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\begin{document}

\title{Politicians' Social Welfare Criteria \\ \large An Experiment With German Legislators\footnote{Corresponding author: Sandro Ambuehl, Department of Economics and UBS Center for Economics in Society, University of Zurich, Bl\"uemlisalpstrasse 10, 8006 Z\"urich, Switzerland, sandro.ambuehl@econ.uzh.ch.
Blesse: Leipzig University, KOMKIS (Competence Center for Local Infrastructure Saxony) as well as ZEW Mannheim and Ifo Institute, Universit\"atsstrasse 16, 04109 Leipzig, blesse@wifa.uni-leipzig.de. Doerrenberg: University of Mannheim \& ZEW Mannheim, Schloss, Ostfl\"ugel, 68161 Mannheim, Germany, doerrenberg@uni-mannheim.de.
Feldhaus: Ruhr-University Bochum, Universits\"atsstrasse 150, 44801 Bochum, Germany, christoph.feldhaus@rub.de. Ockenfels: University of Cologne, Albertus-Magnus-Platz, 50923 K\"oln, Germany, ockenfels@uni-koeln.de.
We thank Peter Buchmann (ZEW) and Timo Huber (University of Zurich) for his excellent coding of the surveys. We are grateful to Michel Mar\'{e}chal and to participants at several conferences and workshops for valuable comments. We especially thank Jonas Schwarze, as well as Sebastian Jancic, Kirsten Marx, Julian Reifenstein, Konstantin Scheuermann, Rosa Wolf, and Hannah Zick for excellent research assistance. This project was funded by the Collaborative Research Centre (SFB) 884 ``The Political Economy of Reforms'', the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 741409 - EEC), the German Research Foundation (DFG) under Germany's Excellence Strategy (EXC 2126/1390838866) and by the Department of Economics at the University of Zurich. Blesse is grateful to the KOMKIS for financial support. The project was approved by the Ethics Review Board (ERB) of the Faculty of Management, Economics, and Social Sciences, University of Cologne. The study is preregistered at the AEA RCT Registry under the number AEARCTR-0007689 (\url{https://doi.org/10.1257/rct.7689-1.1}).} }
\author{Sandro Ambuehl\\ Sebastian Blesse\\ Philipp Doerrenberg\\ Christoph Feldhaus\\ Axel Ockenfels}

\maketitle


\begin{abstract}
Much economic analysis derives policy recommendations based on social welfare criteria intended to model policymakers’ preferences. Yet, little is known about policymakers’ normative views suitable for this use. Our behavioral experiment elicits German legislators’ welfare criteria. When resolving preference conflicts \emph{across} individuals, they place substantially more importance on least-favored alternatives than on most-favored alternatives, contrasting with common aggregation mechanisms and the equal weighting inherent in utilitarianism. When resolving preference conflicts \emph{within} individuals, politicians choose inconsistently with behavioral economists’ most common approach to welfare analysis with time-inconsistent preferences. In distribution, politicians largely support the same welfare criteria as the public.
\medskip\\
\quad \textit{JEL codes: C9, D6}\\
\quad \textit{Keywords: Positive welfare economics, politicians, preference aggregation, paternalism}


\end{abstract}


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\section{Introduction}\label{intro}


Much economic analysis derives policy recommendations based on assumed social welfare criteria that are typically left to policymakers to judge. Yet, little is known about what criteria policy makers actually support \citep{GulPesendorferWelfare}. Empirical evidence that infers policy makers' preferences from actual policies, from policy makers' voting behavior, from party election proposals, or from survey responses \citep[][ respectively]{Heathcoteetal., butler2011can, jacobs2017revealed, heinemann2011viewing} is arguably confounded by political economy considerations, strategic motives, and social signaling concerns. Nonetheless, politicians' personal inclinations can substantially affect their policymaking in democracies \citep{Washington.Daughters, Besleyetal.EducatedLeaders, Leeetal., AmsalemSheffer}.\footnote{The first three papers show, respectively, that the number of daughters affects US congresspersons' voting on reproductive rights issues, that the personality traits Openness to Experience and Extraversion predict stated policy preferences of elected representatives in democracies, and that more highly educated leaders cause higher economic growth in democracies. \citet{Leeetal.}  shows that voters merely decide who is elected into office, but cannot change any given candidate's voting behavior, due to the candidates' lack of commitment power.} To study the social welfare criteria German federal and state legislators support while minimizing political economy constraints, we conduct an anonymized behavioral experiment with real consequences. Critically, our work does \emph{not} seek to improve predictions of politicians' real-world behavior, which may be shaped by political economy and signaling constraints. Rather, we seek to identify the social welfare criteria politicians \emph{genuinely} consider normatively appealing. The aim is to reduce the potential mismatch between the social welfare criteria on which economists' policy recommendations typically rely and those that policy makers endorse.


When economists derive optimal policy recommendations, they must often take a stance not only on how to resolve preference conflicts \emph{across} individuals (social aggregation), but also on how to adjudicate conflicts between mutually inconsistent preferences \emph{within} the same individual---especially when time-inconsistent preferences affect optimal sin taxes \citep{GruberKoszegi.AddictionRational, oDonoghueRabin.SinTaxes,Allcottetal.SodaTaxes}, optimal social security systems \citep{Bisinetal.}, the regulation of payday loans \citep{allcott2022high}, optimal energy policy \citep{AllcottTaubinsky.Lightbulbs}, or similar policies that are reviewed in \citet{BernheimTaubinsky}. The criteria that economists typically assume might deviate from those that politicians support in both of these domains. We thus measure how politicians resolve preference conflicts both across and within individuals.


To resolve preference conflicts across individuals, economists often assign equal weight to those who most prefer the chosen policy and to those who least prefer it. This is the case, for instance, when policy recommendations derive from the maximization of a utilitarian social welfare function, or when policies are ranked according to the Kaldor-Hicks criterion \citep{HausmanMcPherson}. Our experiment lets us characterize how politicians impartially aggregate both when only ordinal information is available (as in the framework of \citealp{Arrow1950}), and when cardinal preferences play a role (as in utilitarian aggregation). We design the experiment in a way that lets us infer ordinal aggregation preferences because cardinal information is often difficult to obtain, either because no meaningful cardinal measures may exist (for instance, in the case of setting priorities) or because it may not be feasible to measure them. We focus on the relative weights politicians implicitly assign to citizens as a function of the preference rank of the alternative they obtain, or of the cardinal utility associated with that alternative.


In the domain of preference conflicts within individuals, the case of time-inconsistency has received particular attention \citep[see][for a review]{EricsonLaibson}. To derive policy recommendations, economists commonly take the stance, known as the \emph{long-run criterion},  that choices involving immediate rewards are mistaken (due to the lure of immediacy), but choices only involving future rewards reveal `true' preferences \citep[e.g.][]{oDonoghueRabin,oDonoghueRabin.SinTaxes,GruberKoszegi.AddictionRational,GruberKoszegi.TaxIncidence,DellaVignaMalmendier.ContractDesign, Allcottetal.SodaTaxes}. We test whether politicians' preferences are consistent with the long-run criterion. In this context, we also study how politicians resolve the tradeoff between respecting the autonomy of a voting-age German citizen and ensuring that he receives an outcome the politician regards as good. We regard this decision, too, as the resolution of a conflict of preferences within an individual---those that the citizen reveals in his choices, and those which, in the mind of the politician, he would have if he understood what is truly good for himself.


To study how politicians resolve preference conflicts across individuals, we use the method developed in \citet{AmbuehlBernheim}. Each respondent learns that she is matched, with some chance, to a group of five German voting-age citizens. Each of them has ranked three given political foundations in the order in which he would like them to receive a donation of \EURdig30 that cannot be split. The respondent observes this preference profile, along with five other profiles. To ensure that respondents reveal their aggregation preferences rather than their own political leanings, we anonymize the foundations. For each profile, the respondent decides which of the anonymized foundations will receive the donation. The respondent knows that one of these preference profiles is real and that her decision on that profile will be carried out. Because we do not reveal the identity of the real profile, the respondent has an incentive to make a serious choice on each profile (as long as she is not completely indifferent to the group of citizens). We then elicit the respondents' beliefs about citizens' willingness to pay to trigger or prevent the donation to whichever foundation they ranked first, second, and last. These cardinal beliefs let us compare respondents' aggregation decisions to a utilitarian benchmark.

To study how politicians resolve preference conflicts within individuals, we rely on the method used in \citet{AmbuehlBernheimOckenfels}. Respondents make choices concerning the conflict between respecting other individuals' autonomy and ensuring they receive outcomes considered good. Each respondent is matched, with some chance, with a German voting-age citizen who can choose how to receive payment for his study participation. Each choice option consists of two payments, one disbursed six months before the other. Impatience is costly: larger, earlier payments are associated with smaller total payments. The respondent's task is to construct the choice set from which the citizen will choose. Our interest centers on the number and type of options the respondent includes in that set. To identify implicit support for the long-run criterion, the respondent constructs one choice set from options that involve immediate payments, and another one from options in which even the early payment occurs one month after the study.


We invited all 2,588 members of German state and federal parliaments to participate in our study, and obtained 423 responses (16.3\%) from across the political spectrum. We weight all responses in our analysis by age, education, and political affiliation to address any remaining selection issues. Because the ultimate sovereign of a democracy is its citizenry, we conduct parallel experiments with a general population sample of well over a thousand members of the German voting-age general public. The comparison between this sample and politicians allows us to check the alignment of lawmakers' social welfare criteria with those of the general public.

We obtain three main findings. First, when politicians resolve preference conflicts across individuals, they deviate from economists' typical practice of weighing them equally. Instead, the average politician promotes compromise by placing substantially more weight on minimizing the number of individuals who receive their least-favored of the three alternatives than on maximizing the number of citizens who obtain their most-favored alternative. This behavior also contrasts with aggregation mechanisms commonly used in practice, such as the plurality vote, that place disproportionate emphasis on first-ranked alternatives and thus implement majoritarian choices. The weight on the least-preferred alternatives varies with a politician’s party’s position. It is weakest for representatives of the far left party and is especially pronounced for center parties. In this sense, more centrist politicians focus on the losers of collective decision-making to a greater extent. When we use politicians' beliefs about citizens' willingness to pay (WTP) to trigger a donation to their most, second-most, and least preferred foundations, we find that utilitarian aggregation based on these beliefs would produce far more majoritarian choices than we actually observe. Politicians' choices are better rationalized by a welfare function that applies a concave transformation to WTP beliefs, consistent with a motive to reduce inequality in utility.

Second, when politicians resolve potentially conflicting preferences within individuals, they deviate from the long-run criterion commonly used in economics in two ways. First, the choice sets they construct are largely independent of the availability of immediate rewards, in contrast to what one would expect if the lure of immediacy were of particular concern. Second, even when all rewards are delayed, more than half of all politicians remove impatient alternatives. This behavior conflicts with the long-run criterion's prescription to defer to the agent's preferences when no immediate rewards are available. Briefly, politicians seek to impose a minimum level of patience that is independent of whether immediate payments are available. Moreover, representatives of parties further to the political right intervene more often in otherwise autonomous choices.

Third, the social welfare criteria of elected politicians broadly align with those of the general public, though there are subtle differences. While politicians and the general public both prefer compromise solutions over majoritarian solutions, that preference is slightly more pronounced among politicians. In the domain of interpersonal preference conflicts, the general public shows as little support for the long-run criterion as politicians, though its members intervene slightly more often to prevent impatient (and in some cases patient) choice.  In spite of the similarities in the public's and politicians' welfare criteria, and in spite of monetary incentives for accuracy, the public's beliefs about others' choices are systematically biased. They underestimate both politicians' and the public's preference for compromise, and they substantially overestimate politicians' interventionism. The latter bias is so severe that subjects believe politicians intervene more often than the general public, even though the opposite is true.\footnote{When asked whether to delegate decisions to either politicians or to other members of the general public, respondents display such a strong aversion to delegating to politicians that beliefs about the two subject pools' choices have no explanatory power.}


There are three advantages of obtaining these results through an experiment, aside from minimizing political economy confounds. First, we can present our experimental decisions in a largely context-free way. Thus, we obtain plausibly context-independent results that generalize to the various policy domains in which economists use social welfare criteria to derive policy recommendations. Second, the stylized nature of our tasks makes it difficult to infer what choice would be socially desirable, which diminishes the types of experimenter demand effects that plague other research with elected representatives. Third, diverging assessments of real-world policies in other research might stem from both heterogeneous beliefs about likely consequences and from differences in value judgments. By contrast, our paper mutes the belief channel and can thus unambiguously trace the reasons for subjects' choices to their value judgments.\footnote{This point is of particular concern when comparing politicians' inferred value judgments to those of the general public, given that politicians are often demonstrably better informed than citizens \citep{Leeetal.}.}

Our results provide guidance to research in economics that derives policy advice based on social welfare criteria. It is important to align the welfare criteria that underlie such research with those that policymakers support. Policy recommendations based on value judgments that decision makers reject are less likely to be followed \citep{SapienzaZingales}.

Our results also inform the literature on \emph{positive welfare economics}, which empirically studies how individuals resolve normative questions \citep{Konow.reviewFairness, Almasetal., Cappelenetal.Meritocratic, Andreonietal.WhenFairIsntFair, AckfeldOckenfels, Bartlingetal., chen2022rejecting}.
That literature largely relies on laboratory and general population samples.\footnote{As an exception, \citet{Fismanetal.Elite} consider the distributional preferences of a likely future elite. A complementary literature shows that general population subjects' decisions in laboratory-type tasks predict real-world political choices \citep[e.g.][]{Fismanetal.Political,Epperetal.}.} Our finding that politicians’ welfare criteria generally mirror the public’s---despite some specific differences---suggests that the broad conclusions of positive welfare economics in other domains can be expected to apply to politicians as well.


Finally, this paper contributes to an evolving literature that studies the preferences, traits, and biases of elected representatives \citep{Fatasetal., Hessetal.,  Hanania, Lindeetal., Shefferetal., ShefferLoewen, JanezicGallego}.
Despite a recent rise in survey and experimental work with political elites, behavioral experiments with real-world policy makers remain scarce  \citep{kertzer2022experiments}. Extant research has not measured the social welfare criteria of politicians. Our finding that the social welfare criteria of politicians and that of the general public broadly align, but that the general public believes in substantial differences, resonates with a recent meta-analysis that finds that elite-public gaps in decision-making are often smaller than commonly believed \citep{Kertzer}. By studying the general preference of politicians to compromise and intervene (largely abstracting from re-election context), our paper thus also speaks to a larger body of political economy papers that study issues of voter representation by political elites as well as political selection and behavior in office.\footnote{Other papers, for instance, study whether politicians know the preferences of their electorate \citep{broockman2018bias}, discard voter opinion selectively or discriminate against voters \citep[e.g.][]{butler2016politicians,butler2011politicians}, represent their voters' interests \citep[e.g.][]{Baileretal.}, violate rational choice theory \citep{Fatasetal.} or are factually more knowledgeable \citep{lee2021policy}. A recent meta-study of \citet{Kertzer} shows that differences in political attitudes between politicians and the broader public may actually be overstated and that these differences may be rather due to demographic differences rather than genuine differences between these groups.  A more distantly related literature, reviewed in \citet{gulzar2021enters}, studies selection into political office depending on factors such as competence and socio-economic background \citep[e.g.][]{DalBoetal.Politicians,jaakoetal2022}, earning levels in politics \citep[e.g.][]{fisman2015labor}, institutional incentives \citep[e.g.][]{galasso2017political}, outside options and intrinsic motivation \citep{besley2005political} as well as social motivators \citep[e.g.][]{gulzar2017citizens}.}

The remainder of this paper proceeds as follows. Section \ref{sec:Design} presents our experimental design. Section \ref{sec:samplesAndContext} provides institutional context and explains our samples and weighting strategies. Section \ref{sec:politiciansMain} contains our main results about politicians' social welfare criteria. Section \ref{citizens} compares them to the welfare criteria of the general public. Section \ref{sec:resultsBeliefs} analyzes respondents' beliefs about the decisions that politicians and other general population respondents have made. Finally, Section \ref{conclusion} concludes.


\section{Design}\label{sec:Design}
Our main experiment has two parts. They allow us to answer, respectively, how politicians aggregate preferences (conflicting preferences \emph{across} individuals), and how politicians resolve the tradeoff between respecting individual liberty and ensuring that individuals obtain outcomes the politician regards as desirable (conflicting preferences \emph{within} individuals, i.e., paternalism). A fraction of the general population respondents complete two additional parts. We explain each part in turn.


\subsection{Conflicting preferences \emph{across} individuals}\label{sec:aggregation}

In this part of the experiment, the politician's task is to aggregate the preferences of five voting-age German citizens that we call stakeholders.

Stakeholders have preferences over three social alternatives, $A$, $B$, and $C$. The politician observes the preference profile of the five stakeholders over the three alternatives as in Panel A of Figure \ref{decisionInterface} and then chooses one of the social alternatives to be carried out. (\citet{AmbuehlBernheim} show that ordinal preference aggregation is largely robust to the specific graphical presentation of preference profiles.) Politicians make one such decision for each of six preference profiles. The politician knows that with a 10\% chance, one of these profiles represents the preferences of a real group of five stakeholders and that her decision on that profile will be carried out, but she does not know which profile that is. Consequently, as long as she is not entirely indifferent towards all stakeholders, she has an incentive to reveal her aggregation preferences truthfully for every preference profile she encounters.

We inform politicians that each social alternative is a non-divisible donation of \EUR30 to one of three political foundations (\textit{Hans B\"ockler Stiftung}, \textit{Bund der Steuerzahler Deutschland},  and \textit{Ludwig Erhard Stiftung}).\footnote{On their websites, the foundations describe themselves as follows. The \textit{Hans B\"ockler} foundation deals with co-determination, research linked to the world of work, and the support of students on behalf of the confederation of German trade unions. The \textit{Bund der Steuerzahler Deutschland} is devoted to combat wasteful spending of taxpayer money and tax revenues, and advocates the rights of taxpayers. The \textit{Ludwig Erhard} foundation aims to promote the principles of liberty in politics and economy, to foster freedom and responsibility, and to strengthen the development of the social-market economy.} To ensure that politicians reveal their aggregation preferences rather than their political leanings, we anonymize the foundations, representing them with individually randomized letter labels $A$, $B$, and $C$.

In a preliminary online survey, stakeholders ranked the three foundations according to their preference. We incentivized stakeholders to answer truthfully. They knew that with a 1 in 40 chance, the ranking they provided would determine the recipient of the \$30 (the first- and second-ranked foundations would receive it with a 2/3 and 1/3 chance, respectively). Stakeholders did not learn how the recipient of the \$30 would be determined otherwise. Hence, they could not strategically misrepresent their preferences. We enroll stakeholders solely to incentivize politicians who make aggregation choices.

After making these aggregation decisions, politicians indicate their beliefs about stakeholders' willingness to pay to trigger the donation to their first, second, and least preferred foundation. We ask politicians to enter a negative amount if they believe stakeholders would pay to prevent rather than trigger the donation.


\begin{figure}[htbp!]
\caption{Main decisions \label{decisionInterface}}
\begin{center}
\textbf{A. Preference Aggregation (to resolve conflicting preferences \emph{across} individuals)}\medskip \\
\includegraphics[width = 0.8\textwidth]{pics/decisionInterfaceAggregation} \bigskip \\
\textbf{B. Choice set construction  (to resolve conflicting preferences \emph{within} individuals)}\medskip \\
\includegraphics[width = 0.8\textwidth]{pics/decisionInterfacePaternalism}
\end{center}
\footnotesize
\end{figure}

\paragraph{Identification.} Following \citet{AmbuehlBernheim}, we use a revealed preference approach to infer how politicians aggregate ordinal preferences. Specifically, we ask what theoretical ordinal preference aggregation function best matches the sequence of choices a politician makes across the six preference profiles. The literature emphasizes two classes of such aggregation functions, \emph{scoring rules} and \emph{Condorcet extensions} \citep[see e.g.][]{GaertnerBook}.

Scoring rules assign values 1, $s$, and 0 to each stakeholder's most, middle, and least-preferred alternatives, respectively. They then select the alternative for which the sum of scores across the stakeholders is highest. The case $s=1/2$ is the Borda rule, and the case $s=0$ is the plurality rule (one person, one vote, and the alternative with the largest number of votes wins). Scoring rules encompass a judgment on how important it is that many stakeholders get their most-preferred rather than the second-most-preferred option, relative to how many get their least-preferred rather than their second-least-preferred option. The Borda rule weighs both considerations equally. The plurality rule only places weight on the first consideration, and hence is \emph{majoritarian}. Its polar opposite, the antiplurality-rule ($s=1$), only places weight on the second consideration.\footnote{The antiplurality rule can be implemented through negative voting: each stakeholder receives one vote he can use against his least preferred alternative. The alternative with the fewest votes against it wins.} We interpret the parameter $s$ as a preference for compromise---the higher the $s$, the more willing is a politician to prevent a stakeholder from obtaining his least preferred alternative by selecting an alternative for which most other stakeholders only have a middling preference.\footnote{All else equal, scoring rules with $s>\frac{1}{2}$ favors an option ranked $n$-th by two people over an alternative ranked $(n-1)$-th by one and $(n+1)$-th by another. Scoring rules with $s<\frac{1}{2}$ do the opposite because they place more weight on gains from high ranks. All else equal, they favor an option ranked $(n-1)$-th by one person and $(n+1)$-th by another over an alternative ranked $n$-th by both.}

Rules in the class of \emph{Condorcet extensions} select the social alternative that wins a pairwise majority vote against every other alternative (the \emph{Condorcet winner}) if such an alternative exists. They differ according to the selections they make when no Condorcet-winner exists.


We select our six preference profiles so we can identify the scoring parameter $s$ for individuals whose choices are consistent with a scoring rule. In conjunction with the others, one profile helps separate the use of the Condorcet rule from scoring rules. While this set of rules is only a subset of those considered in the theoretical literature on ordinal preference aggregation, these are the only rules that receive non-trivial empirical support in the comprehensive classification of \citet{AmbuehlBernheim}.

Table \ref{aggregationProfiles} lists the preference profiles we use. Each profile is associated with a value $\bar{s}$ such that scoring rules with $s\leq \bar{s}$ select alternative $A$ (the \emph{majority winner}) and scoring rules with $s\geq \bar{s}$ select alternative $B$ (the \emph{compromise option}). Scoring rules never strictly prefer alternative $C$ in any of the profiles, though $C$ ties for majority with $A$ in profile 1 and will be selected along with $A$ if $s=0$. In order to ensure that one of the six profiles corresponds to the real stakeholder preferences, we form groups of five stakeholders each such that each group matches one of the six preselected profiles. Appendix \ref{stakeholderImplementation} presents details.


\begin{table}[htb]
\caption{Preference profiles. \label{aggregationProfiles}}
\begin{center}
\begin{small}
\begin{tabular}{cccccccccccc}
\toprule
 Index &  \multicolumn{5}{c}{Preference profile}   & \multicolumn{4}{c}{Scoring rules}& Condorcet \\
\cmidrule(r){8-10}
&&&&&&  $\bar{s}$ & $0\leq s<\bar{s}$ & $s=\bar{s}$ & $\bar{s}<s\leq 1$   \\
\\
1&  B & C & C & A & A & 1/3      	& A$^{a)}$         &\{A,B\}  & B      &B      \\
 & A & B & B & B & B &                  &             &                     \\
 & C & A & A & C & C &                 &             &                       \medskip \\
2&  B&B&A&A&A & 1/3      		& A      &\{A,B\}      & B      &A        \\
 & C&C&B&B&B &                   	&                         \\
 & A&A&C&C&C &                   	&                        \medskip \\
3&  A&A&A&B&B & 1/2        	& A          &\{A,B\}  & B       &A       \\
 & C&B&B&C&C&                   	&                        \\
 & B&C&C&A&A&                    	&                       \medskip \\
4&  C&C&A&A&A & 3/5         	& A      &\{A,B\}      & B       &A        \\
 & B & B & B & B & B &                   	&                        \\
 & A & A &  C&C&C&                 	&                      \medskip  \\
5&  C&A&A&A&B & 2/3        	& A     &\{A,B\}       & B       &A      \\
& B & B & B & B & A &                   	&                       \\
& A&C&C&C&C&                    	&                    \medskip   \\
6&  C&A&A&A&A& 4/5         	& A      &\{A,B\}      & B      &A     \\
 & B& B & B & B & B &                   	&                        \\
 & A&C&C&C&C&                     	&                     \medskip   \\
 \bottomrule
\end{tabular}

\end{small}
\end{center}
\footnotesize
$^{a)}$ If $s = 0$, then the set of selected options is $\{A, C\}$.\medskip\\
\textit{\textbf{Notes}:} Each profile is displayed as a 3$\times$5-matrix. Columns correspond to stakeholders, rows correspond to preference ranks. A stakeholder's first, second, and third-ranked alternatives are listed in the first, second, and third rows, respectively.
\end{table}


\subsection{Conflicting preferences \emph{within} individuals}\label{sec:paternalism}
In this part of the experiment, politicians learn that they may be paired with a German voting-age citizen who will participate in a follow-up study and who will choose how to receive his study payment from a choice set. We refer to that citizen as \emph{Chooser}. The politician's task is to construct the choice set that will be available to the citizen from a menu, using a decision screen as in Panel B of Figure \ref{decisionInterface}. The sole restriction is that at least one alternative must be available.  Each option in that menu is a pair of payments, one received half a year before the other.  In each menu, impatience is costly: the citizen can only increase his early payment by reducing his overall payment. In a follow-up session, the citizen selects one of the options the politician made available, without learning how the choice set was determined.

To measure implicit support for the long-run criterion, politicians construct choice sets from two different menus. In one of them, the citizen receives the early payment immediately, and the late payment with a half-year delay. In the other, the early payment is paid one month after the citizen's participation, and the late payment is paid 7 months after. In addition to this variation in the availability of immediate payments, we vary the menus within individuals to prevent artificially consistent choices. Table \ref{paternalismMenus} displays the menus from which politicians construct choice sets. Politicians are assigned to construct choice sets either from menus 1A and 1B or from menus 2A and 2B. The menus in a pair are rescalings of each other. The amounts in 1A are $\frac{2}{3}$ of the amounts in 1B, and the amounts in menu 2A are $\frac{3}{4}$ of the amounts in menu 1B.

\begin{table}
\caption{Menus from which respondents construct choice sets \label{paternalismMenus}}
\begin{centering}
\begin{tabular}{lcccccc}
\toprule
 & \multicolumn{2}{c}{Menu 1A} &  & \multicolumn{2}{c}{Menu 1B}  \\
\cmidrule(r){2-3} \cmidrule(r){5-6}
  & Early  & Late  &  & Early  & Late  \\
Most patient  	& 0 \EURdig\ & 16 \EURdig\  &  	& 0 \EURdig\  & 24 \EURdig\  \\
Middle  		& 2 \EURdig\  & 10 \EURdig\  &  	& 3 \EURdig\  & 15 \EURdig\  \\
Least patient  	& 4 \EURdig\  & 2 \EURdig\  &  		& 6 \EURdig\ & 3 \EURdig\ \\
\cmidrule(r){2-3} \cmidrule(r){5-6}
  &  &  &  &  &  &  \\
 & \multicolumn{2}{c}{Menu 2A} &  & \multicolumn{2}{c}{Menu 2B} \\
\cmidrule(r){2-3} \cmidrule(r){5-6}
   & Early  & Late  &  & Early  & Late  \\
Most patient  	& 0 \EURdig\ & 18 \EURdig\ &  	& 0 \EURdig\ & 24 \EURdig\ \\
Middle  		& 3 \EURdig\ & 12 \EURdig\ &  	& 4 \EURdig\ & 16 \EURdig\ \\
Least patient  	& 6 \EURdig\ & 3 \EURdig\  &  		& 8 \EURdig\ & 4 \EURdig\ \\
\cmidrule(r){2-3} \cmidrule(r){5-6}
\bottomrule
\end{tabular}\\
\end{centering}
\footnotesize
\end{table}


After constructing choice sets, politicians report their beliefs about the choices previous citizens made from each of two unrestricted choice sets. For each of the three options in each of two menus, they predict how many out of ten randomly chosen people chose that option. These beliefs allow us to measure the probability with which politicians believe they impose binding restrictions by excluding options from choice sets.


\subsection{Delegation}
\label{sec:delegation}

Respondents from the general public make the same decisions as politicians. Some of them additionally decide which of three groups of individuals to delegate their preference aggregation and choice set construction decisions to, after making these choices themselves. They make two such delegation decisions, either in the context of aggregation decisions or in the context of choice set construction, depending on the survey version. Respondents can delegate the decision to either of three groups: (i) federal and state legislators, (ii) the general public, and (iii) a sample of the general public selected to match the demographic characteristics of federal and state legislators.\footnote{In case of group (iii), we inform respondents about the average demographic characteristics (gender, age, university education and marital status) of the general population subjects to whom a delegation is possible.} We inform respondents that the members of these groups have previously participated in the same survey and that they have already made their decisions. Respondents further learn that we have randomly chosen one member of each group and that the decision of this selected member is implemented if the respondent decides to delegate to the corresponding group.


\subsection{Beliefs about other participants' aggregation and intervention decisions} \label{sec:beliefsArchitect}

From a part of the general population respondents, we elicit beliefs about how politicians, other members of the general public, and a sample of the general public that matches the politicians' demographic characteristics decided when aggregating preferences. They indicate, for each respondent group, how many out of 10 respondents from that group chose option $A$, $B$, and $C$ when aggregating the preferences in profile 2 of Table \ref{aggregationProfiles}. We elicit the same beliefs corresponding to preference profile 5.
Other respondents from the general public report beliefs about what choice sets each of the three respondent groups constructed. They indicate how many out of 10 respondents decided for each of the following choice sets: $\{A\}, \{A,B\}, \{A,B,C\}$, and `other.' They provide one set of such beliefs for each of the two menus from which they have previously constructed choice sets themselves.


\subsection{Additional elicitations}\label{sec:questions}

We complement politicians' responses with background information using publicly available data. Since such data is not available for general population respondents, we survey relevant background characteristics for the general public. Respondents from the general public report their gender, year of birth, German citizenship status, federal state of residence, highest educational attainment, marital status, monthly household income, and own monthly net income. They further indicate which political party they most identify with, whether they are a member of a political party, the extent to which they are politically active, and their own political attitude on a left/right scale.\footnote{In addition, both politicians and respondents from the general public answer the three questions about risk, time, and social preferences from \citet{Falketal.Global}, and they indicate their support for additional compulsory retirement savings. In light of the recent study by \citet{chapman2025reassessing} which demonstrates a lack of support for the use of experimentally-validated qualitative self-assessments as proxies for choice-based preference measures, we interpret these responses with caution and defer their analysis to Appendix Sections \ref{appendixPrefs} and \ref{appendixRetirementSavings}.}

We asked another subset of respondents from the general public whether they believe politicians have a stronger or weaker preference for majoritarian over compromise solutions than the average German citizen, and whether they intervene more or less in others' choice sets. For each of these two domains, subjects rated their agreement with a statement that the difference between politicians and the general public arises as an effect of selection into political office and a statement that the difference arises due to experience in office.


\subsection{Implementation}


\paragraph{Survey versions.}\label{surveyVersions} Our main analyses rest on four versions of the survey, summarized in Table \ref{surveyVersions}.\footnote{See Appendix \ref{instructions} for English translations of each survey version. The surveys were custom-coded by the ZEW --- Leibniz Centre for European Economic Research.} The first two versions (\emph{Politicians 1} and \emph{Public 1}) include the preference aggregation and intervention parts (Subsections \ref{sec:aggregation} and \ref{sec:paternalism}) as well as the questions listed in Subsection \ref{sec:questions}.  All politicians complete this version, as does a sample of the general public that matches the universe of federal and state legislators on observables (details below). These two versions differ only in that general population respondents answer additional demographic and attitudinal questions. We administer the remaining two versions to the general public. Version \emph{Public 2} only contains decisions and questions regarding preference aggregation. Version \emph{Public 3} only includes decisions and questions regarding interventions. Both of these versions include the delegation stage (Subsection \ref{sec:delegation}) and the prediction stage (Subsection \ref{sec:beliefsArchitect}) for the corresponding domain (aggregation or intervention). They also include the questions of Subsection \ref{sec:questions}.

All politicians complete the preference aggregation part before the intervention part. In anticipation of attrition in the politician sample, we chose this fixed ordering to increase the chance of achieving sufficient statistical power for at least one of the two parts.

Within each part of each survey version, we randomize the order of decisions and the parameters displayed. We use block-randomization to limit the number of non-identical surveys. This limit arises from the fact that politicians receive a paper version of the survey, sent through postal mail (along with a link through which they can participate online). Each respondent who makes aggregation decisions is randomly assigned to one of the following four orders in which the preference profiles are presented: $(2, 3, 4, 5, 1, 6)$, $(6, 1, 5, 4, 3, 2)$, $(6, 2 , 1, 3, 5, 4)$, $(4, 5, 3, 1, 2, 6)$. Independently, the display of preference profiles randomizes the table rows that correspond to the social alternatives into one of the following orders: $(A, B, C)$, $(C, B, A)$, $(B, A, C)$, $(C, A, B)$. Each respondent who constructs choice sets does so by selecting options either from menus 1A and 1B or from menus 2A and 2B. The respondent reveals beliefs about Choosers' unrestricted choices regarding the corresponding left-out menus. We include immediate payments in either menus 1A and 2A, or in menus 1B and 2B. Finally, a respondent either first sees the menus with immediate payouts, both in the decision and belief elicitation stage, or she first sees the menus without immediate payouts in each of these stages.


Overall, there are thus 16 variations of the preference aggregation part and 8 versions of the intervention part. In order to limit the total number of survey variations to 16, we deterministically pair each variation in the aggregation part with a variation in the intervention part. See Appendix Table \ref{surveyVersionsDetailed} for details.


The versions for general population respondents include comprehension tests.\footnote{We did not include such tests for politicians, both to keep the survey brief, and to avoid the risk of appearing patronizing.} The comprehension test on aggregation decisions serves to ensure that respondents understand the display of the preference profile. They view a preference profile and indicate (i) how many stakeholders rank alternative A in the middle, and (ii) which alternative is preferred most by three people. The comprehension test on interventions serves to ensure that respondents understand the menu of options from which they construct choice sets. For a given menu in which the least patient option is marked as unavailable, subjects indicate (i) the maximal amount that the Chooser can obtain immediately from that choice set, and (ii) the amount of money the Chooser receives with a six-month delay if he chooses to receive \EUR3 immediately. The comprehension checks precede decision-making for each domain. Subjects who make decisions only in one domain (\emph{Public 2} and \emph{Public 3}) complete only one of these checks. Subjects have three attempts to answer correctly; otherwise, the survey terminates. We provide an email address to which subjects can send questions if they feel they have understood the instructions but still cannot continue.

An additional version of the study with general population respondents, \emph{Public 4}, replicates \emph{Public 1} but includes additional elements to address questions that arose after completion of the main surveys, which we detail later.


\paragraph{Incentives.} All politicians and general population respondents learn that a random 10\% of them are paired with either a group of five real stakeholders or with a real voting-age German citizen choosing from a choice set they construct, and that one decision from the study affecting these subjects will be selected at random and carried out. Because respondents do not know whether they are among these 10\%, and because they do not know which decision will be carried out, they have an incentive to reveal their preferences truthfully, as long as they are not completely indifferent towards all other study participants (see Appendix \ref{stakeholderImplementation} for the implementation of these decisions). While decisions affecting others have real consequences, they do not affect the respondent's own study payout.


All respondents indicate beliefs about stakeholders. We refrain from incentivizing these elicitations for politicians because sending money to elected representatives would raise bribery concerns. To permit a clean comparison to the general public, we also refrain from incentivizing the corresponding questions for the latter respondents. We do, however, incentivize the elicitation of beliefs about participants' aggregation and intervention decisions. In each of these prediction problems, the respondent assigns ten identical hypothetical individuals to three or four categories to predict the probability distribution across these categories. If the respondent's prediction matches the true distribution, she receives \EUR5 in the form of our survey provider's panel currency. Otherwise, the respondent loses \EUR0.50 for each of the hypothetical individuals we need to assign to a different category until the elicited and observed distributions coincide. We pay these incentives to a random 10\% of respondents, for one randomly selected prediction problem. Respondents are aware of this scheme.


\begin{table}[]
\caption{Survey versions \label{surveyVersions}}
\begin{center}
\begin{tabular}{lcccc}
\toprule
Version                         & Politicians                 & Public 1       & Public 2       & Public 3      \\
\midrule
Participants \\
\quad Politicians & \checkmark \\
\quad General population &  & \checkmark  & \checkmark & \checkmark  \\
\midrule
Main decisions \\
\quad Preference aggregation          & \checkmark          & \checkmark & \checkmark &            \\
\quad Choice set construction                     & \checkmark          & \checkmark &            & \checkmark \\
\multicolumn{2}{l}{Beliefs about other participants} &            &            &            \\
\quad Preference aggregation            &                     &            & \checkmark &            \\
\quad Choice set construction           &                     &            &            & \checkmark \\
Delegation             &                     &            &            &            \\
\quad Preference aggregation                     &                     &            & \checkmark &            \\
\quad Choice set construction                     &                     &            &            & \checkmark \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Main decisions also include the elicitation of beliefs about stakeholders' willingness to pay to trigger or prevent the donation and about the choices unrestricted Choosers would have made.
\end{table}


\section{Samples and context}\label{sec:samplesAndContext}
Our study enrolls German politicians and the German general public.  Germany is a federation with 16 constituent states that each have their own state parliament and legislature. The federation and all states are multiparty representative democracies. The federal level features a bicameral system. The 736 members of the federal parliament (\textit{Bundestag}) represent the votes of the German population approximately proportionally. It is roughly comparable to the US House of Representatives. Our study does not target the second chamber called \textit{Bundesrat}, which is roughly comparable to the US Senate.
Since 2017, the Bundestag has consisted of members of six major parties, along with a handful of organizations with a single representative each, as well as a small number of representatives without party affiliation.\footnote{These six parties are the centre-right conservative union of Christ-Democrats and their Bavarian sister party, the Christ-Social Union, which we treat together as CDU/CSU, the centre-left Socio-Democrats (SPD), the Greens (Gr\"une), the center-right Liberals (FDP), the left-wing party Die Linke as well as the right-wing party Alternative f\"ur Deutschland (AfD).} Each of the 16 federal states has a single-chamber system with a parliament (\textit{Landtage}) whose members are full-time politicians (except for the chambers of the small city states Bremen and Hamburg). The six major parties of the Bundestag are also the dominant parties in the state parliaments. The federal states have far-reaching political power and autonomy in many important policy fields. For example, they have full legislative autonomy in the fields of education, culture, police system, penal system, social housing and regulative law, and they independently govern taxes on the transfer of land and real estate. State parliaments have additional political power through their representation in the Bundesrat, which needs to approve many federal-level legislative proposals and which can veto any proposed changes to the federal constitution.\footnote{A brief overview of the German political system is available, for example, at \citet{expatrio2023german}.}


\begin{table}
\caption{Politician responses \label{politResponses}}
\begin{center}
\begin{tabular}{lccc}
\toprule
Party & Answers & Total & Share \\
\midrule
AFD & 56 & 336 & 16.67\% \\
CDU/CSU & 117 & 814 & 14.37\% \\
FDP & 47 & 196 & 23.98\% \\
Gruene & 67 & 346 & 19.36\% \\
Linke & 43 & 214 & 20.09\% \\
Fraktionlos & 11 & 62 & 17.74\% \\
SPD & 82 & 619 & 13.25\% \\
Total & 423 & 2587 & 16.35\% \\
\bottomrule
\end{tabular}

\end{center}
\footnotesize
\textbf{\textit{Notes:}} A legislator is \emph{fraktionslos} if they are not associated with a government party. Appendix \ref{nonresponse} splits this table by parliament type.
\end{table}


\paragraph{Politician sample.}
We fielded the politician survey between late May and mid-September 2021. We invited politicians through the ZEW Mannheim (Leibniz Centre for European Economic Research), which had conducted surveys with politicians for many years on a regular basis \citep[e.g.][]{Heinemann-etal-2016-jpube}. Politicians thus recognized and trusted the organization and its surveys. We invited all 709 members of parliament (MPs) in the federal parliament (Bundestag) and all 1879 MPs in each of the 16 state parliaments, both by email and by postal mail, and asked politicians to respond personally. Politicians could answer the online survey on a mobile or desktop device. Alternatively, they could submit their responses to the printed version through postal mail, fax, or email. The invitation letter and emails promised strict confidentiality, as did the survey itself.\footnote{To address concerns that MPs outsourced the completion of the surveys to staffers, we categorized all the emails that politicians or their offices sent to us during the time we fielded the study. Emails containing suggestions or complaints about the survey always came from MPs directly. A handful of politicians informed us that they had just participated. Emails from staffers usually stated that the MP invited for participation did not have the capacity to complete the study. These interactions suggest that politicians largely completed the survey themselves.} In order to increase response rates, we repeatedly contacted MPs both by email and by phone if they had neither participated nor explicitly declined to do so. We did not require politicians to answer every question, but the online interface displayed a message in case of skipped questions or invalid answers.

We obtained responses from 423 legislators, of whom 342 are members of state parliaments and 81 are members of the federal parliament.\footnote{Response modes are the following: 56 postal mail, 354 online on a desktop device, and 13 online on a mobile device. The median response time among politicians responding online for the total survey is just under 12 minutes (691 seconds). Their response time for the six aggregation decisions is 102, 150, and 271 seconds at the 25th, 50th, and 75th percentiles. The corresponding response times for the two intervention decisions are 40, 62, and 91 seconds, respectively.} These numbers correspond to a response rate of 16.3\% across parliaments. Table \ref{politResponses} displays the party composition of our sample and compares it to the composition of the parliaments. We obtained good coverage across the political spectrum; for each of the six government parties, we have responses from at least 43 representatives. Response rates vary from 13.25\% to 23.98\% across the political parties. The differences in response rates do not obviously relate to party position. Detailed non-response analysis shows that participation rates are higher among those over 60 years of age, those who were elected directly rather than through party lists, those who have completed tertiary education, those in state parliaments rather than the federal parliament, and those with fewer years of experience in the federal parliament or in a state parliament. Appendix \ref{nonresponse} presents details.


\paragraph{General population surveys.}


The commercial provider Norstat fielded our surveys \emph{Public} 1, 2, and 3 with the voting-age German general public between May and September 2021. This period coincides with our fielding of the politicians' survey. All respondents completed the survey online. For all survey versions, we defined quotas and tracked respondent characteristics during sample collection. In response, Norstat adjusted the targeting of invitation emails to meet the quotas. For survey \emph{Public 1}, we recruited 609 individuals with demographic characteristics that match those of the universe of federal and state politicians as follows. We required a high net household income (\EUR4000 or more per month\footnote{The mean household net income in Germany was \EUR3612 per month in 2020 \citep{stabua-2022}.}) and a tertiary degree. We further matched the sample to politicians on age, gender, political attitude (left or right, self-stated in the case of the general public), and all interactions between these variables. For surveys \emph{Public 2} and \emph{Public 3}, we recruited 810 and 708 respondents, respectively. We sought representativeness to the German general population along the attributes age, gender, political attitudes (left or right), all interactions of the foregoing three attributes, state of residence, education level, and net household income. For each survey version, we ensured that each federal state is represented proportionally. Appendix \ref{summaryStats} presents summary statistics.\footnote{Response times amongst general population respondents for the aggregation decisions are 80, 114, and 170 seconds at the 25th, 50th, and 75th percentile, respectively. The corresponding response times for the intervention decisions are 56, 86, and 134 seconds, respectively.} Norstat fielded the survey \emph{Public 4} in December 2024 to obtain 1220 responses following the same procedures and targeting representativeness along the same dimensions as surveys \emph{Public 2} and 3. We use the results of that survey to answer ancillary questions that previous waves cannot answer. Because we ran that survey at a different time than the main surveys, however, we do not integrate it into our main study sample.


\paragraph{Survey weighting.}

In order to obtain estimates that are representative of our baseline populations along observables, we will use weighted regression throughout. To determine the population distribution among politicians, we hand-collected all state and federal legislators' age (categories 18 to 40, 40 to 59, 60 or over), gender, education level (no college, college, doctorate), and political party affiliation. We weight our politician sample on all these attributes both in levels and in interactions.\footnote{To ensure sufficient bin size for all interactions, we combine the SPD and Left party into a single category.} We weight our general population sample along the same dimensions, both in levels and in interactions. We obtain the baseline distribution from the 2018 wave of the German Socioeconomic Panel \citep[SOEP, ][]{SOEP} which includes the party the respondent reports to have voted for in the 2017 federal election. Appendix \ref{summaryStats} displays summary statistics.


\section{Politicians' social welfare criteria}\label{sec:politiciansMain}
We begin by examining how politicians resolve preference conflicts across individuals (Subsection \ref{sec:resultsAggregation}). Subsection \ref{patPoliticians} considers politicians' intervention choices (preference conflicts within individuals). We compare politicians' decisions to those of the general public in Section \ref{citizens}. Section \ref{sec:resultsBeliefs} then studies the alignment between respondents' beliefs about politicians and their actual choices.

\subsection{Aggregation: Preference conflicts across individuals} \label{sec:resultsAggregation}
How do politicians aggregate ordinal preferences when acting as an impartial social planner? What do they genuinely consider good for a heterogeneous group of stakeholders when no strategic or reputational concerns shade decisions?

We study these questions by first examining whether politicians follow the majority preference and select alternative $A$, or whether they choose the compromise option $B$ and thus defy the majority preference. Figure \ref{empiricalFingerprintLines} shows the distribution of politicians' choices across all preference profiles, using data from the 345 politicians who made a choice on each of the six profiles. In profile 1, slightly over 80\% of politicians favor the compromise option $B$ over the tied majority winners $A$ and $C$. In profile 2, where three stakeholders rank alternative $A$ first, around three-quarters of politicians opt for the compromise option $B$. Even on profile 6, in which four out of five stakeholders most prefer the majority winner $A$, more than a quarter of politicians still opt for the compromise option. Overall, politicians place more weight on preventing stakeholders from having to live with a last-ranked option than on making sure stakeholders receive their first-ranked option. There is substantial support for compromise.

The Condorcet rule selects the majority winner throughout. This is inconsistent with a preference for preventing stakeholders from having to live with their last-ranked option. In fact, for profile 2, where $A$ is the Condorcet winner, only one in five politicians select that option (and a part of that fraction might represent the usage of a scoring runoff rule rather than Condorcet decision making, as the scoring runoff rules with $s \in (\nicefrac{1}{4}, 1)$ yield the same predictions as the Condorcet rule).\footnote{In profiles 2 to 6, the Condorcet winner coincides with the plurality winner and can be determined easily. In profile 1, the Condorcet winner can be determined as follows. Three people rank $C$ last, so $B$ wins a pairwise majority vote against $C$. Hence, if there is a Condorcet winner, it must be $B$. $B$ also wins a pairwise majority vote against $A$. The reason is that any stakeholder who ranks $A$ last must rank it below $B$. But the one stakeholder who ranks $A$ second must also rank it below $B$, because no stakeholder ranks $B$ last. Hence, $B$ wins a pairwise majority vote against both $A$ and $C$, making it the Condorcet winner.} We conclude that politicians strongly favor scoring rules over Condorcet extensions.


Figure \ref{empiricalFingerprintLines} also shows that politicians respond sensitively to the rank distributions of the three alternatives. As we consider preference profiles with higher indexes, the rank-distribution of the majority-preferred alternative improves, and the rank-distribution of the compromise option worsens. Politicians respond to this variation by selecting the majority winner $A$ more frequently, at the expense of the compromise option $B$. This pattern is what one would expect if politicians choose consistently with scoring rules but are heterogeneous in their preference for compromise (the scoring parameter $s$). Appendix \ref{comprehensiveClassification} corroborates this analysis by assigning each politician to the one of 21 theoretically possible aggregation rules that best describes her behavior. In that analysis, 79\% of politicians are assigned to a scoring rule, and of these, 84\% place more weight on preventing last-ranked choices than on realizing first-ranked choices (scoring parameter $s> 0.5$).


\begin{figure}[htp]
\caption{Politicians' preference aggregation choices \label{empiricalFingerprintLines}\label{bayesClassification}\label{aggregationVparty}}
\begin{center}
\begin{tabular}{cc}
A. Distribution of politicians' choices \\
\includegraphics[width = 0.6\textwidth]{graphsNew/Figure2A} \\
B. Preference for compromise, by political party \\
\includegraphics[width = 0.6\textwidth]{graphsNew/Figure2B}  \\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Panel A: Distribution of preference aggregation decisions.  Profiles are numbered as in Table \ref{aggregationProfiles}.  In each profile, politicians using a scoring rule with $s < \bar{s}$ will choose option A, and politicians with $s > \bar{s}$ will choose option B (except that rule $s=0$ generates a tie between $A$ and $C$ on profile 1). The threshold value $\bar{s}$ increases from left to right and is given by $\nicefrac{1}{3}, \nicefrac{1}{3}, \nicefrac{1}{2}, \nicefrac{3}{5}, \nicefrac{2}{3},$ and $\nicefrac{4}{5}$, respectively. Panel B: Best-fitting scoring parameters, by party position. Whiskers denote 95\% confidence intervals, with standard errors clustered by subject.  Position on political spectrum from \citet{chapelHillSurvey}.
\end{figure}

The way in which politicians resolve interpersonal preference conflicts might depend on their position on the political spectrum---some politicians might be more centrist precisely because they have a stronger preference for compromise than members of more extreme parties. To test this hypothesis, we assign each politician the scoring parameter that best describes her sequence of choices, using the procedure of \citet{AmbuehlBernheim}.\footnote{Specifically, we use a Bayesian classifier. That classifier addresses the issue that less resolute rules (rules that create more ties) can mechanically better match any given choice sequence; it appropriately penalizes such rules. It rests on four assumptions. First, the prior probability over rules is uniform. Second, for each of the six profiles, the respondent follows her assigned rule with probability $1 - \epsilon$. She uniformly randomizes across all options with probability $\epsilon$. Third, decision errors are independent across outcomes. Fourth, when a rule creates ties, the respondent uniformly randomizes over the prescribed choices. The classifier calculates the posterior probability for each rule and a noise parameter $\epsilon$ conditional on the respondent's choice sequence. It assigns each respondent the rule and noise parameter for which this posterior is highest. The procedure is robust to any variation in the choice of prior that does not alter the mode of the posterior distribution. \citet{AmbuehlBernheim} demonstrate the robustness of this approach in multiple ways. First, a clustering procedure shows that the set of aggregation rules considered does not omit empirically relevant rules. Second, an identification strategy that does not rely on classification corroborates the results obtained from the Bayes classifier. Third, \citet{AmbuehlBernheim} show that their classification achieves high predictive power not only out of sample, but even out of context.\label{bayesClassifier}} We allow for the possibility that a politician may aim to harm rather than help the group of stakeholders assigned to her. For politicians classified as benevolent, we relate the estimated scoring parameter to the position of her party on the left/right spectrum as measured on a 1 to 10 scale by \citet{chapelHillSurvey}.\footnote{The party alliance of the CDU and the CSU acts as a union. We elicited membership and party preference for the union, but not separately for the constituent parties. \citet{chapelHillSurvey} list the left/right position separately for the member parties. We average these positions to the position of the alliance as follows. The CSU represents the union in the state of Bavaria, whereas the CDU represents the union in all states outside of Bavaria. Hence, we calculate the position of the union on the political spectrum as the weighted average of the CSU and the CDU's position, with the weight of the CSU equal to the relative population share of Bavaria compared to the German population. The population of Bavaria is 13.08 million, compared to a total German population of 83.02 million, which yields a weight of 0.1576 for the CSU.}

Our procedure classifies 325 of our 345 respondents as benevolent. Of these, 9 are not affiliated with a party. For the remaining 316 politicians, Panel B of Figure \ref{bayesClassification} plots the relation between her preference for compromise (measured by the best-fitting scoring parameter) and the position of her party.\footnote{In our experiment, most scoring parameters are interval-identified. We use midpoints for analysis.} The figure shows a relatively weak association between party position and aggregation preferences, though we observe a trend for a weaker preference for compromise toward the left of the political spectrum. Yet members of each party display a preference for compromise over majoritarian solutions. Specifically, the mean best-fitting scoring parameter is either higher or statistically indistinguishable from $\frac{1}{2}$ (the Borda rule). The members of each party place at least as much weight on preventing last-ranked outcomes as on realizing first-ranked outcomes, on average.


\begin{table}
\caption{Rank distributions of stakeholder preferences over the foundations \label{stakeholderPreferencesMain}}
\begin{center}
\begin{tabular}{lccc} \toprule Charity & Ranked 1st & Ranked 2nd & Ranked 3rd \\ \midrule
Ludwig Erhard Stiftung	&	0.27	&	0.33	&	0.40	\\
Hans Böckler Stiftung	&	0.28	&	0.39	&	0.32	\\
Bund der Steuerzahler	&	0.45	&	0.27	&	0.27	\\
\bottomrule \end{tabular}

\end{center}
\footnotesize
\textbf{\textit{Notes:}} Numbers show the fraction of subjects in the stakeholder sample that place a given foundation in the indicated rank position.
\end{table}


To document our results more formally, we regress politicians' scoring parameters on the political position of their party and on its square. Column 1 in Table \ref{mainTableAggregation} shows the result. The linear effect is statistically significant at the 5\% level. The negative estimate of the square term indicates a concave relationship between party position and preference for compromise, though that estimate is significant only at the 10\% level. Column 2 adds demographic control variables (age, gender, education), an indicator for whether the respondent is a federal or state politician, and fixed effects for each state parliament. It also controls for the respondent's political experience in state and federal parliaments in years. Adding these controls only exerts minor effects on estimated parameter values. While we find a Null effect of years of political experience, federal parliamentarians exhibit a significantly higher preference for compromise ($<0.05$). One hypothesis consistent with this finding is that a greater preference for compromise increases a politician's odds of running for or being elected to the federal parliament. The remaining control variables are far from statistically significant.


\begin{table}[htbp!]
\caption{Politicians' aggregation preferences \label{mainTableAggregation}}
\begin{center}
\begin{tabular}{lcccc} \toprule
& \multicolumn{1}{c}{(1)}
& \multicolumn{1}{c}{(2)}
& \multicolumn{1}{c}{(3)}
& \multicolumn{1}{c}{(4)}
\\ \medskip
\shortstack{VARIABLES\\ \strut \\ \strut}&\multicolumn{2}{c}{\shortstack{Preference for compromise\\(scoring parameter s\\scaled from 0 to 100)}}&\multicolumn{2}{c}{\shortstack{Assigned to\\benevolent rule\\ \strut}}\\\cmidrule(lr){2-3}\cmidrule(lr){4-5}
\medskip
\\ Political party position&               &               &               &               \\
\quad Linear&       1.478** &       1.653** &       0.006   &       0.003   \\
            &     (0.673)   &     (0.777)   &     (0.007)   &     (0.009)   \\
\quad Square&      -0.453*  &      -0.515*  &      -0.003   &      -0.004   \\
            &     (0.266)   &     (0.298)   &     (0.003)   &     (0.003)   \\
Federal parliamentarian&               &       9.493   &               &      -0.030   \\
            &               &     (8.235)   &               &     (0.024)   \\
Political experience (years)&               &      -0.012   &               &       0.000   \\
            &               &     (0.287)   &               &     (0.002)   \\
Female      &               &       3.507   &               &      -0.022   \\
            &               &     (3.312)   &               &     (0.032)   \\
Age         &               &       0.038   &               &      -0.000   \\
            &               &     (0.169)   &               &     (0.001)   \\
\medskip \\
Observations&         317   &         317   &         336   &         336   \\
\bottomrule \end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Columns 1 and 2 only use politicians assigned to a benevolent rule. Regressions do not include respondents not associated with a government party (``fraktionslos''). Political experience measures the total years of experience in federal and state parliaments. Columns 2 and 4 include fixed effects for the 16 state parliaments. Party position represents overall ideological stance on the left/right spectrum from \citet{chapelHillSurvey}, measured on a scale from 1 to 10, de-meaned. $^*p<0.1, ^{**}p<0.05, ^{***}p<0.01$.
\end{table}


Columns 3 and 4 show that our results are not an artifact of focusing on respondents classified as benevolent. We regress an indicator for whether a politician is assigned to a benevolent rule on the same predictor variables as in columns 1 and 2, respectively. All coefficient estimates are close to zero and statistically insignificant.

While our results document the relation between aggregation preferences and political attitudes, we can only speculate about the reasons underlying that relation. In light of the null effect of experience, any relation likely represents a selection effect. The tendency toward concavity is consistent with the fact that Germany's center parties feature a more pronounced compromise culture than those further from the center. Indeed, leading centrist political figures such as former prime ministers Konrad Adenauer, Willy Brandt, and Helmut Schmidt have viewed compromise as key for a functioning democracy.\footnote{After the political vote on the constitution in 1949, Adenauer (Germany's first prime minister) stated that ``compromise also has a great advantage: It teaches the parties, who [are] thus forced to work together, to appreciate the committed, honest opponent even in their political opponents'' \citep[p. 241-243]{ParlamentarischerRat}. Brandt referred to compromises as the ``essence of democracy'' \citep{Tagesschau2025}. Schmidt said ``Anyone who cannot compromise is useless for democracy'' (\textit{ibid.}).}


\begin{table}[htbp!]
\caption{Beliefs about willingness to pay and implied utilitarian choice \label{impliedUtilitarian}}
\begin{center}
\begin{tabular}{lclcc} \toprule &(1)&&(2)&(3)\\ & WTP in \EUR && Implied utilitarian choice & Actual choice \\ \cmidrule(r){2-2}\cmidrule(r){4-5} {Preference rank}  & & Choice Option \\ 	\quad Best	&	18.188	&	\quad A (majority winner)	&	0.660	&	0.383	\\
		&(	0.795	)&		&(	0.019	)&(	0.020	)\\
	\quad Middle	&	6.010	&	\quad B (compromise option)	&	0.302	&	0.591	\\
		&(	0.432	)&		&(	0.020	)&(	0.020	)\\
	\quad Worst	&	-1.860	&	\quad C	&	0.039	&	0.026	\\
		&(	0.579	)&		&(	0.006	)&(	0.008	)\\
\\ \bottomrule \end{tabular}

\end{center}
\footnotesize
\textbf{\textit{Notes:}} WTP is the believed amount in \EUR\; a politician believes a stakeholder would be willing to pay to trigger or prevent (negative WTP) a donation to the foundation in his first, second, and third preference rank, respectively. Implied utilitarian choice is the choice a politician would make if she selected alternatives by maximizing the sum of believed WTP. Actual choice is the actual choice the politician made. For profile one, choice option A (majority winner) coincides with the tied majority winners A and C. Standard errors in parentheses, clustered by subject. Table based on data from politicians who provided complete and consistent WTP beliefs about each preference rank.
\end{table}


Our data also let us interpret politicians' choices in the framework of cardinal welfare maximization. 212 politicians provided complete and consistent beliefs about stakeholders' willingness to pay (WTP) to trigger the donation of \EUR30 to their first, second, and last-ranked foundation, or to prevent it (indicated by negative numbers). If politicians chose social alternatives by maximizing the sum of these willingnesses-to-pay, what choice distribution would we observe? Column 1 of Table \ref{impliedUtilitarian} lists mean WTP beliefs by preference rank.\footnote{We restrict the believed WTP to range from $-$\EUR$30$ to \EUR$30$. To check if our results depend on excluding WTP values outside this range, we rerun the analysis on the sample of 227 complete responses, allowing unrestricted beliefs on others' WTP. Aside from more extreme values for the mean WTP held by politicians, the results remain consistent. Additionally, two politicians provided inconsistent beliefs about stakeholders' WTP. We again rerun the analysis after including these inconsistent responses. The results remain virtually unchanged.} The difference in WTP between obtaining one's first-ranked rather than second-ranked alternative is more than one-and-a-half times as high as that between obtaining one's second-ranked rather than third-ranked alternative. Accordingly, if politicians aggregated preferences according to a utilitarian welfare function, they would frequently prefer the majority option over the compromise option. As column 2 shows, utilitarian maximization selects options $A$, $B$, and $C$ in 66\%, 30\%, and 4\% of the cases, respectively. These values differ substantially from the actual choice frequencies for this subsample, listed in column 3. The majority-preferred option $A$ is chosen 27 \%-points less often than predicted by the utilitarian benchmark, whereas the compromise option $B$ is chosen 28 \%-points more often. Hence, on the assumption that politicians aggregate preferences based on their beliefs about cardinal utilities, they place substantially more weight on equity than utilitarians.\footnote{Appendix section \ref{estimationCardinal} estimates the parameter of a constant-elasticity cardinal welfare function in which cardinal utility $u$ enters welfare through the concave transformation $f(u)=\frac{1}{\alpha}u^\alpha$. We obtain a parameter estimate of 0.67. Due to the limited sample size and possibly noisy elicited beliefs, the estimate is imprecise, with a standard error of 0.28.}

A potential concern about our results on preference aggregation is the possibility that politicians may have tried to infer the identity of the political foundations that could receive the donation and may have chosen to satisfy their private preferences rather than what they believe best reflects the will of the stakeholders. This interpretation is implausible ex ante because such an inference would be extremely weak, for three reasons. First, politicians know that five of the six preference profiles they see are hypothetical, and they do not know which profile is real. This factor greatly dilutes inference.\footnote{Because we needed to make the survey available in pen-and-paper format, all six preference profiles were pre-determined. To implement respondents' decisions, we matched stakeholder preferences to one of the six profiles ex-post. Therefore, unbeknownst to respondents, no inference can in fact be drawn from the six preference profiles.} Second, each politician observes the preference rankings of only five stakeholders. Such a small sample leads to highly uncertain inference. Third, real stakeholder preferences are diverse. Table \ref{stakeholderPreferencesMain} displays the rank distribution of each foundation. These distributions are close to uniform, except that the Ludwig Erhard Stiftung is ranked last slightly more often than one-third, and the Bund der Steuerzahler is ranked first slightly more often.
Because these distributions are close to uniform, ordinal preferences provide little information about the identity of the foundations.

Empirical evidence from our survey \emph{Public 4} additionally suggests that inference about the identity of the political foundations, to the extent it happened, does not act as a confound, though it may lead to choices that appear as noise in our data. Specifically, because we randomize the assignment of letter labels to social alternatives, any inference drawn from information presented earlier than the preference profiles (such as the order in which the instructions present the foundations) acts as noise. Choice based on the believed identity of the foundations acts as a confound only if inference is based on the preference profiles as such.\footnote{Respondents might observe, for instance that a given alternative is often ranked either at the top or at the bottom across the preference profiles, and may thus conclude that that alternative likely does not represent a foundation that is viewed as politically centrist.} Survey \emph{Public 4} randomizes the order in which the instructions present the foundations. It also elicits subjects' beliefs about which letter represents which foundation, as well as the respondent's preferred foundation. This data shows that the former type of inference indeed affects choice. To check for effects of the second type of inference, we test whether subjects who prefer the arguably most centrist foundation Bund der Steuerzahler select the compromise option $B$ disproportionately often. We also check whether subjects who prefer one of the other foundations select the compromise option $B$ disproportionately rarely. Neither is the case, which suggests that the second, problematic type of inference does not play a substantial role in \emph{Public 4}. Appendix \ref{app:public4} presents details.


Overall, our analysis shows that politicians' social welfare criteria in the domain of preference aggregation substantially deviate from those commonly employed in economics research. They demonstrate a strong preference for compromise, especially among members of moderate parties. They place significantly more weight on stakeholders who end up with their least-preferred alternative than the utilitarian or Condorcet-type aggregation often employed in economics.


\subsection{Intervention: Preference conflicts within individuals}\label{patPoliticians}


We now turn to politicians' intervention decisions. How do politicians trade off the objective of making sure citizens make choices they consider good on the one hand, and the objective of respecting others' autonomy on the other hand?  A subset of politicians provided valid responses in this second part of our survey (191 and 193 for the case without and with front-end delay, respectively, with 181 politicians providing valid responses in both cases).\footnote{Six observations from five politicians who answered the pen-and-paper version of the survey are invalid because the choice sets they constructed in these cases are empty.} Appendix \ref{app:attrition} details the difference between the two samples. Attrition does not differ by political preference or rank, but older politicians ($p<0.05$) attrit more often.\footnote{Choices in the preference aggregation part do not differ depending on whether a politician attrited or completed the entire survey.} We account for the slightly changed composition of this smaller sample by recalculating our regression weights.\footnote{Evidence from survey \emph{Public 4} considers the effect of making intervention decisions after rather than before making preference aggregation decisions. In line with a hypothesis of decreasing attention over time, we find that when intervention choices come second, general population subjects remove the most patient alternative more often and the least patient alternative less often. Fatigue might exert a similar effect on politicians' decisions. Appendix \ref{app:orderEffects} presents details.}

Politicians' resolution of the tradeoff becomes apparent in Panel A of Figure  \ref{paternalismMain}. Two features stand out. First, politicians intervene frequently. They exclude the least patient option from the choice set about half the time. Second, politicians intervene to impose patient choice. The choice sets they construct exclude less patient options much more often than more patient options when rewards are immediate. Most politicians' decisions on whether to intervene are consistent across the two rounds. Only 7.3\% of our respondents intervene in only one of the two rounds, whereas 62.2\% intervene in both decisions, and 30.5\% in neither.

Are politicians' decisions consistent with the long-run criterion, according to which citizens' intertemporal choices should be respected unless the lure of immediacy might bias them? The answer is No, for two reasons. First, as the figure shows, politicians do not remove alternatives substantially more often when immediate payments are available than when all payments are delayed. The lure of immediacy plays practically no role in politicians' intervention decisions. Second, politicians' choices are inconsistent with the view that citizens' intertemporal choices should be respected if potential bias caused by the availability of immediate payments is not an option. Even when all payments are delayed by at least one month, politicians still exclude the least patient alternative more than half the time.\footnote{The availability of immediate payments neither affects politicians' beliefs about unrestricted citizens' choices, nor does it affect the actual choices of these subjects (see Appendix \ref{appendixImpPat}). Even so, politicians violate the long-run criterion by intervening when no immediate rewards are available.}


Politicians do not simply remove options they believe no citizen would have chosen. Rather, their decisions reflect a genuine tradeoff between respecting citizen autonomy and enforcing patient decisions. Recall that each politician indicated the probability with which she believes an unrestricted citizen would have chosen a given option, for each option in the menu.\footnote{We exclude four politicians providing beliefs not summing up to 10 from our analysis. To test whether the presented results depend on this exclusion, we perform the same analysis on the sample, including these inconsistent responses. We find no difference in the results.} This data shows that politicians assign 25.0\% probability mass to options they excluded. Hence, politicians who remove options believe their decisions will change the choice of nearly one quarter of citizens.\footnote{As described in Section \ref{surveyVersions}, to prevent artificially consistent choices, we elicit beliefs about slightly different menus than those from which the respondent has previously constructed choice sets. Strictly speaking, beliefs and intervention choices are thus not directly comparable. We estimate a model of how beliefs respond to the six specific payment amounts that characterize a menu. We estimate a two-equation system that expresses the elicited beliefs that an unrestricted citizen would choose options A and B, respectively, on all payment amounts and on the relative prices of gaining one additional present Euro in terms of future Euro from selection option A rather than B, as well as the respondent's intervention choices. We obtain estimates of the beliefs about unrestricted citizens that are corrected for the difference in payment amounts as the predicted values of this regression. This correction lowers the believed effectiveness of the chosen interventions from 25.0\% to 23.5\%.}


\begin{figure}[htbp!]
\caption{Politicians' intervention decisions \label{paternalismMain}}
\begin{center}
A. Interventions by front-end delay \\
\includegraphics[width = 0.6\textwidth]{graphsNew/Figure3A} \\
B. Interventions by political party \\
\includegraphics[width = 0.6\textwidth]{graphsNew/Figure3B}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Panel A displays the fraction of cases for which politicians exclude options from the choice set they construct, categorized by relative patience. The solid (dashed) line corresponds to choice sets constructed from menus that include (exclude) immediate rewards. Panel B displays the average number of excluded options by party position, measured as ideological stance on the left/right spectrum by \citet{chapelHillSurvey}. Whiskers denote 95\% confidence intervals, standard errors clustered by subject.
\end{figure}


Next, we study whether and how politicians' interventions depend on their position on the political spectrum. Three possibilities are plausible. On one hand, traditional conservatives’ more libertarian attitudes suggest a negative relationship between interventions and conservatism. On the other hand, both conservatives’ tendency to uphold traditional values like patience and the recent link between conservatism and authoritarian tendencies suggest the opposite. It is also possible that centrists, who tend to favor compromise and respect minority preferences, are more likely to defer to citizen autonomy. The latter hypothesis predicts a U-shaped relationship between interventions and political position.


In fact, we find that politicians towards the right of the spectrum tend to exclude more options from the choice sets they construct, as Panel B of Figure \ref{paternalismMain} shows. This finding parallels the positive relationship between conservatism and interventionism that \citet{ambuehl2025interventionist} find in a sample of U.S. voters. To document this effect econometrically, Column 1 of Table \ref{paternalismPartyTable} regresses the number of excluded options on party position measured on a scale from 1 to 10 by \citet{chapelHillSurvey} and its square. We find that a politician on the very right of the political spectrum excludes an additional 0.70 alternatives per menu than a politician on the very left ($p<0.05$). This effect increases to 0.90 and becomes statistically significant at $p<0.01$ as we include demographic control variables (age, gender, education, state), an indicator for whether the respondent is a federal or state politician, and the respondent's political experience in state and federal parliaments in years. The coefficient on the square term is close to zero and far from statistically significant in both cases, in contrast to the hypothesis that centrist politicians respect citizen preferences more.

Unlike in the case of preference aggregation, we find no effect of being a federal parliamentarian; preferences for paternalistic interventions do not appear to predict running for or being elected to federal office. There is a substantial effect of gender, however, as column 2 shows. Female politicians remove an additional 0.32 options ($p<0.05$).


\begin{table}[htb!]
\caption{Politicians' intervention decisions \label{paternalismPartyTable}}
\begin{center}
\begin{tabular}{lcccc} \toprule
& \multicolumn{1}{c}{(1)}
& \multicolumn{1}{c}{(2)}
& \multicolumn{1}{c}{(3)}
& \multicolumn{1}{c}{(4)}
\\
VARIABLES   &\multicolumn{2}{c}{Number of options removed}&\multicolumn{2}{c}{Believed restrictiveness}\\\cmidrule(lr){2-3}\cmidrule(lr){4-5}
\\

Mean of the dependent variable&       0.895         &       0.895         &       0.177         &       0.177         \\
            &     (0.064)         &     (0.064)         &     (0.021)         &     (0.021)         \\

\midrule
\\ Political party position&               &               &               &               \\
\quad Linear&       0.057** &       0.082***&       0.017*  &       0.023***\\
            &     (0.028)   &     (0.029)   &     (0.009)   &     (0.009)   \\
\quad Square&       0.010   &       0.009   &       0.000   &      -0.001   \\
            &     (0.012)   &     (0.011)   &     (0.004)   &     (0.004)   \\
Federal parliamentarian&               &      -0.143   &               &       0.018   \\
            &               &     (0.295)   &               &     (0.172)   \\
Political experience (years)&               &      -0.008   &               &      -0.001   \\
            &               &     (0.010)   &               &     (0.003)   \\
Female      &               &       0.319** &               &       0.072** \\
            &               &     (0.124)   &               &     (0.034)   \\
Age         &               &       0.007   &               &      -0.000   \\
            &               &     (0.006)   &               &     (0.002)   \\
Front-end delay&      -0.040   &      -0.049   &      -0.002   &      -0.002   \\
            &     (0.037)   &     (0.038)   &     (0.020)   &     (0.022)   \\

Fixed Effects&               &               &               &               \\
\quad Education&               &  \checkmark   &               &  \checkmark   \\
\quad State &               &  \checkmark   &               &  \checkmark   \\
\\ Subjects &         195   &         195   &         186   &         186   \\
Observations&         369   &         369   &         350   &         350   \\
\bottomrule \end{tabular}

\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions.  Regressions do not include respondents not associated with a government party (``fraktionslos''). Political experience measures the total years of experience in federal and state parliaments. Columns 2 and 4 include fixed effects for the 16 state parliaments. Columns 3 and 4 include a smaller number of observations due to non-responses on beliefs. Party position represents overall ideological stance on the left/right spectrum from \citet{chapelHillSurvey}, measured on a scale from 1 to 10, de-meaned. Standard errors in parentheses, clustered by subject. $^*p<0.1, ^{**}p<0.05, ^{***}p<0.01$.
\end{table}

Are politicians further to the right knowingly more restrictive? An alternative hypothesis is that these politicians simply remove options they believe nobody would choose, but differ in their beliefs about what these options are. To distinguish between these mechanisms, we calculate the probability with which a politician believes her intervention changes the citizen's choice. As column 3 shows, this \emph{believed restrictiveness} increases with political party position both when we control for demographic and political background variables ($p<0.01$, column 4) and when we do not ($p<0.1$, column 3). A politician on the very right believes it is 23 percentage points more likely that her intervention forces a citizen to change his choice than a politician on the very left. Hence, politicians further to the right know that their more restrictive interventions will change the choice of a larger number of citizens.\footnote{This result would obtain mechanically if beliefs-data were pure noise. Contrary to this interpretation, our beliefs data is highly predictive, as Appendix \ref{appendixBeliefNoise} shows.} This finding is consistent with recent cross-country survey evidence that shows that right-wing populist voters favor stronger, more interventionist leaders \citep{donovan2021right}. Likewise, female politicians know that their stricter interventions restrict a larger number of people.

Overall, these results show that many politicians across the spectrum---but especially on the right---readily intervene to enforce choices they regard as good, even if that comes at a cost in terms of citizen autonomy. Yet, their decisions show no sign of support for the long-run criterion that is frequently used in economics research.


\section{Welfare criteria of the general public}\label{citizens}

Representative democracy is based on the idea that politicians' value judgments represent those of the general public. Moreover, much research in positive welfare economics studies normative views of the general public \citep{Almasetal., Bartlingetal., AmbuehlBernheimOckenfels, AmbuehlBernheim}, even though actual policy making often falls to elected representatives, which raises a question of external validity. Therefore, we now examine the alignment between politicians' social welfare criteria and those of the general public. Throughout, we pool the data in survey versions \emph{Public 2} and \emph{Public 3} with the corresponding parts of version \emph{Public 1}.


\subsection{Aggregation: Preference conflicts across individuals}\label{citizensAggregation}

The distribution of aggregation choices among the general public is similar to that of politicians, as Panel A of Figure \ref{figGenPop} shows. Averaged across preference profiles, the general public selects alternatives A, B, and C in 38.5\%, 53.1\%, and 8.4\% of cases, compared to politicians' 37.2\%, 56.5\%, and 6.3\%. Statistically, there are no significant differences between these distributions. We also observe no significant difference in the scoring parameters of politicians and the general population. Yet, in particular, among preference profiles 1 to 3, which require lower scoring parameters $s$ to rationalize the choice of option B, politicians select the compromise option B more often.

Looking at the distribution of best-fitting choice rules between politicians and the general public (see Appendix \ref{aggregationCitPolit}), we find that members of the general population are significantly more likely assigned scoring rules with low parameters than politicians (specifically, $s=0$ and $s \in (0,\nicefrac{1}{3})$), and are less likely assigned the scoring rules with parameter $s \in (\nicefrac{1}{2}, \nicefrac{3}{5})$.\footnote{We additionally find that general population members are significantly less likely to be classified as following the supermajority rule.} (These results are robust to multiple hypotheses testing using the Romano-Wolf step-down procedure \citep{romano2005stepwise}.) Moreover, the distribution of best-fitting rules in our samples is highly similar to the distribution that \citet{AmbuehlBernheim} find with Swedish and US general population samples in a setting where stakeholder preferences concern the question of which international charity will receive a fixed donation amount, and with Swiss student samples in which stakeholders have preferences about which Swiss party will receive a donation of Fr. 30 that cannot be split. This similarity suggests that our results are robust to variations in the domain of stakeholder preferences.

Overall, as a group, when deciding as impartial social planners, politicians resolve preference conflicts across individuals similarly to the general public, though in a slightly less majoritarian fashion.

\begin{figure}[htbp!]
\caption{Social welfare criteria of the general public \label{figGenPop}}
\begin{center}
\begin{tabular}{cc}
\textbf{A. Aggregation} &
\textbf{B. Interventions by the general public} \\
\includegraphics[width = 0.5\textwidth]{graphsNew/Figure4A} &
\includegraphics[width = 0.5\textwidth]{graphsNew/Figure4B} \\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Whiskers denote 95\% confidence intervals, standard errors clustered by subject.
\end{figure}


Yet, in contrast to politicians, the political position of the preferred party of members of the general public does not relate to their aggregation decisions, as Panel A of Figure \ref{figGenPopParty} shows. The difference in party-dependence between politicians and the general public may have multiple causes, including the possibility that a politician's party affiliation is more strongly tied to her identity than a general population member's party preference is to his.\footnote{We do not condition on whether general population respondents are registered party members. The reason is that only a small minority of Germans are members of a political party.  In the year 2019, the six government parties had a total of 1.2 million active members \citep{Niedermayer}, which is 1.4\% of the 83 million German population, or 2\% of the 60 million Germans entitled to vote \citep{Bundeswahlleiter}. In our sample, 90 respondents making aggregation decisions and 75 respondents making intervention decisions report that they are members of a political party. While these respondents represent 6.3\% and 5.7\% of our respective samples, they are still too few to permit statistical inference. Appendix \ref{appendixPoliticalInvolvement} shows that our conclusions pertaining to the general public remain unchanged once we account for political involvement measured as voting regularly in elections. } To test this possibility, we regress best-fitting scoring parameters on an indicator for the general public, the position of a respondent's preferred political party, and its quadratic, as well as on the interaction between the general public indicator and the party position variables. Column 1 of Table \ref{tableGenPop} shows that the difference in party dependence across the two populations is statistically significant. This  conclusion is unchanged once we control for age, gender, education, and state (column 2). This finding implies that some parties' electorates endorse systematically different social welfare criteria than the politicians representing those parties in parliament.

When we consider the general public's beliefs about stakeholders' WTP to trigger or prevent a donation to their first, second, and third-ranked foundation, respectively, our results again parallel those of the politician sample. In particular, implied utilitarian aggregation based on these beliefs yields a choice distribution that places dramatically more weight on the majority winner than the general public's actual choices. See Appendix \ref{sec:impliedUtilitarianGenPop} for details.\footnote{As is the case for politicians, these results do not depend on the exclusion of believed WTP outside the range of $-$\EUR$30$ to \EUR$30$.}


\begin{figure}[htbp!]
\caption{Social welfare criteria of the general public by party position \label{figGenPopParty}}
\begin{center}
\begin{tabular}{cc}
\textbf{A. Preference for compromise} &
\textbf{B. Interventions } \\
\includegraphics[width = 0.5\textwidth]{graphsNew/Figure5A} &
\includegraphics[width = 0.5\textwidth]{graphsNew/Figure5B} \\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Whiskers denote 95\% confidence intervals, with standard errors clustered by subject. Party position represents overall ideological stance on the left / right spectrum from \citet{chapelHillSurvey}.
\end{figure}


\begin{table}[htb!]
\caption{General population vs. politicians \label{tableGenPop}}
\begin{center}
\begin{tabular}{lcccc} \toprule
& \multicolumn{1}{c}{(1)}
& \multicolumn{1}{c}{(2)}
& \multicolumn{1}{c}{(3)}
& \multicolumn{1}{c}{(4)}
\\
\shortstack{VARIABLES\\ \strut \\ \strut}&\multicolumn{2}{c}{\shortstack{Preference for compromise\\(scoring parameter s\\scaled from 0 to 100)}}&\multicolumn{2}{c}{\shortstack{Number of options removed\\ \vspace{7mm}}}\\\cmidrule(lr){2-3}\cmidrule(lr){4-5}
\\
Political party position&       1.478** &       1.757** &       0.057** &       0.057** \\
            &     (0.671)   &     (0.709)   &     (0.028)   &     (0.028)   \\
(Political party position)$^2$&      -0.453*  &      -0.529*  &       0.010   &       0.009   \\
            &     (0.265)   &     (0.280)   &     (0.012)   &     (0.011)   \\
General public&               &               &               &               \\
\quad $\times$ 1&      -4.022   &      -4.008   &       0.354***&       0.272***\\
            &     (2.566)   &     (2.503)   &     (0.083)   &     (0.083)   \\
\quad $\times$ political party position&      -1.161   &      -1.355   &      -0.057*  &      -0.059*  \\
            &     (0.876)   &     (0.886)   &     (0.031)   &     (0.031)   \\
\quad $\times$ (political party position)$^2$&       0.628*  &       0.654*  &      -0.016   &      -0.013   \\
            &     (0.342)   &     (0.348)   &     (0.013)   &     (0.012)   \\
Female      &               &       3.606*  &               &       0.028   \\
            &               &     (2.097)   &               &     (0.049)   \\
Age         &               &      -0.056   &               &       0.010***\\
            &               &     (0.077)   &               &     (0.002)   \\

Fixed Effects&               &               &               &               \\
\quad Education&               &  \checkmark   &               &  \checkmark   \\
\quad State &               &  \checkmark   &               &  \checkmark   \\
\\ \shortstack[l]{p-values\\ Significance of party position \\for general public}&               &               &               &               \\
\quad linear term&       0.573   &       0.471   &       0.990   &       0.878   \\
\quad square term&       0.415   &       0.567   &               &               \\
Observations&       1,660   &       1,660   &       3,003   &       3,003   \\
Subjects    &       1,660   &       1,660   &       1,512   &       1,512   \\
\bottomrule \end{tabular}

\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions.  Regressions exclude politicians not associated with a government party (``fraktionslos'').  Party position represents overall ideological stance on the left / right spectrum from \citet{chapelHillSurvey}, measured on a scale from 1 to 10, de-meaned. Standard errors in parentheses, clustered by subject. $^*p<0.1, ^{**}p<0.05, ^{***}p<0.01$.
\end{table}


\subsection{Interventions: Preference conflicts within individuals}

As is the case for politicians, the general public's choices are inconsistent with the long-run criterion. Panel B of Figure \ref{figGenPop} plots the intervention rates of the general public by front-end delay. It shows that the availability of immediate rewards has no effect on the frequency with which members of the general public exclude any alternative. Moreover, even absent immediate rewards, members of the general public intervene to remove the least patient alternative more than half the time, in contrast to the criterion's prescription to defer to citizens' preferences in that case. Additionally, both politicians and general population respondents remove the most patient alternative slightly more often than the middle alternative.\footnote{This pattern is not driven by spite towards other subjects. To check this possibility, respondents decided whether to costlessly increase the Chooser's payment by \EURdig0.50, to leave it unchanged, or to decrease it by \EURdig0.50. 91\% chose to increase the recipient's payment, only 3\% chose to decrease it. The extent of non-monotonicity in intervention decisions remains nearly unchanged once we exclude spiteful respondents.} The difference in removal rates between the most patient and middle options, however, is an order of magnitude smaller than that between the middle and least patient option. Hence, for both samples, enforcing patient choice is the dominant concern.

There is, however, one difference between politicians' and general population respondents' interventions. General population respondents intervene more often than politicians. While politicians exclude 0.90 choice options on average from the choice sets they construct, general population respondents exclude 1.16  ($p<0.01$).


Panel B of Figure \ref{figGenPopParty} shows that there is no relation between interventions and political party preference among the general public, just as in the case of preference aggregation, and possibly for the same reasons. To document the effect of party position econometrically, we regress the number of excluded options on an indicator for general population respondents, the position of the preferred political party, and the interaction between the two. Column 3 of Table \ref{tableGenPop} displays the result. The negative coefficient on the interaction term almost exactly offsets the effect of party position for politicians, leading to a $p$-value of 0.93 for a dependence on political position among the general public. These results remain largely unchanged once we control for gender, age, education, and state fixed effects.

Just as in the case of preference aggregation, the fact that politicians' intervention decisions depend on their political position while those of the general public do not implies that the politicians of some parties more closely represent the social welfare criteria of the parties' supporters. With the exception of the Green Party and the AfD, politicians of each party intervene statistically significantly less than their supporters. Controlling for gender, age, education, and state fixed effects renders the effect for CDU/CSU statistically insignificant. All findings are robust to multiple hypothesis testing.


\section{Beliefs about politicians' decisions}\label{sec:resultsBeliefs}

Having shown that politicians’ and the public’s welfare criteria broadly align yet differ in specifics, we now assess whether respondents are aware of these similarities and differences.

For the preference aggregation domain, panel A of Figure \ref{delegationBeliefsFigure} compares general population respondents' beliefs about other decision makers to their actual choices (focusing on the two profiles for which we elicited beliefs). Beliefs about politicians and about the general public resemble each other closely. Yet, they diverge from the actual choice probabilities. Most notably, while both politicians and the general public chose the compromise option substantially more often than the majority winner, respondents believe the opposite would be the case ($p<0.01$ both for beliefs about politicians and beliefs about the general public, see Appendix  \ref{beliefsEconometrics}). Hence, general population respondents underestimate others' preference for compromise, both in politicians and in other general population respondents.\footnote{Subjects also overestimate the frequency with which the rank-dominated option C would be chosen, possibly indicating their beliefs about how (in)attentively others would choose. Our results are unlikely to be a consequence of inattention to the belief elicitation itself, however. As Appendix \ref{beliefSensitivity} shows, elicited beliefs vary sensitively with the particular preference profile for which we elicit these beliefs.}

In the domain of intervention decisions, beliefs are systematically distorted, too, as Panel B of Figure  \ref{delegationBeliefsFigure} shows. For easier visibility, we center the actual choice frequencies at zero and show the deviations from that benchmark. Subjects systematically overestimate how often politicians enforce patient choice. They overestimate the fraction of politicians who remove all but the most patient alternative $A$ by about 20 \%-points, they correctly estimate the fraction of politicians who exclude only the least patient option, and they underestimate the fraction of politicians who include all alternatives by about 10 \%-points. In contrast, beliefs about the general public deviate from the truth non-monotonically. Respondents overestimate both the extent to which general population respondents remove all but the most patient alternative, and the extent to which they include all options. Overall, while beliefs about general population subjects are inconclusive, respondents  greatly overestimate politicians' interventionism, both compared to their actual choices and compared to the general population. Appendix \ref{beliefsEconometrics} documents these effects econometrically.

To examine the mechanisms responsible for such beliefs, our ancillary survey \emph{Public 4} asks respondents whether they believe politicians have a stronger or weaker preference for majoritarian over compromise solutions than the average German citizen. Of our respondents, 62.2\% believe that politicians prefer more majoritarian aggregation than average citizens (s.e. 1.4\%). To study the origins of these beliefs, we asked subjects to rate their agreement with two statements: that politicians' aggregation preferences differ from those of the average citizen because individuals with such a preference are more likely to be elected to political office, and that they differ because experience in politics causes politicians to form such a preference. Respondents do not believe that experience plays a substantially different role in explaining these preferences than selection. Encoding subjects' responses on the six-point scale with integers 1 to 6, their agreement with the selection and experience statements is 4.32 (s.e. 0.03) and 4.36 (s.e. 0.03), respectively.

Subjects answer parallel questions concerning intervention decisions. Corroborating the result in Figure \ref{delegationBeliefsFigure}, 71.7\% of our respondents believe that politicians will make fewer alternatives available than the average citizen (s.e. 1.6\%). As in the case of aggregation preferences, subjects' attribution of the difference to selection and experience in politics is nearly equal, though at a slightly lower level (the mean agreement is 3.94 (s.e. 0.036) for both statements).

Overall, respondents appear to have relatively clear ideas about how politicians differ from average citizens, but lack specific hypotheses about the causes of that difference.


\begin{figure}[htbp!]
\caption{Beliefs about politicians' and other general population respondents' choices \label{delegationBeliefsFigure}}
\begin{center}
\begin{tabular}{cc}
A. Aggregation & B. Interventions \\
\includegraphics[width = 0.47\textwidth]{graphsNew/Figure6A}  &
\includegraphics[width = 0.47\textwidth]{graphsNew/Figure6B}  \\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Panel A aggregates across preference profiles 2 and 5, for which we elicited beliefs. Panel B aggregates across menus.
\end{figure}


\section{Conclusion}\label{conclusion}

Economic analysis often derives policy recommendations based on assumed social welfare criteria. The appropriateness of these criteria is typically left to the policymaker to judge. Little is known, however, about policymakers' normative views in a way that is amenable to this use. Our study begins to fill this gap. We conduct a behavioral experiment with German federal and state politicians that avoids confounds from political economy considerations. We complement it with parallel experiments with the German voting-age general public.

We document three main findings. First, when resolving preference conflicts across individuals, politicians assign substantially more importance to individuals who obtain their least-favored alternative than to receive their most-favored alternative, in contrast to both common aggregation mechanisms and the equal weighting inherent in utilitarianism and the Kaldor-Hicks criterion. Second, when resolving preference conflicts within individuals, specifically that between respecting citizen autonomy and ensuring outcomes that politicians believe to be good for the citizen, politicians place substantial weight on the latter motive.
Their choices are inconsistent with the long-run criterion that is frequently used in behavioral public economics as a foundation for policy recommendations. Not only has the availability of immediate rewards virtually no effect on politicians' choices, but politicians still intervene frequently to enforce patient choice when we rule out the possibility that the lure of immediacy might bias citizens' choices. Third, the welfare criteria of politicians and those of the general public broadly align---though general population respondents (wrongly) believe that politicians are less willing to compromise and more willing to limit others' autonomy, both compared to general population respondents and compared to politicians' actual choices.

Our results provide guidance to researchers who derive policy recommendations based on social welfare criteria, so that they reflect those of policymakers and of the general public. Additionally, our result that politicians' and the general public's welfare criteria broadly align is encouraging for the field of positive welfare economics, which typically relies on general population samples. It increases our confidence that its broad results will generalize to elected policy makers both in Germany and in other representative democracies.

\newpage


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{\Huge \textbf{ONLINE APPENDIX}}\\
\vspace{1cm}
{\Large   \textbf{\LARGE Politicians’ Social Welfare Criteria}  \\  \bigskip \bigskip Sandro Ambuehl \space \space Sebastian Blesse \space \space Philipp Doerrenberg\\\medskip Christoph Feldhaus \space \space  Axel Ockenfels}
\end{center}


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\section{Design details}

Table \ref{surveyVersionsDetailed} presents the 16 parametric variations of the survey. Each survey version \emph{Politicians 1}, and \emph{Public 1, 2, 3, 4}, use this parametric variation. Variations differ regarding the order in which the preference profiles were presented, the labels used to represent each social alternative, and the menus of choice options available for choice set construction.


\setlength{\tabcolsep}{2pt}
\begin{table}[htp]
\caption{Survey variants \label{surveyVersionsDetailed}}
\begin{tabular}{lccccccccccc}
\toprule
               &             \multicolumn{2}{c}{Aggregation}        &             \multicolumn{8}{c}{Intervention}   \\
\cmidrule(r){2-3}\cmidrule(r){4-11}

&&&              \multicolumn{2}{c}{Decision 1} & \multicolumn{2}{c}{Decision 2} & \multicolumn{2}{c}{Beliefs 1} & \multicolumn{2}{c}{Beliefs 2} \\
\cmidrule(r){4-5}
\cmidrule(r){6-7}
\cmidrule(r){8-9}
\cmidrule(r){10-11}
Survey  & Order pref.  & Permutation  & Menu       & Immediate                           & Menu       & Immediate                           & Menu      & Immediate                           & Menu      & Immediate                           \\
 Variant &  profiles &  soc. alternatives &         & rewards                           &        & rewards                           &       & rewards                           &       & rewards                           \\

                          \\
1              & 234516                    & ABC                             & 1A         &                               & 1B         & \checkmark      & 2A        &                               & 2B        & \checkmark      \\
2              & 234516                    & CBA                             & 1A         &                               & 1B         & \checkmark      & 2A        &                               & 2B        & \checkmark      \\
3              & 234516                    & BAC                             & 1A         & \checkmark      & 1B         &                               & 2A        & \checkmark      & 2B        &                               \\
4              & 234516                    & CAB                             & 1A         & \checkmark      & 1B         &                               & 2A        & \checkmark      & 2B        &                               \\
5              & 615432                    & ABC                             & 1B         &                               & 1A         & \checkmark      & 2B        &                               & 2A        & \checkmark      \\
6              & 615432                    & CBA                             & 1B         &                               & 1A         & \checkmark      & 2B        &                               & 2A        & \checkmark      \\
7              & 615432                    & BAC                             & 1B         & \checkmark      & 1A         &                               & 2B        & \checkmark      & 2A        &                               \\
8              & 615432                    & CAB                             & 1B         & \checkmark      & 1A         &                               & 2B        & \checkmark      & 2A        &                               \\
9              & 621354                    & ABC                             & 2A         &                               & 2B         & \checkmark      & 1A        &                               & 1B        & \checkmark      \\
10             & 621354                    & CBA                             & 2A         &                               & 2B         & \checkmark      & 1A        &                               & 1B        & \checkmark      \\
11             & 621354                   & BAC                             & 2A         & \checkmark      & 2B         &                               & 1A        & \checkmark      & 1B        &                               \\
12             & 621354                    & CAB                             & 2A         & \checkmark      & 2B         &                               & 1A        & \checkmark      & 1B        &                               \\
13             & 453126                    & ABC                             & 2B         &                               & 2A         & \checkmark      & 1B        &                               & 1A        & \checkmark      \\
14             & 453126                    & CBA                             & 2B         &                               & 2A         & \checkmark      & 1B        &                               & 1A        & \checkmark      \\
15             & 453126                    & BAC                             & 2B         & \checkmark      & 2A         &                               & 1B        & \checkmark      & 1A        &                               \\
16             & 453126                    & CAB                             & 2B         & \checkmark      & 2A         &                               & 1B        & \checkmark      & 1A        &                              \\
\bottomrule
\end{tabular}
\footnotesize
\textit{\textbf{Notes:}} Table \ref{paternalismMenus} lists the options in menus 1A, 1B, 2A, and 2B.
\end{table}


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\section{Stakeholder preference elicitation and outcome implementation}\label{stakeholderImplementation}


To implement the decisions, we randomly chose 10 \% of the respondents of each of the four survey versions (Politicians, Public 1, Public 2, Public 3). For each of the four versions separately, we then selected one of the choices which we implemented for all participants in that survey version (either preference aggregation, choice set construction, or delegation).

\subsection{Preference aggregation}\label{sec:stakeholderPrefs}

We recruited stakeholders for the preference aggregation part from survey company \emph{Pollfish}, using voting-age German citizens. Stakeholders ranked the three political foundations from the most preferred to the least preferred. Stakeholders had incentives to truthfully reveal their preferences over the foundations. Specifically, they learned that with a 1 in 40 chance, their choice would determine which of the three foundations received the donation. They learned that in this case, we would randomly select two of the three foundations, and send the donation to the foundation the respondent ranked higher amongst the two. Stakeholders further learned that with the remaining chance, their decision `contributes to determining' which of the three foundations would receive the donation, together with that of five other participants. As we left the corresponding process unspecified, stakeholders could not have reported preferences strategically.

We used the elicited stakeholder preferences to construct groups of five German voting-age citizens to match one of the six preference profiles. The corresponding politicians' or general population respondents' aggregation decision was then implemented, and it determined which of the three foundations received the donation.

Table \ref{stakeholderPreferencesMain} in the main text summarizes the stakeholder preferences.


\subsection{Interventions}\label{appendixImpPat}

For each respondent for whom a decision is implemented, a Chooser decision was collected in the Cologne Laboratory for Economic Research after finalizing the survey data collection process. We made sure that all Choosers are German citizens. In the sessions, Choosers made eight decisions, one for each menu in Table \ref{paternalismMenus}, one with and once without front-end delay. Each subject went through the rounds in an individually random order. In each round, the subject ranked all three alternatives according to her preference, not knowing whether any alternative might be unavailable, if any. Subjects did see any advice that previous participants had decided to disburse, for the decision problem for which the advice had been given. Subjects were paid according to the round about which the previous participant had decided. Subjects received the most preferred alternative from those that participants had made available. Subjects only knew that one round would be chosen for payment, but did not know which one.


A total of 79 subjects finished these decisions on February 9, 2023. Table \ref{impDataPat} shows these subjects' preferences. For each alternative, it lists the fraction of subjects who prefer that alternative most (Panel A), middle (Panel B), or least (Panel C), separately by whether immediate rewards are available or not. We find only a weak preference for less patient alternatives when immediate rewards are available compared to when they are unavailable, which is not statistically significantly different from zero ($p=0.35$).


\begin{table}
\caption{German subjects' preferences over payment bundles \label{impDataPat}}
\begin{center}
\begin{tabular}{lcccc} \toprule & \multicolumn{3}{c}{Alternative} \\ \cmidrule(r){2-4} Immediate rewards available & Most patient & Middle & Least patient \\ \midrule \\&\multicolumn{3}{c}{A. Highest preference} \\ \cmidrule(r){2-4}
Yes	&	0.905	&	0.057	&	0.038	\\
No	&	0.927	&	0.041	&	0.032	\\
\\&\multicolumn{3}{c}{B. Middle preference} \\ \cmidrule(r){2-4}
Yes	&	0.063	&	0.902	&	0.035	\\
No	&	0.044	&	0.921	&	0.035	\\
\\&\multicolumn{3}{c}{C. Lowest preference} \\ \cmidrule(r){2-4}
Yes	&	0.032	&	0.041	&	0.927	\\
No	&	0.028	&	0.038	&	0.934	\\
\bottomrule \end{tabular}

\end{center}
\end{table}


\newpage
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\section{Summary statistics and non-responses}

\subsection{Summary statistics}\label{summaryStats}


Table \ref{summaryStatsTable} presents summary statistics for our four samples and population statistics.

\begin{table}[htb]
\caption{Sample characteristics \label{summaryStatsTable}}
\begin{center}
\begin{footnotesize}
\begin{tabular}{lccccc} \toprule Survey & \emph{Politicians} & \emph{Public 1}  & \emph{Public 2}  & \emph{Public 3} & SOEP benchmark \\  \midrule
Political party	&		&		&		&		&		\\
\quad Linke	&	0.10	&	0.05	&	0.12	&	0.13	&	0.08	\\
\quad Gruene	&	0.16	&	0.24	&	0.21	&	0.24	&	0.24	\\
\quad SPD	&	0.19	&	0.13	&	0.17	&	0.16	&	0.19	\\
\quad FDP	&	0.11	&	0.18	&	0.14	&	0.13	&	0.06	\\
\quad CDU/CSU	&	0.28	&	0.34	&	0.22	&	0.24	&	0.34	\\
\quad AfD	&	0.13	&	0.06	&	0.14	&	0.10	&	0.07	\\
\quad Other	&	0.03	&		&		&		&	0.02	\\
Political activity	&		&		&		&		&		\\
\quad Inactive	&		&		&	0.30	&	0.29	&		\\
\quad Vote sometimes	&		&		&	0.06	&	0.06	&		\\
\quad Vote regularly	&		&		&	0.73	&	0.75	&		\\
\quad Actively involved (e.g. collecting votes)	&		&		&	0.10	&	0.09	&		\\
\quad Politician (communal level)	&		&		&	0.01	&	0.01	&		\\
Male	&	0.67	&	0.67	&	0.54	&	0.54	&	0.49	\\
Age	&		&		&		&		&		\\
\quad 18-29	&	0.03	&	0.00	&	0.10	&	0.25	&	0.17	\\
\quad 30-39	&	0.12	&	0.16	&	0.20	&	0.13	&	0.16	\\
\quad 40-49	&	0.19	&	0.32	&	0.20	&	0.20	&	0.19	\\
\quad 50-59	&	0.30	&	0.37	&	0.23	&	0.18	&	0.21	\\
\quad 60-69	&	0.32	&	0.15	&	0.20	&	0.17	&	0.14	\\
\quad 70+	&	0.04	&	0.00	&	0.07	&	0.06	&	0.13	\\
Bundesland	&		&		&		&		&		\\
\quad BB	&	0.04	&	0.03	&	0.04	&	0.03	&	0.04	\\
\quad BE	&	0.05	&	0.05	&	0.05	&	0.04	&	0.04	\\
\quad BW	&	0.10	&	0.11	&	0.12	&	0.13	&	0.11	\\
\quad BY	&	0.11	&	0.14	&	0.14	&	0.16	&	0.15	\\
\quad HB	&	0.03	&	0.00	&	0.02	&	0.01	&	0.01	\\
\quad HE	&	0.05	&	0.09	&	0.08	&	0.06	&	0.07	\\
\quad HH	&	0.07	&	0.06	&	0.03	&	0.02	&	0.02	\\
\quad MV	&	0.02	&	0.01	&	0.03	&	0.02	&	0.02	\\
\quad NI	&	0.04	&	0.11	&	0.09	&	0.09	&	0.10	\\
\quad NW	&	0.08	&	0.19	&	0.19	&	0.20	&	0.21	\\
\quad RP	&	0.05	&	0.07	&	0.04	&	0.05	&	0.05	\\
\quad SH	&	0.04	&	0.04	&	0.04	&	0.04	&	0.04	\\
\quad SL	&	0.02	&	0.02	&	0.02	&	0.02	&	0.01	\\
\quad SN	&	0.05	&	0.03	&	0.05	&	0.05	&	0.06	\\
\quad ST	&	0.03	&	0.02	&	0.02	&	0.03	&	0.03	\\
\quad TH	&	0.03	&	0.02	&	0.03	&	0.04	&	0.03	\\
Monthly household income	&		&		&		&		&		\\
\quad 1500 or less	&		&		&	0.20	&	0.21	&	0.15	\\
\quad 1500 - 3000	&		&		&	0.38	&	0.35	&	0.33	\\
\quad 3000 - 4500	&		&		&	0.25	&	0.26	&	0.27	\\
\quad 4500 - 6000	&		&		&	0.12	&	0.11	&	0.13	\\
\quad 6000 - 7500	&		&		&	0.02	&	0.03	&	0.06	\\
\quad 7500 or more	&		&		&	0.03	&	0.03	&	0.06	\\
Highest education completed	&		&		&		&		&		\\
\quad Hauptschule or Mittlere Reife	&	0.00	&	0.00	&	0.38	&	0.36	&	0.36	\\
\quad Abitur or equivalent	&	0.19	&	0.26	&	0.30	&	0.35	&	0.38	\\
\quad Hochschule	&	0.68	&	0.67	&	0.30	&	0.27	&	0.27	\\
\quad Promotion	&	0.13	&	0.07	&	0.02	&	0.02	&		\\
Marital status	&		&		&		&		&		\\
\quad Married or engaged	&		&	0.91	&	0.58	&	0.59	&	0.59	\\
\quad Single	&		&	0.05	&	0.26	&	0.28	&	0.25	\\
\quad Divorced, separated, widowed	&		&	0.04	&	0.16	&	0.12	&	0.15	\\
N	&	423	&	609	&	810	&	708	&		\\
\\ \bottomrule \end{tabular}

\end{footnotesize}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Omitted cells were not asked, or were not available as answer options to subjects. Population statistics are based on weighted data from the 2019 German Socioeconomic Panel (SOEP). In the SOEP, the education category \emph{Promotion} (PhD or professional doctorate) is not elicited separately, the corresponding individuals are included in the category \emph{Hochschule}.
\end{table}

\FloatBarrier

\subsection{Non-response analysis for the politician sample}\label{nonresponse}
Table \ref{nonresponse_table} compares observable characteristics between politicians who elected to participate in our study (N$=$423) and those who did not (N$=$2164). Table \ref{politResponsesParliamentType} shows response rates separately for federal and state parliamentarians.


\begin{table}[htb]
\caption{Selection into participation for politician sample\label{nonresponse_table}}
\begin{center}
{\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\begin{tabular}{lccccc}
\toprule
 & \multicolumn{2}{c}{Non-participants} & \multicolumn{2}{c}{Participants} & Difference \\\\
 & Mean & SD & Mean & SD & $\Delta$ \\
\hline
Male                &       0.692&       0.462&       0.667&       0.472&       0.026   \\
Age $\leq 39$       &       0.142&       0.349&       0.147&       0.354&      -0.005   \\
Age $\geq 40$ \& $\leq 59$&       0.603&       0.489&       0.489&       0.500&       0.114***\\
Age $\geq 60$       &       0.255&       0.436&       0.364&       0.482&      -0.109***\\
Non-tertiary education&       0.238&       0.426&       0.194&       0.396&       0.045** \\
Tertiary education  &       0.645&       0.479&       0.681&       0.467&      -0.036   \\
Doctorate           &       0.116&       0.321&       0.125&       0.331&      -0.009   \\
AFD                 &       0.129&       0.336&       0.132&       0.339&      -0.003   \\
CDU/CSU             &       0.322&       0.467&       0.277&       0.448&       0.045*  \\
FDP                 &       0.069&       0.253&       0.111&       0.315&      -0.042***\\
Gruene              &       0.129&       0.335&       0.158&       0.366&      -0.029   \\
Linke               &       0.079&       0.270&       0.102&       0.303&      -0.023   \\
Fraktionlos         &       0.024&       0.152&       0.026&       0.159&      -0.002   \\
SPD                 &       0.248&       0.432&       0.194&       0.396&       0.054** \\
Bundestag           &       0.290&       0.454&       0.191&       0.394&       0.099***\\
Landtag             &       0.710&       0.454&       0.809&       0.394&      -0.099***\\
Directly elected    &       0.523&       0.500&       0.593&       0.492&      -0.070***\\
Years in Parliament &       9.033&       7.175&       7.544&       6.714&       1.489***\\
\midrule
Observations & 2164 &  & 423 &  & 2587 \\
\bottomrule
\end{tabular}
}

\end{center}
\footnotesize
\textbf{\textit{Notes:}} This table compares differences in observable characteristics between the non-participants (N$=$2164) and participants (N$=$423) among all German legislators in our experiment (N$=$2587). The first two columns in each comparison report the mean values and standard deviations for either 'Non-participants' or 'Participants' politicians, respectively. The last column reports the difference of a means test of equivalence (Wald--test). All characteristics on politicians were hand--collected by the authors from web--resources. Non-tertiary education, completed tertiary education, and having a doctorate refer to International Standard Classification of Education (ISCED) category codes 4, 5, and 6, respectively. A legislator is `fraktionslos' if they are not associated with a government party. One legislator's age is not publicly available. $^*p<0.1, ^{**}p<0.05, ^{***}p<0.01$.
\end{table}


\begin{table}[ht]
\caption{Politician responses by parliament type \label{politResponsesParliamentType}}
\begin{center}
\begin{tabular}{lcccccc}
\toprule
 & \multicolumn{3}{c}{State Parliaments} & \multicolumn{3}{c}{Federal Parliament} \\
Party & Answers & Total & Share & Answers & Total & Share \\
\midrule
AFD & 42 & 244 & 17.21\% & 14 & 92 & 15.22\% \\
CDU/CSU & 97 & 568 & 17.08\% & 20 & 246 & 8.13\% \\
FDP & 30 & 116 & 25.86\% & 17 & 80 & 21.25\% \\
Gruene & 62 & 279 & 22.22\% & 5 & 67 & 7.46\% \\
Linke & 27 & 145 & 18.62\% & 16 & 69 & 23.19\% \\
Fraktionlos & 11 & 60 & 18.33\% & 0 & 2 & 0.00\% \\
SPD & 73 & 466 & 15.67\% & 9 & 153 & 5.88\% \\
Total & 342 & 1878 & 18.21\% & 81 & 709 & 11.42\% \\
\bottomrule
\end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} A legislator is \emph{fraktionslos} if they are not associated with a government party.
\end{table}


\FloatBarrier

\newpage
\subsection{Analysis of attrition in the politician sample}\label{app:attrition}


The fact that relatively many politicians exit the survey after the aggregation choices raises questions about the possibility of differential attrition. To assess this possibility, we regress each of several variables on an indicator for whether the politician completed part 2 of the survey. Table \ref{tab:attrition} displays the results. There is no substantial effect on the median response time. Preference aggregation choices differ only to the extent that subjects who complete Part 2 of the survey less often choose the rank-dominated alternative C. This effect is consistent with the hypothesis that politicians who exit after part 1 of the survey pay less attention to their choices. Beliefs about the willingness to pay for triggering donations are slightly lower for politicians who complete part 2 of the survey. Note, however, that some politicians who do not complete part 2 also do not provide WTP estimates. Reassuringly, we find no statistically significant differential attrition by political attitudes or membership in the federal parliament. The only statistically significant demographic effect is that older politicians complete part 2 substantially less often.


\begin{table}[htb]
\caption{Effect of completion of the entire survey}
\label{tab:attrition}
\begin{center}
\begin{tabular}{lccc}
\toprule
\cmidrule(r){2-3}
  & Effect size estimate & Observations  \\
 \midrule
Median response time &    20.000  & 345 \\                                      & (   15.023 ) & \\ \multicolumn{3}{l}{\textit{Aggregation choices}} \\ \quad A selected&     0.020  & 2070 \\                                      & (    0.028 ) & \\ \quad B selected&     0.022  & 2070 \\                                      & (    0.030 ) & \\ \quad C selected&    -0.043 **& 2070 \\                                      & (    0.018 ) & \\ \multicolumn{3}{l}{\textit{Beliefs about WTP}} \\ \quad Best &    -3.125  & 285 \\                                      & (    3.888 ) & \\ \quad Middle &    -0.209  & 234 \\                                      & (    1.226 ) & \\ \quad Worst &    -2.418 *& 230 \\                                      & (    1.277 ) & \\ \multicolumn{3}{l}{\textit{Political background}} \\ \quad Political attitude &    -0.055  & 336 \\                                      & (    0.244 ) & \\ \quad Federal parliament &    -0.032  & 345 \\                                      & (    0.042 ) & \\ \multicolumn{3}{l}{\textit{Demographics}} \\ \quad Age &    -2.681 **& 345 \\                                      & (    1.234 ) & \\ \quad Male &     0.080  & 345 \\                                      & (    0.050 ) & \\ \quad Univ. degree &     0.096  & 345 \\                                      & (    0.090 ) & \\ \bottomrule\\
\end{tabular}
\end{center}
\textbf{\textit{Notes}}: Each row corresponds to a separate regression of the listed variable on an indicator for whether the politician completed part 2 of the survey. The effect on response time is estimated using quantile regression. All other effects are estimated using OLS. Regressions concerning aggregation choices contain multiple observations per subject. In these cases, we cluster the standard errors on the subject level. Political attitude is measured by party membership projected on a 1-to-10 left-right scale following \citetapp{chapelHillSurvey}.
\end{table}

\FloatBarrier


\newpage
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\section{Additional analysis: Preference aggregation}

\subsection{Comprehensive classification}\label{comprehensiveClassification}


To obtain a more granular understanding of politicians' preference aggregation, we next assign each politician to one of a set of theoretical preference aggregation rules that best describe that politician's choice sequence, following the methods developed in \citetapp{AmbuehlBernheim}. We make no claim that politicians aggregate preferences following any specific \emph{procedure}, just as economists do not usually believe that individuals make decisions under risk by explicitly calculating the expectation of their utility function. We merely use the aggregation rules as a succinct description of the collection of choices made by politicians, in the sense of revealed preference. Given our selection of preference profiles, we can distinguish between the Condorcet pairwise majority rule, and eight scoring rules characterized by $s$ = 0, $s$ = $\nicefrac{1}{3}$ and the six intervals  $(0, \nicefrac{1}{3}), (\nicefrac{1}{3}, \nicefrac{1}{2}), (\nicefrac{1}{2}, \nicefrac{3}{5}), (\nicefrac{3}{5}, \nicefrac{2}{3}), (\nicefrac{2}{3}, \nicefrac{4}{5}),$ and $(\nicefrac{4}{5}, 1)$ into which $s$ may fall. In addition, we can identify the use of the antiplurality runoff rule,\footnote{The winner is calculated as follows. First, calculate the score of each option using the scoring parameter $s = 1$. Second, remove the option with the lowest score from consideration. Third, select the option with the highest score among the remaining options. In case two options tie for the worst score in the first step, drop both of them and use the remaining option as the winner.} the 80\% supermajority rule, and the unanimity rule. In order to address the issue that less resolute rules can mechanically better match any given choice sequence, we use a Bayesian classifier which appropriately penalizes less resolute rules \citepapp{AmbuehlBernheim}.\footnote{Footnote \ref{bayesClassifier} in Section \ref{sec:resultsAggregation} lists details.}

Figure \ref{aggregationCitPolit} displays the resulting classification. Three features stand out. First, as mentioned in Section \ref{citizensAggregation}, there are only a few notable differences between politicians and the general public. Second, more than three-quarters of respondents make choices consistent with a scoring rule. Only slightly over 10\% are best-described as following the Condorcet rule.\footnote{One potential reason for this finding could be that the predictions of the Condorcet rule coincide with those of the scoring runoff rules when $s\in (\nicefrac{1}{4},1)$.} Third, respondents assigned to a scoring rule are overwhelmingly characterized by a scoring parameter $s > \nicefrac{1}{2}$. We also find that just under 10\% of respondents are assigned to a malevolent rule. Given the relatively small number of preference profiles we use to classify respondents, we conjecture that assignments to malevolent rules might reflect noise (though note that in a simulated dataset of uniformly random aggregation choices, 37\% of artificial subjects are assigned to a malevolent rule).


Could our results be driven by attempts to save cognitive effort? Arguably, the plurality rule is a candidate for the least effortful rule, as it allows the respondent to ignore most of the information contained in a preference profile and focus exclusively on first ranks. The fact that we find only a small minority whose choices align with the plurality rule provides evidence against this hypothesis. Moreover, response times do not predict the aggregation rule to which a respondent is assigned in our data. A related concern is the possibility that our particular way of displaying preference profiles to respondents might bias our findings towards certain kinds of rules. While the limited sample size prevented us from experimenting with display formats, \citetapp{AmbuehlBernheim} comprehensively investigate the effect of display formats. Their results are largely independent of the display format.


\begin{figure}[htp!]
\caption{Best-fitting aggregation rules, politicians and general public \label{aggregationCitPolit}}
\begin{center}
\includegraphics[width = 0.6\textwidth]{graphsNew/FigureD1}
\end{center}
\footnotesize
\end{figure}


\subsection{Willingness to pay beliefs and implied utilitarian choice of the general public}\label{sec:impliedUtilitarianGenPop}

Table \ref{impliedUtilitarianGenPop} replicates Table \ref{impliedUtilitarian} for the general public. Utilitarian aggregation places slightly more weight on the compromise option than in the case of politicians, but the implied utilitarian choice distribution still differs dramatically from the actual distribution of choices. Again, these results remain consistent even when responses with inconsistencies or extreme values are included.

\begin{table}[htbp!]
\caption{Willingness to pay beliefs and implied utilitarian choice of the general public \label{impliedUtilitarianGenPop}}
\begin{center}
\begin{tabular}{lclcc} \toprule &(1)&&(2)&(3)\\ & WTP in \EUR && Implied utilitarian choice & Actual choice \\ \cmidrule(r){2-2}\cmidrule(r){4-5} {Preference rank}  & & Choice Option \\ 	\quad Best	&	19.669	&	\quad A (majority winner)	&	0.615	&	0.430	\\
		&(	0.425	)&		&(	0.013	)&(	0.014	)\\
	\quad Middle	&	7.165	&	\quad B (compromise option)	&	0.340	&	0.520	\\
		&(	0.456	)&		&(	0.013	)&(	0.015	)\\
	\quad Worst	&	-1.080	&	\quad C	&	0.045	&	0.050	\\
		&(	0.425	)&		&(	0.004	)&(	0.008	)\\
\\ \bottomrule \end{tabular}

\end{center}
\footnotesize
\textbf{\textit{Notes:}} WTP is the believed amount in \EUR\; a general population member believes a stakeholder would be willing to pay to trigger or prevent (negative WTP) a donation to the foundation in his first, second, and third preference rank, respectively. Implied utilitarian choice is the choice a general population member would make if she selected alternatives by maximizing the sum of believed WTP. Actual choice is the actual choice the general population members made. For profile one, choice option A (majority winner) coincides with the tied majority winners A and C. Standard errors in parentheses, clustered by subject. Table based on data from politicians who provided WTP beliefs about each preference rank.
\end{table}


\subsection{Estimation of a cardinal social welfare function}\label{estimationCardinal}

We posit that politicians make social choices by maximizing a cardinal welfare function defined on beliefs about stakeholders' willingness to pay to trigger or prevent (if negative) a donation to their first, second, and third-ranked foundation, respectively. We ask what form of that function best describes their choices between social alternatives. Letting $u_1$, $u_2$, and $u_3$ denote a politician's beliefs about stakeholders' WTP corresponding to their first, second, and third-ranked foundation, we consider the function
\bmathnn
W_\alpha(X) = \sum_{i=1}^5 f_\alpha(u_{r_i(X)}+\underline{u})
\emathnn
where $r_i(X)$ is the preference rank that stakeholder $i$ assigns to alternative $X$ and $\underline{u}$ is a location parameter. We assume a constant-elasticity form $f_\alpha(x) =   \frac{x^{\alpha}-1}{\alpha}$. Our interest centers on the parameter $\alpha$, which captures how politicians resolve equity-efficiency tradeoffs, where lower values of $\alpha$ indicate a greater weight on equity. Because the constant-elasticity function is only defined on positive values, we set $\underline{u} = 30$.

We estimate this function focusing on the binary choice between the majority-preferred option $A$ and the compromise option $B$, using the 92.99\% of cases in which a politician selected either of these alternatives.  We assume that the politician selects alternative $A$ over $B$ with probability
\bmath \label{logitModel}
\left( 1+\exp\left(-\sigma\left[W_\alpha(A) - W_\alpha(B)\right]\right)\right)^{-1}
\emath
We estimate $\alpha$ and $\sigma$ using nonlinear least squares. Because of the reduced sample size due to non-responses on the questions about WTP beliefs, we have limited statistical power.

We obtain estimates $\hat{\alpha} = 0.65$ (s.e. 0.26) and $\hat{\sigma} = 0.04$ (s.e. 0.03). While the estimate of $\alpha$ is substantially below the utilitarian benchmark of 1, the diminished sample size and the resulting imprecise estimates mean that we cannot reject the hypothesis that $\hat{\alpha}$ equals 1 at conventional levels of significance. When we let $\alpha$ depend on a politician's party's position and its square, we similarly find statistically insignificant results that are directionally consistent with the party-dependence of ordinal aggregation documented in Panel B of Figure \ref{bayesClassification}.


\newpage
\setcounter{table}{0}
\setcounter{figure}{0}
\section{Additional analysis: Interventions}


\subsection{Predictive power of elicited beliefs}\label{appendixBeliefNoise}

Do our elicited beliefs contain information, or are they mostly noise? To answer this question, we calculate the maximum amount the Chooser can receive early according to the choice set the politician or general population respondent has constructed for him.\footnote{For better comparability across decisions, we normalize the payoff amounts. Specifically, we multiply the amounts of Menu 1B by $\frac{2}{3}$ to make them equal to those in Menu 1A, and we multiply the amounts of Menu 2B by $\frac{3}{4}$ to make them equal to those in Menu 2A. } We also use the respondents' beliefs about the Chooser's unrestricted choice to calculate the amount the respondent expects the Chooser would select early, absent restrictions.  We then regress the former variable on the latter, controlling for gender, age, state, and education fixed effects.

Table \ref{paternalismTableAppendixBeliefs} displays the results. Pooling across samples, we find a highly statistically significant coefficient of 0.50. If interventions moved one-for-one with beliefs, the coefficient would be 1; if beliefs were pure noise, the coefficient would be zero. Columns 2 and 3 run the same regression on the sample of politicians and on the sample of general population subjects, respectively. In the case of politicians, the relation is highly statistically significant 0.77, indicating a large amount of information in elicited beliefs data. On the general population sample, the relation is 0.48 and highly statistically significant, indicating substantial information in beliefs.\footnote{Some politicians reported inconsistent beliefs that summed to more than 100\%. Our results remain unchanged when these politicians are included.}


\begin{table}[htbp!]
\caption{Relation between beliefs and interventions \label{paternalismTableAppendixBeliefs}}
\begin{center}
\begin{tabular}{lccc} \toprule
& \multicolumn{1}{c}{(1)}
& \multicolumn{1}{c}{(2)}
& \multicolumn{1}{c}{(3)}
\\
\shortstack{VARIABLES\\ \strut}&\multicolumn{3}{c}{\shortstack{Maximal amount allowed\\ to be received early}}\\\cmidrule(lr){2-4}
\\ Politicians & \checkmark & \checkmark & \quad \\ General public & \checkmark & \quad & \checkmark \\ \midrule
Believed amount chosen early&       0.505***&       0.772***&       0.481***\\
            &     (0.056)   &     (0.118)   &     (0.060)   \\
Age         &      -0.014***&      -0.020   &      -0.013** \\
            &     (0.005)   &     (0.013)   &     (0.005)   \\
Male        &       0.081   &       0.584*  &       0.027   \\
            &     (0.140)   &     (0.302)   &     (0.153)   \\

\\ Observations&       2,997   &         363   &       2,634   \\
Subjects    &       1,510   &         193   &       1,317   \\
\bottomrule \end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Pooled across observations with and without front-end delay. Politicians who did not provide beliefs data are excluded from this analysis. Standard errors in parentheses, clustered by subject.  $^*p<0.1, ^{**}p<0.05, ^{***}p<0.01$.
\end{table}


\subsection{Effect of political involvement among general population subjects}\label{appendixPoliticalInvolvement}

Here, we check whether the absence of an effect of political party preferences among the general population respondents on aggregation and intervention decisions might be caused by a weaker identification of these subjects with their preferred party relative to the case of elected representatives. To this end, we repeat the analysis of Table \ref{tableGenPop}, including only citizens who are politically active, defined as voting regularly or being more politically involved than that. Amongst general population respondents who completed the aggregation decisions, 75.3\% report being involved in politics of at least voting regularly. Amongst those who completed the intervention decisions, the corresponding number is 76.3\%. Notably, all but a handful of respondents achieve this level of involvement exclusively by voting regularly (i.e., they are not more actively involved than as regular voters).

Table \ref{tableGenPopPolActive} displays the result. In the case of preference aggregation, we continue to observe a coefficient on the square of the political party position that largely offsets that of politicians, though the linear term for the general public is now statistically significant, as the corresponding $p$-value near the bottom of the table indicates. In the case of interventions, there remains no statistically significant effect of party position for the general public.

\begin{table}[ht!]
\caption{Politically active citizens vs. politicians \label{tableGenPopPolActive}}
\begin{center}
\begin{tabular}{lcccc} \toprule
& \multicolumn{1}{c}{(1)}
& \multicolumn{1}{c}{(2)}
& \multicolumn{1}{c}{(3)}
& \multicolumn{1}{c}{(4)}
\\
\shortstack{VARIABLES\\ \strut \\ \strut}&\multicolumn{2}{c}{\shortstack{Preference for compromise\\(scoring parameter s\\scaled from 0 to 100)}}&\multicolumn{2}{c}{\shortstack{Number of options removed\\ \vspace{7mm}}}\\\cmidrule(lr){2-3}\cmidrule(lr){4-5}
\\
Political party position&       1.238** &       1.400** &       0.057** &       0.064** \\
            &     (0.579)   &     (0.608)   &     (0.028)   &     (0.028)   \\
(Political party position)$^2$&      -0.382*  &      -0.445*  &       0.010   &       0.008   \\
            &     (0.223)   &     (0.241)   &     (0.012)   &     (0.011)   \\
General public&               &               &               &               \\
\quad $\times$ 1&      -4.481*  &      -3.438   &       0.298***&       0.217** \\
            &     (2.649)   &     (2.708)   &     (0.089)   &     (0.091)   \\
\quad $\times$ political party position&       0.035   &      -0.018   &      -0.057*  &      -0.068** \\
            &     (0.900)   &     (0.913)   &     (0.032)   &     (0.031)   \\
\quad $\times$ (political party position)$^2$&       0.465   &       0.538   &      -0.013   &      -0.008   \\
            &     (0.345)   &     (0.354)   &     (0.013)   &     (0.012)   \\
Female      &               &       3.655   &               &       0.039   \\
            &               &     (2.258)   &               &     (0.059)   \\
Age         &               &      -0.027   &               &       0.009***\\
            &               &     (0.080)   &               &     (0.002)   \\

Fixed Effects&               &               &               &               \\
\quad Education&               &  \checkmark   &               &  \checkmark   \\
\quad State &               &  \checkmark   &               &  \checkmark   \\
\\ \shortstack[l]{p-values\\ Significance of party position \\for general public}&               &               &               &               \\
\quad linear term&       0.065   &       0.043   &       0.976   &       0.779   \\
\quad square term&       0.754   &       0.732   &               &               \\
Observations&         887   &         887   &       1,449   &       1,449   \\
Subjects    &         887   &         887   &         735   &         735   \\
\bottomrule \end{tabular}

\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Regressions exclude politicians not associated with a government party (``fraktionslos'').  Party position represents overall ideological stance on the left / right spectrum from \citetapp{chapelHillSurvey}, measured on a scale from 1 to 10, de-meaned. Standard errors in parentheses, clustered by subject. $^*p<0.1, ^{**}p<0.05, ^{***}p<0.01$.
\end{table}

\FloatBarrier


\subsection{Self-reported risk, time, and social preferences}\label{appendixPrefs}


\begin{table}[ht!]
\caption{Self-reported preferences by political party\label{preferencesVpartyTable}}
\begin{center}
\begin{tabular}{lcccccc} \toprule
& \multicolumn{1}{c}{(1)}
& \multicolumn{1}{c}{(2)}
& \multicolumn{1}{c}{(3)}
& \multicolumn{1}{c}{(4)}
& \multicolumn{1}{c}{(5)}
& \multicolumn{1}{c}{(6)}
\\VARIABLES
& \multicolumn{2}{c}{Altruism}
&  \multicolumn{2}{c}{Risk aversion}
&  \multicolumn{2}{c}{Patience}
\\
\\

Mean of the dependent variable&\multicolumn{2}{c}{5.106}         &\multicolumn{2}{c}{4.550}         &\multicolumn{2}{c}{5.001}         \\
            &\multicolumn{2}{c}{(0.039)}         &\multicolumn{2}{c}{(0.044)}         &\multicolumn{2}{c}{(0.037)}         \\

\cmidrule(r){2-3} \cmidrule(r){4-5} \cmidrule(r){6-7}
Politician  &       0.360***&       0.517***&      -0.686***&      -0.094   &       0.717***&       0.510***\\
            &     (0.123)   &     (0.135)   &     (0.095)   &     (0.131)   &     (0.080)   &     (0.120)   \\
Federal parliamentarian&               &      -0.702   &               &      -0.171   &               &       0.151   \\
            &               &     (0.478)   &               &     (0.343)   &               &     (0.253)   \\
Political experience (years)&               &       0.024   &               &      -0.030** &               &       0.000   \\
            &               &     (0.015)   &               &     (0.013)   &               &     (0.011)   \\
Male        &               &      -0.442***&               &      -0.562***&               &      -0.116   \\
            &               &     (0.074)   &               &     (0.081)   &               &     (0.073)   \\
Age         &               &      -0.003   &               &       0.017***&               &      -0.004   \\
            &               &     (0.003)   &               &     (0.003)   &               &     (0.003)   \\

Fixed Effects&               &               &               &               &               &               \\
\quad Education&               &  \checkmark   &               &  \checkmark   &               &  \checkmark   \\
\quad State &               &  \checkmark   &               &  \checkmark   &               &  \checkmark   \\
\\ Subjects &       2,421   &       2,421   &       2,421   &       2,421   &       2,421   &       2,421   \\
\bottomrule \end{tabular}

\end{center}
\textbf{\textit{Notes:}} Weighted regressions. Standard errors in parentheses, clustered by subject. $^*p<0.1, ^{**}p<0.05, ^{***}p<0.01$.
\end{table}


Are there differences between politicians and the general public in terms of the standard economic time, risk, and social preferences? To answer this question, we turn to respondents' self-assessments based on the questions of \citetapp{Falketal.Global}. 294 politicians provided answers to these three questions, as did all general population respondents. Because of this limited sample, we reweight the sample of politicians. In light of \citetapp{chapman2025reassessing}'s evidence that demonstrates that self-reported qualitative preference measures are not valid proxies for behavioral measures, however, we interpret these results with caution.


Odd-numbered columns in Table \ref{preferencesVpartyTable} regress each of the preference self-reports on an indicator for whether the respondent is a politician. Even-numbered columns add controls for being a federal parliamentarian, political experience in years, gender, age, as well as education and state fixed effects. We find that politicians rate themselves as more altruistic, less risk-averse, and more patient than the general public ($p<0.01$) in each case. Only the parameter estimate for risk aversion renders statistically insignificant once we include statistical controls. We find some, but not all of demographic effects on preferences that typically appear with preference measures. Specifically, males rate themselves as significantly less altruistic and risk-averse (both $p<0.01$).

Figure \ref{preferencesVpartyFigure} further shows how self-reported preferences depend on the political position of the party a respondent supports. Individuals on the political right rate themselves as less altruistic, both among politicians and among the general public. Politicians on the right also rate themselves as less risk-averse; an effect not present for the general public. Self-reported patience, by contrast, does not  vary substantially across the political spectrum in either population.


\begin{figure}[ht!]
\caption{Self-reported preferences by political party\label{preferencesVpartyFigure}}
\begin{center}
\textbf{A. Altruism} \\
\includegraphics[width = 0.5\textwidth]{graphsNew/FigureE1A} \\
\textbf{B. Risk aversion} \\
\includegraphics[width = 0.5\textwidth]{graphsNew/FigureE1B} \\
\textbf{C. Patience} \\
\includegraphics[width = 0.5\textwidth]{graphsNew/FigureE1C} \\
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Whiskers denote 95\% confidence intervals, with standard errors clustered by subject. Party position represents overall ideological stance on the left / right spectrum from \citetapp{chapelHillSurvey}.
\end{figure}


\newpage
\subsection{Support for retirement savings mandate}\label{appendixRetirementSavings}

We check whether experimental behavior correlates with attitudes to real-world policy issues. All politicians and all respondents to \emph{Public 1} and \emph{Public 3} indicated their agreement with the following statement: `\textit{All employees in Germany should be obligated to save a certain portion of their labor income as part of a private pension plan to achieve a pension level that exceeds the pension entitlement from the statutory pension insurance.}' Upon completion of data collection, we noticed several shortcomings of this question. Attitudes towards it may differ for many reasons other than variation in intervention preferences, such as the respondent's opinion about the specific implementation, different interpretations of the vague term `a certain portion,'  heterogeneity in knowledge about the level of the `pension entitlement from the statutory pension insurance,' and differences in whether one would like to be affected by such a policy rather than whether one would like to supply it to others. Hence, we refrain from further analysis of this question, except by noting that individuals who never withhold alternatives in the experiment are less likely to express agreement with the statement.


\subsection{Whose beliefs are more accurate?}\label{wrongBeliefs}


Who has more accurate beliefs about politicians' and the general public's choices? We use the Euclidian distance between a general population respondent's beliefs and the true choice distribution as measure of belief accuracy. We regress it on an indicator for whether beliefs concern politicians, an indicator for preference profile 5 (the other belief decision concerns profile 2), gender, age, political party position, an indicator for political inactivity, and education and state fixed effects. Column 1 of Table \ref{wrongBeliefsPaternalismTable} displays the result. Subjects have slightly less accurate beliefs about politicians' choices, though this difference is minor. Column 2 considers the difference in the belief accuracy measure by whether beliefs concern politicians or the general public. No demographic characteristics significantly relates to the difference in belief accuracy.

Column 3 replicates column 1 in the domain of intervention decisions. Here, respondents have significantly more accurate beliefs about politicians' choices than about those of the general public ($p<0.01$), though accuracy decreases further to the right of the political spectrum ($p<0.05$). Column 4 replicates column 2 without finding any significant effects.


\begin{table}[htb!]
\caption{Divergence between beliefs and truth by respondent characteristics \label{wrongBeliefsPaternalismTable}}
\small
\begin{center}
\begin{tabular}{lcccc} \toprule
& \multicolumn{1}{c}{(1)}
& \multicolumn{1}{c}{(2)}
& \multicolumn{1}{c}{(3)}
& \multicolumn{1}{c}{(4)}
\\
            &\multicolumn{2}{c}{Preference aggregation}&\multicolumn{2}{c}{Interventions}\\\cmidrule(lr){2-3}\cmidrule(lr){4-5}
\shortstack{VARIABLES\\ \strut\\ \strut\\ \quad}&\shortstack{Belief divergence\\ \strut\\ \strut\\ \quad}   &\shortstack{Difference\\belief divergence\\politicians vs.\\general public}   &\shortstack{Belief divergence\\ \strut\\ \strut\\ \quad}   &\shortstack{Difference\\belief divergence\\politicians vs.\\general public}   \\
\midrule \\
Beliefs concern politicians&       0.066***&               &      -2.945***&               \\
            &     (0.010)   &               &     (0.918)   &               \\
Preference profile 5&      -0.075***&      -0.088***&               &               \\
            &     (0.011)   &     (0.018)   &               &               \\
Front-end delay&               &               &       0.760   &       0.674   \\
            &               &               &     (0.875)   &     (1.396)   \\
Male        &      -0.008   &       0.025   &      -3.683*  &      -0.358   \\
            &     (0.020)   &     (0.019)   &     (2.035)   &     (1.646)   \\
Age         &       0.001   &      -0.000   &       0.110   &      -0.062   \\
            &     (0.001)   &     (0.001)   &     (0.076)   &     (0.055)   \\
Political party position&      -0.002   &      -0.001   &       1.091** &      -0.233   \\
            &     (0.004)   &     (0.004)   &     (0.446)   &     (0.404)   \\
Politically inactive&       0.048   &       0.002   &      -1.093   &       0.066   \\
            &     (0.034)   &     (0.029)   &     (2.873)   &     (2.014)   \\

Fixed Effects&               &               &               &               \\
\quad Education&  \checkmark   &  \checkmark   &  \checkmark   &  \checkmark   \\
\quad State &  \checkmark   &  \checkmark   &  \checkmark   &  \checkmark   \\
\\ Observations&       3,240   &       1,620   &       2,832   &       1,416   \\
Subjects    &         810   &         810   &         708   &         708   \\
\bottomrule \end{tabular}

\end{center}
\textbf{\textit{Notes:}} Weighted regressions. Standard errors in parentheses, clustered by subject. $^*p<0.1, ^{**}p<0.05, ^{***}p<0.01$.
\end{table}


\newpage
\setcounter{table}{0}
\setcounter{figure}{0}
\section{Additional analysis: Beliefs and Delegation}

\subsection{Beliefs \label{beliefsEconometrics}}


To document the comparison between beliefs and actual choices econometrically, Table \ref{delegationBeliefsMain} lists the data of Figure \ref{delegationBeliefsFigure} numerically, along with hypothesis tests. Columns 3 and 6 of panel A show that the difference between believed and actual choice frequencies is statistically significant in all but one case in the preference aggregation domain. In the intervention domain, the difference is significant in all but two cases (panel B). Column 7 further shows that there are no statistically significant differences in beliefs about politicians and the general population in the domain of preference aggregation. By contrast, differences in beliefs about the frequency with which politicians construct choice sets $\{A\}$ and $\{A, B, C\}$ compared to how often the general public does so are highly statistically significant.


Finally, we check whether the accuracy of beliefs about politicians' and other general population respondents' choices is related to demographic characteristics. We do not find any robust effects in either domain (see Appendix \ref{wrongBeliefs} for details). We also examine whether beliefs explain subjects' preferences to delegate choices to politicians or general population members. We find little variation in delegation preference; more than half of all respondents most prefer delegating to a general population member and least prefer delegating to a politician, with delegation to a member of the subgroup of general population respondents with the same demographics as politicians ranking in the middle.\footnote{This result might reflect general distrust in politicians. An alternative hypothesis is that subjects attempt to equalize decision power across populations and thus delegate to individuals who do not usually have that power.} Due to these strong preferences, we do not find any relation between beliefs and delegation choices (see Appendix \ref{analysisDelegation} for details).


\begin{table}[htbp!]
\caption{Beliefs about politicians' and other general population respondents' choices \label{delegationBeliefsMain}}
\begin{center}
\begin{tabular}{lccccccc} \toprule        &(1)    &(2)    &(3)    &(4)    &(5)    &(6)    &(7)\\          &       \multicolumn{3}{c}{Politicians} &       \multicolumn{3}{c}{General population}  &       Difference in beliefs   \\      \cmidrule(r){2-4}\cmidrule(r){5-7}      & True  & Believed      & $p$-value    & True  & Believed      & $p$-value    & $p$-value  \\                \\      &       \multicolumn{5}{c}{A. Aggregation}      \\      \cmidrule(r){2-8}       \textit{Choice option}  \\	\quad A (majority winner)	&	0.391	&	0.416	&	0.004	&	0.416	&	0.425	&	0.274	&	0.256	\\
	\quad B (compromise option)	&	0.565	&	0.365	&	0.000	&	0.531	&	0.365	&	0.000	&	0.984	\\
	\quad C	&	0.044	&	0.219	&	0.000	&	0.054	&	0.210	&	0.000	&	0.242	\\

\\ &     \multicolumn{5}{c}{B. Interventions}    \\      \cmidrule(r){2-8}       \textit{Choice set}     \\	\quad \{A\}	&	0.132	&	0.323	&	0.000	&	0.117	&	0.195	&	0.000	&	0.000	\\
	\quad \{A, B\}	&	0.291	&	0.283	&	0.356	&	0.403	&	0.279	&	0.000	&	0.691	\\
	\quad \{A, B, C\}	&	0.330	&	0.222	&	0.000	&	0.130	&	0.361	&	0.000	&	0.000	\\
	\quad Other	&	0.247	&	0.172	&	0.247	&	0.350	&	0.165	&	0.000	&	0.625	\\
\bottomrule \end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Columns 1, 2, 4, and 5 each display a probability distribution over the choice options or choice sets listed on the left. Panel A aggregates across preference profiles. Panel B aggregates across menus. For profile one, choice option A (majority winner) coincides to the tied majority winner A and C. Columns 3 and 6 display the $p$-values for tests that the mean of the beliefs equals the point estimate of the true distribution. Column 7 displays the $p$-values for tests that the mean beliefs of politicians and the general public coincide. $^*p<0.1, ^{**}p<0.05, ^{***}p<0.01$.
\end{table}


\subsection{Delegation}\label{analysisDelegation}


Respondents in the general population samples ranked three groups of individuals to whom they prefer to delegate the preference aggregation (\emph{Public 2}) or intervention decisions (\emph{Public 3}): (i) elected representatives, (ii) general population subjects, (iii) general population subjects with a distribution of demographic characteristics like elected representatives. Figure \ref{delegationPreferences} displays respondents' delegation preferences. Panel A focuses on preference aggregation. In this domain, nearly 70\% of subjects most prefer delegating to a general population individual, and just over 10\% most prefer delegating to an elected representative. Conversely, nearly 70\% of subjects least prefer delegating to an elected representative, and just over 10\% least prefer delegating to a general population member. Indeed, 51.54\% of subjects most prefer delegating to a general population member, least prefer delegating to an elected representative, and have a middling preference for delegating to a general population member with demographic characteristics of elected representatives. Panel B shows that these results are qualitatively and quantitatively highly similar in the intervention domain. We obtain highly similar results when we disaggregate these graphs by the specific preference profile to be aggregated or by the menu from which to construct choice sets (not shown). This similarity across domains and parametrizations suggests that respondents' delegation decisions reflect generalized confidence in the delegates rather than domain- or task-specific beliefs. In fact, we obtain this result in spite of the fact that beliefs about preference aggregation choices depend sensitively on the profile of preferences to be aggregated, as panel A of Figure \ref{delegationBeliefs} shows.


\begin{figure}[H]
\caption{Delegation preferences \label{delegationPreferences}}
\begin{center}
\begin{tabular}{cc}
A. Aggregation & B. Interventions \\
\includegraphics[width = 0.5\textwidth]{graphsNew/FigureF1A}
&
\includegraphics[width = 0.5\textwidth]{graphsNew/FigureF1B} \\
\end{tabular}
\end{center}
\footnotesize
\end{figure}


\begin{figure}[H]
\caption{Beliefs about delegates' choices \label{delegationBeliefs}}
\begin{center}
\begin{tabular}{cc}
A. Aggregation & B. Interventions \\
\includegraphics[width = 0.47\textwidth]{graphsNew/FigureF2A}
&
\includegraphics[width = 0.47\textwidth]{graphsNew/FigureF2B}
\\
\end{tabular}
\end{center}
\footnotesize
\end{figure}


\paragraph{Beliefs about delegates' choices.}\label{beliefSensitivity}


Panel A of Figure \ref{delegationBeliefs} shows subjects' beliefs about the option that members of each group of delegates would choose when aggregating preferences. Beliefs are strikingly similar across groups. Because of this lack of variation across delegation groups, beliefs are not systematically related to delegation preferences. This result does not simply reflect inattention. In fact, subjects' beliefs vary substantially with the preference profile. Moreover, subjects' beliefs vary substantially with their own scoring parameter. Rather, we conjecture that the pronounced unwillingness to delegate to politicians thus appears to reflect a general distrust rather than a factor specific to preference aggregation.


Panel B shows what choice sets respondents believe delegates would construct. As highlighted in Section \ref{sec:resultsBeliefs}, respondents believe politicians (and members of a sample of the general public with demographic characteristics that match politicians) would construct substantially different choice sets. Beliefs do not systematically differ, however, depending on whether or not the subjects' choices involve a front-end delay. Again, this effect is unlikely due to limited attention because beliefs vary substantially with the group of delegates.


\newpage
\setcounter{table}{0}
\setcounter{figure}{0}
\section{Additional survey \emph{Public 4}}\label{app:public4}


\subsection{Design}

Our additional survey \emph{Public 4} is an adaptation of \textit{Public 1} (see section \ref{surveyVersions}) that serves three main purposes. First, we seek to examine the possibility that subjects may draw inferences about the identity of the political foundations and the sources of that inference. Second, we seek to test the effect of randomizing the order of the preference aggregation and intervention tasks. Third, we seek to further understand the public's perceptions of the differences between elected representatives and average citizens, as well as their beliefs about why these differences arise.


\paragraph{Additional randomization and elicitations concerning inference}

To examine whether subjects attempt to infer which label represents which foundation, the information they use to draw that inference, and the effect of this inference on choice, we randomized the order in which the instructions present the foundations. To understand the design and results, consider that our design permutes the assignment of the letters to the social alternatives as defined by the preference profiles. Throughout, $A$ is majority-preferred in every profile, $B$ is the compromise option in every profile, and $C$ is the rank-dominated option. We use Greek letters $\alpha, \beta, \gamma$ to denote the labels presented to subjects. For instance,  the assignment of letters to social alternatives is $(A,B,C) = (\alpha, \beta, \gamma)$ for some subjects, $(C, B, A) = (\alpha, \beta, \gamma)$ for others, and yet other permutations for the remaining subjects. We call $(A, B, C)$ the \emph{de-permuted labels} and $(\alpha, \beta, \gamma)$ the permuted labels. We let $\lambda_i$ denote the permutation in place for subject $i$. If, for instance, for subject $i$, $\lambda_i(A) = \gamma$, then this subject sees the majority-preferred alternative labeled as the third letter of the alphabet.

Our randomization of the order in which the instructions present the foundations lets us test the hypothesis that subjects infer that letters $\alpha$, $\beta$, and $\gamma$ correspond to the foundation listed first, second, and third, respectively. Recall that this type of inference would appear as noise in the aggregation decisions rather than as a confound.

Two additional elicitations measure subjects' beliefs about the identity of the foundations. To prevent the possibility that these belief questions influence the way in which subjects make aggregation decisions, we ask them only once subjects have completed all aggregation decisions.

The first question presents the names of the foundations openly and asks the subject to choose one foundation to receive an additional EUR30, or to equally distribute the EUR30 across the three foundations. The subject knows that her choice will be carried out with a 5\% chance; the order in which the foundations are presented in this question is randomized.

If the subject has a preferred foundation (i.e., does not choose to equally distribute the EUR30), the next question asks which letter, $\alpha$, $\beta$, or $\gamma$ represents the foundation she preferred in the question just prior. The subject can also select ``I do not have a strong conjecture which letter represents this foundation.'' On the same page, subjects indicate their subjective level of certainty in their inference on a ten-point scale that ranges from ``unsure'' to ``very sure.''


\paragraph{Randomization of the order of the parts} A random half of the subjects in survey \emph{Public 4} first completed the preference aggregation part, the other half first completed the paternalism part.


\paragraph{Questions concerning the difference between politicians and the general public.\\}

We ask the following questions to understand the public's perceptions of the differences between elected representatives and average citizens, as well as their beliefs about why these differences arise.

\textbf{\textit{Questions concerning preference conflicts within individuals}}

\begin{itemize}

   \item \textit{\textbf{Q1:} If elected German politicians and average German citizens were to make the same kinds of decisions you made most recently, how would they decide? Please tell us whether you think politicians would make more or fewer options available than average citizens.}\\

    \begin{itemize}
        \item \textit{Politicians make significantly fewer options available}
        \item \textit{Politicians make somewhat fewer options available}
        \item \textit{Politicians make somewhat more options available}
        \item \textit{Politicians make significantly more options available}
    \end{itemize}

\vspace{1mm}
\textit{You said that politicians would make [significantly fewer / somewhat fewer / somewhat more / significantly more] options available. Why do you think that?}\\

\item \textit{\textbf{Q2:} Experience in politics leads politicians to intervene less in others’ decision options than average citizens.}
\textit{Answering options:}\\

 \begin{itemize}
        \item \ \textit{Likert Scale:  \hfill \ [Totally disagree (1), Totally agree (6)]}\hspace*{1cm}\quad\
    \end{itemize}
\vspace{1mm}
\item \textit{\textbf{Q3:} People who intervene less in others’ decisions are more likely to be elected to political office.}
\textit{Answering options:}\\

 \begin{itemize}
        \item \ \textit{Likert Scale: \hfill \ [Totally disagree (1), Totally agree (6)]}\hspace*{1cm}\quad\
    \end{itemize}


\vspace{1mm}
\item \textit{\textbf{Q4:} Can you think of any additional reasons? If so, please describe them.[Open text response]}
\end{itemize}

\pagebreak
\textbf{\textit{Questions concerning preference conflicts across individuals.}}


\textit{When you aggregate several people’s preferences over two different alternatives in a decision (similar to the decisions you made earlier), the following initial situation can arise:}
\begin{itemize}
\item \textit{Alternative 1 is neither strongly rejected nor strongly preferred by anyone.}
\item \textit{Alternative 2 is strongly preferred by a majority and strongly rejected by some.}
\end{itemize}
\textit{People who choose Alternative 1 prefer balancing interests. People who choose Alternative 2 prefer majority decisions.}

\textit{Now imagine that elected German politicians and average German citizens must make such a choice.}\\
\begin{itemize}
\vspace{1mm}
\item \textit{\textbf{Q5:} How do you think politicians and average citizens differ in this respect, if at all?}

 \begin{itemize}
        \item \textit{Politicians prefer majority decisions clearly more than average citizens}
        \item \textit{Politicians prefer majority decisions slightly more than average citizens}
        \item
\textit{Politicians prefer balancing interests slightly more than average citizens}
\item \textit{Politicians prefer balancing interests clearly more than average citizens}
    \end{itemize}


\vspace{1mm}
\item \textit{\textbf{Q6:} People who prefer balancing interests more than the average citizen are more likely to be elected to political office.}  \begin{itemize}
        \item \ \textit{Likert Scale:  \hfill \ [Totally disagree (1), Totally agree (6)]}\hspace*{1cm}\quad\
    \end{itemize}
    \vspace{1mm}
\item \textit{\textbf{Q7:} Experience in politics leads politicians to prefer balancing interests more than average citizens.}
\begin{itemize}
        \item \ \textit{Likert Scale:  \hfill \ [Totally disagree (1), Totally agree (6)]}\hspace*{1cm}\quad\
    \end{itemize}

\vspace{1mm}
\item \textit{\textbf{Q8:} Can you think of any additional reasons? If so, please describe them. [Open text response]}
\end{itemize}


\newpage

\subsection{Order effects}\label{app:orderEffects}

Table \ref{tab:orderEffectsPat} considers the effect of the order in which the two main parts of the survey are presented on interventions. For preference aggregation decisions, we do not find a significant difference in the distributions of any alternative being selected as a function of the order in which the parts were shown to subjects, as panel A shows. Panel B does show the effects of order on intervention decisions. Consistent with the hypothesis that subjects' attention decreases as the experiment progresses, we observe a shift towards a more uniform distribution of available options, as one would expect if fatigue makes subjects decide more randomly which options to include or exclude from the choice set they construct.


\begin{table}[htb]
\caption{Order effects}
\label{tab:orderEffectsPat}
\begin{center}
\textbf{A. Aggregation} \\
\begin{tabular}{lccc}
\toprule
& \multicolumn{2}{c}{Part shown first} & Difference \\
\cmidrule(r){2-3}
  & Interventions & Aggregation  \\
 \midrule
Option & \\
\quad A &     0.289 &     0.267 &    -0.022  \\  & (    0.016) & (    0.016) & (    0.022) \\ \quad B &     0.441 &     0.468 &     0.027  \\  & (    0.013) & (    0.016) & (    0.021) \\ \quad C &     0.271 &     0.265 &    -0.005  \\  & (    0.016) & (    0.016) & (    0.023) \\ \\
\bottomrule
\end{tabular}
\medskip

\textbf{B. Interventions} \\
\begin{tabular}{lccc}
\toprule
& \multicolumn{2}{c}{Part shown first} & Difference \\
\cmidrule(r){2-3}
  & Interventions & Aggregation  \\
 \midrule
Option & \\
\quad A &     0.748 &     0.660 &    -0.088** \\  & (    0.021) & (    0.023) & (    0.031) \\ \quad B &     0.835 &     0.807 &    -0.028  \\  & (    0.016) & (    0.019) & (    0.025) \\ \quad C &     0.366 &     0.440 &     0.074** \\  & (    0.022) & (    0.024) & (    0.033) \\ \\
\bottomrule
\end{tabular}
\end{center}
\textbf{\textit{Notes}}: Panel A lists the distribution of choices in aggregation decisions in the survey \emph{Public 4} separately for individuals who first completed the intervention decisions and for those who first completed the aggregation decisions. Panel B lists the fraction of subjects that made any given alternative available. These fractions do not sum to 1 because respondents could make multiple alternatives available.
\end{table}


\subsection{Inference about the identity of the foundations}\label{app:inference}

Recall that inference about the identity of the foundations can stem from two sources. Only the first of these -- choice based on inference about the identity of the foundations drawn from the preference profiles---can confound our main results. We analyze this possibility in subsection \ref{app:inferenceProfiles}. The second of these---choice-based inference about the identity of the foundations from the presentation of the foundations in the instructions---acts as noise because of the permutation of the assignment of labels to foundations. We study this effect in subsection \ref{app:inferenceOrder}.


\subsubsection{Inference from preference profiles}\label{app:inferenceProfiles}


If participants were able to infer their preferred option from the information in the preference profiles, then the random assignment of labels to social alternatives should predict their inference.

Let $\lambda_i(B)$ denote the letter assigned to the compromise alternative $B$ in subject $i$'s survey.

Bund der Steuerzahler (henceforth: Bund) is less clearly associated with any side of the political spectrum than our other two foundations. In other words, it is the centrist foundation. If participants associate the compromise option $B$ with Bund, then we should observe that they believe Bund is associated with label $\lambda_i(B)$. We only observe what label a subject associates with Bund if Bund is their preferred foundation (29.5\% of subjects).


We have the pertinent data for subjects whose preferred foundation is Bund and who make a guess about the letter representing that foundation. Of these subjects, 31.27\% of  believe Bund is associated with $\lambda_i(B)$, the letter used for the compromise alternative $B$, compared to 40.63\% who associate it with the letter of the majority-preferred alternative $A$, $\lambda_i(A)$ and 28.36\% who associate it with the letter used for the rank-dominated alternative $\lambda_i(C)$. By contrast, they associate it with the (intrinsically meaningless) labels $\alpha, \beta, \gamma$ with probabilities 30.18\%, 45.82\%, and 24.00\%, respectively. This evidence is inconsistent with the hypothesis that subjects' choices are strongly driven by inferences about the foundations' identities from the preference profiles.

We can perform a parallel analysis for subjects who prefer foundations other than Bund. For these subjects, we do not observe what letter they believe is associated with Bund, but we see whether they believe their preferred foundation is associated with the letter representing the compromise option disproportionately rarely. On the contrary, subjects whose preferred foundation is Hans Böckler Stiftung believe their preferred foundation to be associated with the letters representing the majority, compromise, and rank-dominated alternatives, $\lambda_i(A)$, $\lambda_i(B)$, and $\lambda_i(C)$, with probabilities 34.84\%, 39.35\%, and 25.81\%. Similarly, among subjects who prefer the Ludwig Erhard Stiftung, the corresponding numbers are 41.32\%, 33.53\%, and 25.15\%. In neither case do subjects believe their preferred foundation to be associated with the compromise option disproportionately rarely.

These data stem from general population subjects, not from politicians. However, if politicians engaged in attempts to identify the identity of their preferred foundation from the preference profiles to a significantly larger extent, we should observe much bigger differences between the aggregation criteria we infer from politicians and those we infer from general population subjects.


\subsubsection{Inference from information other than preference profiles}\label{app:inferenceOrder}

We also test whether subjects use the order in which the foundations were presented in the initial instructions as an indication of the label associated with their preferred foundation, and whether they make aggregation choices based on this inference.

\begin{table}
\caption{Inference about identity of the foundations from the presentation order}
\label{tab:inference}
\begin{center}
\setlength{\tabcolsep}{6pt}
\begin{tabular}{lccc}
\toprule
Preferred foundation presented in position& 1 & 2 & 3 \\
\midrule
A. Belief that preferred foundation has label \\
\quad $\alpha$ & 0.516 & 0.160 & 0.110 \\ \quad $\beta$ & 0.176 & 0.539 & 0.176 \\ \quad $\gamma$ & 0.080 & 0.078 & 0.435 \\ \quad None & 0.228 & 0.223 & 0.278 \\  \\
B. Label of chosen foundation \\
\quad $\alpha$ & 0.344 & 0.249 & 0.237 \\ \quad $\beta$ & 0.447 & 0.517 & 0.403 \\ \quad $\gamma$ & 0.209 & 0.234 & 0.361 \\  \\
\bottomrule
\end{tabular}
\end{center}
\end{table}


Panel A of Table \ref{tab:inference} shows that when asked about the identity of the foundations, they mainly take the order in which the foundations were presented in the beginning as an indication. This does not mean that they made decisions based on this inference---the question itself may cause subjects to think about the identity of the foundations.

Panel B shows the label of the foundation subjects chose as a function of the position in which their preferred foundation was presented in the instructions. When their preferred foundation is presented in the first position, then subjects choose the alternative with label $\alpha$ in 34.4\% of cases---less often than they choose the alternative labeled $\beta$---even though 51.6\% of them believe it to be associated with label $\alpha$, as panel A shows. Similarly, when the preferred foundation is presented in the third position, subjects chose the alternative with label $\gamma$ in 36.1\% of cases even though 43.5\% of them believe it to be associated with that label. In contrast, when the preferred foundation is presented in the middle position, 51.7\% choose the alternative labeled $\beta$. Hence, at least in some cases, subjects' choices between alternatives are consistent with the hypothesis that subjects infer the label of their preferred foundation from the order in which it is presented in the beginning and then select the alternative with the corresponding label.

To assess the magnitude of this effect on choice, we regress an indicator for whether a subject chooses the foundation labeled $\alpha$ on an indicator for whether the subject believes that label represents her preferred charity. We perform parallel regressions for the remaining labels. Panel A of Table \ref{tab:inferenceRegressions} shows the results.

Because the OLS regression could represent endogeneity, as would arise, for instance, if the reported beliefs about the identity of the preferred foundation were an ex-post rationalization of the choices made earlier, we also perform instrumental variables estimations, instrumenting the belief that the preferred foundation is labeled $\alpha$ with an indicator for whether the subjects' preferred foundation was presented on the very left. We perform parallel regressions for the remaining labels. Panel B shows the results.

Hence, in our general population sample, the inference from the position in which the foundation was presented on the choice of the foundation is relatively pronounced. Crucially, however, this behavior does not confound our main estimation results because the randomization of labels to foundations makes it appear as noise.


\begin{table}
\caption{Choice based on inference about identity of the foundations from the presentation order}
\label{tab:inferenceRegressions}
\begin{center}
\begin{tabular}{lccc}
\toprule
Foundation with given label chosen & $\alpha$ & $\beta$ & $\gamma$ \\
\midrule
\textbf{A. OLS} \\
Belief that preferred foundation has label \\

                    &               &               &               \\

\quad $\alpha$      &       0.113***&               &               \\
                    &     (0.026)   &               &               \\
\quad $\beta$       &               &       0.098***&               \\
                    &               &     (0.021)   &               \\
\quad $\gamma$      &               &               &       0.193***\\
                    &               &               &     (0.031)   \\
Constant               &       0.254***&       0.445***&       0.238***\\
                    &     (0.009)   &     (0.009)   &     (0.009)   \\


 \\
\textbf{B. IV} \\
Belief that preferred foundation has label \\

                    &               &               &               \\

\quad $\alpha$      &       0.203***&               &               \\
                    &     (0.051)   &               &               \\
\quad $\beta$       &               &       0.155***&               \\
                    &               &     (0.044)   &               \\
\quad $\gamma$      &               &               &       0.317***\\
                    &               &               &     (0.060)   \\
Constant               &       0.239***&       0.434***&       0.223***\\
                    &     (0.012)   &     (0.012)   &     (0.010)   \\


 \\
\bottomrule
\end{tabular}
\end{center}
\textbf{\textit{Notes}}: Standard errors in parentheses, clustered by subject.
\end{table}


\newpage
\section{Survey}\label{instructions}

\includepdf[pages=-]{versionForScreenshots_professionaltranslation}


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