypothesis, we elicited spectators’ beliefs about the same attributes
used in the experiment (e.g., the number of hours worked by individuals earning more than
$100,000) presented in the conjoint tables. We then created dummy variables taking a value
of 1 if a participant believed that the entrepreneur has a merit-worthy characteristic with
respect to each of the profiles. For instance, the dummy would take a value of 1 if the
participant believes that the entrepreneur donates a lot, employs many workers, etc. The
questions to measure these beliefs can be found in C.2.2.

Figure A.5.1 plots the distributions of spectators beliefs about stakeholders’ real-life attributes. Regarding beliefs about the attributes related to the TD, panel (a) shows that rich spectators believe that entrepreneurs donate more than what poor spectators believe (Rank-sum test for differences in distributions: z = -10.799, p < 0.01). Indeed, 44.68% of rich spectators and 27.50% of poor ones believe entrepreneurs donated more than $3,600 in the last year. Conversely, 5.41% of rich and 17.92% of poor spectators believe that entrepreneurs donated less than $20 last year. A similar trend is depicted in Panel (b) with respect to beliefs about entrepreneurs’ number of employees: rich spectators are more likely than poor ones to believe that rich entrepreneurs employ many employees in their firm (Rank-sum test: z = -9.735, p < 0.01). 27.8% of rich and 13.67% of poor spectators believe that entrepreneurs employ more than 1,000 people, while 5.32% of rich and 12.50% of poor spectators believe that rich business-owners have less than 5 employees. Finally, there are no differences between rich and poor spectators in their beliefs related to the innovation of rich business owners (Rank-sum test: z = -0.197, p = 0.8437), with most spectators believing that the number of patents is between 1 and 180 (76.26% of rich and 75.33% of poor spectators).

Regarding the beliefs about the source of firm ownership, Panel (d) reports that most rich (86.58%) and poor spectators (82.58%) believe that the share of business owners who have founded their firm is between 0 and 49% compared to having inherited, purchased, or received as a gift. The difference between rich and poor spectators, albeit small, is statistically significant (Rank-sum test: z = 2.602, p < 0.01).
Panel (e) shows spectators' beliefs about poor individuals' working hours and Panel (f) shows their beliefs about rich individuals' working hours. We observe that both rich and poor spectators believe that the rich work more than the poor. Further, both rich and poor spectators have similar beliefs on the number of the poor working between 6 and 10 hours (52.25% of poor and 51.75% spectators). However, a greater share of rich (38.92%) than poor spectators (35.33%) believe that recipients work less than 6 hours, while a greater share of poor (12.42%) than rich spectators (9.34%) believe that recipients work more than 10 hours. A Rank-sum test confirms the difference in distributions of beliefs on the recipients' working hours between the two groups ($z = 2.395, p = 0.0166$). Finally, while most of our respondents, irrespective of their income status, believe that wealthy entrepreneurs work between 6 and 10 hours, a similar share of poor spectators, 21.7% and 19.8% respectively, believe that entrepreneurs work either less than 6 hours and more than 10 hours. A Rank-sum test finds no significant difference in the distribution of beliefs about entrepreneurs' working hours between rich and poor spectators ($p = 0.49$).

In general, our hypothesis of a “surprise effect” does not seem to be upheld, if not occasionally. In particular, the attribute relative to the working hours of entrepreneurs appears to be subject to this surprise effect in three of the six specifications examined (Table A.5.5). We do not find any significant effect for the other attributes, and in some cases the interaction has the opposite sign to what we expected. For instance, we find that spectators who believe that the rich contribute significantly to innovation reward the rich innovator even more (Table A.5.5, column 6). This can be explained by the fact that these relatively few wealthy spectators ($N_R = 70$) have also a more positive view of business owners (McNemar’s test: $p < 0.01$) and of technological innovation ($p < 0.01$). We then conclude that prior beliefs on the attribute do not appear to exert any systematic effect on redistributive choices.
<figure><img src="image_8.png" /><figcaption>Figure A.5.1: Beliefs about stakeholders’ real-life attributes</figcaption></figure>
<table><caption>Table A.5.5: Tobit regression results - Beliefs about rich and poor individuals</caption><thead><tr><th rowspan="2"></th><th colspan="2">Treat:Donation</th><th colspan="2">Treat:Employment</th><th colspan="2">Treat:Innovation</th></tr><tr><th>(1)<br>Poor</th><th>(2)<br>Rich</th><th>(3)<br>Poor</th><th>(4)<br>Rich</th><th>(5)<br>Poor</th><th>(6)<br>Rich</th></tr></thead><tbody><tr><td>Low effort recipient</td><td>-0.049***<br>(0.009)</td><td>-0.014**<br>(0.007)</td><td>-0.034***<br>(0.01)</td><td>-0.064***<br>(0.01)</td><td>-0.049***<br>(0.01)</td><td>-0.052***<br>(0.01)</td></tr><tr><td>High effort entrepreneur</td><td>-0.023***<br>(0.007)</td><td>-0.014***<br>(0.005)</td><td>-0.038***<br>(0.008)</td><td>-0.026***<br>(0.008)</td><td>-0.032***<br>(0.008)</td><td>-0.040***<br>(0.006)</td></tr><tr><td>Founder</td><td>-0.0059<br>(0.006)</td><td>-0.0067<br>(0.004)</td><td>-0.013**<br>(0.006)</td><td>-0.0078<br>(0.006)</td><td>-0.0021<br>(0.005)</td><td>-0.0040<br>(0.005)</td></tr><tr><td>TD:Donation</td><td>-0.032***<br>(0.008)</td><td>-0.0056<br>(0.007)</td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 6 hours</td><td>-0.051<br>(0.04)</td><td>-0.070**<br>(0.03)</td><td>-0.097**<br>(0.04)</td><td>-0.0079<br>(0.05)</td><td>-0.11***<br>(0.03)</td><td>-0.041<br>(0.03)</td></tr><tr><td>More than 10 hours</td><td>0.0021<br>(0.05)</td><td>0.082<br>(0.06)</td><td>0.039<br>(0.07)</td><td>0.073<br>(0.07)</td><td>0.028<br>(0.05)</td><td>0.11**<br>(0.05)</td></tr><tr><td>Less than 6 hours</td><td>-0.034<br>(0.04)</td><td>0.076**<br>(0.04)</td><td>0.016<br>(0.04)</td><td>0.00087<br>(0.05)</td><td>-0.013<br>(0.03)</td><td>0.087**<br>(0.04)</td></tr><tr><td>More than 10 hours</td><td>-0.041<br>(0.05)</td><td>-0.073*<br>(0.04)</td><td>0.068<br>(0.05)</td><td>0.0048<br>(0.07)</td><td>-0.024<br>(0.04)</td><td>0.041<br>(0.04)</td></tr><tr><td>Belief founder</td><td>0.091*<br>(0.05)</td><td>-0.074<br>(0.05)</td><td>-0.065<br>(0.05)</td><td>-0.061<br>(0.07)</td><td>-0.033<br>(0.04)</td><td>-0.039<br>(0.05)</td></tr><tr><td>Less than 20 USD</td><td>-0.040<br>(0.06)</td><td>0.0058<br>(0.10)</td><td></td><td></td><td></td><td></td></tr><tr><td>More than 3600 USD</td><td>-0.062*<br>(0.04)</td><td>0.042<br>(0.03)</td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 6 hours × Low effort recipient</td><td>0.042***<br>(0.01)</td><td>-0.017<br>(0.01)</td><td>-0.0052<br>(0.02)</td><td>0.029<br>(0.02)</td><td>0.027*<br>(0.01)</td><td>-0.0031<br>(0.02)</td></tr><tr><td>More than 10 hours × Low effort recipient</td><td>0.040**<br>(0.02)</td><td>-0.0074<br>(0.02)</td><td>-0.014<br>(0.03)</td><td>0.027<br>(0.02)</td><td>0.017<br>(0.02)</td><td>0.0083<br>(0.03)</td></tr><tr><td>Less than 6 hours × High effort entrepreneur</td><td>0.0093<br>(0.01)</td><td>0.012<br>(0.01)</td><td>0.021<br>(0.01)</td><td>0.024**<br>(0.01)</td><td>0.0011<br>(0.01)</td><td>0.037***<br>(0.01)</td></tr><tr><td>More than 10 hours × High effort entrepreneur</td><td>0.0061<br>(0.01)</td><td>0.024**<br>(0.01)</td><td>0.062***<br>(0.02)</td><td>0.0061<br>(0.02)</td><td>0.032**<br>(0.01)</td><td>0.015<br>(0.01)</td></tr><tr><td>Founder × Belief founder</td><td>0.016<br>(0.01)</td><td>0.0092<br>(0.01)</td><td>0.0073<br>(0.02)</td><td>-0.0035<br>(0.02)</td><td>0.0028<br>(0.02)</td><td>-0.026**<br>(0.01)</td></tr><tr><td>Less than 20 USD × TD:Donation</td><td>-0.010<br>(0.03)</td><td>0.034<br>(0.04)</td><td></td><td></td><td></td><td></td></tr><tr><td>More than 3600 USD × TD:Donation</td><td>0.0014<br>(0.01)</td><td>0.0090<br>(0.010)</td><td></td><td></td><td></td><td></td></tr><tr><td>TD:Employment</td><td></td><td></td><td>-0.0033<br>(0.006)</td><td>0.012*<br>(0.007)</td><td></td><td></td></tr><tr><td>Less than 5</td><td></td><td></td><td>0.082<br>(0.07)</td><td>0.044<br>(0.1)</td><td></td><td></td></tr><tr><td>More than 1000</td><td></td><td></td><td>-0.034<br>(0.05)</td><td>0.022<br>(0.04)</td><td></td><td></td></tr><tr><td>Less than 5 × TD:Employment</td><td></td><td></td><td>0.0068<br>(0.01)</td><td>0.0095<br>(0.03)</td><td></td><td></td></tr><tr><td>More than 1000 × TD:Employment</td><td></td><td></td><td>0.0071<br>(0.02)</td><td>-0.026*<br>(0.01)</td><td></td><td></td></tr><tr><td>TD:Innovation</td><td></td><td></td><td></td><td></td><td>0.0076<br>(0.007)</td><td>-0.0028<br>(0.005)</td></tr><tr><td>0</td><td></td><td></td><td></td><td></td><td>-0.097<br>(0.07)</td><td>0.036<br>(0.09)</td></tr><tr><td>More than 180</td><td></td><td></td><td></td><td></td><td>0.023<br>(0.04)</td><td>0.083**<br>(0.03)</td></tr><tr><td>0 × TD:Innovation</td><td></td><td></td><td></td><td></td><td>-0.028<br>(0.02)</td><td>-0.024*<br>(0.01)</td></tr><tr><td>More than 180 × TD:Innovation</td><td></td><td></td><td></td><td></td><td>0.0027<br>(0.02)</td><td>-0.028**<br>(0.01)</td></tr><tr><td>Constant</td><td>0.54***<br>(0.07)</td><td>0.40**<br>(0.2)</td><td>0.55***<br>(0.09)</td><td>0.52***<br>(0.2)</td><td>0.61***<br>(0.06)</td><td>0.75***<br>(0.1)</td></tr><tr><td>Observations</td><td>7200</td><td>7200</td><td>4800</td><td>4800</td><td>7200</td><td>6336</td></tr><tr><td>Pseudo R-squared</td><td>0.032</td><td>0.057</td><td>0.048</td><td>0.058</td><td>0.047</td><td>0.15</td></tr></tbody></table>

Notes: The dependent variable is the percentage of money transferred from the entrepreneur to the recipient *TD:Donation* is a dummy = 1 if the entrepreneur donated more than $3,600 in the last 12 months. *TD:Employment* equals 1 if the entrepreneur's firm has over 1000 employees. *TD:Innovation* equals 1 if the entrepreneur's firm obtained over 180 patents. *Founder* is a dummy = 1 if the entrepreneur founded the firm they own. *High effort entrepreneur* equals 1 if the entrepreneur works more than 10 hours daily. *Low effort recipient* equals 1 if the recipient works less than 6 hours daily. The beliefs variables are the correspondent dummies: for instance, *Belief Low effort recipient* is equal to 1 if the spectator believes people like Person 2 work less than 6 hours daily. Note that the variable *Belief patents* was measured only in wave 3. Controls include age, gender, region of residence, education level, and a dummy capturing the order in which the tables were presented. Standard errors clustered at the individual level in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%.
## A.6 Heterogeneity analysis

This Section investigates further the heterogeneity analysis of our AME: we here provide additional evidence on certain dimensions where differences among groups can be relevant.

### A.6.1 Gender

Only 28% of our *Poor* group comprises males. Our sample under-represents this category, which is, nonetheless, unbalanced even in the US population, since only 41% of the US poor are males (see Appendix: Table A.1.1). Gender is, nonetheless, insignificant for the *Poor* group (Table A.6.2, column 3), thus suggesting that the under-sampling of poor males did not bias our results. In contrast, the share of males in the *Rich* group (48%) is in line with the actual share in the US population (51%). Previous studies suggest that women favor more progressive redistributive policies than men across all income groups (Alesina and Giuliano, 2011), possibly because of lower overconfidence (Buser et al., 2020), and that wealthy men and women have similar redistributive preferences (Page et al., 2013). Although women are normally “believed” to be more pro-social than men (Exley et al., 2025), experimental studies reveal on average no clear trend (Croson and Gneezy, 2009), although women’s preferences could be more responsive to the details of the interaction, thus behaving more pro-socially in some contexts (Marianne, 2011).

In our sample, rich women redistribute significantly less than rich men ($p = 0.025$; Table A.6.1, column 2) and reward more both stakeholders when they work more than 10 hours (Table A.6.2, column 4), suggesting that they are more meritocratic than rich men. Some gender differences also emerge with respect to the TD attributes, as women redistribute *less* to the poor when the entrepreneur owns an innovative firm and *more* when the entrepreneur employs more than 1000 workers in comparison to rich men.

### A.6.2 Prospect of upward mobility

Benabou and Ok (2001) conjecture that low-income individuals who expect to earn above the mean income in the future may rationally prefer to demand low-income taxation in the present. Even if, in our experiment, spectators' decisions are independent of any self-interest component, participants expecting to join the top income quintile should redistribute less as they identify more with rich individuals. To explore whether the Prospect of Upward Mobility (POUM) mechanism can be relevant, we provide evidence of heterogeneous treatment effects between poor spectators who have future expectations of social mobility and those who have not. Overall, only a minority of respondents identify their position in the income scale correctly. Only 15% of bottom-income respondents and 12% of top-income respondents stated that their income belonged to the bottom and the top 20% of the income distribution, respectively. In contrast, 48% of bottom-income and 47% of top-income participants believed their income belonged to the intermediate quintile, in line with the well-known tendency for both the rich and the poor to predominantly think of themselves
as “middle class” ( Cruces et al., 2013). Poor spectators who believe they will join the top 20% only represent 3.58% of the poor sample (according to Chetty et al., 2014b, the actual transition probability is 7.8%), and those who believe they will end up in the top 40% are 11.83% (20% in the Chetty et al., 2014b) (see the transition matrix in table A.7.1 for more detail). As expected, these participants feel closer to rich individuals than those who do not expect to climb the social ladder (t-test on differences on the synthetic variable described in Section 4.3.4: *p* < 0.01). When we analyze the correlation between a dummy equal to 1 if poor spectators believe they will join the top quintile, and the redistribution decision in Table A.6.1, we find no statistically significant effect suggesting a lack of evidence for our conjecture. Additionally, poor spectators who believe they will climb the social ladder in the future do not react differently to stakeholders’ attributes than those who believe they will not climb it (Table A.6.2, column 5). The only difference is concerning the attribute related to the effort of the entrepreneur: poor spectators who believe they will climb the social ladder are more likely than those who believe they will not climb it to redistribute to the poor if the entrepreneur exerts more effort.

### A.6.3 Trust in government, experience of social mobility, techno-optimism

We report here some heterogeneity analyses on dimensions that were not pre-registered. We study variation of behavior with respect to individual trust in institutions (Kuziemko et al., 2015). We introduce in the analysis the indicator variable *Trust*, taking value of 1 if the spectators believe that “they can trust the federal government in Washington, D.C., to do what is right” always or most of the time. 22.9% of poor spectators and 54.5% of rich spectators (Chi-Squared test: *p* < 0.01) believe this is the case. We find that the coefficients for *Founder*, *Low effort recipient*, and *High effort entrepreneur* are significantly higher for rich spectators who trust the government (*p* < 0.05 for *Founder* and *p* < 0.01 for the other two attributes; Table A.6.2, column 8), while we do not observe any significant interaction between trust in government and the stakeholders’ attributes for poor spectators (Table A.6.2, column 7). This means that rich spectators who trust the government reward individual merit *less* than those who do not trust it. Presumably, those who trust the government believe in the government’s ability to redistribute income - as in (Kuziemko et al., 2015)-, thus attributing less importance to “meritocracy”.

We also study the variation in behavior for the variable *Experience*, which is equal to one if rich spectators believed that they were in the bottom 80% of the income distribution when they grew up. 91.8% of rich spectators fall into this category. This high value is arguably due to the tendency by top income earners to underestimate their position in the income scale (see section A.6 and Cruces et al., 2013). People believing they belonged to the bottom 80% of the income distribution redistributed less on average (*p* < 0.01; Table A.6.1, column 2 and 4), in line with what found by Cohn et al., 2023. Moreover, they rewarded the entrepreneur who works more than 10 hours and punished the recipient who works less
than 6 hours ($p < 0.01$; Table A.6.2, column 6). Hence, not only did top income-earners who believed they experienced social mobility redistribute less than other top income-earners, but also they were more inclined to reward individual merit, although this attitude did not extend to entrepreneurs who founded their firm.

Finally, we analyze the redistributive choices of techno-optimists, defined as respondents who agree or strongly agree that technological innovation is essential to solve society's problems. Even if techno-optimists did not behave differently from others in the main regression (Table A.6.1, column 3.4), poor spectators who were skeptical of technology's role in addressing societal challenges redistributed more when the entrepreneur was described as an innovator (Table A.6.2, column 9). By contrast, the AME of *TD:Innovation* turns negative and insignificant for poor spectators who trust technological innovation. Among rich spectators, the effect of *TD:Innovation* is negative and significant for those who believe in technological advances (Table A.6.2, column 10). These results suggest that the propensity to reward innovative entrepreneurs is influenced by individuals' beliefs about the role of technological progress in solving societal issues.

<table><caption>Table A.6.1: Regression analysis - Socio-demographic variables</caption><thead><tr><th rowspan="3"></th><th colspan="2">Treat: Pooled</th><th colspan="2">Treat: Innovation</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>Poor</th><th>Rich</th><th>Poor</th><th>Rich</th></tr></thead><tbody><tr><td>Liberal</td><td>0.098<sup>***</sup><br>(0.02)</td><td>0.053<sup>***</sup><br>(0.02)</td><td>0.086<sup>**</sup><br>(0.03)</td><td>0.088<sup>***</sup><br>(0.03)</td></tr><tr><td>Female</td><td>0.010<br>(0.02)</td><td>-0.040<sup>**</sup><br>(0.02)</td><td>0.0031<br>(0.03)</td><td>-0.13<sup>***</sup><br>(0.03)</td></tr><tr><td>POUM</td><td>0.027<br>(0.04)</td><td></td><td>0.0029<br>(0.06)</td><td></td></tr><tr><td>Experience mobility</td><td></td><td>-0.11<sup>***</sup><br>(0.03)</td><td></td><td>-0.083<sup>*</sup><br>(0.05)</td></tr><tr><td>Trust in government</td><td>-0.00022<br>(0.02)</td><td>0.039<sup>**</sup><br>(0.02)</td><td>-0.0085<br>(0.03)</td><td>0.026<br>(0.03)</td></tr><tr><td>Techno-optimist</td><td></td><td></td><td>0.034<br>(0.03)</td><td>0.039<br>(0.04)</td></tr><tr><td>Constant</td><td>0.50<sup>***</sup><br>(0.04)</td><td>0.68<sup>***</sup><br>(0.08)</td><td>0.52<sup>***</sup><br>(0.06)</td><td>0.87<sup>***</sup><br>(0.1)</td></tr><tr><td>Observations</td><td>19168</td><td>18304</td><td>7184</td><td>6336</td></tr><tr><td>Pseudo R-squared</td><td>0.016</td><td>0.064</td><td>0.021</td><td>0.12</td></tr></tbody></table>

**Notes:** The dependent variable is the percentage of money transferred from the entrepreneur to the recipient. *Liberal* is a dummy = 1 if the spectator is liberal. *Female* is a dummy = 1 if the spectator is a female. *POUM* is a dummy = 1 if poor spectators believe they will join the top quintile. *Experience mobility* is a dummy = 1 if rich spectators state they were not in the top quintile when they were young. *Trust in government* is a dummy = 1 if spectators believe they can trust the federal government in Washington, D.C. to do what is right always or most of the time. *Techno-optimist* is a dummy = 1 if spectators agree and strongly agree that technology is needed to solve society's problems. Controls include age, gender, region of residence, education level, conjoint-table fixed effects, treatment dummies, and the interactions between table fixed effects, and treatment dummies. Standard errors clustered at the individual level in parentheses. \* significant at 10%, \*\* significant at 5%, \*\*\* significant at 1%.
<table><caption>Table A.6.2: Tobit regression - Heterogeneity analysis</caption><thead><tr><th rowspan="2"></th><th colspan="2">Ideology<br>(B = 1 if Liberal)</th><th colspan="2">Gender<br>(B = 1 if Female)</th><th colspan="2">Social mobility<br>(B=1 if POUM/Experience)</th><th colspan="2">Trust in Gov<br>(B = 1 if Trust)</th><th colspan="2">Techno-optimist<br>(B = 1 if Techno-opt.)</th></tr><tr><th>Poor</th><th>Rich</th><th>Poor</th><th>Rich</th><th>Poor</th><th>Rich</th><th>Poor</th><th>Rich</th><th>Poor</th><th>Rich</th></tr></thead><tbody><tr><td>B</td><td>0.11***<br>(0.02)</td><td>0.037*<br>(0.02)</td><td>0.020<br>(0.02)</td><td>-0.022<br>(0.02)</td><td>0.012<br>(0.04)</td><td>-0.086***<br>(0.03)</td><td>-0.022<br>(0.02)</td><td>-0.0021<br>(0.02)</td><td>0.047<br>(0.03)</td><td>0.056<br>(0.04)</td></tr><tr><td>TDE:Donation</td><td>-0.031***<br>(0.01)</td><td>0.016<br>(0.01)</td><td>-0.074***<br>(0.02)</td><td>-0.0078<br>(0.01)</td><td>-0.032***<br>(0.007)</td><td>-0.027<br>(0.03)</td><td>-0.032***<br>(0.010)</td><td>0.0016<br>(0.02)</td><td></td><td></td></tr><tr><td>TDE:Donation x B</td><td>-0.0037<br>(0.03)</td><td>-0.032<br>(0.02)</td><td>0.057*<br>(0.03)</td><td>0.018<br>(0.02)</td><td>-0.000090<br>(0.06)</td><td>0.029<br>(0.04)</td><td>0.00078<br>(0.03)</td><td>-0.0032<br>(0.02)</td><td></td><td></td></tr><tr><td>TDE:Employment</td><td>-0.00045<br>(0.01)</td><td>0.00046<br>(0.02)</td><td>0.045<br>(0.03)</td><td>-0.029*<br>(0.02)</td><td>-0.0046<br>(0.006)</td><td>0.025<br>(0.06)</td><td>-0.0085<br>(0.01)</td><td>0.0038<br>(0.02)</td><td></td><td></td></tr><tr><td>TDE:Employment x B</td><td>-0.0040<br>(0.04)</td><td>0.0065<br>(0.03)</td><td>-0.065<br>(0.04)</td><td>0.072**<br>(0.03)</td><td>0.10<br>(0.08)</td><td>-0.023<br>(0.07)</td><td>0.029<br>(0.04)</td><td>-0.00030<br>(0.03)</td><td></td><td></td></tr><tr><td>TDE:Innovation</td><td>0.011<br>(0.009)</td><td>-0.026**<br>(0.01)</td><td>0.017<br>(0.02)</td><td>0.043**<br>(0.02)</td><td>0.0071<br>(0.007)</td><td>-0.0064<br>(0.04)</td><td>0.0027<br>(0.009)</td><td>-0.014<br>(0.01)</td><td>0.017**<br>(0.008)</td><td>-0.00048<br>(0.007)</td></tr><tr><td>TDE:Innovation x B</td><td>-0.018<br>(0.03)</td><td>0.039<br>(0.02)</td><td>-0.015<br>(0.03)</td><td>-0.081***<br>(0.03)</td><td>-0.022<br>(0.05)</td><td>-0.0026<br>(0.04)</td><td>0.017<br>(0.03)</td><td>0.011<br>(0.02)</td><td>-0.027**<br>(0.01)</td><td>-0.011<br>(0.009)</td></tr><tr><td>Founder</td><td>-0.0016<br>(0.004)</td><td>-0.0097***<br>(0.004)</td><td>-0.0020<br>(0.005)</td><td>-0.0038<br>(0.004)</td><td>-0.0044<br>(0.003)</td><td>-0.0018<br>(0.008)</td><td>-0.0070**<br>(0.003)</td><td>-0.014***<br>(0.004)</td><td>-0.0018<br>(0.006)</td><td>-0.0052<br>(0.008)</td></tr><tr><td>Founder x B</td><td>-0.012*<br>(0.007)</td><td>0.0056<br>(0.005)</td><td>-0.0038<br>(0.006)</td><td>-0.0062<br>(0.005)</td><td>-0.010<br>(0.02)</td><td>-0.0056<br>(0.009)</td><td>0.0096<br>(0.007)</td><td>0.014**<br>(0.005)</td><td>0.00034<br>(0.010)</td><td>-0.0030<br>(0.010)</td></tr><tr><td>Low effort recipient</td><td>-0.035***<br>(0.005)</td><td>-0.052***<br>(0.007)</td><td>-0.024***<br>(0.007)</td><td>-0.021***<br>(0.005)</td><td>-0.035***<br>(0.004)</td><td>-0.0091<br>(0.010)</td><td>-0.038***<br>(0.005)</td><td>-0.068***<br>(0.007)</td><td>-0.036***<br>(0.009)</td><td>-0.057***<br>(0.02)</td></tr><tr><td>Low effort recipient x B</td><td>0.0011<br>(0.009)</td><td>0.026***<br>(0.009)</td><td>-0.014*<br>(0.009)</td><td>-0.037***<br>(0.009)</td><td>0.020<br>(0.02)</td><td>-0.033***<br>(0.01)</td><td>0.016*<br>(0.009)</td><td>0.051***<br>(0.009)</td><td>-0.0011<br>(0.01)</td><td>0.0061<br>(0.02)</td></tr><tr><td>High effort entrepeneur</td><td>-0.022***<br>(0.004)</td><td>-0.028***<br>(0.004)</td><td>-0.022***<br>(0.006)</td><td>-0.013***<br>(0.004)</td><td>-0.023***<br>(0.003)</td><td>0.0023<br>(0.009)</td><td>-0.024***<br>(0.004)</td><td>-0.038***<br>(0.005)</td><td>-0.024***<br>(0.008)</td><td>-0.028**<br>(0.01)</td></tr><tr><td>High effort entrepeneur x B</td><td>-0.0019<br>(0.008)</td><td>0.016***<br>(0.007)</td><td>0.00029<br>(0.006)</td><td>-0.016***<br>(0.006)</td><td>0.024**<br>(0.01)</td><td>-0.025***<br>(0.009)</td><td>0.0073<br>(0.008)</td><td>0.032***<br>(0.006)</td><td>-0.0011<br>(0.01)</td><td>-0.0071<br>(0.01)</td></tr><tr><td>Constant</td><td>0.51***<br>(0.04)</td><td>0.67***<br>(0.08)</td><td>0.50***<br>(0.04)</td><td>0.63***<br>(0.08)</td><td>0.51***<br>(0.04)</td><td>0.70***<br>(0.08)</td><td>0.52***<br>(0.04)</td><td>0.63***<br>(0.08)</td><td>0.53***<br>(0.06)</td><td>0.81***<br>(0.1)</td></tr><tr><td>Observations</td><td>19200</td><td>18336</td><td>19200</td><td>18336</td><td>19200</td><td>18336</td><td>19168</td><td>18304</td><td>7200</td><td>6336</td></tr><tr><td>Pseudo R-squared</td><td>0.016</td><td>0.055</td><td>0.017</td><td>0.057</td><td>0.016</td><td>0.061</td><td>0.016</td><td>0.058</td><td>0.021</td><td>0.11</td></tr><tr><td>TDE:Donation (B=1)</td><td>-0.034<br>(0.022)</td><td>-0.016<br>(0.012)</td><td>-0.017<br>(0.011)</td><td>0.010<br>(0.014)</td><td>-0.032<br>(0.055)</td><td>0.002<br>(0.006)</td><td>-0.031<br>(0.025)</td><td>-0.002<br>(0.011)</td><td></td><td></td></tr><tr><td>TDE:Employment (B=1)</td><td>-0.004<br>(0.028)</td><td>0.007<br>(0.016)</td><td>-0.019<br>(0.012)</td><td>0.043**<br>(0.018)</td><td>0.099<br>(0.082)</td><td>0.002<br>(0.007)</td><td>0.021<br>(0.030)</td><td>0.004<br>(0.015)</td><td></td><td></td></tr><tr><td>TDE:Innovation (B=1)</td><td>-0.007<br>(0.025)</td><td>0.013<br>(0.014)</td><td>0.002<br>(0.011)</td><td>-0.038***<br>(0.010)</td><td>-0.015<br>(0.043)</td><td>-0.009<br>(0.006)</td><td>0.020<br>(0.025)</td><td>-0.003<br>(0.014)</td><td>-0.010<br>(0.011)</td><td>-0.012**<br>(0.005)</td></tr><tr><td>Founder (B=1)</td><td>-0.013**<br>(0.006)</td><td>-0.004<br>(0.004)</td><td>-0.006<br>(0.004)</td><td>-0.010***<br>(0.003)</td><td>-0.015<br>(0.015)</td><td>-0.007***<br>(0.003)</td><td>0.003<br>(0.006)</td><td>-0.001<br>(0.004)</td><td>-0.001<br>(0.008)</td><td>-0.008<br>(0.005)</td></tr><tr><td>High effort entrepreneur x B (B=1)</td><td>-0.023***<br>(0.006)</td><td>-0.013***<br>(0.004)</td><td>-0.022***<br>(0.004)</td><td>-0.028***<br>(0.004)</td><td>0.001<br>(0.011)</td><td>-0.023***<br>(0.003)</td><td>-0.016**<br>(0.007)</td><td>-0.006*<br>(0.003)</td><td>-0.025***<br>(0.008)</td><td>-0.035***<br>(0.006)</td></tr><tr><td>Low effort recipient (B=1)</td><td>-0.033***<br>(0.007)</td><td>-0.026***<br>(0.005)</td><td>-0.038***<br>(0.005)</td><td>-0.057***<br>(0.007)</td><td>-0.015<br>(0.017)</td><td>-0.042***<br>(0.005)</td><td>-0.022***<br>(0.008)</td><td>-0.017***<br>(0.005)</td><td>-0.037***<br>(0.011)</td><td>-0.051***<br>(0.010)</td></tr></tbody></table>

Notes: The dependent variable is the percentage of money transferred from the entrepreneur to the recipient TD:Donation is a dummy = 1 if the entrepreneur donated more than $3,600 in the last 12 months. TD:Employment equals 1 if the entrepreneur's firm has over 1000 employees. TD:Innovation equals 1 if the entrepreneur's firm obtained over 180 patents. Founder is a dummy = 1 if the entrepreneur founded the firm they own. High effort entrepreneur equals 1 if the entrepreneur works more than 10 hours daily. Low effort recipient equals 1 if the recipient works more than 10 hours daily. Liberal is a dummy = 1 if the spectator is liberal. Female is a dummy = 1 if the spectator is a female. POUM is a dummy = 1 if poor spectators believe they will join the top quintile. Experience mobility is a dummy = 1 if rich spectators state they were not in the top quintile when they were young. Trust in government is a dummy = 1 if spectators believe they can trust the federal government in Washington, D.C. to do what is right always or most of the time. Techno-optimist is a dummy = 1 if spectators if spectators agree and strongly agree that technology is needed to solve society's problems. The question on people's views on technology was asked only in the third survey wave, so for the analysis in columns 9 and 10 we only use data from the wave. Controls include age, region of residence, education level, and a dummy for the order in which the tables were presented. Standard errors clustered at the individual level in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%.
## A.7 Social mobility transition matrices

<table><caption>Table A.7.1: POUM Transition matrix - Poor spectators</caption><thead><tr><th></th><th>First quintile</th><th>Second quintile</th><th>Third quintile</th><th>Fourth quintile</th><th>Fifth quintile</th></tr></thead><tbody><tr><th>First quintile</th><td>10.08%</td><td>6.67%</td><td>4.67%</td><td>0.83%</td><td>0.83%</td></tr><tr><th>Second quintile</th><td>3.83%</td><td>15.42%</td><td>16.50%</td><td>2.33%</td><td>0.42%</td></tr><tr><th>Third quintile</th><td>0.92%</td><td>3.17%</td><td>25.83%</td><td>4.50%</td><td>0.92%</td></tr><tr><th>Fourth quintile</th><td>0.08%</td><td>0.08%</td><td>0.42%</td><td>0.50%</td><td>0.42%</td></tr><tr><th>Fifth quintile</th><td>0.08%</td><td>0.08%</td><td>0.33%</td><td>0.08%</td><td>1%</td></tr></tbody></table>

**Notes:** In the left column, each quintile represents participants' perception of their current position in the income distribution. In the upper row, each quintile represents participants' beliefs regarding their future position in the income distribution.

<table><caption>Table A.7.2: Experience of social mobility transition matrix - Rich spectators</caption><thead><tr><th></th><th>First quintile</th><th>Second quintile</th><th>Third quintile</th><th>Fourth quintile</th><th>Fifth quintile</th></tr></thead><tbody><tr><th>First quintile</th><td>0.44%</td><td>0.79%</td><td>1.40%</td><td>0.35%</td><td>0%</td></tr><tr><th>Second quintile</th><td>1.40%</td><td>0.87%</td><td>7.42%</td><td>2.79%</td><td>0.44%</td></tr><tr><th>Third quintile</th><td>1.05%</td><td>2.71%</td><td>28.10%</td><td>12.91%</td><td>1.31%</td></tr><tr><th>Fourth quintile</th><td>0.35%</td><td>0.61%</td><td>9.34%</td><td>14.31%</td><td>5.24%</td></tr><tr><th>Fifth quintile</th><td>0.09%</td><td>0.09%</td><td>0.26%</td><td>2.79%</td><td>4.97%</td></tr></tbody></table>

**Notes:** In the left column, each quintile represents participants' perception of their past position in the income distribution. In the upper row, each quintile represents participants' beliefs regarding their current position in the income distribution.
# B Additional analysis and robustness checks

## B.1 Incentivized behaviors and preferences

<figure><img src="image_9.png" /><figcaption>Figure B.1.1: Distribution of spectators' donation choices, by income group</figcaption></figure>
<table><caption>Table B.1.1: Correlation analysis - Incentivized charity choice and political preferences</caption><thead><tr><th></th><th>Charity donation</th><th>Joe Biden</th><th>Inequality aversion</th><th>Progressive taxes</th><th>Education policies</th><th>Gov. regulations</th><th>Gov. transfers</th></tr></thead><tbody><tr><td>Redistribution choice</td><td>9.55***<br>(1.4)</td><td>0.13***<br>(0.03)</td><td>0.24***<br>(0.03)</td><td>0.22***<br>(0.03)</td><td>0.15***<br>(0.03)</td><td>0.26***<br>(0.03)</td><td>0.27***<br>(0.03)</td></tr><tr><td>Rich</td><td>9.19***<br>(1.1)</td><td>0.053**<br>(0.02)</td><td>0.046**<br>(0.02)</td><td>0.14***<br>(0.02)</td><td>0.18***<br>(0.02)</td><td>0.12***<br>(0.02)</td><td>0.012<br>(0.02)</td></tr><tr><td>Constant</td><td>14.0***<br>(2.2)</td><td>0.11**<br>(0.05)</td><td>0.44***<br>(0.05)</td><td>0.14***<br>(0.05)</td><td>0.080*<br>(0.05)</td><td>0.16***<br>(0.05)</td><td>0.29***<br>(0.05)</td></tr><tr><td>Observations</td><td>21136</td><td>37536</td><td>37536</td><td>37536</td><td>37536</td><td>37536</td><td>37536</td></tr><tr><td>R-squared</td><td>0.122</td><td>0.132</td><td>0.087</td><td>0.107</td><td>0.088</td><td>0.094</td><td>0.077</td></tr></tbody></table>

Notes: OLS regression results. The dependent variables are: *Charity donation*, a variable between 0 and 50 representing participants' incentivized donation choice to “Feeding America”; “Joe Biden”, a dummy = 1 if the spectator voted Biden in the 2016 presidential election; *Inequality aversion*, a dummy = 1 if spectators agree or strongly agree with the statement “Income differences between the rich and the poor in this country should be reduced”. *Progressive taxes*, *Education policies*, *Government regulations*, and *Government transfers*, dummies equal to 1 if spectators believe that these policies are “Very important” or “Extremely important” to address inequality in the US. Controls include age, gender, region of residence, education level, political ideology (liberal or non-liberal), conjoint-table fixed effects, treatment dummies, and the interactions between table fixed effects, and treatment dummies. Standard errors clustered at the individual level in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%.
<table><caption>Table B.1.2: Tobit regression - PCA</caption><thead><tr><th rowspan="2"></th><th colspan="2">All treatments</th><th colspan="2">Treat:Donation</th><th colspan="2">Treat:Employment</th><th colspan="2">Treat:Innovation</th></tr><tr><th>(1)<br>Poor</th><th>(2)<br>Rich</th><th>(3)<br>Poor</th><th>(4)<br>Rich</th><th>(5)<br>Poor</th><th>(6)<br>Rich</th><th>(7)<br>Poor</th><th>(8)<br>Rich</th></tr></thead><tbody><tr><td>Belief in TD (pca)</td><td>-0.020**<br>(0.008)</td><td>0.011<br>(0.007)</td><td>-0.0063<br>(0.01)</td><td>0.0068<br>(0.01)</td><td>-0.020<br>(0.02)</td><td>0.025*<br>(0.01)</td><td>-0.029**<br>(0.01)</td><td>0.011<br>(0.01)</td></tr><tr><td>TD:Donation</td><td></td><td></td><td>-0.040***<br>(0.009)</td><td>-0.00073<br>(0.006)</td><td></td><td></td><td></td><td></td></tr><tr><td>TD:Donation × Belief in TDE (pca)</td><td></td><td></td><td>-0.011*<br>(0.006)</td><td>0.00079<br>(0.004)</td><td></td><td></td><td></td><td></td></tr><tr><td>TD:Employment</td><td></td><td></td><td></td><td></td><td>0.0018<br>(0.006)</td><td>0.0061<br>(0.006)</td><td></td><td></td></tr><tr><td>TD:Employment × Belief in TDE (pca)</td><td></td><td></td><td></td><td></td><td>0.0062<br>(0.005)</td><td>-0.0024<br>(0.004)</td><td></td><td></td></tr><tr><td>TD:Innovation</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.0056<br>(0.007)</td><td>-0.0088*<br>(0.005)</td></tr><tr><td>TD:Innovation × Belief in TDE (pca)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.00081<br>(0.005)</td><td>-0.00068<br>(0.003)</td></tr><tr><td>Founder</td><td>-0.0048<br>(0.003)</td><td>-0.0070***<br>(0.003)</td><td>-0.0035<br>(0.005)</td><td>-0.0054<br>(0.004)</td><td>-0.012**<br>(0.006)</td><td>-0.0083<br>(0.006)</td><td>-0.0016<br>(0.005)</td><td>-0.0075*<br>(0.004)</td></tr><tr><td>High effort entrepreneur</td><td>-0.022***<br>(0.003)</td><td>-0.021***<br>(0.003)</td><td>-0.019***<br>(0.005)</td><td>-0.0094**<br>(0.004)</td><td>-0.022***<br>(0.006)</td><td>-0.022***<br>(0.006)</td><td>-0.025***<br>(0.006)</td><td>-0.033***<br>(0.005)</td></tr><tr><td>Low effort recipient</td><td>-0.034***<br>(0.004)</td><td>-0.039***<br>(0.004)</td><td>-0.030***<br>(0.006)</td><td>-0.021***<br>(0.006)</td><td>-0.037***<br>(0.009)</td><td>-0.051***<br>(0.009)</td><td>-0.037***<br>(0.007)</td><td>-0.052***<br>(0.008)</td></tr><tr><td>Constant</td><td>0.48***<br>(0.04)</td><td>0.64***<br>(0.08)</td><td>0.46***<br>(0.06)</td><td>0.43**<br>(0.2)</td><td>0.50***<br>(0.08)</td><td>0.54***<br>(0.2)</td><td>0.54***<br>(0.06)</td><td>0.85***<br>(0.10)</td></tr><tr><td>Observations</td><td colspan="2">19200</td><td colspan="2">18336</td><td colspan="2">7200</td><td colspan="2">6336</td></tr><tr><td>Pseudo R-squared</td><td colspan="2">0.018</td><td colspan="2">0.055</td><td colspan="2">0.021</td><td colspan="2">0.110</td></tr></tbody></table>

**Notes:** The dependent variable is the percentage of money transferred from the entrepreneur to the recipient *TDE:Donation* is a dummy = 1 if the entrepreneur donated more than $3,600 in the last 12 months. *TDE:Employment* equals 1 if the entrepreneur's firm has over 1000 employees. *TDE:Innovation* equals 1 if the entrepreneur's firm obtained over 180 patents. *Founder* is a dummy = 1 if the entrepreneur founded the firm they own. *High effort entrepreneur* equals 1 if the entrepreneur works more than 10 hours daily. *Low effort recipient* equals 1 if the recipient works more than 10 hours daily. Controls include age, gender, region of residence, education level, political ideology, and a dummy for the order in which the tables were presented. Standard errors clustered at the individual level in parentheses. \* significant at 10%, \*\* significant at 5%, \*\*\* significant at 1%.