\documentclass[smallheadings]{article}


\usepackage[latin9]{inputenc}
\usepackage[margin=1in]{geometry}
\geometry{verbose}
\usepackage{color}
\usepackage{array}
\usepackage{float}
\usepackage{booktabs}
\usepackage{calc}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{amsthm}
\usepackage{bm}

\usepackage{graphicx}
\usepackage{setspace}
\usepackage[authoryear]{natbib}
\setstretch{1.5}
\usepackage[colorlinks = true, citecolor = blue, linkcolor = blue, urlcolor  = blue,]{hyperref}

\makeatletter
    \usepackage{paralist}


\DeclareTextSymbolDefault{\textquotedbl}{T1}

\providecommand{\\}{\\}


\setlength{\parskip}{0cm}
    \setlength{\parindent}{1em}

    \usepackage[compact]{titlesec}
    \titlespacing{\section}{0pt}{2ex}{2ex}
    \titlespacing{\subsection}{0pt}{2ex}{1ex}
    \titlespacing{\subsubsection}{0pt}{1ex}{0ex}


\usepackage{tabularx}
\usepackage{adjustbox}
\usepackage{array}


\usepackage{afterpage}


\usepackage{soul}


\usepackage{mathrsfs}
\usepackage{mdwlist}
\usepackage{eucal}
\usepackage{marvosym}
\usepackage{amsthm}
\usepackage{epstopdf}
\usepackage{dsfont}
\usepackage{bm}
\usepackage{enumerate}
\usepackage{multirow}
\usepackage{pdfpages}
\usepackage{pdflscape}
\usepackage{rotating}
\usepackage[labelfont=bf]{caption}


\usepackage{minitoc}
\usepackage[toc,page,header]{appendix}


\usepackage{multibib}
\newcites{appendix}{References}
\renewcommand{\thepart}{}
\renewcommand{\partname}{}

\renewcommand{\thesection}{\Roman{section}}
\renewcommand{\thesubsection}{\Alph{subsection}}
\renewcommand{\thesubsubsection}{\arabic{subsubsection}}
\makeatletter    \def\p@subsection{\thesection.}   \makeatother
\makeatletter    \def\p@subsubsection{\thesection.\thesubsection.}   \makeatother


\newcommand{\tabitem}{~~\llap{\textbullet}~~}

\usepackage{makecell}

\definecolor{darkgreen}{rgb}{0.1, 0.45, 0.1}

\newcommand{\red}{\color{red}}
\newcommand{\black}{\color{black}}
\newcommand{\green}{\color{darkgreen}}


\newcommand{\emptybox}[2][\textwidth]{
  \begingroup
  \setlength{\fboxsep}{-\fboxrule}
  \noindent\framebox[#1]{\rule{0pt}{#2}}
  \endgroup
}

\renewcommand{\theadalign}{tc}
\renewcommand{\theadfont}{}
\renewcommand{\theadgape}{\Gape[4pt]}
\renewcommand{\cellgape}{\Gape[4pt]}

\usepackage{array}
\newcolumntype{L}[1]{>{\raggedright\let\newline\\\arraybackslash\hspace{0pt}}m{#1}}
\newcolumntype{C}[1]{>{\centering\let\newline\\\arraybackslash\hspace{0pt}}m{#1}}
\newcolumntype{R}[1]{>{\raggedleft\let\newline\\\arraybackslash\hspace{0pt}}m{#1}}


\newcommand{\setrow}[1]{\gdef\rowmac{#1}#1\ignorespaces}
\newcommand{\clearrow}{\global\let\rowmac\relax}
\clearrow


\clubpenalty = 1000
\widowpenalty = 1000

\DeclareMathOperator{\plim}{plim}

\renewcommand{\theenumi}{(\roman{enumi})}
\renewcommand{\labelenumi}{\theenumi}
\newcommand{\bmath}{\begin{eqnarray}}
\newcommand{\emath}{\end{eqnarray}}
\newcommand{\bmathnn}{\begin{eqnarray*}}
\newcommand{\emathnn}{\end{eqnarray*}}
\newcommand{\bi}{\begin{itemize}}
\newcommand{\ei}{\end{itemize}}
\newcommand{\I}{\mathcal{I}}
\newcommand{\var}{\mathrm{var}}
\newcommand{\logit}{\mathrm{logit}}
\newcommand{\abs}{\mathrm{abs}}
\newcommand{\sgn}{\mathrm{sgn}}
\newcommand{\del}{\partial}
\newcommand{\w}{\omega}

\usepackage{bbold}
\newcommand{\one}{\mathbb{1}}

\renewcommand{\footnotesize}{\scriptsize\selectfont}


\setcounter{MaxMatrixCols}{10}

\theoremstyle{plain}

\newtheorem{proposition}{Proposition}\newtheorem{prediction}{Prediction}\newtheorem*{example}{Example}\setcounter{section}{0}
\newtheorem{theorem}{Theorem}\newtheorem{lemma}{Lemma}\newtheorem{corollary}{Corollary}\newtheorem{result}{Result}\newtheorem{hypothesis}{Hypothesis}\theoremstyle{definition}
\newtheorem*{definition}{Definition}\newtheorem{assumption}{Assumption}\newtheorem*{remark}{Remark}\theoremstyle{plain}
\DeclareMathOperator*{\argmax}{argmax}
\DeclareMathOperator*{\argmin}{argmin}
\DeclareMathOperator{\core}{Core}
\DeclareMathOperator{\cov}{cov}


\usepackage{xpatch}

\xpatchcmd{\@thm}{\thm@headpunct{.}}{\thm@headpunct{}}{}{}


\makeatother

\begin{document}
\title{\textbf{What Motivates Paternalism?}\\
 \textbf{ An Experimental Study} \\
 }


\author{Sandro Ambuehl, B. Douglas Bernheim, and Axel Ockenfels\thanks{\baselineskip = 0.6\normalbaselineskip Ambuehl: University of Zurich,
Department of Economics, Bl\"uemlisalpstrasse 10, 8006 Z\"urich, Switzerland,
sandro.ambuehl@econ.uzh.ch. Bernheim: Stanford University, Department
of Economics, 579 Serra Mall, Stanford, CA, 94305, USA, bernheim@stanford.edu.
Ockenfels: University of Cologne, Department of Economics, Universit\"atsstrasse
22a, 50923 Cologne, Germany, ockenfels@uni-koeln.de. This paper previously
circulated under the title ``Projective Paternalism.'' We thank
the editor, Stefano DellaVigna, as well as three anonymous referees,
Joshua Knobe, Yoram Halevy, Armin Falk, Glenn W. Harrison, Kiryl Khalmetski,
Frank Schilbach, Cass Sunstein, Dmitry Taubinsky, Stefan Trautmann,
and seminar and conference audiences in Berkeley, Copenhagen, Cork,
Cornell, Heidelberg, Innsbruck, Nijmegen, NYU, Stanford, Toronto,
and Zurich for helpful comments and discussions. Max Rainer Pascal
Grossmann and Yero Samuel Ndiaye provided excellent research assistance.
Ambuehl gratefully acknowledges support through a University of Toronto
Connaught New Researcher Award and the University of Toronto Scarborough
Wynne and Bertil Plumptre Fellowship. Parts of this paper were written
while Ambuehl visited the briq Institute in Bonn. This research has
been approved by the University of Toronto Research Ethics Board (protocol
10279). Ambuehl and Bernheim gratefully acknowledge funding from the
Alfred P. Sloan foundation (grant number G-2017-9017). Ambuehl and
Ockenfels gratefully acknowledge funding from the European Research
Council (ERC) under the European Union's Horizon 2020 research and
innovation programme (grant agreement No 741409); the results reflect
only the authors' views; the ERC is not responsible for any use that
may be made of the information it contains. Ockenfels gratefully acknowledges
funding by the German Research Foundation (DFG) under Germany's Excellence
Strategy (EXC 2126/1-390838866).}}

\maketitle
\thispagestyle{empty}
\begin{abstract}
We study experimentally when, why, and how people intervene in others'
choices. Choice Architects (CAs) construct opportunity sets containing
bundles of time-indexed payments for Choosers. CAs frequently prevent
impatient choices despite opportunities to provide advice, believing
Choosers benefit. They violate common behavioral welfare criteria
by removing impatient options even when all payoffs are delayed. CAs
intervene not by removing options they wish they could resist when
choosing for themselves (\emph{mistakes-projective paternalism}),
but rather as if they seek to align others' choices with their own
aspirations (\emph{ideals-projective paternalism}). Laboratory choices
predict subjects' support for actual paternalistic policies.
\end{abstract}
\vspace{0cm}


\doparttoc
\faketableofcontents

\newpage{}


\label{intro} \setcounter{page}{1}

{Normative} discussions of paternalism have featured in economics,
philosophy, and public policy for centuries \citep{Locke,Mill,ThalerSunstein}.
A wide range of government regulations, such as retirement savings
mandates, restrictions on payday loans and investment products, various
forms of consumer protection, the criminalization of suicide, and
legal doctrines concerning undue inducement and unconscionability
all address paternalistic concerns \citep{DworkinBook,Zamir}. Paternalistically
motivated social programs play a large role in the U.S. economy \citep{MulliganPhilipson,MoffittJEP,currieGahvari},
and in some instances even the business models of private companies
are paternalistically motivated.\footnote{The Vanguard Group, for instance, argues that financial advisors should
attempt to help clients by ``providing discipline and reason to clients
who are often undisciplined and emotional'' and that they ``can
act as emotional circuit breakers ... by circumventing their clients'
tendencies'' \citep{Vanguard.AdvisorsAlpha}. } Despite the prevalence of paternalism, a \emph{positive} investigation
of the phenomenon is largely lacking. Significantly, paternalistic
decision making often falls to voters \citep{Faravellietal.}, low-level
government officials \citep{MoffittEJ}, and managers of private firms,
rather than to experts, omniscient benevolent planners \citep{Besley.MeritGoods},
or despotic autocrats \citep{Glaeser}. A large body of research catalogues
the fallibility of human judgment when people choose for themselves
\citep{ThinkingFastAndSlow}. It is reasonable to assume that normative
judgments that guide paternalism are also susceptible to systematic
confusion or bias \citep{RizzoWhitman}.


This paper studies experimentally when, why, and how people act paternalistically.
These questions are difficult to address in naturally occurring contexts
because real-world policies with paternalistic elements generally
implicate non-paternalistic concerns, such as externalities, and in
any event people usually disagree about their efficacy. Conducting
a laboratory experiment allows us to remove extraneous factors and
study paternalistic behavior in isolation. In order to demonstrate
external validity, we show that our main results extend to subjects'
assessments of real-world paternalistic policies, and that these assessments
are directly related to subjects' decisions within the experiment.

Subjects in the role of Choice Architects construct choice sets that
determine the opportunities available to others in the role of Choosers.
Each choice option consists of two payments, one received the day
of the experiment (``sooner'') and the other received half a year
later (``later''). Impatience is costly: larger earlier payments
are associated with smaller total payments. We employ the intertemporal
choice domain because of its central importance across all of economics,
because of \emph{ex ante} reasons to believe that certain choices
would meet with disapproval, and because it allows us to examine normative
judgments that feature prominently in applications of behavioral welfare
economics, including in a substantial and rapidly expanding literature
\citep[reviewed in][]{BernheimTaubinsky} that argues for a wide range
of paternalistic policies.

In the first part of our analysis, we show that Choice Architects
frequently choose to restrict others' choice options, then we establish
several fact patterns that shed light on their motivations. Significantly,
subjects frequently withhold options from Choosers despite ample opportunities
to provide advice. The typical intervention requires the Chooser to
exercise a minimum level of patience. For instance, when the available
options include receiving \EURdig0 sooner and \EURdig15 later, 15\%
of Choice Architects withhold the opportunity to receive \EURdig2
sooner and \EURdig9 later, and 31\% withhold the opportunity to receive
\EURdig4 sooner and \EURdig2 later. Furthermore, Choice Architects
are more likely to withhold options when the relative price of current
consumption is greater. Advice concerning the options that remain
in Choosers' opportunity sets follows similar patterns. Thus, Choice
Architects suppress impatience either by preventing it or by advising
against it.

Next we ask whether these interventions are paternalistic, in the
sense that Choice Architects believe they improve the Choosers' well-being
\citep{Dworkin}. According to both incentive-compatible and non-incentivized
measures, Choice Architects by and large believe the restrictions
they impose are helpful. Furthermore, those who impose more severe
restrictions believe restrictions are more beneficial. We rule out
alternative motives, such as the possibility that subjects intervene
simply because they prefer to do something rather than remain idle,
or because they enjoy exercising control over others.

The next step in our analysis is to investigate whether Choice Architects'
interventions conform to the types of normative judgments that often
appear in applications of behavioral welfare economics. One common
position maintains that the availability of immediate rewards, coupled
with a tendency to place ``too much'' weight on near-term experiences
(``present bias''), generate excessive impatience \citep[e.g.][]{oDonoghueRabin,oDonoghueRabin.SinTaxes,GruberKoszegi.AddictionRational,GruberKoszegi.TaxIncidence,DellaVignaMalmendier.ContractDesign}.
The findings summarized above are potentially consistent with the
hypothesis that Choice Architects adopt that perspective. Is the alleviation
of perceived present bias in fact a central objective of the observed
interventions? If it is, removing the lure of immediacy by delaying
the receipt of all payments should substantially reduce interventions.
Yet we find that people impose slightly \emph{more} patience in otherwise
equivalent decision problems with ``front-end delay.'' Thus, their
behavior indicates that they generally disapprove of impatience, not
that they are concerned about present bias.

Another hypothesis of interest is that paternalists make decisions
for others based exclusively on their own assessments of right and
wrong without regard to the preferences or subjective experiences
of the affected individuals. We find, on the contrary, that even non-libertarians
respect Choosers' preferences to a degree, but are unwilling to tolerate
sufficiently impatient choices. In particular, Choice Architects respond
to information about Choosers' preferences in an accommodating direction.
For instance, they impose substantially more patience on Choosers
who describe themselves as impatient and unhappy than on those who
claim to be content with their impatience.

The second part of our investigation asks how Choice Architects decide
which options are good for others and which are bad.
Their sensitivity to Choosers' inclinations and subjective experience
implies that these considerations play important roles in Choice Architects'
evaluations. But when their information about the Chooser is incomplete,
how do they arrive at their evaluations? In \emph{The Theory of Moral
Sentiments}, Adam Smith argued that such inferences follow from self-examination:
\textit{\emph{``As we have no immediate experience of what other
men feel, we can form no idea of the manner in which they are affected,
but by conceiving what we ourselves should feel in the like situation}}\textit{.''}
In this spirit, we identify two distinct types of departures from
the benchmark case of perfect knowledge concerning others' well-being
and biases, distinguished according to whether paternalists reason
about others based on their own mistakes, or based on their own preferences.
A \emph{mistakes-projective paternalist} assumes others tend to share
her susceptibility to error. She behaves as if she tries to help others
avoid choices she herself would like to reject, but chooses nevertheless.
We demonstrate that this inclination generates a \emph{negative correlation}
between the choices she makes for herself and the restrictions she
imposes on others. In contrast, an \emph{ideals-projective paternalist}
behaves as if she assumes her own preferences are relevant for others,
either because she thinks they tend to share her values, or because
she simply believes her perspective is valid and theirs are not. Ideals-projective
paternalism generates a \emph{positive correlation} between the choices
paternalists make for themselves and the restrictions they impose
on others.

We find strong support for ideals-projective paternalism. More patient
Choice Architects impose greater patience on others, and this pattern
reflects their judgments about what the Choosers ought to do, rather
than a greater inclination to intervene. They also believe more strongly
that restrictions enhance the Choosers' well-being.

Next we ask whether paternalists simply express their welfare judgments
based on accurate beliefs about others, or whether paternalistic interventions
are susceptible to systematic cognitive biases. To answer this question,
we focus on Choice Architects' beliefs about the choices unrestricted
Choosers would make. We examine how those beliefs relate not only
to the restrictions Choice Architects impose, but also to their own
preferences. We find that interventions are closely related to beliefs
about Choosers' inclinations. Moreover, those beliefs are systematically
biased: subjects assume (incorrectly) that the choices of others tend
to resemble their own (a \emph{false consensus effect}, \citealp{Rossetal.FalseConsensus}).
As a result, despite the tendency for more patient Choice Architects
to impose greater patience, patient and impatient Choice Architects
expect their mandates to bind with about the same frequency. We conclude
that interventions are systematically misguided, even according to
the Choice Architects' aims.

The third part of our analysis explores the external validity of our
findings. We elicit subjects' support for imposing sin taxes and for
regulation of high-interest, short-term lending in a neighboring country.
We also measure subjects' own consumption of the targeted products.
We find that real-world preferences for paternalistic policies that
only impact others are strongly correlated with Choice Architects'
decisions in the behavioral portion of our experiment. Additionally,
the relationships between policy preferences and own consumption are
consistent with ideals-projective paternalism. For instance, lighter
drinkers express significantly more support for an increase in alcohol
taxes in another jurisdiction.

Our work is related to a small empirical literature on paternalistic
behavior. The closest parallels are \citet{Uhl} and \citet{KrawczykWozny}.
In both studies, subjects choose between one of two options for themselves
and decide whether to eliminate one of the options for others.\footnote{In \citet{KrawczykWozny}, the options are two lunch items, one healthy,
the other unhealthy. In \citet{Uhl}, the options are whether to make
a second choice (i.e., choosing the point in time at which to collect
a rising payment) either in advance or in the moment. The empirical
literature on paternalistic behavior also includes \citet{Jacobsoetal.,Gangadharanetal.,Lusketal.,Epleyetal.}.} While these studies examine the proclivity to intervene, as well
as correlations between chosen interventions and the options subjects
select for themselves, they do so in settings that preclude the dispensation
of advice. Accordingly, it is impossible to tell whether the subjects
see intervention as intrinsically desirable, or merely as the only
feasible method of expressing their opinions. Furthermore, the close
juxtaposition of essentially identical decisions (for the subjects
themselves and for others) may well introduce spurious correlation
through anchoring and/or a demand for consistency, which undermines
inferences concerning the connection between own-preferences and interventions.
Setting these important design issues aside, these papers have more
limited objectives than ours, and consequently do not address the
other important questions that structure our analysis: whether interventions
are benevolently motivated; whether paternalists care about and respond
to information concerning the preferences of the impacted parties;
whether they exhibit less inclination to intervene when those parties
make their choices under conditions that are commonly thought to mitigate
decision biases; whether paternalistic interventions implicate systematic
cognitive biases; and whether paternalism in the lab relates to real-world
policy preferences. Likewise, they do not address the distinction,
central to this paper, between ideals-projective and mistakes-projective
paternalism. In a more recent paper, \citet{Bartlingetal.} focus
on decisions to intervene paternalistically by removing dominated
options. They largely abstract from motivations involving disapproval
of others' preferences, which is a central feature of the current
paper.

Several other lines of work bear on the empirical analysis of paternalism.
One examines the tendency for professional advisors to steer their
clients toward the same options they choose for themselves \citep{Foersteretal.,Linnainmaaetal.}
-- a possible reflection of ideals-projective paternalism. A second
studies how people feel about being in situations where others can
influence or constrain their choices \citep{HerzAuthority,HerzECMA,Katariaetal.,LubbeckeSchnedler,AckfeldOckenfels}.\footnote{A larger theoretical literature relating to paternalism includes \citet{SaintPaul,CarlinetGervaisManso,BisinLizzeriYariv,Altmannetal.,Laibson.PrivatePaternalism}.}
A third studies social disapproval of ostensibly \emph{repugnant transactions}
\citep{Roth.repugnance}, such as paid organ donation \citep{BasuJEP,Basu.Coercion,LeiderRoth,Eliasetal.prostitution,Eliasetal.kidneys,Eliasetal.EfficiencyMoralityTradeoffs,JMP,AmbuehlOckenfelsAERPP,Clemens,ExleyKesslerMoneyTime}.\footnote{\citet{Ambuehletal.Repugnance} sketch a model with agents who exhibit
a form of ideals-projective paternalism that explains why the introduction
of monetary incentives can cause people to judge transactions as unethical
(e.g. paid kidney donation), even though they approve of the same
transaction in the absence of such incentives (e.g. in-kind kidney
exchange).} A fourth explores how people make surrogate choices for others in
settings where the surrogate cannot leave the affected individual
with flexibility \citep[see][for a review]{ifcherZarghamee}. \hide
Finally, the literature on social preferences studies the general
question of how people make decisions that affect others \citep[see][for reviews]{FehrFischbacherReview,CooperKagel.Handbook}.
\unhide

The empirical study of paternalism connects with various other branches
of the literature. The literature on \emph{libertarian paternalism}
argues that authorities can use \emph{nudges}, rather than coercion,
to adjust behavior in directions the authorities (or their policy
analysts) deem beneficial (\citealp{ThalerSunstein}; \citealp[see][for reviews]{LoewensteinHaisley,Benartzietal.InvestMoreInNudging}).
Our analysis sheds light on the types of paternalistic judgments that
motivate nudges, and it identifies common biases that nudge designers
could in principle learn to avoid.
We contribute more broadly to a literature in cognitive science on
moral heuristics \citep[see][for reviews]{Sunstein.MoralHeuristics,Gigerenzer.MoralHeuristics}.
There is also a connection to a literature on \emph{projection bias}
\citep{vanBovenetal.,Loewensteinetal.ProjectionBias,Madarasz.InformationProjection},
in that we exhibit a manifestation of this phenomenon in the normative
domain. Finally, a collection of recent empirical studies evaluate
particular paternalistic policies by deploying normative principles
from behavioral welfare economics; see \citet{BernheimTaubinsky}
for a review, and \citet{BernheimRangelWelfare} or \citet{BernheimGoodBad}
for foundations.

The remainder of this paper proceeds as follows. Section \ref{design}
outlines our experimental design. Section \ref{anatomy} demonstrates
that Choice Architects frequently force others to choose patiently,
and that they believe these interventions benefit the Choosers. It
also explores the responsiveness of paternalistic interventions to
the introduction of front-end delay, and of information about Choosers'
preferences. In Section \ref{determinants}, we investigate hypotheses
about the formation of Choice Architects' views concerning Choosers.
We begin by formalizing the paternalists' decision problem, introducing
the concepts of ideals-projective and mistakes-projective paternalism,
and deriving their implications. After documenting patterns that point
to ideals-projective paternalism, we explore the prevalence and implications
of mistaken beliefs about the selections Choosers would make in absence
of interventions. Section \ref{realWorldPolicy} demonstrates that
laboratory choices predict support for real-world paternalistic policies,
and it documents ideals-projective paternalism regarding those policies.
Finally, Section \ref{conclusions} outlines directions for further
research.

\section{Experiment Design}

\label{design}

We begin by explaining the design of our experiment. Section \ref{paradigm}
describes the main types of decision problems we use to investigate
paternalism. Section \ref{treatments} provides an overview of the
structure of the experiment. The remaining subsections then present
details concerning incentivization (Section \ref{incentives}), the
Choosers' decisions (Section \ref{choosers}), and implementation
(Section \ref{implementation}). For easier readability, this section
condenses the presentation of our design. A comprehensive description
appears in Appendix \ref{experimentFullDetails}.

\subsection{Main elements of the experiment}

\label{paradigm}

Each subject in our experiment is either a Choice Architect or a Chooser.
Our interest is in the Choice Architects, who determine the set of
options that will be available to Choosers. The experiment's main
elements, described below, shed light on the following four questions:
(i) How do Choice Architects construct opportunity sets for Choosers?
(ii) Do Choice Architects believe that withholding options helps or
hurts Choosers? (iii) What options do Choice Architects select for
themselves? (iv) What do they believe Choosers would select absent
restrictions? We discuss each of these elements in turn.


\paragraph{Construction of opportunity sets}

The Choice Architect constructs the Chooser's opportunity set from
a menu of three options, as illustrated in Panel A of Figure \ref{mainTreatmentLayout}.
Each option is a bundle of two monetary payments, one received the
day of the experiment, the other received with a half-year delay.
We design the options so that a Chooser can increase his present payment
only by accepting a smaller amount overall. The Choice Architect must
\emph{actively} decide whether the opportunity set will include each
option; neither inclusion nor exclusion is a default. The sole restriction
is that each opportunity set must include at least one option. We
emphasize to subjects that there are no right or wrong answers, and
that they should construct opportunity sets based on their genuine
views.


\begin{figure}[htb!]
\vspace{-0.3cm}
\noindent \caption{\textbf{Decision screens for the Choice Architect in the Main condition.}
\label{mainTreatmentLayout}}

\setstretch{1}
\begin{centering}
\medskip{}
\textbf{A. Constructing the Chooser's opportunity set.} \medskip{}
 \\
\par\end{centering}
\noindent \begin{centering}
\fbox{\parbox[c]{0.8\textwidth}{
\centering \textsf{\small{}Which of the choice options will be available
to the future participant?}{\small{} }\\
{\small{} }\textsf{\small{}(You must make at least one option available)}{\small{}}\\
{\small{} \includegraphics[width=0.65\textwidth]{publicationFigures/basicTreatmentConcrete}}\\
{\small{} \medskip{}
}\textsf{\small{}If you have a message for the future participant,
enter it here:}{\small{}}\\
{\small{} \emptybox[0.65\textwidth]{1cm}}
}}
\par\end{centering}
\centering{}\bigskip{}
 \textbf{B. Beliefs about effect on Chooser's well-being.}\medskip{}
 \\

\fbox{\parbox[c]{0.8\textwidth}{
\centering

\includegraphics[width=0.55\textwidth]{publicationFigures/welfareSelfStated}\\
 \includegraphics[width=0.6\textwidth]{publicationFigures/cvElicitation} \\

}}
\end{figure}


We study the characteristics of the opportunity sets Choice Architects
construct. Three design features limit the possible explanations for
imposing restrictions.
 First, Choice Architects can advise Choosers. Specifically, in each
round, the Choice Architect can write an unrestricted message to the
Chooser, which the Chooser observes before making his decision. The
Choice Architect can also convey disapproval of any option by clicking
a button. In that case, the Chooser sees a red asterisk next to the
corresponding option, accompanied by a statement that a previous participant
advises against it. The existence of these opportunities is central
to our objective. Without them, Choice Architects might remove options
simply because they have no other way to convey opinions and advice,
rather than because they perceive a genuine need for restrictions.
Accordingly, this feature allows us to detect situations in which
Choice Architects feel they cannot trust informed Choosers to make
good decisions (an expression of paternalism), rather than situations
in which Choice Architects merely believe they have better information
than Choosers (a state of affairs that does not necessarily implicate
paternalism).

Second, we ensure that Choice Architects can only influence Choosers'
outcomes, not their decision processes. In particular, the Choice
Architect cannot save the Chooser time or effort, or spare him the
ordeal of resisting temptation. The reason is that the Chooser ranks
all three of the options that might be in his opportunity set, without
knowing which are actually available or how their availability is
determined. He then receives the option he has ranked most highly
among those that are actually available. Choice Architects are aware
of this procedure.

Third, the Choice Architect's material payoff is independent of her
decisions concerning the Chooser's opportunity sets. Accordingly,
the Choice Architect's only plausible motivations involve her feelings
about the Chooser's autonomy or consequences. Here, our objective
is to avoid confounding paternalistic motivations with conflicts of
interest. This feature mirrors an important property of many paternalistic
decisions. For example, members of Internal Review Boards charged
with protecting human subjects are usually precluded from having personal
interests in any research that is subject to their oversight.

Three additional features of this setting merit emphasis. First, our
experiment focuses on \emph{hard paternalism} (restricting opportunity
sets) rather than \emph{soft paternalism} (influencing choice without
changing opportunity sets). Soft paternalism introduces other potentially
confounding considerations. For example, the attractiveness of employing
a nudge depends in part on beliefs about its efficacy. In our setting,
efficacy is unambiguous. Second, we study paternalistic decisions
by individuals, rather than groups. While many paternalistic policies
result from group decision making (e.g., through voting), the judgments
of individuals are always central. Third, by using opportunity sets
involving bundles of immediate and delayed monetary payments, our
experiment introduces a plausible \emph{a priori} rationale for paternalism:
people commonly view patience as virtuous and impatience as reflecting
weakness.

\paragraph{Elicitation of beliefs about the welfare effects of restrictions}

A Choice Architect's decision to withhold options is paternalistic
only if she believes it promotes the Chooser's well-being \citep{Dworkin}.
We measure these beliefs in two ways on a single screen, as illustrated
in Panel B of Figure \ref{mainTreatmentLayout}. The Choice Architect
sees an opportunity set on the left, along with a subset of the same
options on the right. As explained subsequently, in some rounds the
subset on the right is the one the Choice Architect constructed; in
other rounds it is given exogenously.\footnote{In most cases, the opportunity set on the left contains three options.
However, for half of the rounds involving exogenous restrictions,
the opportunity set on the left contains two options, and the one
on the right contains only the most patient option. See Section \ref{otherMotives}
and Appendix \ref{exogenousRemovalCondition} for additional details.}

Choice Architects first answer a simple non-incentivized question:
\textit{Which opportunity set is better for the future participant?}
They select between \textit{Opportunity Set Left}, \textit{Both equal},
and \textit{Opportunity Set Right}. Second, Choice Architects complete
a decision list that elicits their beliefs about the welfare effects
of restrictions. Assuming they are not entirely indifferent towards
Choosers, the elicitation is incentive-compatible. Each line of the
list presents a binary choice of the following form: \textit{The payment
of the future participant should be determined by ... (i) the opportunity
set on the left, and the participant's completion payment will remain
unchanged, OR (ii) the opportunity set on the right, and the participant's
completion payment will be raised / lowered by \EURdig$p$}, {with}
$p\in\{1,0.5,0.3,0.1,0.05,0,-0.05,-0.1,-0.3,-0.5,-1\}$. A Choice
Architect may believe, for instance, that enlarging an opportunity
set requires positive compensation of \EURdig0.4 to the Chooser if
the additional choice options create opportunities for errors. A benevolent
Choice Architect will prefer the first option if $p=-0.5$, but will
prefer the second if $p=-0.3$. Thus, the transfer $p$ at which a
benevolent Choice Architect switches from \textit{(i)} to \textit{(ii)}
reveals her beliefs about the payment that compensates the Chooser
for receiving the opportunity set on the right over the one on the
left. We impose no restrictions on how subjects fill in these lists
such as monotonicity. Because we implement (at most) one decision
from one list, the Choice Architect has an incentive to choose in
accordance with her genuine preferences, regardless whether she is
benevolent or malevolent.\footnote{Our elicitation differs in two ways from the types of incentive-compatible
belief elicitation techniques commonly used in experimental economics.
First, we add or subtract the amount $p$ to or from the Chooser's
completion payment, not the Choice Architect's completion payment,
even though the Choice Architect is the party expressing the beliefs.
Second, we do not compensate the Choice Architects based on the distance
between their expressed beliefs and some objective truth. Our approach
more closely resembles the incentive-compatible methods used to elicit
willingness-to-pay rather than those used to elicit beliefs.}

\paragraph{Elicitation of Choice Architects' time preferences}

Our investigation of paternalistic mechanisms involves comparisons
between the opportunity sets Choice Architects construct for Choosers
and the choices they make for themselves. All Choice Architects complete
six decision lists such as the one shown in Figure \ref{onlineList}.
Each line is a choice between \EURdig$x_{\textit{early}}$ the day
of the experiment and \EURdig$x_{\textit{late}}$ $t$ months later.\footnote{We use $(x_{\textit{early}},x_{\textit{late}})\in\{(2,10),(5,10),(8,10),(2,15),(7,15),(12,15)\}$
and $t$ between 1 and 6 months in steps of one month.\label{fnOnlineTime}}


\begin{figure}[htbp!]
\setstretch{1} \caption{\textbf{Choice Architects' own intertemporal choices.}\label{onlineList}}
\vspace{-0.3cm}
\noindent \begin{centering}
\fbox{\parbox[c]{0.9\textwidth}{
\begin{center}
\vspace{-0.3cm}
 \textsf{\setlength{\tabcolsep}{2pt} On each line, choose the option
you genuinely prefer: }\\
 \textsf{ }
\fbox{
\begin{tabular}{cccl}
\textsf{\EURdig 8 on day of experiment}  & \textsf{$\square$}  & \textsf{$\square$}  & \textsf{\EURdig 10 in 1 month after the experiment }\tabularnewline
\textsf{\EURdig 8 on day of experiment}  & \textsf{$\square$}  & \textsf{$\square$}  & \textsf{\EURdig 10 in 2 months after the experiment }\tabularnewline
\textsf{\EURdig 8 on day of experiment}  & \textsf{$\square$}  & \textsf{$\square$}  & \textsf{\EURdig 10 in 3 months after the experiment }\tabularnewline
\textsf{\EURdig 8 on day of experiment}  & \textsf{$\square$}  & \textsf{$\square$}  & \textsf{\EURdig 10 in 4 months after the experiment }\tabularnewline
\textsf{\EURdig 8 on day of experiment}  & \textsf{$\square$}  & \textsf{$\square$}  & \textsf{\EURdig 10 in 5 months after the experiment }\tabularnewline
\textsf{\EURdig 8 on day of experiment}  & \textsf{$\square$}  & \textsf{$\square$}  & \textsf{\EURdig 10 in 6 months after the experiment }\tabularnewline
\end{tabular}} \vspace{-0.3cm}
\par\end{center}
}}
\par\end{centering}
\vspace{-0.2cm}
\end{figure}


To ensure that anchoring or a demand for consistency does not produce
an artificial relationship between the decisions Choice Architects
make for themselves and those that impact others, we make it difficult
for them to compare options across these settings. When choosing for
themselves, the amount of delay differs across the options but the
monetary payments are the same. When constructing opportunity sets
for Choosers, the monetary payments differ across options but the
amount of delay is the same. We also limit the potential influence
of confounding mechanisms by requiring Choice Architects to make choices
for themselves online three to six days before the laboratory session,
and by interspersing these tasks with decision lists involving risk
taking to obfuscate their purpose.


The temporal separation between the online session and the lab session
implies that the earlier payments in the online session are not immediate.
This lack of immediacy could in principle cause Choice Architects
to think about the options available for themselves in the online
session and those available to Choosers in the laboratory session
somewhat differently. Consequently, towards the end of the laboratory
session, Choice Architects make additional selections for themselves
from each three-option set for which they previously constructed Choosers'
opportunity sets. While these choices potentially raise greater concerns
about anchoring or a demand for consistency than those made in the
online session, we find little evidence that these confounds are in
fact present in our experiment; see Appendix Section \ref{anchoringDemandForConsistency}.

\paragraph{Elicitation of beliefs about Choosers' unrestricted choices}

We elicit Choice Architects' beliefs about the distributions of unrestricted
choices made by ten previous subjects for each menu encountered elsewhere
in the experiment. Choice Architects drag and drop ten tags labeled
``Participant'' into bins representing each of the three choice
options. Systematic discrepancies between these beliefs and the actual
(known) distribution of unrestricted choices may imply that paternalistic
interventions are predicated on mistaken perceptions of Choosers'
tendencies. To ensure incentive compatibility, a Choice Architects'
compensation may be based entirely on the accuracy of their reported
beliefs.

\subsection{Structure of the experiment and additional elicitations}

\label{treatments}\label{additionalElicitations}

As noted above, the experiment consists of an online component and
a laboratory component. The online component elicits Choice Architects'
own intertemporal preferences. The laboratory component consists of
three stages. Stage 1 includes 14 rounds of paternalistic decisions
and assessments of perceived welfare effects that employ various menus
of options from which Choice Architects construct choice sets. The
\emph{Main} condition comprises four rounds, each of which proceeds
as described above. These rounds employ menus 1 to 4 shown in Table
\ref{choiceSets}. For each subject, we randomly select the round
involving either menu 1 or 2 and delay both the early and the late
payment for each option in that menu by one week. As we discuss subsequently,
the introduction of front-end delay allows us to test some specific
ideas about the nature of paternalism. Each Choice Architect also
participates in three additional conditions (ten rounds in total)

 for which we alter the decision problems described above to test
specific hypotheses about the determinants of paternalism. These include
the \emph{Exogenous Restriction} condition (four rounds using menu
6), the \emph{Chooser Information} condition (four rounds using menu
5), and the \emph{Induced Chooser Preference} condition (two rounds
using menus 3 and 4).
We describe these conditions when we encounter the hypotheses they
address in Sections \ref{benevolence}, \ref{paternalismInfo}, and
\ref{otherMotives}, respectively.

\begin{table}[htbp!]
\setstretch{1}
\begin{centering}
\caption{\textbf{Menus of options from which Choice Architects construct opportunity
sets.}\label{choiceSets}}
\begin{tabular}{lcccccccccc}
 & \multicolumn{2}{c}{Menu 1} &  & \multicolumn{2}{c}{Menu 2} &  & \multicolumn{2}{c}{Menu 3} &  & \tabularnewline
\cmidrule(r){2-3} \cmidrule(r){5-6} \cmidrule(r){8-9} Option  & Today  & In 6 months  &  & Today  & In 6 months  &  & Today  & In 6 months  &  & \tabularnewline
Most patient  & \$0  & \$15  &  & \$0  & \$15  &  & \$0  & \$15  &  & \tabularnewline
Middle  & \$3  & \$10  &  & \$3  & \$9  &  & \$2  & \$12  &  & \tabularnewline
Least patient  & \$5  & \$1  &  & \$6  & \$1  &  & \$4  & \$2  &  & \tabularnewline
\cmidrule(r){2-3} \cmidrule(r){5-6} \cmidrule(r){8-9}  &  &  &  &  &  &  &  &  &  & \tabularnewline
 & \multicolumn{2}{c}{Menu 4} &  & \multicolumn{2}{c}{Menu 5} &  & \multicolumn{2}{c}{Menu 6} &  & \tabularnewline
\cmidrule(r){2-3} \cmidrule(r){5-6} \cmidrule(r){8-9} Option  & Today  & in 6 months  &  & Today  & In 6 months  &  & Today  & In 6 months  &  & \tabularnewline
Most patient  & \$0  & \$15  &  & \$0  & \$15  &  & \$0  & \$15  &  & \tabularnewline
Middle  & \$2  & \$9  &  & \$4  & \$6  &  & \$3  & \$7  &  & \tabularnewline
Least patient  & \$4  & \$2  &  & \$5  & \$1  &  & \$4  & \$1  &  & \tabularnewline
\cmidrule(r){2-3} \cmidrule(r){5-6} \cmidrule(r){8-9}  &  &  &  &  &  &  &  &  &  & \tabularnewline
\end{tabular}
\par\end{centering}
\vspace{-0.3cm}
 \medskip{}
\textbf{\scriptsize{}Notes:}{\scriptsize{}  Menus
1 to 4 correspond to the Main condition. Menu 5 corresponds to the
Chooser Information condition (section \ref{paternalismInfo}). Menu
6 corresponds to the Exogenous Removal condition (section \ref{otherMotives}). }{\scriptsize\par}
\vspace{-0.3cm}
\end{table}

Each subject proceeds through the rounds of Stage 1 in an individually
randomized order. In each round, the Choice Architect first constructs
the Chooser's opportunity set, and then reveals her beliefs about
whether the three-option opportunity set or a subset thereof is better
for the Chooser. In the Main condition, the comparator subset is the
one the Choice Architect constructed herself.


In Stage 2, we collect additional data that shed light on aspects
of the Stage 1 decisions. First, Choice Architects make surrogate
choices for Choosers. These tasks are identical to those in Stage
1, except we require Choice Architects to select a single option.
Assuming Choice Architects are benevolent, these decisions reveal
the options they deem best for Choosers. Unlike decisions to restrict
opportunity sets, surrogate choices do not implicate Choice Architects'
willingness to intervene. Second, to incentivize attentiveness, we
administer an eight-question test on specific features of the experiment.
We tell subjects about this test in advance, and advise them that
their performance on it could completely determine their earnings.\footnote{Appendix \ref{attentionTest} lists the test questions and frequencies
of correct responses. Subjects do not learn anything about the content
or focus of the test before making decisions concerning the Chooser.
The test consists of eight questions about the stimuli the Choice
Architects encountered. It does not refer to Choice Architects' own
decisions.} Third, we elicit beliefs about previous Choosers' unrestricted choices,
as described above.\footnote{Half of our subjects, chosen at random, complete these elicitations
before Stage 1. We control for this order in all of our regressions.} Fourth, Choice Architects select an option for themselves from each
menu, also as described above. Fifth, we ask Choice Architects to
adjust the completion payment of a different Chooser. They can costlessly
increase that payment by \EURdig1, leave it unchanged, or decrease
it by \EURdig1. We use their responses to gauge whether they are
benevolent or spiteful toward Choosers.

In Stage 3, subjects express opinions about four real-world paternalistic
policy proposals, and then provide information about their own inclinations
to engage in the affected activities. We use this information to evaluate
the generalizability of our findings. The experiment ends with a brief
memory check concerning choices subjects made in the Online component.

Appendix \ref{experimentFullDetails} presents comprehensive detail
about all design elements.


\subsection{Incentives}

\label{timing} \label{incentives}

\paragraph{Choice Architects' decisions concerning Choosers.}

There is a 25\% chance that we match any given Choice Architect with
a Chooser. For each match, we randomly draw one of the rounds in which
the Choice Architect makes a decision concerning the Chooser. With
50\% probability, we implement the decision from the first half of
that round. With the remaining 50\% probability, we determine the
Chooser's opportunity set by randomly drawing a line from the decision
list in the second half of the round (which elicits the perceived
benefit of a restriction) and implementing the Choice Architect's
selection. Separately, with 25\% probability, we implement the Choice
Architect's chosen adjustment to the completion payment for a different
Chooser who we assign at random. Choice Architects know that Choosers
will participate in a subsequent laboratory session. We inform Choice
Architects of the matching and implementation probabilities, and explain
that, if they are matched to a Chooser, no other subject will influence
the Chooser's opportunities. We also let Choice Architects know that
no other subject has manipulated the choice problems that determine
their own payments.

\paragraph{Choice Architects' own payment.}

A Choice Architect's payment is determined either by the online component
or by one of the following: the attention test, elicitation of beliefs
concerning Choosers' unrestricted selections, or choices made for
themselves. Each of these four alternatives is equally likely.\footnote{Subjects learn at the beginning of the online component that there
is a 25\% chance a single decision from that component will determine
their payment, and a 75\% chance the laboratory component will determine
it, but they do not learn at the outset what the latter possibility
will entail.} For the elicitation of beliefs concerning unrestricted Choosers'
selections, the Choice Architect receives \EURdig10 minus the number
of tags we must reassign to make the elicited and observed distributions
of choices coincide.\footnote{Our elicitation procedure is the balls-in-bins method described in
\citet{Delavandeetal.}.
Truthful revelation is optimal for a risk-neutral subject. Subjects
understand this scheme more easily than alternatives. While risk aversion
theoretically generates a tendency for measured beliefs to be overly
dispersed, Choice Architects' risk preferences, elicited in the online
component, predict neither the location nor the dispersion of elicited
belief distributions.} For the attention test, the Choice Architect receives \EURdig1 for
each correct answer. For the online component or the Choice Architects'
decisions for herself in the laboratory component, we implement one
randomly selected decision within the corresponding task block. Each
Choice Architect also receives a show-up payment of \EURdig4.5 and
a completion payment of \EURdig8.\footnote{The completion payment in the first two sessions was \EURdig5, which
we increased after feedback that subjects perceived the study payment
to be low.}

\subsection{Choosers}

\label{choosers}

Choosers participate after all Choice Architect sessions are completed.
Each Chooser first selects one of four self-descriptive statements
(see Section \ref{paternalismInfo}) and then ranks the elements of
one menu. He receives the option he ranks highest among those his
matched Choice Architect makes available.
 Each Chooser also receives a show-up payment of \EURdig4.5 and a
completion payment of \EURdig8. If the Choice Architect task selected
for implementation involves the incentive-compatible elicitation of
beliefs concerning welfare effects, we raise or lower the corresponding
Chooser's completion payment based on the Choice Architect's decision.

\subsection{Implementation}

\label{implementation}

We conducted 16 sessions with a total of 303 Choice Architects during
the summer of 2018 at the Cologne Laboratory for Economic Research.
Each lasted approximately 90 minutes. We recruited 100 additional
subjects to study other hypotheses (see the discussion of the Choice
Distribution Information condition in Appendix \ref{projPatInfoAppendix}).
Separately, 124 subjects participated as Choosers (see Appendix \ref{ChooserChoices}).

The experiment is computer-based. We display instructions on-screen,
and intersperse comprehension checks which subjects must complete
correctly to continue. Those failing the comprehension checks can
review the instructions and try again (all subjects eventually passed).
The comprehension checks emphasize that there are no right or wrong
answers for decisions affecting the Choosers. We process all incentive
payments through PayPal and cover all transaction fees.
\vspace{-0.3cm}

\section{Properties of paternalistic interventions}\label{anatomy}\label{analysis}
\vspace{-0.3cm}
\subsection{Do people intervene and, if so, in what way?}

\label{InterventionResults}

The first step in our analysis is to ask whether people intervene
into others' decisions and to characterize the nature of those interventions.
We find that interventions are common, and that they generally limit
impatient choices, especially when impatience is costly.

Figure \ref{resultsFig1} shows the frequencies with which Choice
Architects make specific types of options unavailable, averaged across
rounds in the Main condition (excluding those with front-end delay).
The prevailing tendency is for Choice Architects to prevent impatient
choice. They remove the least patient option 32.7\% of the time, the
middle option 11.6\% of the time, and the most patient option 5.1\%
of the time. When we limit attention to the 86.3\% of Choice Architects
who are altruistic (in the sense that they costlessly increase a second
Chooser's completion payment in Stage 2), the frequency with which
they remove the most patient option drops by half (to 2.5\%). The
removal frequency for the middle option falls slightly (to 10.6\%),
and there is no change for the least patient option. As Table \ref{fracPrevented}
shows, the pattern of withholding impatient options rather than patient
ones emerges for all four menus.

\begin{figure}[htbp!]
\setstretch{1}
 \caption{\textbf{Withheld and discouraged options.}\label{resultsFig1}}
\vspace{-0.2cm}

\begin{centering}
\includegraphics[width=0.55\textwidth]{publicationFigures/preventionBasicStacked}


\par\end{centering}
\vspace{-0.2cm}
 \textbf{\scriptsize{}Notes:}{\scriptsize{} The solid line displays
the fraction of cases for which the Choice Architect withholds options,
categorized by relative patience, from the Chooser. The vertical distance
between the solid and dashed lines represents the fraction of cases
in which the Choice Architect advises against the option but does
not withhold it. We average the data across the menus (1 to 4) in
the Main condition, excluding rounds with front-end delay. Whiskers
indicate 95\%-confidence intervals based on estimates of standard
errors clustered at the subject level. }{\scriptsize\par}\vspace{-0.3cm}
\end{figure}

Imposing a minimum degree of patience is also the modal behavioral
pattern on the individual level: 44.9\% of our subjects remove at
least one option from at least one of the opportunity sets, and never
remove an option without also excluding less patient options. Choice
Architects who never remove any option in the Main condition comprise
the second largest category (38.3\%). We refer to this group as {Libertarians}.
A small fraction (4.3\%) of Choice Architects impose an upper bound
on patience.\footnote{These subjects remove at least one option from at least one choice
set, and never remove an option without also excluding more patient
options.} The remaining 12.5\% of Choice Architects impose non-monotonic restrictions.

Choice Architects' decisions depend not only on the rank (by patience)
of an option within a menu, but also on the cost of impatience, defined
as the total amount of money the Chooser forfeits by acting impatiently.
In menu 3, for instance, the Chooser sacrifices a total of \EURdig1
(\EURdig15 $-$ \EURdig14) by selecting the middle option rather
than the most patient option. In menus 1, 2, and 4, the corresponding
figures are \EURdig2, \EURdig3, and \EURdig4, respectively. Removal
rates for the middle option increase monotonically with the forfeited
amount: they are 8.3\% for menu 3, 9.3\% for menu 1, 14.4\% for menu
2, and 14.5\% for menu 4 (see Table \ref{fracPrevented}). A similar
pattern holds for the least patient option. By selecting the least
patient option from menu 2 rather than the most patient option, a
Chooser forfeits \EURdig8 in total, whereas the corresponding amount
is \EURdig9 for each of the remaining menus. The removal rate for
the least patient option is 23.5\% for menu 2, compared with 32\%,
38.9\%, and 31.4\% for menus 1, 3, and 4, respectively. To quantify
these relationships, we estimate an OLS regression relating the availability
of an option to the total amount of money a Chooser would forfeit
by selecting it rather than the most patient option, controlling for
the rank of the option within the choice set, as well as session and
order fixed effects. We find that, for each additional Euro sacrificed
when selecting an impatient option, the frequency with which Choice
Architects withhold that option increases by 2.9 percentage points
($p<0.01$, see Appendix \ref{priceSensitivity} for details).


\begin{table}[htbp!]
\setstretch{1} \caption{\textbf{Fraction of options withheld in Main condition.}\label{fracPrevented}}
\vspace{-0.2cm}

\begin{centering}
\begin{tabular}{cccccc}
\toprule
Menus  & \multicolumn{3}{c}{Percent option unavailable} & Observations  & Subjects \tabularnewline
\cmidrule(r){2-4}  & Most patient  & Middle  & Least patient  &  & \tabularnewline
\midrule
1, 2, 3, 4  & 5.1  & 11.6  & 32.7  & 909  & 303\tabularnewline


1  & 6.0  & 9.3  & 32.0  & 150  & 150\tabularnewline

2  & 5.2  & 14.4  & 23.5  & 153  & 153\tabularnewline

3  & 4.3  & 8.3  & 38.9  & 303  & 303 \tabularnewline

4  & 5.3  & 14.5  & 31.4  & 303  & 303\tabularnewline
\midrule


 &  &  &  &  & \tabularnewline
\end{tabular}
\par\end{centering}
\vspace{-0.3cm}
 \textbf{\scriptsize{}Notes:}{\scriptsize{} Decisions with front-end
delay are excluded. }{\scriptsize\par}
\vspace{-0.3cm}
\end{table}

The frequency with which subjects advise against options but do not
withhold them exhibits similar patterns; see Figure \ref{resultsFig1}.
Subjects advise against impatient options substantially more often
than against patient ones, just as they tend to remove the former
more than the latter. Thus, patterns of hard paternalism (prohibitions)
and soft paternalism (advice) mirror each other. Significantly, the
pattern of advice is essentially the same, both qualitatively and
quantitatively, for subjects who never withhold options. Subjects
write free-form messages much less frequently (only 21.5\% of subjects
ever send a message exceeding 5 characters in length), but when they
do, they typically articulate paternalistic perspectives concerning
the desirability of patience. Examples include
``\textit{Patience is a virtue},'' ``\textit{Stay rational. Don't
weigh the present more than the future},'' and ``\textit{It is important
to practice patience}.''

\subsection{Do people believe their interventions are beneficial?}

\label{benevolence}

Having shown that interventions occur in our experiment, we now ask
whether they are paternalistic. A restriction on free choice is deemed
paternalistic only if the party responsible for it believes that it
benefits those it affects (\citealp{Dworkin}). In this section, we
show that our Choice Architects think the restrictions they impose
are beneficial, and that those who impose stricter mandates believe
the benefits are greater. Accordingly, we are indeed observing paternalism.

\begin{table}[htb!]
\setstretch{1} \caption{\textbf{Subjects' beliefs about the welfare effects of withholding
options.}\label{welfareBeliefs}}
\vspace{-0.3cm}

\begin{small}

\begin{centering}
\begin{tabular}{lcccc}
\toprule
 & (1)  & (2)  & (3)  & (4) \tabularnewline
Dependent variable  & \multicolumn{2}{c}{Belief smaller opportunity set } & \multicolumn{2}{c}{Negative of }\tabularnewline
 & \multicolumn{2}{c}{ better for Chooser} & \multicolumn{2}{c}{Compensating Variation}\tabularnewline
\cmidrule(r){2-3} \cmidrule(r){4-5} Smaller set  & Chosen  & Exogenous  & Chosen  & Exogenous \medskip{}
 \tabularnewline
Menus  & 1 - 4  & 6  & 1 - 4  & 6 \tabularnewline
 & without FED  &  & without FED  & \tabularnewline
 &  &  &  & \tabularnewline
 & \multicolumn{4}{c}{\textbf{Summary statistics for dependent variables} \black }\tabularnewline
\midrule
Distribution of beliefs  &  &  &  & \tabularnewline
\hspace{0.4cm} \emph{Evaluation opportunity set is}  &  &  &  & \tabularnewline
\hspace{0.8cm}\emph{ better}  & 0.296  & 0.345  &  & \tabularnewline
\hspace{0.8cm}\emph{ same}  & 0.600  & 0.107  &  & \tabularnewline
\hspace{0.8cm}\emph{ worse}  & 0.105  & 0.548  &  & \tabularnewline

\hspace{0.4cm} \emph{Mean negative CV}  &  &  & 0.021  & -0.224 \medskip{}
\tabularnewline
Distribution of beliefs if options withheld  &  &  &  & \tabularnewline
\hspace{0.4cm} \emph{Evaluation opportunity set is}  &  &  &  & \tabularnewline
\hspace{0.8cm} \emph{better}  & 0.652  & 0.455  &  & \tabularnewline
\hspace{0.8cm} \emph{same}  & 0.091  & 0.118  &  & \tabularnewline
\hspace{0.8cm} \emph{worse}  & 0.257  & 0.428  &  & \tabularnewline
\hspace{0.4cm} \emph{Mean negative CV}  &  &  & 0.051  & -0.122 \medskip{}
 \tabularnewline
 & \multicolumn{4}{c}{\textbf{Regression results} \black }\tabularnewline
\midrule


1 option withheld  & 0.320{*}{*}{*}  &  & 0.019  & \tabularnewline
 & (0.068)  &  & (0.050)  & \tabularnewline
2 options withheld  & 0.496{*}{*}{*}  &  & 0.113  & \tabularnewline
 & (0.104)  &  & (0.079)  & \tabularnewline
Mean \# options withheld  &  & 0.686{*}{*}{*}  &  & 0.251{*}{*}{*} \tabularnewline
\hspace{0.4cm} in Main condition  &  & (0.073)  &  & (0.055) \tabularnewline
Comparison set contains  &  & -0.182{*}{*}  &  & -0.253{*}{*}{*} \tabularnewline
\hspace{0.4cm} most patient option only  &  & (0.088)  &  & (0.062) \medskip{}
\tabularnewline
Observations  & 909  & 606  & 866  & 574 \tabularnewline
Number of subjects  & 303  & 303  & 297  & 292 \tabularnewline
\bottomrule
\end{tabular}
\par\end{centering}
\end{small} \textbf{\scriptsize{}Notes:}{\scriptsize{} The top half
of the table provides summary statistics for the dependent variables,
and the bottom half presents regression results. FED stands for front-end
delay. Unit of observation: subject-round pair. {Method}: Columns
1 and 2, OLS; columns 3 and 4, interval regression. {Dependent variables}:
The dependent variable for columns 1 and 2 measures whether the Choice
Architect considers the smaller opportunity set better, equally good,
or worse for the Chooser than the unrestricted set, coded as 1, 0,
and -1, respectively. For columns 3 and 4, it is the negative of the
Choice Architect's beliefs about the compensating variation (CV) of
reducing the opportunity set. For all columns 1 and 3, the smaller
opportunity set is the one the Choice Architect has constructed; for
columns 2 and 4, we construct it by exogenously removing either the
least patient option, or the least patient and middle options. {Controls}:
All regressions control for altruism and spite, and include session,
and order fixed effects. Columns 1 and 3 also control for menu fixed
effects {Samples}: Columns employ data from different conditions,
as indicated. For columns 1 and 3, the row }\emph{\scriptsize{}Distribution
of beliefs if some option withheld}{\scriptsize{} includes all observations
in which the Choice Architect withheld one or more of the options
in rounds of the Main condition without front-end delay. For columns
2 and 4, that row includes observations from all rounds in the Exogenous
Restriction condition for Choice Architects who withheld at least
one option in the Main condition. In the bottom panel, each column
corresponds to a separate regression. Columns 3 and 4 exclude subjects
with multiple switches in the elicitation of compensating variations.
{Standard errors}: clustered by subject. \black $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$. }{\scriptsize\par}
\end{table}

We begin by analyzing subjects' responses to the question of whether
the full opportunity set or the opportunity set they have constructed
is better for the Chooser. Column 1 of Table \ref{welfareBeliefs}
(top half) shows the distribution of responses in the Main condition
(without front-end delay). In 30\% of cases, subjects report that
the choice set they have constructed is better for the Chooser than
the unrestricted set, while 11\% report that it is worse. These frequencies
are artificially attenuated by the presence of libertarian subjects
who never remove options, and who therefore largely report that neither
set is better. For rounds in which subjects imposed restrictions,
65\% describe the restricted set as better, while 25\% describe it
as worse. Overall, subjects are far more likely to believe their interventions
are helpful than hurtful.

To evaluate the statistical significance of these findings, we regress
the subject's report about the welfare effect on indicators for the
number of options removed, encoding the responses ``better,'' ``same,''
and ``worse'' as +1, 0, and -1, respectively (Appendix \ref{welfareBeliefsAppendix}
reports the corresponding ordered probit regressions). We use the
full sample and control for altruism versus spite on the part of the
Choice Architect, i.e., whether she has chosen to costlessly increase
or decrease a second Choosers' completion payment by \EURdig1. We
include session, order, and menu fixed effects, and cluster standard
errors by subject. On average, a Choice Architect is more likely to
believe that her action benefits the Chooser when she removes one
option rather than none, and the difference is highly statistically
significant, as the estimates in column 1 show. When the Choice Architect
removes two options, the estimated coefficient is even larger, but
the increment is only marginally significant ($p=0.11$).\footnote{We also find that spiteful Choice Architects believe much less strongly
that their restrictions benefit Choosers. We caution, however, that
this estimate is based on only nine spiteful subjects.}

One purpose of the \emph{Exogenous Restrictions} condition, which
we mentioned briefly in Section \ref{treatments}, is to allow us
to study beliefs about the welfare effects of prespecified restrictions
whether or not Choice Architects select them. Here we examine two
of the four rounds in this condition (see Section \ref{otherMotives}
for an explanation of the other two rounds). There are two main differences
between the welfare evaluations in these rounds and those in the Main
condition. First, they involve comparisons between the unrestricted
set and an exogenously specified subset that includes either the most
patient option or the two most patient options. The number of items
in the comparison set (one or two) is determined randomly for each
subject and is the same in the both rounds. Second, this condition
employs menu 6 (see Table \ref{choiceSets}).\footnote{For the purpose of examining Choice Architects' beliefs about welfare
effects, the two rounds are essentially identical, so it is especially
important to report standard errors clustered at the subject level
reported in Table \ref{welfareBeliefs}. The distinction between these
rounds is that we provide the Choice Architect with a different default
when asking her to construct the Chooser's opportunity set: in one
round, the default consists of all three options, and the Choice Architect
can remove either the least patient option or the least patient and
middle options; in the other round, the default consists of the most
patient option, and the Choice Architect can add either the middle
option or the middle and least patient options. We examine Choice
Architects' selected opportunity sets for these rounds, as well as
for the other two rounds of the Exogenous Restrictions condition,
in Section \ref{otherMotives}. }


Because exogenously imposed restrictions need not coincide with the
interventions Choice Architects deem best for Choosers, we would expect
to find less optimism about their welfare effects. Indeed, as shown
in top half of Table \ref{welfareBeliefs}, column 2, Choice Architects
say that the restrictions make Choosers better off 35\% of the time
and worse off 55\% of the time. Of course, these figures include libertarians.
Non-libertarian Choice Architects (who, by definition, withhold some
option in the Main treatment) say that the restrictions make Choosers
better off 47\% of the time and worse off 42\% of the time. Moreover,
even non-libertarians may view the removal of two options as excessive;
indeed, in the Main treatment, they remove 0.86 options per round
on average. Non-libertarian Choice Architects say that removing the
most impatient option, and nothing else, makes the Chooser better
off 52\% of the time and worse off 35\% of the time (not shown in
the table). On average, these subjects clearly believe that a minimal
restriction helps: the difference in frequencies is 19 percentage
points ($p=0.078$).

We report associated regression results in the bottom half of Table
\ref{welfareBeliefs}, column 2. We use OLS as a convenient
method for calculating conditional means, but do not interpret the
estimates as causal effects. The regression relates welfare
evaluations of exogenous restrictions, calibrated as before, to a
proxy for the Choice Architect's proclivity to intervene (the average
number of options removed in the Main condition), and a dummy variable
indicating that the comparison set is limited to the most patient
item. Once again, we estimate the regression with the full sample,
control for altruism versus spite on the part of the Choice Architect,
and include session and order effects. Two conclusions follow from
this regression. First, Choice Architects who have demonstrated a
greater proclivity to restrict Choosers in the Main condition are
substantially more likely to believe that exogenously specified restrictions
are beneficial. Second, Choice Architects are more likely to believe
that a restriction is beneficial if it removes one item rather than
two.

Columns 5 and 6 replicate columns 1 and 2 using dependent variables
based on our second measure of welfare effects: beliefs about compensating
variations, elicited through an incentive-compatible procedure. Here
we estimate interval regressions, including only those subjects whose
choices in the multiple-decision lists are consistent with a preference
for increasing the Chooser's payment.\footnote{Data from the multiple decision lists are sometimes censored at the
smallest and largest amounts specified in the lists. In column 3 of
Table \ref{welfareBeliefs}, 61 (7.04\%) observations are censored
above and 49 (5.66\%) are censored below. In column 4, these figures
are 25 (4.36\%) and 99 (17.25\%), respectively.}
Results for evaluations of exogenous restrictions corroborate our
findings based on unincentivized evaluations (compare column 6 to
column 2). Naturally, magnitudes differ because the scales of the
dependent variables are not comparable. Results for evaluations of
chosen restrictions are consistent with our findings based on unincentivized
evaluations (compare column 5 to column 1), but the coefficients are
not statistically distinguishable from zero at conventional levels
of confidence. This loss of statistical precision may reflect the
complexity of the incentive compatible elicitation, which could introduce
noise. While this evidence is correlational, Section \ref{paternalismInfo}
shows that exogenously varying information about Choosers alters beliefs
about welfare effects. Moreover, Section \ref{otherMotives} provides
evidence against the hypothesis that causality flows from choices
to beliefs through a rationalization mechanism.

Subjects' own explanations for the restrictions they impose are consistent
with our interpretation. At the end of the survey, we asked subjects
why they constructed choice sets as they did. Subjects who withheld
options typically cited paternalistic motives. Examples include, ``\textit{I
wanted to force the future experiment participant to make the decision
that is right for him},'' ``\textit{A discount factor of 1/5 is
very extreme. Other options offer a justifiable discount factor},''


and ``\textit{I wanted to protect him from himself}.'' Subjects
who did not withhold options, in contrast, often described libertarian
reasoning: ``\textit{The more options, the better},''
 and ``\textit{I will not dictate anyone's choice, even though I
have a strong opinion}.''

Overall, we conclude that, for the most part, Choice Architects believe
the restrictions they impose are beneficial, and therefore these interventions
are consistent with paternalism. However, it does not necessarily
follow that Choice Architects intervene \emph{because} of this belief.
Additional evidence, provided in subsequent sections, helps to bridge
this gap. First, we show in Section \ref{paternalismInfo} that interventions
are sensitive to the provision of information that changes beliefs
about the benefits of restrictions. Second, we provide evidence in
Section \ref{otherMotives} that causality does not flow from interventions
to beliefs through a rationalization mechanism.


\subsection{Do people intervene to correct for ``present bias''?}

\label{situationalDeterminants}\label{FED}

So far, we have seen that many people intervene with benevolent intentions
to prevent impatient choices that favor immediate payments over delayed
rewards. These findings are consistent with the objectives that behavioral
economists who believe decision makers suffer from \emph{present bias},
defined as the tendency to favor immediate gratification excessively,
commonly attribute to benevolent planners. Consequently, the next
step in our analysis is to ask whether the alleviation of perceived
present bias is in fact a central objective of the observed interventions.

As is well-known, introducing front-end delay (that is, delaying all
payments by a fixed amount of time) induces people to choose more
patiently for themselves \citep{Fredericketal.timeInconsistent}.
Under the hypothesis that people suffer from present bias, this effect
occurs because front-end delay removes the lure of immediacy that
is responsible for excessively impatient choice. Thus, if people believe
others suffer from present bias, front-end delay reduces the perceived
benefits of paternalistic intervention. Indeed, under the particular
positive and normative assumptions behavioral economists commonly
invoke, front-end delay eliminates those benefits entirely.\footnote{Most of the pertinent literature assumes that people have quasihyperbolic
preferences, which take the following form: $U_{t}(x_{t},x_{t+1},...)=u(x_{t})+\beta\sum_{k=1}^{\infty}\delta^{k}u(x_{t+k})$,
with $\beta,\delta<1$. A common normative interpretation of this
model is that $\delta$ captures true intertemporal preferences, while
any deviation of $\beta$ from unity constitutes a bias. In that case,
a benevolent planner would try to maximize $\sum_{t=0}^{\infty}\delta^{t}u(x_{t})$
(the long-run criterion). \citet{BernheimTaubinsky} provide a critical
evaluation of the foundations for this criterion.} Thus, if Choice Architects share the perspectives that behavioral
economists often attribute to benevolent planners -- if they indeed
seek to correct for perceived present bias -- the introduction of
front-end delay should reduce or eliminate interventions. As we will
see, our data reject this hypothesis in favor of the view that Choice
Architects simply disapprove of impatient choices.

\begin{table}[htb!]
\setstretch{1}

\caption{\textbf{Effects of front-end delay and information about the Chooser.}\label{frontEndDelay}
\label{projPatInfo} }

\vspace{-0.2cm}

\begin{centering}
\begin{tabular}{lcccccc}
\toprule
 & (1)  & (2)  & (3)  & (4)  & (5)  & \tabularnewline
 &  &  &  &  &  & \tabularnewline
VARIABLES  & \EURdig\; Choice  & Belief \EURdig  & Max. \EURdig  & Max. \EURdig  & Belief \EURdig  & \tabularnewline
 & Architect  & Chooser  & Chooser  & Chooser  & Chooser  & \tabularnewline
 & takes early  & takes early  & takes early  & takes early  & takes early  & \tabularnewline
\midrule
 &  &  &  &  &  & \tabularnewline
Front-end delay  & -0.312{*}{*}{*}  & -0.228{*}{*}{*}  & -0.104  &  &  & \tabularnewline
 & (0.088)  & (0.049)  & (0.093)  &  & \medskip{}
  & \tabularnewline
Chooser information  &  &  &  &  &  & \tabularnewline
\hspace{0.4cm} \textit{Patient, happy}  &  &  &  & -0.454{*}{*}{*}  & -1.480{*}{*}{*}  & \tabularnewline
 &  &  &  & (0.084)  & (0.061)  & \tabularnewline
\hspace{0.4cm} \textit{Patient, unhappy}  &  &  &  & -0.049  & -1.057{*}{*}{*}  & \tabularnewline
 &  &  &  & (0.072)  & (0.054)  & \tabularnewline
\hspace{0.4cm} \textit{Impatient, unhappy}  &  &  &  & -0.312{*}{*}{*}  & -0.223{*}{*}{*}  & \tabularnewline
 &  &  &  & (0.076)  & (0.043)  & \tabularnewline
 &  &  &  &  &  & \tabularnewline
Mean of dep. var.  & 0.965  & 1.929  & 4.548  & 3.246  & 1.521  & \tabularnewline
 & (0.098)  & (0.062)  & (0.082)  & (0.058)  & (0.045)  & \tabularnewline
 &  &  &  &  &  & \tabularnewline
Observations  & 606  & 606  & 606  & 1,212  & 1,212  & \tabularnewline
Number of subjects  & 303  & 303  & 303  & 303  & 303  & \tabularnewline
\bottomrule
\end{tabular}
\par\end{centering}
\textbf{\scriptsize{}Notes:}{\scriptsize{}
Method: OLS. Unit of observation: subject-round pair. Dependent variables:
}\emph{\scriptsize{}\EURdig\; Choice Architect takes early}{\scriptsize{}
is the amount of Euros the Choice Architect chooses to receive early
when choosing for herself in stage 2 of the laboratory component.
}\emph{\scriptsize{}Belief Chooser takes early}{\scriptsize{} is the
Choice Architect's beliefs about the mean amount a Chooser will receive
early if allowed to choose without restrictions. }\emph{\scriptsize{}Max.
\EURdig\; Chooser takes early}{\scriptsize{} is the maximal amount
of Euros the Choice Architect permits the Chooser to receive early.
Controls: }\emph{\scriptsize{}Front-end delay}{\scriptsize{} is an
indicator for whether all payments in a menu are delayed by a week.
}\emph{\scriptsize{}Chooser information}{\scriptsize{} is a set of
dummies that indicate the statement a Chooser selects from Table \ref{chooserInfo}
to describe himself. For columns 4 and 5, the omitted category is
}\textit{\scriptsize{}impatient, happy}{\scriptsize{}. Other controls
include session, order, and (for columns 1-3) menu fixed effects.
Samples: columns 1-3 use menus 1 and 2 from the Main condition; columns
4-5 use all rounds of the Chooser Information condition. Standard
errors: clustered by subject. $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$. }{\scriptsize\par}
\vspace{-0.4cm}
\end{table}


Focusing on the decisions Choice Architects make for themselves in
Stage 2 of the laboratory session, we begin by replicating the usual
finding that front-end delay increases patience. Column 1 of Table
\ref{frontEndDelay} presents a regression of the early payment the
Choice Architect selects for herself on an indicator for front-end
delay. The regression employs all of the decisions for menus 1 and
2 in the Main condition. Recall that each Choice Architect makes one
of these two decisions, selected at random, with front-end delay.
We include session, order, and menu fixed effects, and cluster standard
errors by subject. As expected, the estimates show that the addition
of front-end delay yields a statistically significant decrease in
the selected early payment (\EURdig0.31, which corresponds to 5.6\%
of the greatest possible treatment effect).

Next, we ask whether Choice Architects anticipate Choosers' responses
to front-end delay. Recall that, in Stage 2 of the laboratory component,
we elicit Choice Architects' beliefs about the selections Choosers
would make from all unrestricted menus. To generate the results in
column 2 of Table \ref{frontEndDelay}, we regress the mean of the
early payment for this elicited distribution on the same set of variables
used in column 1. The results show that Choice Architects expect unrestricted
Choosers to exhibit more patience with front-end delay, although the
effect is slightly smaller than the impact on their own choices (\EURdig0.23).


\begin{figure}[htbp!]
\setstretch{1} \caption{\textbf{Effect of front-end delay.} \label{resultsFigFED}}
\vspace{-0.4cm}
\begin{centering}
\includegraphics[width=0.55\textwidth]{publicationFigures/preventionFED}
\par\end{centering}
\vspace{-0.3cm}
\textbf{\scriptsize{}Notes:}{\scriptsize{} Whiskers display 95\%-confidence
intervals with estimates of standard errors clustered at the subject
level. Graph based on all decisions in the Main condition for menus
1 and 2. }{\scriptsize\par}\vspace{-0.3cm}
\end{figure}


How does front-end delay affect mandates? Figure \ref{resultsFigFED}
shows the frequency with which Choice Architects withhold each type
of choice, averaged over all rounds in the Main condition that involve
menus 1 and 2 (half of which include front-end delay). If anything,
the interventions are slightly \emph{more} common with front-end delay,
not less. Column 3 of Table \ref{frontEndDelay} makes the same point
in a regression format. For this purpose, we define a Choice Architect's
\emph{mandate} as the largest early payment she allows the Chooser
to receive. For example, if a Choice Architect offers the most patient
and middle alternatives from menu 1 of Table \ref{choiceSets}, the
mandate is \EURdig3.\footnote{All our results are robust with respect to alternative definitions
of this variable, such as the smallest amount of money the Choice
Architect forces the Chooser to receive with delay.} Here we have regressed mandates for the same rounds on the same set
of variables as in columns 1 and 2. The estimated effect of front-end
delay is negative, meaning that mandates with front-end delay are
more restrictive, not less, although the effect is not statistically
significant. Further, Choice Architects are significantly \emph{more}
likely to think that their chosen restrictions are beneficial in the
presence of front-end delay than without it, as Appendix \ref{welfareBeliefsAppendix}
shows. Thus, we find no evidence that Choice Architects intervene
to counter present bias. Rather, they appear to regard patience as
generally virtuous, and intervene to enforce it.


\subsection{Are interventions sensitive to information about the affected party's
inclinations and subjective experiences?}

\label{paternalismInfo}

Having ruled out the possibility that our Choice Architects act on
the types of objectives and perspectives that behavioral economists
often attribute to benevolent planners, we consider a second, simpler
hypothesis: paternalists make decisions for others based exclusively
on their own conceptions of right and wrong without regard to the
preferences or subjective experiences of the affected individuals.
As we explain in this section, our data strongly reject this hypothesis.
This finding leads naturally to the question we address in Section
\ref{projectivePaternalism}: In light of the fact that paternalists'
beliefs about others' inclinations and subjective experiences affect
their interventions, how do they form those beliefs?

\begin{table}[htb!]
\setstretch{1} \caption{\textbf{Statements for the Chooser Information condition.}\label{chooserInfo}}
\vspace{-0.2cm}

\centering{}
\begin{minipage}[t]{0.9\textwidth}
\hrulefill{} \begin{compactitem}

\item \textsf{I am a patient person. I am happy with this. (I often
forego things in the present with regard to the future).}

\item \textsf{I am an impatient person. I am happy with this. (I
rarely forego things in the present with regard to the future).}

\item \textsf{I am a patient person. I often regret my decisions.
(Perhaps too often, I forego things in the present with regard to
the future).}

\item \textsf{I am an impatient person. I often regret my decisions.
(Perhaps too rarely, I forego things in the present with regard to
the future).} \end{compactitem} \vspace{-0.2cm}
 \hrulefill{}
\end{minipage}
\end{table}

\label{info}

The analysis of this section employs data from the \emph{Chooser Information}
condition, which we mentioned briefly in Section \ref{treatments}.
It resembles the Main condition, except that we provide Choice Architects
with information about their Choosers. Specifically, before making
the decisions described in Section \ref{choosers}, each Chooser endorses
one of the four statements listed in Table \ref{chooserInfo}, which
describe them as either patient or impatient, and as either generally
happy or unhappy with their intertemporal choices. The condition consists
of four rounds, one for each of the four statements. The Choice Architect
knows her Chooser will fall into one of these categories, but does
not know which one. Moreover, Choice Architects believe that a substantial
fraction of Choosers falls into each of the four categories.\footnote{On average, Choice Architects believe that 33.1\% of Choosers will
classify themselves as patient and happy, 21.3\% as impatient and
happy, 16.5\% as patient and unhappy, and 29.1\% as impatient and
unhappy. We elicited these beliefs either directly before or directly
after eliciting beliefs about the distribution of choices by ten unrestricted
subjects (randomized on the individual level). } Consequently, they have reason to treat their decisions in each round
as consequential. All four rounds involve menu 5 (see Table \ref{choiceSets}).\footnote{We ask subjects in the role of Choosers to select the statement that
describes them best at the start of their session, before they receive
any other information. If the Chooser Information condition is selected
for implementation, the Chooser receives the choice set the Choice
Architect constructed for the corresponding statement.}

\begin{figure}[htb!]
\setstretch{1} \caption{\textbf{Effect of information about the Chooser.} \label{resultsFigChooserInfo}}
\vspace{-0.2cm}
\begin{centering}
\begin{tabular}{cc}
\textbf{A. Chooser impatient}  & \textbf{B. Chooser patient} \tabularnewline
\includegraphics[width=0.5\textwidth]{publicationFigures/preventionChooserInfoImpatient}  & \includegraphics[width=0.5\textwidth]{publicationFigures/preventionChooserInfoPatient} \tabularnewline
\end{tabular}
\par\end{centering}
\vspace{-0.2cm}
 \textbf{\scriptsize{}Notes:}{\scriptsize{} Whiskers indicate 95\%
confidence intervals with standard errors clustered by subject. Graph
based on data from the Chooser Information condition, in which decisions
involve menu 5.}{\scriptsize\par}\vspace{-0.3cm}
\end{figure}

Figure \ref{resultsFigChooserInfo}, which displays the frequency
of withheld options for each Chooser statement, establishes that Choice
Architects respond to this information. Panel A shows interventions
affecting Choosers who describe themselves as impatient. Choice Architects
impose more patience if an impatient Chooser claims to be unhappy
with his impatience than if he claims to be happy with it. Thus, Choice
Architects appear to select interventions that ostensibly help Choosers
achieve their own goals. For Choosers who describe themselves as patient
(Panel B), this pattern reverses. Choice Architects impose more patience
if a patient Chooser claims to be happy with his patience (perhaps
to prevent the Chooser from accidentally selecting impatient options)
than if he claims to be unhappy with it.

Column 4 of Table \ref{projPatInfo} makes these same points in a
regression format. The dependent variable is our measure of the Choice
Architect's mandate, defined as in the Section \ref{situationalDeterminants}.
Using data from the Chooser Information condition, we regress mandates
on indicators for each of the four Chooser self-descriptions (omitting
one), as well as session and order fixed effects, clustering standard
errors at the subject level. The results establish that Choice Architects'
mandates differ significantly, both economically and statistically,
across the four types of Choosers.\footnote{Appendix \ref{welfareBeliefsAppendix} shows how Choosers' self-descriptions
relate to Choice Architects' beliefs about the welfare effects of
chosen restrictions. Interventions continue to be consistent with
benevolent motives. The likelihood with which Choice Architects see
their interventions as helpful is highest when the Chooser is impatient
and generally unhappy with his choices, second highest when the Chooser
is patient and generally happy with his choices (possibly because
Choice Architects remove options they think Choosers might select
by accident), and lowest when the Chooser is either impatient and
happy or patient and unhappy (apparently because Choice Architects
are disinclined to enforce impatience).}

Next we explore the mechanisms through which the information we provide
affects interventions. The regression in column 5 of Table \ref{projPatInfo}
shows how Choice Architects' beliefs about Choosers' unrestricted
choices differ across the four self-descriptions. Recall that, in
Stage 2 of the laboratory component, we elicit Choice Architects'
beliefs about the selections Choosers would make from all unrestricted
menus. Here we regress the mean of the early payment for this elicited
distribution on indicators for the self-descriptions. The large and
statistically significant differences between the four groups establish
that the information changes Choice Architects' beliefs about the
Choosers' inclinations. However, it is also apparent from the same
regression that the information does not affect Choice Architects'
interventions \emph{only} by changing beliefs about Choosers' selections:
conditional on knowing that the Chooser is unhappy, also knowing that
he is patient rather than impatient drives beliefs and mandates in
\emph{opposite} directions (compare the pertinent coefficients in
columns 4 and 5). The product of the differences in coefficients is
negative and highly statistically significant ($p<0.01$).\footnote{To perform this test, we estimate columns 4 and 5 jointly in a two-equation
system OLS-regression with bootstrapped standard errors clustered
on the subject level (1,000 samples). }


\subsection{Potential confounds}

\label{otherMotives}

Paternalism is not the only possible motivation for intervention.
To test for specific spurious motives, we supplemented our experiment
with some additional conditions, which we briefly summarize in this
section. Finding little evidence of spurious motives, in particular
for interventions that Choice Architects regard as helpful to Choosers,
we conclude that our results primarily reflect paternalism. To conserve
space, details of these analyses appear in Appendix \ref{additionalConditions}.

The \emph{Induced Chooser Preferences} condition, which we mentioned
briefly in Section \ref{treatments}, addresses the concern that Choice
Architects may intervene out of a general desire either to keep busy
or to exercise active control over Choosers. By deploying Vernon Smith's
induced preferences paradigm \citep{Smith.InducedPreferences}, it
removes reasons to take issue with the Chooser's objectives while
preserving the spurious motives mentioned above, as well as paternalistic
motives unrelated to disapproval of preferences, such as the preemption
of accidental errors.

In this condition, each choice option consist of a pair of immediate
payments, one in Euros, the other in an experimental currency that
converts to Euros at the rate $\hat{\delta}_{i}$, which is known
only to Chooser $i$. Choice Architects merely know the population
distribution of exchange rates. Formally, these decision problems
resemble the intertemporal problems in the Main condition: one can
think of the two payments as ``immediate'' and ``delayed'' (even
though we pay both out right after the session) and interpret $\hat{\delta}_{i}$
as the Chooser's discount factor. The main difference is that, in
this setting, the Choice Architect has no basis for disputing the
normative validity of the Chooser's $\hat{\delta}_{i}$, because we
have exogenously induced it. In contrast, the use of induced preferences
does not remove motivations for intervention stemming from the desire
to keep busy or to exercise active control over Choosers.
 There are two rounds in this condition using, respectively, analogs
of menu 3 and menu 4 (see Table \ref{choiceSets}).

Choice Architects are substantially less likely to withhold options
in the Induced Chooser Preferences condition than in the Main condition.
Moreover, we do not see a pattern in the Induced Chooser Preferences
condition analogous to our finding in the Main condition that Choice
Architects become more inclined to limit impatient choices when impatience
is more costly. Choice Architects are also far less likely to believe
that interventions benefit the Chooser in the Induced Chooser Preferences
condition. Specifically, Choice Architects both restrict choice and
describe the restriction as helpful in 26.0\% in the Main condition
(excluding the rounds with front-end delay), compared to only 11.7\%
of the rounds in the Induced Chooser Preferences condition. We conclude
that the patterns of primary interest in the Main condition likely
reflect paternalism arising from Choice Architects' disapproval of
impatience rather than spurious motivations.

A limitation of the Induced Chooser Preferences condition is that
it cannot rule out a more nuanced hypothesis: Choice Architects may
indulge their desire to exercise active control only if they can rationalize
their actions as beneficial. A second purpose of the Exogenous Restrictions
condition, part of which we discussed in Section \ref{benevolence},
is to allow us to address this possibility.

To understand the logic of our test, consider a Choice Architect who
would have constructed the two-option opportunity set had the default
consisted of the three-option set. If we remove the least patient
option exogenously, constructing the two-option opportunity set no
longer involves an exercise of active control. To affect the outcome,
the Choice Architect must now remove the middle option, which she
can potentially rationalize as beneficial on the grounds that patience
is virtuous. Thus, if the motives for interventions are spurious,
the frequency with which Choice Architects withhold the middle option
should rise. Accordingly, this condition includes a round in which
the Choice Architect can remove options from a three-option choice
set (menu 6), and a round in which we first remove the least patient
option in that set exogenously. To account for unrelated boundary
effects and noisy choosers, we include two otherwise identical versions
of these rounds in which the default opportunity set consists of the
most patient option by itself, so that the Choice Architect adds options
instead of removing them.\footnote{Throughout this condition, when all three options are available, the
Choice Architect can withhold either the least patient option, or
the least patient and middle options; when we remove the least patient
option exogenously, the Choice Architects can withhold the middle
option. Thus, the Choice Architects' alternatives are more restricted
than in the Main condition. In addition, as discussed in Section \ref{welfareBeliefs},
when all three options are available, elicited beliefs about welfare
effects involve comparisons between the three-option set and either
the two-option set consisting of the most patient and middle options,
or the one-option set consisting of the most patient option. When
we remove the least patient option exogenously, these elicited beliefs
involve comparisons between the two-option set and the set consisting
of the most patient option. } These rounds disable the mechanism of concern, because a Choice Architect
who enjoys exercising power, but who regards patience as virtuous,
cannot rationalize the addition of impatient options. We find no evidence
that Choice Architects are primarily motivated by a desire to exert
control whenever they can rationalize active interventions as beneficial.

\section{Projective paternalism}

\label{determinants}

We have shown that Choice Architects often withhold options for paternalistic
reasons. But how do they decide whether particular options are good
or bad for Choosers? By definition, paternalists are hesitant to rely
on the judgments implicit in Choosers' decisions, and indeed may even
question whether Choosers are aware of their own best interests. And
yet we have also seen in Section \ref{paternalismInfo} that paternalists
are sensitive to Choosers' preferences and subjective experiences.
While Choice Architects evidently find this information relevant,
we require them to make decisions about interventions without knowing
much about the Choosers, as is often the case in real-world applications.
In this section, we seek to clarify how Choice Architects arrive at
their evaluations in such situations.


\subsection{Conceptual framework}

\label{model}

To draw sharp inferences concerning the mechanisms governing Choice
Architects' beliefs about Choosers, we interpret their behavior through
the lens of a formal theory. Here, we develop a model of the Choice
Architect's problem, precisely define various paternalistic mechanisms,
and then identify their distinctive empirical signatures so we can
distinguish among them.

\subsubsection{Setting\label{subsec:Preliminaries}}

There are two agents, a Choice Architect (she) and a Chooser (he).
Potential consumption opportunities for the Chooser are indexed by
a real number $c\in\mathbb{R}$. The Choice Architect can set a lower
bound (\emph{mandate}), $\underline{r}\in[-\infty,\infty)$, on the
set of available alternatives, where we interpret $\underline{r}=-\infty$
as the absence of a mandate. She can also set an upper bound (\emph{cap}),
$\overline{r}\in(-\infty,\infty]$, where we interpret $\overline{r}=\infty$
as the absence of a cap. The Chooser then selects an option in $[\underline{r},\overline{r}]$.
A surrogate choice is one in which the Chooser sets these bounds subject
to the restriction that $\underline{r}=\overline{r}$; we use $s$ to denote
this common value. For our intertemporal choice application, the index
captures the balance between earlier and later payments, and the Choice
Architect's selection of restrictions determines the most and least
patient choices a Chooser may make.


\paragraph{The Choice Architect's beliefs about Choosers}

Suppose the Choice Architect believes the Chooser would select $c$
from the unrestricted set $\mathbb{R}$, but that option $u$ would
be best for him. We refer to $u$ as the Chooser's \emph{ideal} choice,
and to $m=u-c$ as the Chooser's \emph{mistake} -- in each case,
\emph{according to the Choice Architect's subjective judgment}, which
may or may not coincide with (i) the Chooser's judgment, (ii) the
Choice Architect's belief about the Chooser's judgment, or (iii) any
external judgment.\footnote{For the intertemporal choice setting, the reader may be tempted to
think of $u$ and $m$ in terms of quasi-hyperbolic discounting and
the long-run criterion, but it is important to remember that our Choice
Architects do not generally embrace that perspective (see Section
\ref{FED}). These variables reflect whatever normative judgments
they actually apply.}

The Choice Architect has imperfect information about the Chooser,
and her beliefs about the Chooser's ideal choices and mistakes are
given by a cumulative distribution function $F(u,m)$. Next we describe
some additional assumptions concerning these beliefs.

In many domains, it is reasonable to assume that perceived mistakes
are unidirectional. Concerning intertemporal choice, there appears
to be a widespread perception that people struggle to act sufficiently
patiently rather than sufficiently impatiently. Indeed, as we saw
in Section \ref{InterventionResults}, the dominant pattern by far
in our experiment is for subjects to impose patience rather than impatience.
Accordingly, letting $P$ denote the probability measure induced by
$F$, we have: \begin{assumption}\label{unidirectionalAssumption}
$P(m\geq0)=1$.\footnote{Our results continue to hold when Assumption \ref{unidirectionalAssumption}
is violated as long as $P(m<0)$ is not too large.} \end{assumption} This assumption involves a sign convention: the
index of the ideal option is always at least as large as that of the
chosen option. Thus, for our intertemporal choice setting, higher
values of the index, $c$, denote greater patience, while $\underline{r}$
and $\overline{r}$ denote, respectively, the lowest and highest degrees
of patience the Choice Architect permits.\footnote{In a setting involving exercise, where the concern is that mistakes
generally involve inactivity, higher values of $c$ would correspond
to more frequent and vigorous workouts. In a setting involving nutrition,
where the concern is that mistakes generally involve excessive quantities
of unhealthy foods, higher value of $c$ would correspond to healthier
consumption.}

We also make some regularity assumptions concerning the Choice Architect's
beliefs. To facilitate the statement of these assumptions, we decompose
$F$ into two components, as follows. We assume the Choice Architect
believes the fraction $\gamma$ of Choosers

make normatively valid choices: $m$ is identically
equal to zero and the CDF of $u$ is given by $Q(u)$. Furthermore,
we assume the Choice Architect believes the fraction $1-\gamma$ of
Choosers

make normatively flawed choices: $m$ is strictly positive and the CDF of $(u,m)$
is given by $H(u,m)$. Thus, $F(u,m)=\gamma I_{m\geq0}Q(u)+(1-\gamma)H(u,m)$,
where $I_{m\geq0}$ equals 1 if $m\geq0$ and $0$ otherwise.\footnote{Studies in Behavioral Public Economics often employ the parallel assumption that the population includes a non-negligible group of ``rational'' consumers along with ``behavioral'' consumers \citep[see][]{BernheimTaubinsky}. Here our assumption concerns the Choice Architect's beliefs about Choosers, rather than their actual characteristics.}

With this structure in place, we can state our technical assumptions:
\begin{assumption}\label{Ref: Regularity}$Q$ and $H$ are atomless
CDFs with well-defined densities, and with  $\mathrm{supp}\left(Q\right)=\left(-\infty,\infty\right)$
and $\mathrm{supp}(H)=(-\infty,\infty)\times[0,\infty)$. Furthermore,
the mean of $u$ is finite for both $Q$ and $H$, and the mean of
$m$ is finite for $H$.\end{assumption}

\paragraph{Objective function}

The Choice Architect constructs the Chooser's opportunity set to maximize
her perception of the Chooser's expected welfare. Specifically, the
Choice Architect believes that if a Chooser's selection $c$ differs
from his ideal $u$, he sustains a welfare loss of $-l(c-u)$. We
assume that $l(z)$ is differentiable with $l(0)=0$, and that $z$
and $l'(z)$ have opposite signs, so that $z=0$ maximizes $l(z)$.
We also assume for technical simplicity that $\left|l'\right|$
is bounded above, and that it is bounded away from zero outside any
neighborhood of $0$.\footnote{This assumption guarantees the existence of various integrals used
in the proofs of our formal results. We can relax the assumption as
long as the existence of these integrals is maintained. }

We can write the Choice Architect's objective function as follows:
\begin{equation}
W(\underline{r},\overline{r})=\int l\big(\varphi_{u,m}(\underline{r},\overline{r})-u\big)dF(u,m),\label{welfareFct}
\end{equation}
where $\varphi_{u,m}(\underline{r},\overline{r})$ denotes the Choice
Architect's belief about the selection a Chooser of type $(u,m)$
will make when choosing from the restricted set $[\underline{r},\overline{r}]$.
We assume the Choice Architect believes the Chooser will select $c=u-m$
if that option is available, and the next closest available option
otherwise. Accordingly, $\varphi_{u,m}(\underline{r},\overline{r})=\max\{\min\{c,\overline{r}\},\underline{r}\}$.

It would be straightforward to add a term, $\kappa(\underline{r},\overline{r})$,
to the objective function for the purpose of capturing the Choice
Architect's feelings (either positive or negative) about restricting
the Choosers' options, other than those arising from anticipated effects
on the Chooser's welfare. For simplicity we focus on the case of $\kappa\equiv0$.\footnote{Our formulation of the Choice Architect's problem, together with the
assumption $\kappa=0$, rules out three types of motives that could
affect interventions. First, we exclude the possibility that Choice
Architects intervene due to a desire to exert control, which we would
represent by assuming that $\kappa(\underline{r},\overline{r})>0$ for
$\underline{r}\neq-\infty$ and/or $\overline{r}\neq\infty$. This assumption
is consistent with the experimental results in Section \ref{otherMotives}.
Second, we abstract from pure libertarianism, which we might otherwise
model by assuming that, for all restrictions, $\kappa$ is negative and extremely
large in magnitude for some fraction of the population. Assuming this concern for
autonomy is uncorrelated with other aspects of preferences, libertarians
and non-libertarians will behave identically when forced to make surrogate
choices for others, consistent with our experimental results in Section
\ref{projectivePaternalism} below. Third, in our model, the Choice
Architect is concerned only with the Chooser's outcome, not with his
choice process. For example, Choice Architects do not restrict Choosers'
opportunity sets to save them the cognitive costs of making more complex
choices, or to spare them the disutility associated with overcoming
temptation (as in \citet{ThalerShefrin} or implicitly in \citet{GulPesendorfer.Temptation}).
Importantly, we structure our experiment in a way that eliminates
motives for intervention such as the cognitive costs or temptation
experienced by Choosers.}

Our results concern the comparative statics governing the optimal
restrictions $[\underline{r}^{*},\overline{r}^{*}]$, defined as the
values that maximize expression (\ref{welfareFct}), as well as the
optimal surrogate choices $s^{*}$.


\paragraph{The Choice Architect's own choices}

Our empirical analysis focuses in large part on the relation between
the choices Choice Architects make for themselves and the restrictions
they impose on others. Accordingly, let $c^{A}$ denote the selection
the Choice Architect makes for herself when faced with the same decision
problem as above. She believes that option $u^{A}$ would be ideal.
If $u^{A}\neq c^{A}$, she acknowledges that her actual choice $c^{A}$
involves a mistake $m^{A}=u^{A}-c^{A}$.

Our next assumption, which concerns the distribution of types among
Choice Architects, will eventually allow us to connect Choice Architects'
own choices, $c^{A}$, to their mandates, $\underline{r}^{*}$.
Formally, define $G_{m^{A}}(\cdot|c^{A})$ as the marginal distribution
of mistakes, and $G_{u^{A}}(\cdot|c^{A})$ as the marginal distribution
of ideals, among Choice Architects who choose $c^{A}$ for themselves.
We say that a CDF $\Gamma$ for a variable $x$ is increasing in a
parameter $\theta$ if, for all $\theta'>\theta''$, $\Gamma(\cdot|\theta')$
first-order stochastically dominates $\Gamma(\cdot|\theta'')$. \begin{assumption}
\label{CAbehavior}\
$G_{u^{A}}(\cdot|c^{A})$ is increasing in $c^{A}$ and $G_{m^{A}}(\cdot|c^{A})$
is decreasing in $c^{A}$. \end{assumption} Intuitively, given the
identity $c^{A}=u^{A}-m^{A}$, a higher value of $c^{A}$ tends to
indicate that $u^{A}$ is higher and $m^{A}$ is lower. Formally,
the assumption rules out a strong positive correlation between ideals
and mistakes among Choice Architects.\footnote{The assumption is satisfied when mistakes and ideals are independently
distributed, when they are negatively correlated, and when they are
not too strongly positively correlated. If $u^{A}$ and $m^{A}$ follow
a bivariate Gaussian distribution with variances $\sigma_{u^{A}}^{2}$
and $\sigma_{m^{A}}^{2}$, respectively, and correlation $\rho^{A}$,
then Assumption \ref{CAbehavior} is satisfied iff $\rho^{A}<\min\{\frac{\sigma_{u^{A}}}{\sigma_{m^{A}}},\frac{\sigma_{m^{A}}}{\sigma_{u^{A}}}\}$.}

\subsubsection{The paternalist's decision} \label{PaternalistTypes}

We begin with the fundamental trade-off between commitment and flexibility
inherent in the paternalist's optimization problem. If the Choice
Architect knew everything about the Chooser including his preferences,
she would form a view as to which alternative is best for him and
then eliminate any potential for mistakes by removing all other options.
However, when the Choice Architect is not perfectly informed about
the Chooser, any restriction may end up ruling out the best
choice. As a result, optimal interventions often leave the Chooser with
a degree of autonomy. Moreover, under Assumption \ref{unidirectionalAssumption}
(unidirectional mistakes), an upper bound on the choice of $c$ imposes
costs on Choosers with high values of $u$ without preventing any
mistakes. Accordingly, as shown in the following proposition, while
the optimal intervention may involve a mandate $(\underline{r}^{*}>-\infty)$,
it never includes a cap (i.e., we always have $\overline{r}^{*}=\infty$).\footnote{For our intertemporal choice problem, given our sign convention, $\underline{r}^{*}$
is the minimum degree of patience the Choice Architect permits, and
hence the maximum amount the Chooser can elect to receive early. Likewise,
$\overline{r}^{*}$ is the maximum degree of patience the Choice Architect
permits, and hence the minimum amount the Chooser can elect to receive
early.} The proposition also guarantees the existence of optimal mandates
and surrogate choices. All proofs appear in Appendix \ref{appendixProofs}.

\begin{proposition}\label{noUpperBound} \
 \begin{compactenum}

\item A Choice Architect does not impose a cap on the Chooser's
options: $\overline{r}^{*}=+\infty$.

\item A Choice Architect may impose a mandate $\underline{r}^*>-\infty$ on the Chooser's
options, but always leaves the Chooser with some flexibility: the
set of optimizers is non-empty, closed, and
bounded.\footnote{The existence of residual flexibility hinges on our assumption that
the underlying set of feasible options is unbounded. When the set
of feasible options is bounded (as in our experiment), it may be optimal
for the Choice Architect to eliminate all discretion.}

\item  A Choice Architect's optimal surrogate choice, $s^{*}$, is well-defined: The set of optimal
surrogate choices is non-empty, closed, and bounded.

\end{compactenum} \end{proposition}

We cannot guarantee the uniqueness of the optimal mandate or surrogate
choice without additional technical restrictions. In what follows, we assume that the Choice Architect breaks ties in favor of the Chooser's autonomy and thus selects the smallest maximizer (which, according to the proposition, is well-defined). The consistent application of other rules, such as selecting the largest
maximizer, yields identical conclusions.

\paragraph{Varieties of paternalism}

We distinguish between various modes of paternalism, differentiated
by the way in which the Choice Architect's beliefs about Choosers
depend on her own type, $(u^{A},m^{A})$. A \emph{projective paternalist}
forms her views about others by extrapolating from her understanding
of herself. A Choice Architect exhibits\emph{ mistakes-projective
paternalism} to the extent she thinks others are similar to her with
respect to the mistakes they make. A Choice Architect exhibits \emph{ideals-projective
paternalism} to the extent she thinks others either have, or should
have, ideals similar to her own. Naturally, a projective paternalist
can be both mistakes-projective and ideals-projective. In contrast,
a \emph{conventional behavioral welfarist} acts as a benevolent social
planner whose information about others may be incomplete, but who has accurate information about the underlying population
distributions of key decision parameters, as envisioned in many standard
behavioral welfare analyses \citep[see, e.g.,][]{BernheimTaubinsky}.
She does not consider her own inclinations when intervening in others'
choices.

To define these concepts precisely, we specialize to a semi-parametric
representation of the Choice Architect's beliefs. Specifically, we
assume that the distribution of beliefs over $(u,m)$ is bivariate
Gaussian, with parameters that depend on the Choice Architect's characteristics,
as specified below. To maintain Assumption \ref{unidirectionalAssumption},
we truncate $m$ at zero:\footnote{It is straightforward to generalize all of our results to a setting
with two jointly Gaussian latent characteristics, $z_{u}$ and $z_{m}$,
such that $u$ is a function of $z_{u}$, $u(z_{u})$, and $m$ is
function of $z_{m}$, $m(z_{m})$, where $u$ and $m$ are increasing
in their arguments, in each case strictly so on some open interval.
This approach allows us to accommodate our assumption that $P(m\geq0)=1$
in a variety of ways without ruling out natural distributional forms.
For example, as an alternative to truncation, one could then assume
that $m(z_{m})=\alpha\exp(z_{m})$.} \black


\begin{assumption}\label{Gaussian} Let $(u,z_{m})\sim\mathcal{N}(\mu_{u},\mu_{m},\sigma_{u},\sigma_{m},\rho$).
Then $(u,m)=\big(u,\max\{0,z_{m}\}\big)$. \end{assumption}


This assumption yields a mass point at $m=0$ that corresponds to
the share $\gamma$ of Choosers who the Choice Architect believes behave according to their ideals. Thus it implies specific functional forms for $Q$ and $H$. We now turn to our formal definitions.

\begin{definition}\ \label{types} \begin{compactenum}

\item Choice Architects practice \emph{mistakes-projective paternalism}
if $\mu_{m}$ is strictly increasing and $\mu_{u}$ is weakly increasing
in $m^{A}$.

\item Choice Architects practice \emph{ideals-projective} paternalism
if $\mu_{u}$ is strictly increasing and $\mu_{m}$ is weakly increasing
in $u^{A}$.

\item A Choice Architect is a \emph{conventional behavioral welfarist}
if $\mu_{u}$ and $\mu_{m}$ are the actual population means (and
hence independent of the Choice Architect's type).

 \end{compactenum} \end{definition}

Each of the first two definitions involves a \emph{direct effect} and
a \emph{compensatory effect}. The direct effects are intuitive: to
the extent Choice Architects assume others either share or should
share their ideals, we would expect $\mu_{u}$ to be strictly increasing
in $u^{A}$ (and similarly for mistakes). To understand the compensatory
effects, imagine Choice Architects have sufficient information about
others' behavior to correctly identify the population average of unrestricted
choice (i.e., the mean of $c$, $\mu_{c}$). Then, if an increase
in $u^{A}$ produces an increase in $\mu_{u}$, it must also produce
a fully compensating increase in $\mu_{m}$. Likewise, with imperfect
information about $\mu_{c}$, one would expect a partially compensating
increase in $\mu_{m}$. Similar considerations apply to the inferences
about others Choice Architects draw from their own mistakes.\footnote{Our notions of projective paternalism assume that the inferences a
Choice Architect draws from her own characteristics pertain to the
means, $\mu_{u}$ and $\mu_{m}$, rather than to the variances or
correlations, which we treat as invariant across Choice Architects.
For the sake of tractability, we abstract from all forms of heterogeneity
among Choice Architects other than in $u^{A}$ and $m^{A}$.}

\paragraph{Testable implications}

Our main theoretical result uses Assumption \ref{CAbehavior} to show
that each mode of paternalism has a distinctive empirical signature
involving the sign of the correlation between, on the one hand, the
selections Choice Architects make for themselves, and on the other
hand, their mandates and surrogate choices.

\begin{proposition}\label{mainProp}\
 \begin{compactenum}

\item Through the mistakes-projective mechanism, the distributions
of the optimal mandate $\underline{r}^{*}$ and optimal surrogate
choice $s^{*}$ conditional on the Choice Architect's own selection,
$c^{A}$, are decreasing in $c^{A}$ (weakly in the case of surrogate
choices).\footnote{Recall that, when we say a distribution is increasing or decreasing
in a parameter, we mean in the sense of first-order stochastic dominance.} \medskip{}

\item Through the ideals-projective mechanism, the distributions
of the optimal mandate $\underline{r}^{*}$ and optimal surrogate
choice $s^{*}$ conditional on the Choice Architect's own selection,
$c^{A}$, are increasing in $c^{A}$. \medskip{}

\item A conventional behavioral welfarist's optimal mandate $\underline{r}^{*}$
and optimal surrogate choice $s^{*}$ are independent of her own choice
$c^{A}$. \end{compactenum} \end{proposition}

The intuition for Proposition \ref{mainProp} is straightforward.
Under Assumption \ref{CAbehavior}, an increase in $c^{A}$ generates
an upward shift in the distributions of $u^{A}$ and a downward shift
in the distribution of $m^{A}$. Concretely, a consumer who acts more
patiently probably aspires to greater patience and is less prone to
mistakes involving excessive impatience. Through ideals-projective
paternalism, the increase in $u^{A}$ leads to an upward shift in
the distribution of $u$ (the main effect), and possibly an upward
shift in the distribution of $m$ (the compensatory effect). Both
of these effects result in higher mandates -- concretely, greater
enforcement of patience. Through mistakes-projective paternalism,
the decrease in $m^{A}$ leads to a downward shift in the distribution
of $m$ (the main effect) and possibly a downward shift in the distribution
of $u$ (the compensatory effect). Both of these effects result in
lower mandates -- concretely, less enforcement of patience. As a
result, greater patience when acting for oneself goes hand-in-hand
with the imposition of greater patience to the extent Choice Architects
are ideals-projective paternalists, and with the imposition of less
patience to the extent they are mistakes-projective paternalists.
For a conventional behavioral welfarist, own choices play no role.
The intuition for surrogate choices is essentially the same, except
that surrogate choice disables effects operating through $m$.

Accordingly, in the next subsection, we examine the empirical relationships
between, on the one hand, the patience Choice Architects display when
choosing for themselves and, on the other hand, their mandates and
surrogate choices. We ask whether these relationships are consistent
with the signature patterns implied by ideals-projective or mistakes-projective
paternalism.


\subsection{Distinguishing between variants of projective paternalism}

\label{projectivePaternalism}


\paragraph{The relationship between patience and mandates}

We have seen that mistakes-projection induces a \emph{negative} relation
between the options Choice Architects select for themselves and those
they force on others, whereas ideals-projection induces a \emph{positive}
relation, and conventional behavioral welfarism implies the absence
of a relation. To differentiate between these hypotheses, we study
this relationship empirically. First we construct a measure of the
degree of patience the Choice Architect displays when choosing for
herself in the experiment's online component. Specifically, we calculate
the percentile rank of the number of months she is willing to delay
the receipt of the larger payment averaged over the six decision lists.
Measuring patience in this way enables us to avoid assumptions about
the structure of Choice Architects' intertemporal preferences. To
avoid ambiguity, we focus on the 291 (of 303) Choice Architects who
respected monotonicity in all of these lists.\footnote{The 3.97\% of subjects with multiple switches is low compared to other
studies using multiple-decision lists, such as \citet{HoltLaury}.} Next we relate this measure of patience to Choice Architects' interventions
in the Main condition, using the \emph{Mandate} variable defined in
Section \ref{situationalDeterminants} (i.e., the maximum amount the
Choice Architect permits the Chooser to receive immediately).

\begin{figure}[htbp!]
\setstretch{1} \caption{\textbf{Ideals-projective paternalism.} \label{projPaternalismFigure} }
\vspace{-0.3cm}
\begin{centering}
\includegraphics[width=0.55\textwidth]{publicationFigures/projectivePaternalismPaperNEW}
\par\end{centering}
\vspace{-0.2cm}
 \textbf{\scriptsize{}Notes:}{\scriptsize{} Dependent variable: Maximum
amount of money the Chooser is allowed to receive immediately as function
of the Choice Architects' own patience. Sample: We exclude subjects
classified as libertarian. Patience percentiles are unrelated to libertarianism,
see Table \ref{projPaternalism}. Whiskers indicate 95\% confidence
intervals. }{\scriptsize\par}\vspace{-0.3cm}
\end{figure}

Figure \ref{projPaternalismFigure}, which excludes libertarian subjects
(38\% of our sample), depicts our main result: those who have chosen
more patiently for themselves in the online component of our experiment
also impose significantly more patience on Choosers. The most patient
non-libertarian Choice Architects' mandates are stricter than those
of the least patient Choice Architects by about \EURdig1. This difference
is just under two-thirds of the standard deviation of mandates (\EURdig1.64)
across this population, and one-fifth of the greatest possible effect
(\EURdig5) in our experiment. According to Proposition \ref{mainProp}
in Section \ref{PaternalistTypes}, this finding is consistent with
the signature pattern associated with ideals-projective paternalism.


\begin{table}[htb!]
\setstretch{1} \caption{\textbf{Relationships between Choice Architects' patience and measures
of their mandates, surrogate choices, and welfare beliefs.} \label{projPaternalism}}
\vspace{-0.3cm}

\begin{centering}
\begin{tabular}{lcccccc}
\toprule
 & (1)  & (2)  & (3)  & (4)  & (5)  & (6) \tabularnewline
VARIABLES  & Mandate  & Mandate  & Libertarian  & Surrogate  & Surrogate  & Welfare \tabularnewline
 &  &  &  & choice  & choice  & belief \tabularnewline
Non-libertarian  &  &  &  &  &  & \tabularnewline
\hspace{0.4cm}subjects only  & Yes  &  &  & Yes  &  & Yes \tabularnewline
\midrule
 &  &  &  &  &  & \tabularnewline
Patience \%-ile  & -1.044{*}{*}{*}  & -0.614{*}{*}{*}  & 0.011  & -1.589{*}{*}{*}  & -1.655{*}{*}{*}  & 0.509{*}{*}{*} \tabularnewline
 & (0.270)  & (0.197)  & (0.091)  & (0.242)  & (0.183)  & (0.130) \medskip{}
\tabularnewline
Mean of dep. var.  & 3.080  & 3.633  & 0.385  & 0.876  & 0.865  & 0.285 \tabularnewline
 & (0.091)  & (0.067)  & (0.029)  & (0.088)  & (0.069)  & (0.044) \medskip{}
\tabularnewline
Observations  & 537  & 873  & 291  & 518  & 837  & 537 \tabularnewline
Number of subj.  & 179  & 291  & 291  & 179  & 291  & 179 \tabularnewline
\bottomrule
\end{tabular}
\par\end{centering}

\vspace{-0.1cm}
 \medskip{}
\textbf{\scriptsize{}Notes:}{\scriptsize{} {Method}: OLS. Unit of
observation: subject-round pairs for column 1, 2, 4, 5 and 6; subjects
for column 3. Dependent variables: }\emph{\scriptsize{}Mandate}{\scriptsize{}
is the maximum amount the Choice Architect allows the Chooser receive
immediately (as in Section \ref{situationalDeterminants}). }\emph{\scriptsize{}Libertarian}{\scriptsize{}
is a dichotomous variable indicating that the Choice Architect never
withholds options. }\emph{\scriptsize{}Surrogate choice}{\scriptsize{}
is the amount the Chooser receives immediately if the Choice Architect's
surrogate choice is implemented. }\emph{\scriptsize{}Welfare belief
}{\scriptsize{}is the Choice Architect's belief about the welfare
effect of the chosen restriction (as in Section \ref{benevolence}).
Controls: }\emph{\scriptsize{}Patience \%-ile}{\scriptsize{} is the
Choice Architect's percentile rank according to the average number
of months she is willing to delay the receipt of the larger payment
in the online tasks. Other controls include session, order, and (with
the exception of columns 3 and 6) menu fixed effects. Samples: All
regressions limited to subjects who responded monotonically to all
multiple-decision lists in the online component. Columns 1, 2, and
3 based on rounds from the Main condition without front-end delay.
Columns 4 and 5 based on the corresponding surrogate choice rounds
from Stage 2 of the laboratory component. The number of observations
is smaller for surrogate choices because some of these choices were
not recorded in the first two sessions. Column 6 based on the same
two rounds of the Exogenous Restrictions condition used in Section
\ref{benevolence}. Columns 1, 4, and 6 restricted to non-libertarian
subjects. Standard errors: clustered at the subject level. $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$. }{\scriptsize\par}
\vspace{-0.3cm}
\end{table}

We formalize these observations by regressing Choice Architects' mandates
in the Main condition on their patience percentiles. The unit of observation
is a single decision in a single round. We control for session, order,
and menu fixed effects, and cluster standard errors by subject. For
the regression in column 1 of Table \ref{projPaternalism}, we exclude
libertarians. As Figure \ref{projPaternalismFigure} suggests, we
find that the mandate tightens by \EURdig1.04 as patience rises from
the bottom of the population distribution to the top, and the effect
is highly statistically significant. Column 2 shows that the effect
is smaller (\EURdig0.61) but still highly statistically significant
once all subjects, including the libertarians, are included. The estimated
effect is attenuated because libertarians never intervene, and because
a Choice Architect's patience percentile does not predict whether
she is libertarian (see column 3, which reports a subject-level regression
of a libertarian indicator variable on the Choice Architect's patience
percentile).

\paragraph{The relationship between patience and surrogate choices}

Next we ask whether Choice Architects differ in their judgments about
what is good for Choosers, or merely in their propensities to intervene
based on those judgments. To address this question, we examine Choice
Architects' \emph{surrogate choices}. Specifically, as mentioned in
Section \ref{treatments}, in Stage 2 of the laboratory session we
require Choice Architects to select a single item for the Chooser
from each menu encountered previously in the experiment. Here we study
each Choice Architect's surrogate choices for all of the Main-condition
menus, excluding the one with front-end delay. Because these surrogate
choices force Choice Architects to intervene, they directly reveal
judgments about what is good for the Chooser. Column 4 shows that
among non-libertarian subjects, the relation between Choice Architects'
surrogate choices and their patience percentiles is even stronger
than the relation between their mandates and their patience percentiles.
Thus, the latter relationship reflects differences in judgments about
what is good for the Chooser, and not merely differences in the propensity
to intervene. Column 5 replicates column 4 using the entire sample.
As the coefficient of interest changes only slightly, we infer that
libertarians are similar to non-libertarians with respect to their
judgments about others, except for their willingness to intervene.
The positive relation between surrogate choices and own choices is
part of the signature pattern associated with ideals-projective paternalism.

\paragraph{The relationship between patience and welfare beliefs}

Next we ask whether patience on the part of Choice Architects is related
to their beliefs about the benefits of exogenously specified restrictions.
For this purpose, we use data from the Exogenous Restrictions condition,
much as in Section \ref{benevolence}. Regressing our measure of beliefs
about the welfare effects of restrictions on our measure of the Choice
Architect's patience along with session and order fixed effects, we
find that more patient Choice Architects are more likely to believe
patience-promoting interventions are beneficial (see column 6 of Table
\ref{projPaternalism}).\footnote{Using our incentivized measure of negative compensating variation,
the corresponding coefficient estimate is 0.191 (s.e. 0.094).} Though not a formal implication of Proposition \ref{mainProp}, this
finding goes hand-in-hand with the tendency for more patient Choice
Architects to impose greater patience, and it suggests that the key
comparative static is indeed a consequence of paternalism.

\paragraph{The relationship between front-end delay and mandates}

In Section \ref{situationalDeterminants}, we rejected the hypothesis
that Choice Architects intervene to control perceived present bias
by showing that the introduction of front-end delay increases the
value Choice Architects believe Choosers derive from restrictions.
According to our estimates, it also increases the restrictiveness
of interventions, and while that effect is not statistically significant,
we certainly find no evidence that interventions decline. A natural
explanation for this initially puzzling phenomenon now emerges: it
is simply a consequence of ideals-projective paternalism. First recall
that the introduction of front-end delay causes Choice Architects
to behave more patiently when choosing for themselves. Perhaps because
they are not fully aware of their inconsistencies (as evidence from
\citealp{Fedyk}, implies), they behave as if their ideal level of
patience, $u^{A}$, is higher with front-end delay than without (consistent
with our Assumption \ref{CAbehavior}). Projecting this greater level
of ideal patience onto others, they impose more restrictive mandates.
Any compensatory change in their beliefs about others' susceptibility
to mistakes reinforces this tendency.

\subsection{Are interventions misguided? \label{subsec:Predictions}}

The fact that people engage in projective paternalism does not necessarily
mean their interventions are objectively misguided. For example, an
ideals-projective paternalist could have correct beliefs about the
choices others would make if given the freedom to choose, but nevertheless
decompose each of those choices into an ideal component and a mistaken
component based on her own subjective values. To conclude that projective
paternalists err when tightening or loosening constraints on others'
choices, we must first demonstrate that their beliefs about others
are systematically mistaken, and then confirm that those beliefs impact
their interventions.

We begin by demonstrating that Choice Architects suffer from \emph{false
consensus bias} with respect to their' beliefs about others' choices.
For the regression shown in column 1 of Table \ref{projPat2}, the
dependent variable is the Choice Architect's beliefs about the mean
amount a Chooser will receive immediately if allowed to choose without
restrictions from a given menu, calculated from the distribution elicited
in the relevant round of Stage 2 during the laboratory session (as
in Section \ref{situationalDeterminants}). Focusing on the menus
presented in the Main condition without front-end delay, we relate
this variable to the Choice Architect's own patience percentile, defined
as in Section \ref{projectivePaternalism}. As with all other regressions
in the table, we exclude libertarian subjects, control for session,
order, and menu fixed effects, and present standard errors clustered
at the subject level. Striking evidence of the false consensus effect
emerges, in the sense that people tend to believe others will behave
as they themselves behave. Compared to the most patient Choice Architects,
the least patient ones think unrestricted Choosers would elect to
receive an additional \EURdig1.30 immediately (26\% of the maximum,
\EURdig5, that Choosers can receive immediately).

Next we ask whether Choice Architects' skewed perceptions of others'
inclinations infect the restrictions they impose. We have already
seen in Section \ref{projPatInfo} that Choice Architects tailor their
interventions based on information they receive about Choosers' propensities.
As a general matter, one would therefore expect to find a strong relationship
between their beliefs about others' unrestricted choices and their
chosen restrictions. The hypothesized relationship is precisely what
we observe in column 2 of Table \ref{projPat2}. For this regression,
the dependent variable is the Choice Architect's mandate, defined
once again as the maximum amount she allows the Chooser to receive
immediately (see Section \ref{situationalDeterminants}). Focusing
on the menus presented in the Main condition without front-end delay
and excluding libertarian subjects, we relate this variable to a measure
of the Choice Architects' beliefs about the Chooser's impatience.
To maintain comparability of the coefficient estimate to the effect
of the Choice Architects' own patience, we use the percentile rank
of the inverse of the mean amount she believes the Chooser will receive
immediately if allowed to choose without restrictions. We also control
for the Choice Architect's patience percentile. Because the Choice
Architect's own preferences could drive their interventions directly
while also incidentally impacting their beliefs about others through
the false consensus effect, the omission of this control variable
could potentially induce spurious correlation between the Choice Architect's
mandate and her beliefs. The significant negative coefficient for
the beliefs percentile implies, as expected, that Choice Architects
impose more restrictive mandates when they believe Choosers are more
patient. Indeed, Choice Architects' mandates are more strongly related
to their percentiles ranks according to their beliefs about Choosers'
unrestricted selections than to their percentiles ranks according
to their own preferences.\footnote{A possible objection to the regression in column 2 is that, because
the two percentiles are strongly correlated, the regression may spuriously
load their effects on the one that contains less measurement error.
Appendix \ref{twoSLS} displays the results of a two-stage least-squares
version of the same regression that addresses this concern. We find
that the coefficient of the beliefs percentile more than doubles,
and it remains highly statistically significant, while the coefficient
of the patience percentile changes sign, declines slightly in magnitude,
and is statistically insignificant.}


\begin{table}[htb!]
\setstretch{1} \caption{\textbf{Accuracy and relevance of beliefs.} \label{projPat2}}
\vspace{-0.3cm}


\begin{centering}
\begin{tabular}{lcccccc}
\toprule
 & (1)  & (2)  & (3)  & (4)  & (5)  & \tabularnewline
VARIABLES  & Belief  & Mandate  & Believed  & Actual  & More restrictive  & \tabularnewline
 & mean  &  & relevance  & relevance  & than believed  & \tabularnewline
\midrule

 &  &  &  &  &  & \tabularnewline
Patience \%-ile  & -1.272{*}{*}{*}  & -0.540{*}  & 0.011  & 0.041{*}{*}{*}  & 0.320{*}{*}{*}  & \tabularnewline
 & (0.167)  & (0.318)  & (0.029)  & (0.013)  & (0.064) \medskip{}
  & \tabularnewline
Belief \%-ile  &  & -1.136{*}{*}{*}  &  &  &  & \tabularnewline
 &  & (0.365)  &  &  & \medskip{}
  & \tabularnewline
Mean of dep. var.  & 1.467  & 3.100  & 0.095  & 0.056  & 0.209  & \tabularnewline
 & (0.062)  & (0.089)  & (0.010)  & (0.004)  & (0.023)  & \tabularnewline
\medskip{}
  &  &  &  &  &  & \tabularnewline
Observations  & 537  & 537  & 537  & 537  & 537  & \tabularnewline
Number of subjects  & 179  & 179  & 179  & 179  & 179 & \tabularnewline
\bottomrule
\end{tabular}
\par\end{centering}

\medskip{}
\textbf{\scriptsize{}Notes:}{\scriptsize{} Method: OLS. Unit of observation:
subject-round pairs. Dependent variables: }\emph{\scriptsize{}Belief
mean}{\scriptsize{} is the Choice Architect's belief about the mean
amount a Chooser will receive immediately if allowed to choose without
restrictions (as in Section \ref{situationalDeterminants}). }\emph{\scriptsize{}Mandate}{\scriptsize{}
is the maximum amount the Choice Architect allows the Chooser to receive
immediately (as in Section \ref{situationalDeterminants}). }\emph{\scriptsize{}Believed
relevance}{\scriptsize{} is the frequency with which the Choice Architect's
mandate would bind according to her beliefs about Choosers' unrestricted
selections. }\emph{\scriptsize{}Actual relevance}{\scriptsize{} is
the frequency with which the Choice Architect's restriction actually
binds according to Choosers' unrestricted selections. }\emph{\scriptsize{}More
restrictive than believed}{\scriptsize{} is a dichotomous variable
indicating whether the actual relevance is greater than the believed
relevance. Controls: }\emph{\scriptsize{}Patience \%-ile}{\scriptsize{}
is the Choice Architect's percentile rank according to the average
number of months she is willing to delay the receipt of the larger
payment in the online tasks. }\emph{\scriptsize{}Beliefs \%-ile}{\scriptsize{}
is the Choice Architects' percentile rank in terms of her beliefs
about the Chooser's patience, as measured by the inverse of the mean
amount the Chooser will receive immediately if allowed to choose without
restrictions. Additional controls include session, order, and menu
fixed effects. Samples: Regressions limited to non-libertarian subjects
who responded monotonically to all multiple-decision lists in the
online component.
Column 1 based on rounds from Stage 2 of the laboratory component
that elicited Choice Architects' beliefs concerning Choosers' selections
for the same menus as in the Main condition (excluding the one with
front-end delay). Columns 2-6 based on rounds from the Main condition
without front-end delay. Columns 4 and 6 also use data on Choice Architects'
beliefs concerning Choosers' selections, gathered in Stage 2 of the
laboratory component, in combination with the Choice Architect's chosen
restriction to construct the dependent variable. Columns 5 and 6 also
use data on unrestricted choices, gathered in Stage 2 of the laboratory
component, in combination with the Choice Architects' chosen restrictions
to construct the dependent variable. Standard errors: clustered at
the subject level. $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$. }{\scriptsize\par}
\vspace{-0.3cm}
\end{table}

So far, we have shown that false consensus bias infects Choice Architects'
beliefs about others' choices, and that those beliefs are closely
related to their mandates. It follows that systematic bias also infects
Choice Architects' decisions about interventions, which means these
decisions are objectively misguided, even accepting the Choice Architects'
aims. For a stark illustration of this point, see columns 3 through
5 of Table \ref{projPat2}. For column 3, the dependent variable is
the ``believed relevance'' of the restriction the Choice Architect
imposes, defined as the fraction of Choosers for whom the Choice Architect
thinks the mandate would bind. Specifically, for each menu encountered
in the Main condition (excluding the round with front-end delay),
we identify the least patient choice the Choice Architect permits,
and then calculate the frequency with which the Choice Architect believes
unrestricted Choosers would select less patient options according
to the subjective distribution elicited in Stage 2 of the laboratory
session. We relate this variable to the Choice Architect's patience
percentile and the usual fixed effects. As indicated in the table,
these subjects believe, on average, that they force 9.5\% of Choosers
to choose more patiently.

The regression reveals no systematic relation between these probabilities
and Choice Architects' own patience: more patient Choice Architects
do not intend to impose restrictions that enforce patience with higher
frequency. And yet, that is precisely what they do. For column 4,
the dependent variable is the ``actual relevance'' of the Choice
Architect's restriction, defined as the fraction of Choosers for whom
the restriction would actually bind. Specifically, for each menu encountered
in the Main condition (excluding the round with front-end delay),
we identify the least patient option the Choice Architect permits,
and calculate the frequency with which unrestricted individuals choose
less patiently than that.\footnote{For the actual unrestricted choice frequencies, we use the distribution
of selections Choice Architects make for themselves from each of the
pertinent menus in Stage 2 of the laboratory session (see Appendix
\ref{caChoicesSection}). Because there is only one Chooser for every
four Choice Architects and each Chooser makes a single unrestricted
choice, estimated frequencies based on Chooser data would be noisy.
Significantly, we select Choice Architects and Choosers from the same
population, and Choice Architects are aware of this fact.}

Because more patient Choice Architects impose more stringent mandates,
an increase in the Choice Architect's patience percentile significantly
increases the fraction of Choosers affected by the selected restriction.
As a result, more patient Choice Architects are substantially more
likely to underestimate the restrictiveness of their interventions.
A simple tabulation reveals that 29.5\% of Choice Architects in the
most patient (non-libertarian) quartile underestimate the restrictiveness
of their mandates, compared with only 9.0\% of those in the least
patient quartile. This conclusion also emerges from the regression
in column 5, which employs a dichotomous dependent variable indicating
whether the actual effect of the Choice Architect's restriction is
greater than she believes. The estimated coefficient for the Choice
Architect's patience percentile is highly statistically significant,
which means that greater patience implies a significantly greater
propensity to underestimate the restrictiveness of chosen mandates.\footnote{While we are mainly interested in the effect of patience on misperceptions
of restrictiveness, it is worth noting that most of our Choice Architects
actually overstate the restrictiveness of their chosen mandates. We
interpret this finding with caution, however, due to the well-known
tendency for elicited beliefs to be biased towards uniformity. Here,
average beliefs about the distribution of choices across types of
options (56\%, 25\%, and 19\% for the most, second-most, and least
patient options, respectively) are more uniform than the actual frequencies
(78\%, 14\%, and 8\%), and this pattern is responsible for the apparent
prevalence of overstated restrictiveness.}

Because Choice Architects' biased beliefs influence their interventions,
it is natural to wonder whether informational policies could correct
these errors and thereby improve the interventions, at least from
the Choice Architects' perspectives. We address this question in Appendix
\ref{projPatInfoAppendix}. Consistent with an existing literature
that documents the resistance of the false consensus effect to informational
interventions \citep{KruegerClement,EngelmannStrobel.FalseConsensusInfo},

we find that neither the Chooser-specific information provided in
the Chooser Information condition, nor information about the distribution
of unrestricted choices, attenuates the false consensus bias or ideals-projective
paternalism.

\section{Support for paternalistic policies}

\label{realWorldPolicy}

We conclude this investigation by showing that subjects' judgments
about real-world paternalistic policy proposals relate meaningfully
to the decisions they make as Choice Architects in our experiment.
Additionally, we demonstrate that ideals-projective paternalism extends
to the policy domain.

To this end, in Stage 3 of the experiment, we ask subjects to rate
four policy proposals involving taxes on sugary drinks, alcohol, and
tobacco, as well as restrictions on short-term, high-interest loans.
Because our subjects live in Germany, we focus primarily on tax policies
for Switzerland, which should purge the ratings of personal interests,
at least in principle. We ask subjects to assume the tax policies
would be budget-neutral, so responses do not reflect general attitudes
about the size of government. For each policy, we elicit the extent
to which the subject supports or opposes its implementation.\footnote{Concerning tax policies, response options are that Switzerland should
``definitely not introduce such a tax,'' ``probably not introduce
such a tax,'' ``probably introduce such a tax,'' or ``definitely
introduce such a tax.'' Because small, short-term, high-interest
loans are not available in Switzerland (possibly due to a lack of
demand), questions regarding lending restrictions pertain to Germany.
The response options are that the market for short-term loans in Germany
should ``be greatly restricted,'' ``be somewhat restricted,''
``remain unchanged,'' ``be somewhat liberalized,'' or ``be greatly
liberalized.'' We encode these options as $2$, $1$, 0, $-1$, and
$-2$, respectively. } We also elicit beliefs about the impact of each policy on the welfare
of the average citizen.\footnote{The question about alcohol taxes concerns adolescents and young adults
rather than average citizens, but is otherwise identical.}

After subjects express these judgments and answer additional non-incentivized
questions, they provide information about themselves that relates
to the impacted activities. These variables allow us to distinguish
between ideals-projective paternalism and mistakes-projective paternalism
in the domain of real-world paternalistic policy proposals. Specifically,
we elicit subjects' body mass index,\footnote{Subjects can click a button to open a window that asks them to enter
their height $h$ in cm and weight $w$ in kg. The window then displays
their body mass index using the formula BMI $=\frac{w}{(h/100)^{2}}$.} their average alcohol consumption, their frequency of binge drinking
(defined as the consumption of four or more units of alcohol for men,
or five or more units for women, within a two-hour period),\footnote{The experiment defines a unit of alcohol as 0.2 liters of beer, 0.1
liter of wine, or 1 shot of schnapps or liquor.} their cigarette consumption, and their experience with short-term,
high-interest loans. In addition, subjects provide information about
their credit card debt in the online portion of the experiment (see
Appendix \ref{experimentFullDetails} for details).\footnote{Misreporting of these characteristics is a potential concern. For
example, people tend to over-report height and under-report weight
\citep{BMIreview}. A strong correlation remains, however, between
reported and measured BMI \citep{BMIcorrelation}. Because our interest
centers on the signs of correlations, underreporting of BMI does not
qualitatively affect our conclusions. Similar statements apply to
self-reported alcohol consumption \citep{alcoholReview} and self-reported
cigarette smoking \citep{smokingReview}.}

\paragraph{Relation of policy support to laboratory behavior}

For each policy proposal, we list the fractions of subjects who express
a given level of support or opposition, as well as the fractions who
provide each possible welfare assessment, in the top half of Table
\ref{experimentVsRealWorld}. Support outweighs opposition in each
case. It is particularly pronounced for increased tobacco taxes and
sugary drinks taxes. The distribution of beliefs about the welfare
effects of the proposed policies mirrors the distribution of support
for each policy.

To examine the relation between these judgments and the decisions
subjects make as Choice Architects in our experiment, we regress measures
of their support for the policies on the average mandates they impose
on Choosers. In the interests of obtaining easily interpretable coefficients,
we use OLS, and defer ordered probit estimates to Appendix \ref{welfareRealWorld}.
We encode strong opposition as $-2$, weak opposition as $-1$, weak
support as 1, and strong support as 2.\footnote{For lending restrictions, we code neutrality as 0.}
We relate these measures of policy support to the Choice Architects'
mandates (i.e., the maximum amount the Choice Architect allows the
Chooser receive immediately, as in Section \ref{situationalDeterminants}),
averaged over rounds involving menus 3 and 4 in the Main condition.\footnote{We confine attention to menus 3 and 4 so that we can take an average
over the same set of menus for each Choice Architect without including
rounds with front-end delay. } Other controls include gender, age, field of study, high school GPA,
monthly expenses, political orientation, and personal behaviors related
to each of the policy domains listed above, as well as session and
order fixed effects.\footnote{Subjects could decline to provide pieces of information such as their
body mass index. In that case, we set the value of the corresponding
variable equal to its population mean, and we include dummy variables
to indicate such replacements. Appendix \ref{realWorldPoliciesNoControls}
displays a version of Table \ref{experimentVsRealWorld} in which
subject-specific control variables are excluded from the regressions.
If anything, the inclusion of control variables leads to an increase
in the magnitude of the coefficient estimates.} Panel B.1 in Table \ref{experimentVsRealWorld} displays the results.
Focusing first on average support for the four paternalistic policies
(column 1), we find that the coefficient of interest is negative and
statistically significant ($p<0.01$). Thus, subjects who impose tighter
restrictions on Choosers in the laboratory also express greater support
for paternalistic policies in the real world. We also perform separate
regressions for each of the policy proposals and report the key coefficient
estimate from each in columns 2 to 5. In each case, we find that Choice
Architects who enforce more patience in the laboratory express greater
support for actual paternalistic interventions. These relations are
significant at the 5\% level for alcohol and tobacco taxes and at
the 10\% level for the remaining two policies.

\begin{table}[htbp!]
\setstretch{1} \setlength{\tabcolsep}{4pt} \caption{\textbf{Experimental decisions and support for real-world paternalistic
policies.}\label{experimentVsRealWorld}}
\begin{centering}
\vspace{-0.3cm}
 \begin{small}


\begin{tabular}{lccccc}
\toprule
 & (1)  & (2)  & (3)  & (4)  & (5) \tabularnewline
Policy  & All  & Increase  & Increase  & Introduce sugary  & Tighten restrictions on \tabularnewline
 &  & alcohol tax  & tobacco tax  & drinks tax  & short-term lending\medskip{}
\tabularnewline
 & \multicolumn{5}{c}{\textbf{A. Summary statistics for dependent variables}}\tabularnewline
\cmidrule(r){2-6} Distribution of support  &  &  &  &  & \tabularnewline
\hspace{0.4cm} \textit{strongly opposed (-2)}  & -  & 0.154  & 0.055  & 0.087  & 0.060 \tabularnewline
\hspace{0.4cm} \textit{weakly opposed (-1)}  & -  & 0.305  & 0.102  & 0.196  & 0.191 \tabularnewline
\hspace{0.4cm} \textit{neutral (0)}  & -  & -  & -  & -  & 0.342 \tabularnewline
\hspace{0.4cm} \textit{weakly in favor (1)}  & -  & 0.328  & 0.280  & 0.362  & 0.278 \tabularnewline
\hspace{0.4cm} \textit{strongly in favor (2)}  & -  & 0.213  & 0.563  & 0.355  & 0.129\medskip{}
\tabularnewline


 & \multicolumn{5}{c}{\textbf{B. Relation between laboratory choice and policy attitudes}}\tabularnewline
\cmidrule(r){2-6} \medskip{}
 & \multicolumn{5}{c}{\textit{B.1 Dependent variable: support for policy proposal}}\tabularnewline
Average mandate imposed on Chooser  & -0.113{*}{*}{*}  & -0.129{*}{*}  & -0.126{*}{*}  & -0.113{*}  & -0.086{*} \tabularnewline
\hspace{0.4cm}{(max. \EURdig\; paid immediately)}  & (0.041)  & (0.066)  & (0.051)  & (0.066)  & (0.052) \tabularnewline
Observations  & 403  & 403  & 403  & 403  & 403 \medskip{}
\tabularnewline
 & \multicolumn{5}{c}{\textit{B.2 Dependent variable: beliefs about welfare effect of policy
proposal}}\tabularnewline
Average belief exog. smaller choice  & 0.185{*}{*}{*}  & 0.220{*}{*}  & 0.235{*}{*}{*}  & 0.210{*}{*}{*}  & 0.075 \tabularnewline
\hspace{0.4cm} set better for Chooser  & (0.054)  & (0.086)  & (0.084)  & (0.076)  & (0.075) \tabularnewline
Observations  & 403  & 403  & 403  & 403  & 403 \medskip{}
\tabularnewline
 & \multicolumn{5}{c}{\textbf{C. Projective paternalism with actual policies}}\tabularnewline
\cmidrule(r){2-6} Alcohol consumption  &  &  &  &  & \tabularnewline
\hspace{0.4cm} \textit{Alcohol units / week}  &  & -0.050  &  &  & \tabularnewline
 &  & (0.034)  &  &  & \tabularnewline
\hspace{0.4cm} \textit{log(days binge drinking / year)}  &  & -0.165{*}{*}{*}  &  &  & \tabularnewline
 &  & (0.055)  &  &  & \tabularnewline
Tobacco consumption  &  &  &  &  & \tabularnewline
\hspace{0.4cm} \textit{Smoker yes / no}  &  &  & -0.881{*}{*}{*}  &  & \tabularnewline
 &  &  & (0.284)  &  & \tabularnewline
\hspace{0.4cm} \textit{Cigarettes / day}  &  &  & -0.046  &  & \tabularnewline
 &  &  & (0.039)  &  & \tabularnewline
Body Mass Index  &  &  &  & -0.062{*}{*}  & \tabularnewline
 &  &  &  & (0.028)  & \tabularnewline
Debt  &  &  &  &  & \tabularnewline
\hspace{0.4cm} \textit{Credit card debt (in \EURdig1,000)} &  &  &  &  & -0.573{*}{*}{*} \tabularnewline
 &  &  &  &  & (0.114) \tabularnewline
\hspace{0.4cm} \textit{Other short-term debt yes / no}  &  &  &  &  & 0.101 \tabularnewline
 &  &  &  &  & (0.388) \medskip{}
\tabularnewline
Controls  & - & Yes  & Yes  & Yes  & Yes\tabularnewline
Observations  & - & 403  & 403  & 398  & 351 \tabularnewline
\midrule


 &  &  &  &  & \tabularnewline
\end{tabular}\end{small}
\par\end{centering}
\vspace{-0.3cm}
\textbf{\scriptsize{}Notes:}{\scriptsize{} Method: OLS. Unit of observation:
subject. Dependent variables: }\emph{\scriptsize{}Support for policy
proposal}{\scriptsize{} is the support expressed for various policies,
coded -2 (strong opposition) to 2 (strong support), averaged over
policies for ``All.'' A ``neutral'' response was possible only
for the question about short-term lending. Because we asked subjects
about }\emph{\scriptsize{}loosening}{\scriptsize{} restrictions on
short-term lending, we reverse-coded these responses for easier comparability
(so that higher values correspond to greater support for tightening
restrictions). }\emph{\scriptsize{}Beliefs about welfare effects}{\scriptsize{}
is the belief about the welfare effect of various policies, coded
-2 (significantly worse off) to 2 (significantly better off), averaged
over policies for ``All.'' Controls: }\emph{\scriptsize{}Average
mandate imposed on Chooser}{\scriptsize{} is the maximum amount the
Choice Architect allows the Chooser receive immediately (as in Section
\ref{situationalDeterminants}), averaged over rounds involving menus
3 and 4 in the Main condition.
}\emph{\scriptsize{}Average belief exog. smaller choice set better
for Chooser}{\scriptsize{} (as in Section \ref{benevolence}) encodes
whether the Choice Architect considers the smaller opportunity set
better, equally good, or worse for the Chooser than the unrestricted
set, coded as 1, 0, and -1, respectively, averaged across the four
rounds of the Exogenous Restriction Condition.
All regressions control for gender, age, self-reported political attitudes,
log monthly expenses, high school GPA, university faculty at which
the subject's major field of study is offered, as well as for session
and order fixed effects. Regressions in Panel B in addition control
for weekly alcohol consumption, log days of binge drinking per year
(defined as the consumption of at least 4 units of alcohol for females,
5 for males, within a period of two hours \citep{NIAAA}), smoking
status, number of cigarettes smoked per day, body mass index, credit
card debt, and for having taken a short term loan. For variables measured
with interval precision, we use midpoints for analysis. For control
variables that subjects chose not to disclose, the regressions in
Panel B impute population means, and include indicators for whether
a variable's value was missing. Samples: includes 303 subjects who
participated in the main experiment, plus 100 subjects who participated
in the Choice Distribution Information Condition (see Appendix \ref{projPatInfoAppendix}).
Regressions in Panel C exclude subjects who chose not to disclose
the personal characteristic of interest. Standard errors: clustered
by subject. $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$. \black }{\scriptsize\par}
\end{table}


Next we examine the relation between beliefs about the welfare effects
of actual paternalistic policies, and those concerning exogenous restrictions
on choice in the laboratory. To measure the latter beliefs, we encode
the Choice Architects' statements as to whether an exogenously specified
restriction would help Choosers, leave them equally well off, or hurt
them, as 1, 0, and -1, respectively (as in Section \ref{benevolence}).
We average this variable across the four rounds of the Exogenous Restrictions
condition. For the regressions shown in Panel B.2 of Table \ref{experimentVsRealWorld},
we include the same control variables as in Panel B.1. On average
across the four policies (column 1), subjects who believe choice restrictions
are beneficial in the laboratory also tend to believe that paternalistic
interventions are beneficial in practice. The same pattern emerges
for each of the sin taxes (columns 2-4). For restrictions on short-term
lending, we do not find statistically significant effects. Because
German universities do not charge tuition, personal debt may be less
salient than alcohol, tobacco, and sugary drinks for our student subjects.


While we cannot exclude the possibility that non-paternalistic considerations
such as the prevention of externalities factor into subjects' assessments
of the four policies, Choice Architects' selections and statements
in the laboratory session are not subject to such confounds. Consequently,
it is reasonable to conclude from the robust relationships documented
in Table \ref{experimentVsRealWorld} that attitudes toward these
policies depend in significant part on the types of paternalistic
inclinations we document in the laboratory.


\paragraph{Projective paternalism with actual policies}

Next we investigate the relationships between respondents' policy
judgments and their own characteristics, with the objective of distinguishing
between mistakes-projective and ideals-projective paternalism. Focusing
on the example of alcohol taxes, mistakes-projective paternalism should
yield a positive relationship between alcohol consumption and support
for alcohol taxation in politically unrelated jurisdictions, where
the impact will fall on others. Intuitively, heavier drinkers are
more likely to be problem drinkers. To the extent they project their
problem drinking on others, they will conclude that others would benefit
from measures that limit alcohol consumption. Ideals-projective paternalism
has the opposite implication. Intuitively, light drinkers are less
likely to enjoy consuming alcohol. To the extent they project their
lack of enjoyment on others, they will conclude that the costs of limitating alcohol consumption are relatively low. Therefore, we should expect to observe a negative relationship between alcohol consumption
and support for alcohol taxes.

To test these competing implications, we estimate OLS regressions
relating subjects' support for each policy to pertinent personal behaviors
and characteristics.\footnote{Appendix \ref{welfareRealWorld} presents corresponding estimates
obtained from ordered probit models.} We include all subjects from all treatments, and include the same
set of additional control variables as in the regressions of Table
\ref{experimentVsRealWorld}.


Panel C displays the results. Column 2 shows that subjects who binge
drink more frequently are significantly \emph{less} likely to express
support for alcohol taxes on others, exactly as ideals-projective
paternalism predicts. Because binge drinking is a good proxy for problem
drinking, this finding strongly contradicts the hypothesis of mistakes-projective
paternalism. Conditional on the level of binge drinking, greater weekly
alcohol consumption also appears to reduce support for alcohol taxes
on others, but the effect is not statistically significant.\footnote{The coefficient on weekly consumption becomes statistically significant (and remains negative) when we remove the control for binge drinking from the regression.} Similarly, column 3 shows that German subjects are significantly
more likely to express support for tobacco taxes in Switzerland if
they do not smoke themselves. In column 4, we find that people with
lower BMIs express stronger support for sugary drinks taxes on others,
again consistent with ideals-projective paternalism. Similarly, subjects
are less likely to support restricting other people's access to the
market for short-term, high-interest lending when they have larger
credit card balances (column 5).\footnote{Only 12 out of 403 subjects report ever having taken a short-term
loan other than through their credit cards.} While results such as those of \citet{Allcottetal.SodaTaxes} suggest that the relationships between policy support and subject characteristics may partially be driven by differences in domain-specific knowledge such as health literacy, this factor cannot plausibly explain the strong relationship between laboratory choice and policy attitudes documented in Panel B of the table. Overall, the data on judgments about real-world paternalistic policies therefore manifests the signature pattern associated with ideals-projective
paternalism.


In principle, the observed relationship between one's own behavior
and support for paternalistic policies may also be due to systematic
variation in beliefs about the effectiveness of a given policy. \hide
This would be the case, for instance, if heavier individuals are more
likely to believe that losing weight is difficult, and hence think
that the demand elasticity for sugary drinks is lower. \unhide Appendix
\ref{mTurkVignette} reports a vignette experiment with US subjects
in which we control for variations in beliefs about efficacy. In cases
where the pattern of support for paternalistic policies points to
ideals-projective paternalism, controlling for beliefs about efficacy
leaves that pattern qualitatively unchanged.\footnote{That survey asks about alcohol taxes, retirement savings mandates, restrictions on short-term, high-interest lending and sugary drinks taxes. We observe statistically significant indications of ideals-projective paternalism for the first two policies. The evidence does not support mistakes-projective paternalism for any policy.}

\section{Conclusion}

\label{conclusions}

This paper examines when, why, and how people intervene in others'
choices. In a setting involving intertemporal tradeoffs, Choice Architects
frequently remove options that are attractive to impatient decision
makers. They believe their interventions benefit the Chooser, and
are thus acting paternalistically. We examine and reject two simple
hypotheses about their motives. First, we rule out the possibility
that Choice Architects intervene with the objective of controlling
excessive impatience arising from perceived present bias, a motive
practitioners of behavioral welfare economics often attribute to benevolent
planners. Second, we rule out the possibility that Choice Architects
intervene simply to impose their own judgments, without regard to
the inclinations or subjective experiences of the affected individuals.

How do Choice Architects judge what is good for others? \hide Arriving
at this evaluation is difficult because, by definition, paternalists
are hesitant to rely on the judgments implicit in Choosers' decisions,
and indeed may even question whether Choosers are aware of their own
best interests. Yet we also know that beliefs about Choosers' proclivities
and experiences factor into Choice Architects' interventions. \unhide
Ideals-projective paternalism emerges from our analysis as the key
mechanism. An ideals-projective paternalist acts \emph{as if} she
believes others share, or ought to share, the ideals to which she
aspires for herself. We show that ideals-projective paternalism is
related to the false consensus effect (an objective fallacy). As a
result, even though more patient Choice Architects do not intend to
force a larger fraction of Choosers to choose more patiently, this
is precisely what they do. We also find that ideals-projective paternalism
extends to assessments of real-world paternalistic policies, which
strongly correlate with behavior in the laboratory. \hide Ideals-projective
paternalism contrasts with mistakes-projective paternalism, according
to which subjects view themselves as prone to particular types of
decision errors, make the assumption that others share that susceptibility,
and intervene to remedy it. It also contrasts with conventional behavioral
welfarism, which holds that subjects either understand the true distribution
of behavioral proclivities or form a belief about it based on information
concerning the Choosers. Both of these alternative hypotheses are
more closely aligned with widespread perspectives in behavioral welfare
economics but, as positive hypotheses, neither finds support in our
data.\unhide

Throughout, we have remained agnostic about the effects of Choice
Architects' interventions on Choosers' welfare. Finding an objective
basis for making such assessments is challenging. For example, from
a libertarian perspective, any intervention is welfare-reducing. Alternatively,
if one believes that, given the high cost of impatience in our experiment,
the most patient option always dominates the other alternatives, then
removing the least patient option, or both the least patient and middle
options, is weakly welfare-enhancing, because it helps Choosers avoid
accidental errors. Existing evidence suggests, however, that people
have a positive willingness to pay for autonomy \citep{HerzAuthority,HerzECMA,OwensEtAl.ControlPremium,LubbeckeSchnedler,AckfeldOckenfels}.

There are many questions we hope future research will clarify. For
example, how much of their own resources are subjects willing to give
up in order to impose paternalistic restrictions on others, and do
these amounts differ for mistakes-projective and ideals-projective
paternalists? We also hope to extend the empirical study of paternalistic
decision making to subject pools consisting of ``professional paternalists''
such as medical doctors and policy makers. In contexts where objective
benchmarks are available, existing evidence suggests that nearly everyone
exhibits behavioral biases \citep{StangoZinmanBcounts}, including
elected politicians \citep{Shefferetal.}.


\newpage\singlespacing \setlength{\bibsep}{0pt plus 0.3ex}

\begin{thebibliography}{99}
\bibitem{Clemens} Clemens, Michael A. Testing for repugnance in economic transactions: Evidence from guest work in the Gulf. \textit{The Journal of Legal Studies}. University of Chicago Press Chicago, IL. vol. 47. no. S1. pp. S5--S44. 2018.
\bibitem{BernheimTaubinsky} Bernheim, B Douglas; Taubinsky, Dmitry. Behavioral public economics. In Handbook of Behavioral Economics: Applications; Foundations 1. Elsevier. vol. 1. pp. 381--516. 2018.
\bibitem{HeidhuesKoszegi} Heidhues, Paul; K{\H{o}}szegi, Botond. Exploiting naivete about self-control in the credit market. \textit{American Economic Review}. vol. 100. no. 5. pp. 2279--2303. 2010.
\bibitem{oDonoghueRabin.SinTaxes} O'Donoghue, Ted; Rabin, Matthew. Optimal sin taxes. \textit{Journal of Public Economics}. Elsevier. vol. 90. no. 10-11. pp. 1825--1849. 2006.
\bibitem{DellaVignaMalmendier.ContractDesign} DellaVigna, Stefano; Malmendier, Ulrike. Contract design; self-control: Theory; evidence. \textit{The Quarterly Journal of Economics}. MIT Press. vol. 119. no. 2. pp. 353--402. 2004.
\bibitem{GruberKoszegi.AddictionRational} Gruber, Jonathan; K{\H{o}}szegi, Botond. Is addiction ``rational''? Theory; evidence. \textit{The Quarterly Journal of Economics}. MIT Press. vol. 116. no. 4. pp. 1261--1303. 2001.
\bibitem{GruberKoszegi.TaxIncidence} Gruber, Jonathan; K{\H{o}}szegi, Botond. Tax incidence when individuals are time-inconsistent: the case of cigarette excise taxes. \textit{Journal of Public Economics}. Elsevier. vol. 88. no. 9-10. pp. 1959--1987. 2004.
\bibitem{RizzoWhitman} Rizzo, Mario J; Whitman, Glen. Escaping Paternalism: Rationality, Behavioral Economics,; Public Policy. Cambridge University Press. 2019.
\bibitem{AckfeldOckenfels} Viola Ackfeld; Axel Ockenfels. Protecting Autonomy versus Intervening to Promote Prosocial Behavior. \textit{unpublished}. 2020.
\bibitem{Allcottetal.SodaTaxes} Allcott, Hunt; Lockwood, Benjamin B; Taubinsky, Dmitry. Regressive sin taxes, with an application to the optimal soda tax. \textit{The Quarterly Journal of Economics}. Oxford University Press. vol. 134. no. 3. pp. 1557--1626. 2019.
\bibitem{Madarasz.ProjectionEquilibrium} Madar{\'a}sz, Krist{\'o}f. Projection equilibrium: Definition; applications to social investment; persuasion. CEPR Discussion Paper No. DP10636. 2015.
\bibitem{Danzetal.BiasesOfOthers} Danz, David; Madar{\'a}sz, Krist{\'o}f; Wang, Stephanie. The Biases of Others: Projection Equilibrium in an Agency Setting. CEPR Discussion Paper No. DP12867. 2018.
\bibitem{Madarasz.InformationProjection} Madarasz, Kristof. Information projection: Model; applications. \textit{The Review of Economic Studies}. Oxford University Press. vol. 79. no. 3. pp. 961--985. 2012.
\bibitem{Bartlingetal.} Bj\"orn Bartling; Alexander Cappelen; Henning Hermes; Marit Skivenes; Bertil Tungodden. Free to Fail? Paternalistic Preferences in the U.S.. \textit{unpublished}. 2020.
\bibitem{Lindvall} Torgny Lindvall. Lectures on the Coupling Method. Dover Publications, Mineola, New York. 2002.
\bibitem{ThalerShefrin} Thaler, Richard H; Shefrin, Hersh M. An economic theory of self-control. \textit{Journal of Political Economy}. The University of Chicago Press. vol. 89. no. 2. pp. 392--406. 1981.
\bibitem{Zlatevetal.} Julian J. {Zlatev}; David Peter {Daniels}; Hajin {Kim}; Margaret A. {Neale}. Default neglect in attempts at social influence. \textit{Proceedings of the National Academy of Sciences of the United States of America}. vol. 114. no. 52. pp. 13643--13648. 2017.
\bibitem{Danielsetal.} David Peter {Daniels}; Julian J. {Zlatev}. Choice Architects Reveal a Bias Toward Positivity; Certainty. \textit{Organizational Behavior; Human Decision Processes}. vol. 151. pp. 132--149. 2019.
\bibitem{RubinsteinSalant} Salant, Yuval; Rubinstein, Ariel. (A, f): choice with frames. \textit{The Review of Economic Studies}. Wiley-Blackwell. vol. 75. no. 4. pp. 1287--1296. 2008.
\bibitem{Amadoretal.} Manuel {Amador}; Ivan {Werning}; George Marios {Angeletos}. Commitment vs. Flexibility. \textit{Econometrica}. vol. 74. no. 2. pp. 365--396. 2006.
\bibitem{BernheimGoodBad} B. Douglas Bernheim. The Good, the Bad,; the Ugly: A Unified Approach to Behavioral Welfare Economics. \textit{Journal of Benefit-Cost Analysis}. vol. 7. no. 1. pp. 12-68. 2016.
\bibitem{ifcherZarghamee} Ifcher, John; Zarghamee, Homa. Behavioral Economic Phenomena in Decision-Making for Others. \textit{The Journal of Economic Psychology}. IZA Discussion Paper. 2020.
\bibitem{StangoZinmanBcounts} Victor Stango; Jonathan Zinman. We are all behavioral, more or less: Measuring; using consumer-level behavioral sufficient statistics. \textit{NBER working paper}. vol. 25540. 2019.
\bibitem{Vanguard.AdvisorsAlpha} Bennyhoff, Donald G; Kinniry Jr, Francis M. Advisor's Alpha. \textit{Vanguard research paper (December). https://advisors. vanguard. com/iwe/pdf/ICRAA. pdf}. 2011.
\bibitem{Linnainmaaetal.} Linnainmaa, Juhani T; Melzer, Brian; Previtero, Alessandro. The misguided beliefs of financial advisors. \textit{Journal of Finance}. forthcoming.
\bibitem{Foersteretal.} Foerster, Stephen; Linnainmaa, Juhani T; Melzer, Brian T; Previtero, Alessandro. Retail financial advice: does one size fit all?. \textit{The Journal of Finance}. Wiley Online Library. vol. 72. no. 4. pp. 1441--1482. 2017.
\bibitem{Dworkin} Dworkin, Gerald. Paternalism. \textit{the Monist}. JSTOR. pp. 64--84. 1972.
\bibitem{Shefferetal.} Sheffer, Lior; Loewen, Peter John; Soroka, Stuart; Walgrave, Stefaan; Sheafer, Tamir. Nonrepresentative representatives: an experimental study of the decision making of elected politicians. \textit{American Political Science Review}. Cambridge University Press. vol. 112. no. 2. pp. 302--321. 2018.
\bibitem{Cohenetal.MEL} Cohen, Jonathan D; Ericson, Keith Marzilli; Laibson, David; White, John Myles. Measuring time preferences. \textit{NBER working paper}. 2016.
\bibitem{Andreonietal.MEL} Andreoni, James; Gravert, Christina; Kuhn, Michael A; Saccardo, Silvia; Yang, Yang. Arbitrage Or Narrow Bracketing? On Using Money to Measure Intertemporal Preferences. \textit{NBER working paper}. 2018.
\bibitem{Andersenetal.ECMA} Andersen, Steffen; Harrison, Glenn W; Lau, Morten I; Rutstr{\"o}m, E Elisabet. Eliciting risk; time preferences. \textit{Econometrica}. Wiley Online Library. vol. 76. no. 3. pp. 583--618. 2008.
\bibitem{smokingReview} West, Robert; Zatonski, Witold; Przewozniak, Krzysztof; Jarvis, Martin J. Can we trust national smoking prevalence figures? Discrepancies between biochemically assessed; self-reported smoking rates in three countries. \textit{Cancer Epidemiology; Prevention Biomarkers}. AACR. vol. 16. no. 4. pp. 820--822. 2007.
\bibitem{alcoholReview} Sobell, Linda C; Sobell, Mark B. Alcohol consumption measures. \textit{Assessing alcohol problems: A guide for clinicians; researchers}. vol. 2. pp. 75--99. 1995.
\bibitem{BMIcorrelation} Nawaz, Haq; Chan, Wendy; Abdulrahman, Mustapha; Larson, David; Katz, David L. Self-reported weight; height: implications for obesity research. \textit{American Journal of Preventive Medicine}. Elsevier. vol. 20. no. 4. pp. 294--298. 2001.
\bibitem{BMIreview} S. Connor {Gorber}; M. {Tremblay}; D. {Moher}; B. {Gorber}. A comparison of direct vs. self-report measures for assessing height, weight; body mass index: a systematic review. \textit{Obesity Reviews}. vol. 8. no. 4. pp. 307--326. 2007.
\bibitem{HarrisonRoss} Harrison, Glenn W; Ross, Don. Varieties of paternalism; the heterogeneity of utility structures. \textit{Journal of Economic Methodology}. Taylor \& Francis. vol. 25. no. 1. pp. 42--67. 2018.
\bibitem{DworkinBook} Gerald Dworkin. Paternalism. Wadsworth Publishing Company. 1971.
\bibitem{FehrFischbacherReview} Fehr, Ernst; Fischbacher, Urs. Why social preferences matter--the impact of non-selfish motives on competition, cooperation; incentives. \textit{The Economic Journal}. Wiley Online Library. vol. 112. no. 478. pp. C1--C33. 2002.
\bibitem{vanBovenetal.} Van Boven, Leaf; Dunning, David; Loewenstein, George. Egocentric empathy gaps between owners; buyers: misperceptions of the endowment effect.. \textit{Journal of Personality; Social Psychology}. American Psychological Association. vol. 79. no. 1. pp. 66. 2000.
\bibitem{Halevy2008} Halevy, Yoram. Strotz meets Allais: Diminishing impatience; the certainty effect. \textit{American Economic Review}. vol. 98. no. 3. pp. 1145--62. 2008.
\bibitem{Ainslie} Ainslie, George. Specious reward: a behavioral theory of impulsiveness; impulse control. \textit{Psychological bulletin}. American Psychological Association. vol. 82. no. 4. pp. 463. 1975.
\bibitem{LoewensteinPrelec} Loewenstein, George; Prelec, Drazen. Anomalies in intertemporal choice: Evidence; an interpretation. \textit{The Quarterly Journal of Economics}. MIT Press. vol. 107. no. 2. pp. 573--597. 1992.
\bibitem{GruberMullainathan} Gruber, Jonathan; Mullainathan, Sendhil. Do cigarette taxes make smokers happier?. In Happiness; Public Policy. Springer. pp. 109--146. 2006.
\bibitem{KoszegiRabinMistakes} K{\H{o}}szegi, Botond; Rabin, Matthew. Mistakes in choice-based welfare analysis. \textit{American Economic Review}. vol. 97. no. 2. pp. 477--481. 2007.
\bibitem{KoszegiRabinHappiness} K{\H{o}}szegi, Botond; Rabin, Matthew. Choices, situations,; happiness. \textit{Journal of Public Economics}. Elsevier. vol. 92. no. 8-9. pp. 1821--1832. 2008.
\bibitem{Locke} Locke, John. Two Treatises of Government. A. Millar et al., London. 1764.
\bibitem{Dawes.FalseConsensus} Dawes, Robyn M. Statistical criteria for establishing a truly false consensus effect. \textit{Journal of Experimental Social Psychology}. Elsevier. vol. 25. no. 1. pp. 1--17. 1989.
\bibitem{Rothetal.KidneyExchange} Roth, Alvin E; S{\"o}nmez, Tayfun; {\"U}nver, M Utku. Kidney exchange. \textit{The Quarterly Journal of Economics}. MIT Press. vol. 119. no. 2. pp. 457--488. 2004.
\bibitem{OwensEtAl.ControlPremium} Owens, David; Grossman, Zachary; Fackler, Ryan. The control premium: A preference for payoff autonomy. \textit{American Economic Journal: Microeconomics}. vol. 6. no. 4. pp. 138--61. 2014.
\bibitem{LubbeckeSchnedler} L{\"u}bbecke, Silvia; Schnedler, Wendelin. Don't patronize me! An Experiment on Rejecting Paternalistic Help. \textit{unpublished}. 2018.
\bibitem{AndreoniMiller} Andreoni, James; Miller, John. Giving according to GARP: An experimental test of the consistency of preferences for altruism. \textit{Econometrica}. Wiley Online Library. vol. 70. no. 2. pp. 737--753. 2002.
\bibitem{VesterlundHandbook} Vesterlund, Lise. Using experimental methods to understand why; how we give to charity. \textit{Handbook of Experimental Economics}. vol. 2. pp. 91--151. 2016.
\bibitem{CooperKagel.Handbook} Cooper, David J; Kagel, John H. Other-regarding preferences. \textit{The handbook of experimental economics}. vol. 2. pp. 217. 2016.
\bibitem{Popeetal.ProjectionBias} Busse, Meghan R; Pope, Devin G; Pope, Jaren C; Silva-Risso, Jorge. The psychological effect of weather on car purchases. \textit{The Quarterly Journal of Economics}. MIT Press. vol. 130. no. 1. pp. 371--414. 2015.
\bibitem{AclandLevy} Acland, Dan; Levy, Matthew R. Naivet{\'e}, projection bias,; habit formation in gym attendance. \textit{Management Science}. INFORMS. vol. 61. no. 1. pp. 146--160. 2015.
\bibitem{Conlinetal.} Conlin, Michael; O'Donoghue, Ted; Vogelsang, Timothy J. Projection bias in catalog orders. \textit{American Economic Review}. vol. 97. no. 4. pp. 1217--1249. 2007.
\bibitem{Sunstein.BetterOff} Sunstein, Cass R. `Better off, as judged by themselves': a comment on evaluating nudges. \textit{International Review of Economics}. Springer. vol. 65. no. 1. pp. 1--8. 2018.
\bibitem{Loibletal.} Loibl, C{\"a}zilia; Sunstein, Cass R; Rauber, Julius; Reisch, Lucia A. Which Europeans like nudges? Approval; controversy in four European countries. \textit{Journal of Consumer Affairs}. Wiley Online Library. 2018.
\bibitem{SunsteinReischRauber} Sunstein, Cass R; Reisch, Lucia A; Rauber, Julius. A worldwide consensus on nudging? Not quite, but almost. \textit{Regulation \& Governance}. Wiley Online Library. vol. 12. no. 1. pp. 3--22. 2018.
\bibitem{SunsteinSystem2nudges} Sunstein, Cass R. People prefer system 2 nudges (kind of). \textit{Duke Law Journal}. HeinOnline. vol. 66. pp. 121. 2016.
\bibitem{Benartzietal.InvestMoreInNudging} Benartzi, Shlomo; Beshears, John; Milkman, Katherine L; Sunstein, Cass R; Thaler, Richard H; Shankar, Maya; Tucker-Ray, Will; Congdon, William J; Galing, Steven. Should governments invest more in nudging?. \textit{Psychological Science}. SAGE Publications Sage CA: Los Angeles, CA. vol. 28. no. 8. pp. 1041--1055. 2017.
\bibitem{Tannenbaumetal.} Tannenbaum, David; Fox, Craig R; Rogers, Todd. On the misplaced politics of behavioural policy interventions. \textit{Nature Human Behaviour}. Nature Publishing Group. vol. 1. no. 7. pp. 0130. 2017.
\bibitem{WeinzierlAERPP} Weinzierl, Matthew. Welfarism's Envy Problem Extends to Popular Judgments. In AEA Papers; Proceedings. vol. 108. pp. 28--32. 2018.
\bibitem{WeinzierlNBER} Weinzierl, Matthew. A Welfarist Role for Nonwelfarist Rules: An Example with Envy. \textit{NBER working paper}. 2017.
\bibitem{Schroyen} Schroyen, Fred. An alternative way to model merit good arguments. \textit{Journal of Public Economics}. Elsevier. vol. 89. no. 5-6. pp. 957--966. 2005.
\bibitem{Fedyk} Fedyk, Anastassia. Asymmetric Naivet{\'e}: Beliefs about Self-Control. \textit{unpublished}. 2017.
\bibitem{TavrisAronson} Tavris, Carol; Aronson, Elliot. Mistakes were made (but not by me): Why we justify foolish beliefs, bad decisions,; hurtful acts. Houghton Mifflin Harcourt. 2008.
\bibitem{HuberPaynePuto} Huber, Joel; Payne, John W; Puto, Christopher. Adding asymmetrically dominated alternatives: Violations of regularity; the similarity hypothesis. \textit{Journal of consumer research}. The University of Chicago Press. vol. 9. no. 1. pp. 90--98. 1982.
\bibitem{PikulinaTergiman} Pikulina, Elena; Tergiman, Chloe. Preferences for Power. \textit{unpublished}. 2018.
\bibitem{RossNisbett} Ross, L; Nisbett, RE. McGraw-Hill series in social psychology. \textit{The person; the situation: Perspectives of social psychology. New York, NY, England: Mcgraw-Hill Book Company}. 1991.
\bibitem{BlockMarschak} Block, Henry David; Marschak, Jacob. Random orderings; stochastic theories of response. \textit{Cowles Foundation for Research in Economics, Yale University}. 1959.
\bibitem{AlosFerrerFehrNetzer} Carlos Al{\'o}s-Ferrer,; Ernst Fehr,; Nick Netzer. Time Will Tell: Recovering Preferences when Choices Are Noisy. \textit{unpublished}. 2018.
\bibitem{NIAAA} National Institutes on Alcohol Abuse; Alcoholism. Recommended Alcohol Questions. 2018.
\bibitem{EngelmannStrobel.FalseConsensusInfo} Engelmann, Dirk; Strobel, Martin. Deconstruction; reconstruction of an anomaly. \textit{Games; Economic Behavior}. Elsevier. vol. 76. no. 2. pp. 678--689. 2012.
\bibitem{KruegerClement} Krueger, Joachim; Clement, Russell W. The truly false consensus effect: An ineradicable; egocentric bias in social perception.. \textit{Journal of personality; social psychology}. American Psychological Association. vol. 67. no. 4. pp. 596. 1994.
\bibitem{Sandmo.ExAnteExPost} Sandmo, Agnar. Ex post welfare economics; the theory of merit goods. \textit{Economica}. JSTOR. vol. 50. no. 197. pp. 19--33. 1983.
\bibitem{MoffittJEP} Moffitt, Robert A. The negative income tax; the evolution of US welfare policy. \textit{Journal of Economic Perspectives}. vol. 17. no. 3. pp. 119--140. 2003.
\bibitem{Besley.MeritGoods} Besley, Timothy. A simple model for merit good arguments. \textit{Journal of Public Economics}. Elsevier. vol. 35. no. 3. pp. 371--383. 1988.
\bibitem{Musgrave.MeritGoods} Musgrave, Richard Abel. Theory of public finance.. McGraw-Hill. 1959.
\bibitem{Laibson.PrivatePaternalism} Laibson, David. Private Paternalism, the Commitment Puzzle,; Model-Free Equilibrium. \textit{American Economic Review, Papers \& Proceedings}. vol. 108. pp. 1--21. 2018.
\bibitem{BisinLizzeriYariv} Bisin, Alberto; Lizzeri, Alessandro; Yariv, Leeat. Government policy with time inconsistent voters. \textit{American Economic Review}. vol. 105. no. 6. pp. 1711--37. 2015.
\bibitem{Faravellietal.} Faravelli, Marco; Man, Priscilla; Walsh, Randall. Mandate; paternalism: a theory of large elections. \textit{Games; Economic Behavior}. Elsevier. vol. 93. pp. 1--23. 2015.
\bibitem{MulliganPhilipson} Mulligan, Casey B; Philipson, Tomas J. Merit motives; government intervention: Public finance in reverse. \textit{National Bureau of Economic Research Working Paper}. vol. 7698. 2000.
\bibitem{currieGahvari} Currie, Janet; Gahvari, Firouz. Transfers in cash; in-kind: Theory meets the data. \textit{Journal of Economic Literature}. vol. 46. no. 2. pp. 333--83. 2008.
\bibitem{MoffittEJ} Moffitt, Robert. Welfare work requirements with paternalistic government preferences. \textit{The Economic Journal}. Wiley Online Library. vol. 116. no. 515. pp. F441--F458. 2006.
\bibitem{BlumenthalDefinition} Blumenthal, Jeremy A. Emotional paternalism. \textit{Florida State University Law Review}. HeinOnline. vol. 35. pp. 1. 2007.
\bibitem{AndreoniCrowding} Andreoni, James. An experimental test of the public-goods crowding-out hypothesis. \textit{The American Economic Review}. JSTOR. pp. 1317--1327. 1993.
\bibitem{ManskiMeasuringExpectations} Manski, Charles F. Measuring expectations. \textit{Econometrica}. Wiley Online Library. vol. 72. no. 5. pp. 1329--1376. 2004.
\bibitem{Delavandeetal.} Delavande, Adeline; Gin{\'e}, Xavier; McKenzie, David. Measuring subjective expectations in developing countries: A critical review; new evidence. \textit{Journal of Development Economics}. Elsevier. vol. 94. no. 2. pp. 151--163. 2011.
\bibitem{Falketal.Validated} Falk, Armin; Becker, Anke; Dohmen, Thomas; Huffman, David; Sunde, Uwe. The preference survey module: A validated instrument for measuring risk, time,; social preferences. 2016.
\bibitem{FalkZimmermannConsistency} Falk, Armin; Zimmermann, Florian. Consistency as a Signal of Skills. \textit{Management Science}. INFORMS. vol. 63. no. 7. pp. 2197--2210. 2016.
\bibitem{MontielOleaStrzalecki} Montiel Olea, Jos{\'e} Luis; Strzalecki, Tomasz. Axiomatization; measurement of quasi-hyperbolic discounting. \textit{The Quarterly Journal of Economics}. MIT Press. vol. 129. no. 3. pp. 1449--1499. 2014.
\bibitem{HaidtKesebir} Haidt, Jonathan; Kesebir, Selin. Handbook of social psychology. John Wiley \& Sons. vol. 2. 2010.
\bibitem{ThalerSunstein} Thaler, Richard H; Sunstein, Cass R. Libertarian paternalism. \textit{American Economic Review}. vol. 93. no. 2. pp. 175--179. 2003.
\bibitem{AradRubinstein} Arad, Ayala; Rubinstein, Ariel. The People's Perspective on Libertarian-Paternalistic Policies. \textit{unpublished}. 2017.
\bibitem{Mill} Mill, John Stuart. On liberty. Longmans, Green, Reader,; Dyer. 1869.
\bibitem{CarlinetGervaisManso} Carlin, Bruce Ian; Gervais, Simon; Manso, Gustavo. Libertarian paternalism, information production,; financial decision making. \textit{The Review of Financial Studies}. Society for Financial Studies. vol. 26. no. 9. pp. 2204--2228. 2013.
\bibitem{Maretteetal.} Marette, Stephan; Lusk, Jayson L; Norwood, F Bailey. Choosing for others. \textit{Applied Economics}. Taylor \&amp; Francis. vol. 48. no. 22. pp. 2093--2111. 2016.
\bibitem{Lusketal.} Lusk, Jayson L; Marette, Stephan; Norwood, F Bailey. The paternalist meets his match. \textit{Applied Economic Perspectives; Policy}. Oxford University Press. vol. 36. no. 1. pp. 61--108. 2013.
\bibitem{OliveiraJacobson} de Oliveira, Angela CM; Jacobson, Sarah. (Im) patience by Proxy: Making Intertemporal Decisions for Others. \textit{unpublished}. 2017.
\bibitem{Albrechtetal.Neuro} Albrecht, Konstanze; Volz, Kirsten G; Sutter, Matthias; Laibson, David I; Von Cramon, D Yves. What is for me is not for you: Brain correlates of intertemporal choice for self; other. \textit{Social cognitive; affective neuroscience}. Oxford University Press. vol. 6. no. 2. pp. 218--225. 2010.
\bibitem{Anderssonetal.DecidingForOthers} Andersson, Ola; Holm, H{\aa}kan J; Tyran, Jean-Robert; Wengstr{\"o}m, Erik. Deciding for others reduces loss aversion. \textit{Management Science}. Informs. vol. 62. no. 1. pp. 29--36. 2014.
\bibitem{oDonoghueRabin.optimalPaternalism} O'Donoghue, Ted; Rabin, Matthew. Studying optimal paternalism, illustrated by a model of sin taxes. \textit{American Economic Review Papers \& Proceedings}. vol. 93. no. 2. pp. 186--191. 2003.
\bibitem{New.Paternalism} New, Bill. Paternalism; public policy. \textit{Economics \& Philosophy}. Cambridge University Press. vol. 15. no. 1. pp. 63--83. 1999.
\bibitem{RabinNBLLN} Benjamin, Daniel J; Rabin, Matthew; Raymond, Collin. A model of nonbelief in the law of large numbers. \textit{Journal of the European Economic Association}. Oxford University Press. vol. 14. no. 2. pp. 515--544. 2016.
\bibitem{WinkelmannBoes} Winkelmann, Rainer; Boes, Stefan. Analysis of microdata. Springer Science \& Business Media. 2006.
\bibitem{HeckmanSelection} Heckman, James J. Sample Selection Bias as a Specification Error. \textit{Econometrica}. vol. 47. no. 1. pp. 153--161. 1979.
\bibitem{DanaCain} Dana, Jason; Cain, Daylian M. Advice versus choice. \textit{Current Opinion in Psychology}. Elsevier. vol. 6. pp. 173--176. 2015.
\bibitem{Gigerenzer.MoralHeuristics} Gigerenzer, Gerd. Moral intuition = fast; frugal heuristics?. In The cognitive science of morality: intuition; diversity. MIT Press. pp. 1--26. 2008.
\bibitem{Sunstein.MoralHeuristics} Sunstein, Cass R. Moral heuristics. \textit{Behavioral; Brain Sciences}. [New York]: Cambridge University Press, 1978-. vol. 28. no. 4. pp. 531--541. 2005.
\bibitem{Baron.Nonconsequentialist} Baron, Jonathan. Nonconsequentialist decisions. \textit{Behavioral; Brain Sciences}. Cambridge University Press. vol. 17. no. 1. pp. 1--10. 1994.
\bibitem{Atanasov.Review} Atanasov, Pavel D. Risk preferences in choices for self; others: Meta analysis; research directions. \textit{unpublishedl}. 2015.
\bibitem{BenabouAli} Ali, S Nageeb; Benabou, Roland. Image versus information: Changing societal norms; optimal privacy. \textit{NBER working paper}. 2016.
\bibitem{Altmannetal.} Altmann, Steffen; Falk, Armin; Grunewald, Andreas. Incentives; information as driving forces of default effects. \textit{unpublished}. 2017.
\bibitem{HausmanMcPhersonSatz} Hausman, Daniel; McPherson, Michael; Satz, Debra. Economic analysis, moral philosophy,; public policy. Cambridge University Press. 2016.
\bibitem{GertCulver} Gert, Bernard; Culver, Charles M. Paternalistic behavior. \textit{Philosophy \& Public Affairs}. JSTOR. pp. 45--57. 1976.
\bibitem{HerzECMA} Bartling, Bj{\"o}rn; Fehr, Ernst; Herz, Holger. The intrinsic value of decision rights. \textit{Econometrica}. Wiley Online Library. vol. 82. no. 6. pp. 2005--2039. 2014.
\bibitem{HerzAuthority} Fehr, Ernst; Herz, Holger; Wilkening, Tom. The lure of authority: Motivation; incentive effects of power. \textit{The American Economic Review}. American Economic Association. vol. 103. no. 4. pp. 1325--1359. 2013.
\bibitem{Shiffrin} Shiffrin, Seana Valentine. Paternalism, unconscionability doctrine,; accommodation. \textit{Philosophy \& Public Affairs}. Wiley Online Library. vol. 29. no. 3. pp. 205--250. 2000.
\bibitem{Gangadharanetal.} Gangadharan, Lata; Grossman, Philip J; Jones, Kristy. Donor types; paternalism. \textit{unpublished}. 2015.
\bibitem{DoepkeZilibotti} Doepke, Matthias; Zilibotti, Fabrizio. Parenting with style: Altruism; paternalism in intergenerational preference transmission. \textit{Econometrica}. Wiley Online Library. vol. 85. no. 5. pp. 1331--1371. 2017.
\bibitem{SaintPaul} Saint-Paul, Gilles. Policy consequences of limited cognitive ability. \textit{Spanish Economic Review}. Springer. vol. 6. no. 2. pp. 97--105. 2004.
\bibitem{Glaeser} Glaeser, Edward L. Paternalism; psychology. \textit{NBER working paper 11789}. 2005.
\bibitem{PollakTiedTransfers} Pollak, Robert A. Tied transfers; paternalistic preferences. \textit{The American Economic Review}. JSTOR. vol. 78. no. 2. pp. 240--244. 1988.
\bibitem{Zamir} Zamir, Eyal. The efficiency of paternalism. \textit{Virginia Law Review}. JSTOR. pp. 229--286. 1998.
\bibitem{BisinVerdierEconLetters} Bisin, Alberto; Verdier, Thierry. Agents with imperfect empathy may survive natural selection. \textit{Economics Letters}. Elsevier. vol. 71. no. 2. pp. 277--285. 2001.
\bibitem{BisinVerdierJET} Bisin, Alberto; Verdier, Thierry. The economics of cultural transmission; the dynamics of preferences. \textit{Journal of Economic Theory}. Elsevier. vol. 97. no. 2. pp. 298--319. 2001.
\bibitem{Pedersenetal.WhoWantsPaternalism} Pedersen, Sofie Kragh; Koch, Alexander K; Nafziger, Julia. Who wants paternalism?. \textit{Bulletin of Economic Research}. Wiley Online Library. vol. 66. no. S1. 2014.
\bibitem{Katariaetal.} Kataria, Mitesh; Levati, M Vittoria; Uhl, Matthias. Paternalism with hindsight: do prot{\'e}g{\'e}s react consequentialistically to paternalism?. \textit{Social Choice; Welfare}. Springer. vol. 43. no. 3. pp. 731--746. 2014.
\bibitem{KrawczykWozny} Krawczyk, Michal; Wozny, Lukasz P. An experiment on temptation; attitude towards paternalism. \textit{unpublished}. 2017.
\bibitem{LoewensteinHaisley} Loewenstein, George; Haisley, Emily C. The economist as therapist: Methodological ramifications of `light' paternalism. Oxford University Press. vol. 1. 2007.
\bibitem{Epleyetal.} Schroeder, Juliana; Waytz, Adam; Epley, Nicholas. Endorsing Help For Others That You Oppose For Yourself: Mind Perception Alters the Perceived Effectiveness of Paternalism.. \textit{Journal of Experimental Psychology: General}. 2017.
\bibitem{Uhl} Uhl, Matthias. Do self-committers mind commitment by others? An experiment on weak paternalism. \textit{Rationality, Markets; Morals}. vol. 2. pp. 13-34. 2011.
\bibitem{BenkertNetzer} Benkert, Jean-Michel; Netzer, Nick. Informational Requirements of Nudging. \textit{Journal of Political Economy}. vol. 126. no. 6. pp. 2323-2355. 2018.
\bibitem{EngelmannStrobel.FalseConsensus} Engelmann, Dirk; Strobel, Martin. The false consensus effect disappears if representative information; monetary incentives are given. \textit{Experimental Economics}. Springer. vol. 3. no. 3. pp. 241--260. 2000.
\bibitem{Jacobsoetal.} Jacobsson, Fredric; Johannesson, Magnus; Borgquist, Lars. Is altruism paternalistic?. \textit{The Economic Journal}. Wiley Online Library. vol. 117. no. 520. pp. 761--781. 2007.
\bibitem{Cunha} Cunha, Jesse M. Testing paternalism: Cash versus in-kind transfers. \textit{American Economic Journal: Applied Economics}. JSTOR. pp. 195--230. 2014.
\bibitem{BordaloGennaioliShleiferConsumer} Bordalo, Pedro; Gennaioli, Nicola; Shleifer, Andrei. Salience; consumer choice. \textit{Journal of Political Economy}. University of Chicago Press Chicago, IL. vol. 121. no. 5. pp. 803--843. 2013.
\bibitem{BordaloGennaioliShleiferRisk} Bordalo, Pedro; Gennaioli, Nicola; Shleifer, Andrei. Salience theory of choice under risk. \textit{The Quarterly journal of economics}. MIT Press. vol. 127. no. 3. pp. 1243--1285. 2012.
\bibitem{Bushongetal.} Bushong, Benjamin; Rabin, Matthew; Schwartzstein, Joshua. A model of relative thinking. \textit{Working paper}. Harvard University Boston MA. 2015.
\bibitem{KoszegiSzeidl} K{\H{o}}szegi, Botond; Szeidl, Adam. A model of focusing in economic choice. \textit{The Quarterly journal of economics}. Oxford University Press. vol. 128. no. 1. pp. 53--104. 2012.
\bibitem{NgangoueWeizsacker} Ngangoue, Kathleen; Weizsacker, Georg. Learning from unrealized versus realized prices. \textit{unpublished}. 2015.
\bibitem{ShengwuJMP} Li, Shengwu. Obviously strategy-proof mechanisms. \textit{American Economic Review}. forthcoming.
\bibitem{MartinezNiederle} Alejandro Martinez; Muriel Niederle. Contingent reasoning: The Special Role of Uncertainty. \textit{unpublished}. 2017.
\bibitem{FadenBeauchamp} Faden, Ruth R; Beauchamp, Tom L. A history; theory of informed consent. Oxford University Press. 1986.
\bibitem{FlachaireHollard} Flachaire, Emmanuel; Hollard, Guillaume. Controlling starting-point bias in double-bounded contingent valuation surveys. \textit{Land economics}. University of Wisconsin Press. vol. 82. no. 1. pp. 103--111. 2006.
\bibitem{Wangetal.ReachingForYield} Lian, Chen; Ma, Yueran; Wang, Carmen. Low Interest Rates; Risk Taking: Evidence from Individual Investment Decisions. \textit{unpublished}. 2016.
\bibitem{Hucketal.PortfolioChoice} Huck, Steffen; Schmidt, Tobias; Weizsacker, Georg. The standard portfolio choice problem in Germany. \textit{unpublished}. 2017.
\bibitem{Cappelenetal.JustLuck} Cappelen, Alexander W; Konow, James; S{\o}rensen, Erik {\O}; Tungodden, Bertil. Just luck: An experimental study of risk-taking; fairness. \textit{The American Economic Review}. American Economic Association. vol. 103. no. 4. pp. 1398--1413. 2013.
\bibitem{SEPinformedConsent} Informed Consent. Stanford Encyclopedia of Philosophy. 2017.
\bibitem{DeanNeligh} Mark Dean; Nathaniel Neligh. Experimental Tests of Rational Inattention. \textit{unpublished}. 2017.
\bibitem{CaplinDeanLeahy} Caplin, Andrew; Dean, Mark; Leahy, John. Rationally Inattentive Behavior: Characterizing; Generalizing Shannon Entropy. 2017.
\bibitem{AbelerJaeger} Abeler, Johannes; J{\"a}ger, Simon. Complex tax incentives. \textit{American Economic Journal: Economic Policy}. American Economic Association. vol. 7. no. 3. pp. 1--28. 2015.
\bibitem{DessiRustichini} Dessi, Roberta; Rustichini, Aldo. Strong intrinsic motivation. \textit{Toulouse School of Economics, Working Paper}. vol. 567. 2015.
\bibitem{Duckworthetal.IQ} Duckworth, Angela Lee; Quinn, Patrick D; Lynam, Donald R; Loeber, Rolf; Stouthamer-Loeber, Magda. Role of test motivation in intelligence testing. \textit{Proceedings of the National Academy of Sciences}. National Acad Sciences. vol. 108. no. 19. pp. 7716--7720. 2011.
\bibitem{Azfar} Azfar, Omar. Rationalizing hyperbolic discounting. \textit{Journal of Economic Behavior \&amp; Organization}. Elsevier. vol. 38. no. 2. pp. 245--252. 1999.
\bibitem{FrechetteSchotterTrevino} Fr{\'e}chette, Guillaume R; Schotter, Andrew; Trevino, Isabel. Personality, Information Acquisition,; Choice Under Uncertainty: An Experimental Study. \textit{Economic Inquiry}. Wiley Online Library. vol. 55. no. 3. pp. 1468--1488. 2017.
\bibitem{Almlundetal.} Almlund, Mathilde; Duckworth, Angela Lee; Heckman, James J; Kautz, Tim D. Personality psychology; economics. \textit{National Bureau of Economic Research}. vol. 16822. 2011.
\bibitem{Eliasetal.EfficiencyMoralityTradeoffs} Elias, Julio; Lacetera, Nicola; Macis, Mario. Paying for kidneys? {A} randomized survey; choice experiment. \textit{American Economic Review}. vol. 109. no. 8. pp. 2855--88. 2019.
\bibitem{ExleyKesslerMoneyTime} Exley, Christine L; Kessler, Judd B. Equity Concerns are Narrowly Framed: Why Money Cannot Buy Time. \textit{unpublished}. 2017.
\bibitem{Deanetal.StatusQuoBias} Dean, Mark; K{\i}br{\i}s, {\"O}zg{\"u}r; Masatlioglu, Yusufcan. Limited attention; status quo bias. \textit{Journal of Economic Theory}. Elsevier. vol. 169. pp. 93--127. 2017.
\bibitem{MatejkaKoszegi} Filip Matejka; Botond Koszegi. Mental Accounting Economizes on Information. \textit{unpublished}. 2017.
\bibitem{EspondaVespa} Esponda, Ignacio; Vespa, Emanuel. Hypothetical thinking; information extraction in the laboratory. \textit{American Economic Journal: Microeconomics}. American Economic Association. vol. 6. no. 4. pp. 180--202. 2014.
\bibitem{MorrisStrack} Morris, Stephen; Strack, Philipp. The Wald Problem; the Equivalence of Sequential Sampling; Static Information Costs. \textit{unpublished}. 2017.
\bibitem{HebertWoodford} H{\'e}bert, Benjamin; Woodford, Michael. Rational inattention with sequential information sampling. \textit{unpublished}. 2017.
\bibitem{ORIV} Gillen, Ben; Snowberg, Erik; Yariv, Leeat. Experimenting with measurement error: techniques with applications to the Caltech cohort study. \textit{Journal of Political Economy}. forthcoming.
\bibitem{Cohnetal.Countercyclical} Cohn, Alain; Engelmann, Jan; Fehr, Ernst; Mar{\'e}chal, Michel Andr{\'e}. Evidence for countercyclical risk aversion: an experiment with financial professionals. \textit{The American Economic Review}. American Economic Association. vol. 105. no. 2. pp. 860--885. 2015.
\bibitem{GabaixSparsity} Gabaix, Xavier. A sparsity-based model of bounded rationality. \textit{The Quarterly Journal of Economics}. MIT Press. vol. 129. no. 4. pp. 1661--1710. 2014.
\bibitem{AgarwalSongYao} Agarwal, Sumit; Song, Changcheng; Yao, Vincent W. Banking competition; shrouded attributes: evidence from the US mortgage market. \textit{unpublished}. 2017.
\bibitem{Devineetal.Concealment} Eric G Devine; Megan E Waters; Megan Putnam; Caitlin Surprise; Katie O'Malley; Courtney Richambault; Rachel L Fishman; Clifford M Knapp; Elissa H Patterson; Ofra Sarid-Segal; Chris Streeter; Laurie Colanari; Domenic A Ciraulo. Concealment; fabrication by experienced research subjects. \textit{Clinical Trials}. vol. 10. pp. 935-948. 2013.
\bibitem{Fredericketal.OpportunityCostNeglect} Frederick, Shane; Novemsky, Nathan; Wang, Jing; Dhar, Ravi; Nowlis, Stephen. Opportunity cost neglect. \textit{Journal of Consumer Research}. University of Chicago Press Chicago, IL. vol. 36. no. 4. pp. 553--561. 2009.
\bibitem{Ambuehletal.ForTheyKnowNot} Sandro Ambuehl; Axel Ockenfels; Colin Stewart. For They Know Not What They Do. Selection Effects of Incentives with Costly Information Acquisition.. \textit{unpublished}. 2017.
\bibitem{EUresearchEthics} Hughes, Jonathan; Hunter, David; Sheehan, Mark; Wilkinson, Stephen; Wrigley, Anthony. European textbook on ethics in research. Publications Office of the European Union. 2010.
\bibitem{repugnantDataEconomist} The Economist. Risk; reward. Data; technology are starting to up-end the insurance business. \textit{The Economist}. 2017.
\bibitem{Dentietal.AxiomaticRI} Denti, Tommaso; Mihm, Maximilian; de Oliveira, Henrique; Ozbek, Kemal. Rationally inattentive preferences; hidden information costs. \textit{Theoretical Economics}. Econometric Society. vol. 12. pp. 621-654. 2016.
\bibitem{Buseretal.Updating} Thomas Buser; Leonie Gerhards; Jo\"el van der Weele. Measuring responsiveness to feedback as a personal trait. \textit{Working paper}. 2016.
\bibitem{KamenicaReview} Kamenica, Emir. Behavioral economics; psychology of incentives. \textit{Annual Review of Economics}. Annual Reviews. vol. 4. no. 1. pp. 427--452. 2012.
\bibitem{SteinerStewartMatejka} Steiner, Jakub; Stewart, Colin; Matejka, Filip. Rational inattention dynamics: Inertia; delay in decision-making. Citeseer. 2015.
\bibitem{SteinerStewart} Steiner, Jakub; Stewart, Colin. Perceiving prospects properly. \textit{The American Economic Review}. American Economic Association. vol. 106. no. 7. pp. 1601--1631. 2016.
\bibitem{BeckerCrime} Becker, Gary S. Crime; punishment: An economic approach. In The Economic Dimensions of Crime. Springer. pp. 13--68. 1968.
\bibitem{Gopalakrishnanetal.} Gopalakrishnan, Arun; Iyengar, Raghuram; Meyer, Robert J. Consumer dynamic usage allocation; learning under multipart tariffs. \textit{Marketing Science}. INFORMS. vol. 34. no. 1. pp. 116--133. 2014.
\bibitem{AgarwalMazumder} Agarwal, Sumit; Mazumder, Bhashkar. Cognitive abilities; household financial decision making. \textit{American Economic Journal: Applied Economics}. American Economic Association. vol. 5. no. 1. pp. 193--207. 2013.
\bibitem{Burksetal.CognitiveSkills} Burks, Stephen V; Carpenter, Jeffrey P; Goette, Lorenz; Rustichini, Aldo. Cognitive skills affect economic preferences, strategic behavior,; job attachment. \textit{Proceedings of the National Academy of Sciences}. National Acad Sciences. vol. 106. no. 19. pp. 7745--7750. 2009.
\bibitem{GabaixLaibsonTimePrediction} Xavier Gabaix; David Laibson. Myopia; Discounting. \textit{NBER working paper}. 2017.
\bibitem{AmbuehlOckenfelsAERPP} Ambuehl, Sandro; Ockenfels, Axel. The Ethics of Incentivizing the Uninformed. A Vignette Study. \textit{The American Economic Review Papers; Proceedings}. vol. 107. no. 5. pp. 91-95. 2017.
\bibitem{Borghansetal.Noncognitive} Borghans, Lex; Meijers, Huub; Ter Weel, Bas. The role of noncognitive skills in explaining cognitive test scores. \textit{Economic Inquiry}. Wiley Online Library. vol. 46. no. 1. pp. 2--12. 2008.
\bibitem{Dohmenetal.PreferencesAndIQ} Dohmen, Thomas; Falk, Armin; Huffman, David; Sunde, Uwe. Are risk aversion; impatience related to cognitive ability?. \textit{The American Economic Review}. American Economic Association. vol. 100. no. 3. pp. 1238--1260. 2010.
\bibitem{Segal.MotivationTestScores} Carmit Segal. Working When No One Is Watching: Motivation, Test Scores,; Economic Success. \textit{Management Science}. vol. 58. no. 8. pp. 1438-1457. 2012.
\bibitem{Benjaminetal.WhoIsBehavioral} Benjamin, Daniel J; Brown, Sebastian A; Shapiro, Jesse M. Who is `behavioral'? Cognitive ability; anomalous preferences. \textit{Journal of the European Economic Association}. Wiley Online Library. vol. 11. no. 6. pp. 1231--1255. 2013.
\bibitem{Wechsler} Wechsler, David. Wechsler Adult Intelligence Scale--Fourth Edition (WAIS--IV). 2014.
\bibitem{RavenAPM} Raven, John C; Raven, John C; John Hugh Court. Advanced progressive matrices. HK Lewis London. 1962.
\bibitem{Kahan2013} Kahan, Dan M. Ideology, motivated reasoning,; cognitive reflection. \textit{Judgment; Decision Making}. vol. 8. no. 4. pp. 407--424. 2013.
\bibitem{Kahanetal.} Kahan, Dan M; Peters, Ellen; Dawson, Erica Cantrell; Slovic, Paul. Motivated numeracy; enlightened self-government. \textit{Cultural Cognition Project Working Paper 116, Yale Law School.}. 2014.
\bibitem{Woloshinetal.} Woloshin, Steven; Schwartz, Lisa M; Moncur, Megan; Gabriel, Sherine; Tosteson, Anna NA. Assessing values for health: numeracy matters. \textit{Medical Decision Making}. Sage Publications. vol. 21. no. 5. pp. 382--390. 2001.
\bibitem{ZikmundFisheretal.} Zikmund-Fisher, Brian J; Smith, Dylan M; Ubel, Peter A; Fagerlin, Angela. Validation of the Subjective Numeracy Scale: effects of low numeracy on comprehension of risk communications; utility elicitations. \textit{Medical Decision Making}. Sage Publications. 2007.
\bibitem{Reynaetal.Review} Reyna, Valerie F; Nelson, Wendy L; Han, Paul K; Dieckmann, Nathan F. How numeracy influences risk comprehension; medical decision making.. \textit{Psychological bulletin}. American Psychological Association. vol. 135. no. 6. pp. 943. 2009.
\bibitem{Sheridanetal.2003} Sheridan, Stacey L; Pignone, Michael P; Lewis, Carmen L. A randomized comparison of patients' understanding of number needed to treat; other common risk reduction formats. \textit{Journal of General Internal Medicine}. Wiley Online Library. vol. 18. no. 11. pp. 884--892. 2003.
\bibitem{JMP} Sandro Ambuehl. An Offer You Can't Refuse? Incentives Change How We Think. \textit{CESifo working paper 6296}. 2017.
\bibitem{BenabouTirole2011} B{\'e}nabou, Roland; Tirole, Jean. Identity, morals,; taboos: Beliefs as assets. \textit{The Quarterly Journal of Economics}. Oxford University Press. vol. 126. no. 2. pp. 805--855. 2011.
\bibitem{BenabouTiroleJEP} B{\'e}nabou, Roland; Tirole, Jean. Mindful Economics: The Production, Consumption,; Value of Beliefs. \textit{The Journal of Economic Perspectives}. American Economic Association. vol. 30. no. 3. pp. 141--164. 2016.
\bibitem{BenabouLaffontLecture} B{\'e}nabou, Roland. The Economics of Motivated Beliefs. \textit{Revue d'{\'e}conomie politique}. Dalloz. vol. 125. no. 5. pp. 665--685. 2015.
\bibitem{BenabouTirole2006MotivationCrowding} B{\'e}nabou, Roland; Tirole, Jean. Incentives; prosocial behavior. \textit{The American Economic Review}. American Economic Association. vol. 96. no. 5. pp. 1652--1678. 2006.
\bibitem{CheMierendorff} Yeon-Koo Che; Konrad Mierendorff. Optimal Sequential Decision with Limited Attention. \textit{unpublished manuscript}. 2016.
\bibitem{Farley} Farley, Melissa. Prostitution, Liberalism,; Slavery. \textit{Logos: A Journal of Modern Society \& Culture}. vol. 46. no. 3. 2013.
\bibitem{Danna} Daniela Danna. Report on prostitution laws in the European Union. \textit{unpublished manuscript}. 2014.
\bibitem{Nussbaum.Objectification} Nussbaum, Martha C. Objectification. \textit{Philosophy \& Public Affairs}. Wiley Online Library. vol. 24. no. 4. pp. 249--291. 1995.
\bibitem{Thiessenetal.} Thiessen, C; Kim, YA; Formica, R; Bia, M; Kulkarni, S. Written informed consent for living kidney donors: practices; compliance with CMS; OPTN requirements. \textit{American Journal of Transplantation}. Wiley Online Library. vol. 13. no. 10. pp. 2713--2721. 2013.
\bibitem{livingdonor101} livingdonor101.com. Living Donor Informed Consent. 2013.
\bibitem{DittoLopez} Ditto, Peter H; Lopez, David F. Motivated skepticism: Use of differential decision criteria for preferred; nonpreferred conclusions. \textit{Journal of Personality; Social Psychology}. American Psychological Association. vol. 63. no. 4. pp. 568. 1992.
\bibitem{EpleyGilovich} Epley, Nicholas; Gilovich, Thomas. The Mechanics of Motivated Reasoning. \textit{Journal of Economic Perspectives}. American Economic Association. vol. 30. no. 3. pp. 133--40. 2016.
\bibitem{chanceetal.2} Chance, Zoe; Gino, Francesca; Norton, Michael I; Ariely, Dan. The slow decay; quick revival of self-deception. \textit{Frontiers in psychology}. Frontiers Media SA. vol. 6. no. 1075. 2015.
\bibitem{chanceetal.1} Chance, Zo{\"e}; Norton, Michael I; Gino, Francesca; Ariely, Dan. Temporal view of the costs; benefits of self-deception. \textit{Proceedings of the National Academy of Sciences}. National Acad Sciences. vol. 108. pp. 15655--15659. 2011.
\bibitem{GinoNortonWeber} Gino, Francesca; Norton, Michael I; Weber, Roberto A. Motivated Bayesians: Feeling Moral While Acting Egoistically. \textit{Journal of Economic Perspectives}. American Economic Association. vol. 30. no. 3. pp. 189--212. 2016.
\bibitem{DohertyThistle} Doherty, Neil A; Thistle, Paul D. Adverse selection with endogenous information in insurance markets. \textit{Journal of Public Economics}. Elsevier. vol. 63. no. 1. pp. 83--102. 1996.
\bibitem{Melzeretal.} Linnainmaa, Juhani T; Melzer, Brian T; Previtero, Alessandro. The Misguided Beliefs of Financial Advisors. \textit{Unpublished manuscript}. 2016.
\bibitem{Wazana} Wazana, Ashley. Physicians; the pharmaceutical industry: is a gift ever just a gift?. \textit{Journal of the American Medical Association}. American Medical Association. vol. 283. no. 3. pp. 373--380. 2000.
\bibitem{Morganetal.PharmaGifts} Morgan, MA; Dana, Jason; Loewenstein, George; Zinberg, S; Schulkin, J. Interactions of doctors with the pharmaceutical industry. \textit{Journal of Medical Ethics}. BMJ Publishing Group Ltd; Institute of Medical Ethics. vol. 32. no. 10. pp. 559--563. 2006.
\bibitem{Campbelletal.PharmaGifts} Campbell, Eric G; Gruen, Russell L; Mountford, James; Miller, Lawrence G; Cleary, Paul D; Blumenthal, David. A national survey of physician--industry relationships. \textit{New England Journal of Medicine}. Mass Medical Soc. vol. 356. no. 17. pp. 1742--1750. 2007.
\bibitem{BazermanLoewensteinMoore} Bazerman, Max H; Loewenstein, George; Moore, Don A. Why good accountants do bad audits. \textit{Harvard business review}. vol. 80. no. 11. pp. 96--103. 2002.
\bibitem{AndersonEthics} Anderson, Elizabeth. Value in ethics; economics. Harvard University Press. 1995.
\bibitem{SenDeweyLectures} Sen, Amartya. Well-being, agency; freedom: the Dewey lectures 1984. \textit{The journal of philosophy}. JSTOR. vol. 82. no. 4. pp. 169--221. 1985.
\bibitem{SenOUP} Sen, Amaryta. On ethics; economics. \textit{OUP Catalogue}. Oxford University Press. 1999.
\bibitem{ExleyJMP} Exley, Christine L. Excusing selfishness in charitable giving: The role of risk. \textit{The Review of Economic Studies}. Oxford University Press. pp. rdv051. 2015.
\bibitem{HaisleyWeber} Haisley, Emily C; Weber, Roberto A. Self-serving interpretations of ambiguity in other-regarding behavior. \textit{Games; Economic Behavior}. Elsevier. vol. 68. no. 2. pp. 614--625. 2010.
\bibitem{boneMarrow} Boo, Michael; van Walraven, Suzanna M; Chapman, Jeremy; Lindberg, Brian; Schmidt, Alexander H; Shaw, Bronwen E; Switzer, Galen E; Yang, Edward; Egeland, Torstein; World Marrow Donor Association. Remuneration of hematopoietic stem cell donors: principles; perspective of the World Marrow Donor Association. \textit{Blood}. Am Soc Hematology. vol. 117. no. 1. pp. 21--25. 2011.
\bibitem{HossainOkui} Hossain, Tanjim; Okui, Ryo. The binarized scoring rule. \textit{The Review of Economic Studies}. Oxford University Press. vol. 80. no. 3. pp. 984--1001. 2013.
\bibitem{Greene.MLfixedEffects} Greene, William. The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects. \textit{The Econometrics Journal}. Wiley Online Library. vol. 7. no. 1. pp. 98--119. 2004.
\bibitem{KamenicaContextualInference} Kamenica, Emir. Contextual inference in markets: On the informational content of product lines. \textit{The American Economic Review}. American Economic Association. vol. 98. no. 5. pp. 2127--2149. 2008.
\bibitem{DongKaiser.panelDoubleHurdle} Dong, Diansheng; Kaiser, Harry M. Studying household purchasing; nonpurchasing behaviour for a frequently consumed commodity: two models. \textit{Applied Economics}. Taylor \&amp; Francis. vol. 40. no. 15. pp. 1941--1951. 2008.
\bibitem{Dongetal.PanelDoubleHurdle} Dong, Diansheng; Chung, Chanjin; Kaiser, Harry M. Modelling milk purchasing behaviour with a panel data double-hurdle model. \textit{Applied Economics}. Taylor \& Francis. vol. 36. no. 8. pp. 769--779. 2004.
\bibitem{Rosato} Rosato, Antonio. Selling Substitute Goods to Loss-Averse Consumers: Limited Availability, Bargains; Rip-offs. \textit{Unpublished manuscript}. 2013.
\bibitem{ZinmanZitzewitz} Zinman, Jonathan; Zitzewitz, Eric. Wintertime for Deceptive Advertising?. \textit{American Economic Journal: Applied Economics}. American Economic Association. vol. 8. no. 1. pp. 177--192. 2016.
\bibitem{Wilkieetal.} Wilkie, William L; Mela, Carl F; Gundlach, Gregory T. Does `bait; switch' really benefit consumers?. \textit{Marketing Science}. INFORMS. vol. 17. no. 3. pp. 273--282. 1998.
\bibitem{HessGerstner} Hess, James D; Gerstner, Eitan. Yes,`bait; switch' really benefits consumers. \textit{Marketing Science}. INFORMS. vol. 17. no. 3. pp. 283--289. 1998.
\bibitem{GerstnerHess} Gerstner, Eitan; Hess, James D. Can bait; switch benefit consumers?. \textit{Marketing Science}. INFORMS. vol. 9. no. 2. pp. 114--124. 1990.
\bibitem{MitchellMoro} Mitchell, Matthew F; Moro, Andrea. Persistent distortionary policies with asymmetric information. \textit{The American Economic Review}. JSTOR. vol. 96. no. 1. pp. 387--393. 2006.
\bibitem{EggDonorSettlement} Kelly Knaub. Egg Donors Get Pay Limits Axed With Antitrust Settlement. \textit{Law360}. 2016.
\bibitem{HoffmanBurks} Mitchell Hoffman; Stephen V. Burks. Training Contracts, Worker Overconfidence,; the Return from Firm-Sponsored General Training. \textit{Unpublished manuscript}. 2015.
\bibitem{Lazear.BaitAndSwitch} Lazear, Edward P. Bait; switch. \textit{Journal of Political Economy}. JSTOR. vol. 103. no. 4. pp. 813--830. 1995.
\bibitem{Suen.SelfPerpetuation} Suen, Wing. The Self-Perpetuation of Biased Beliefs. \textit{The Economic Journal}. Wiley Online Library. vol. 114. no. 495. pp. 377--396. 2004.
\bibitem{Bernheimetal.401k} B. Douglas Bernheim; Andrei Fradkin; Igor Popov. The Welfare Economics of Default Options in 401(k) Plans. \textit{American Economic Review}. vol. 105. no. 9. pp. 2798-2837. 2015.
\bibitem{CaplinAnnualReviews} Andrew Caplin. Measuring; Modeling Attention. \textit{Annual Reviews of Economics}. vol. 8. pp. 379-403. 2016.
\bibitem{KoszegiRabinWelfare} Koszegi, Botond; Rabin, Matthew. Revealed mistakes; revealed preferences. \textit{The foundations of positive; normative economics: a handbook}. Oxford University Press New York. pp. 193--209. 2008.
\bibitem{Ambuehletal.FinEd} Ambuehl, Sandro; Bernheim, B Douglas; Lusardi, Annamaria. Evaluating Financial Competence. \textit{NBER working paper 20618}. 2017.
\bibitem{BenoitDubra} Beno{\^\i}t, Jean-Pierre; Dubra, Juan. Apparent overconfidence. \textit{Econometrica}. Wiley Online Library. vol. 79. no. 5. pp. 1591--1625. 2011.
\bibitem{HuckSzechWenner} Steffen Huck; Nora Szech; Lukas Wenner. More Effort With Less Pay - On Information Avoidance, Belief Design; Performance. \textit{unpublished manuscript, London Business School}. 2015.
\bibitem{Blackwell} Blackwell, David. Equivalent comparisons of experiments. \textit{The Annals of Mathematical Statistics}. Institute of Mathematical Statistics. vol. 24. no. 2. pp. 265--272. 1953.
\bibitem{Rosenberg1} Tina Rosenberg. Need a Kidney? Not Iranian? You'll Wait.. 2015.
\bibitem{Havocscope} Havocscope. Organ Trafficking Prices; Kidney Transplant Sales. 2015.
\bibitem{Epstein} Richard Epstein. The Economics of Organ Donations: EconTalk Transcript. 2006.
\bibitem{FriedmanSavage} Friedman, Milton; Savage, Leonard J. The utility analysis of choices involving risk. \textit{Journal of Political Economy}. JSTOR. vol. 56. no. 4. pp. 279--304. 1948.
\bibitem{Eggsploitation} Center for Bioethics; Culture. Eggsploitation. 2011.
\bibitem{Masseyetal.Regret} Massey, EK; Kranenburg, LW; Zuidema, WC; Hak, G; Erdman, RAM; Hilhorst, Medard; Ijzermans, JNM; Busschbach, JJ; Weimar, Willem. Encouraging psychological outcomes after altruistic donation to a stranger. \textit{American Journal of Transplantation}. Wiley Online Library. vol. 10. no. 6. pp. 1445--1452. 2010.
\bibitem{CaplinDean.AER15} Andrew Caplin; Mark Dean. Revealed Preference, Rational Inattention,; Costly Information Acquisition. \textit{American Economic Review}. vol. 105. no. 7. pp. 2183-2203. 2015.
\bibitem{nytEggs} Tamar Lewin. Egg Donors Challenge Pay Rates, Saying They Shortchange Women. \textit{New York Times}. 2015.
\bibitem{BeckerBlog} Gary S. Becker. Should the Purchase; Sale of Organs for Transplant Surgery be Permitted? BECKER. 2006.
\bibitem{diTellaetal.} Rafael DiTella; Ricardo Perez-Truglia; Andres Babino; Mariano Sigman. Conveniently Upset: Avoiding Altruism by Distorting Beliefs about Others' Altruism. \textit{American Economic Review}. vol. 105. no. 11. pp. 3416-3442. 2015.
\bibitem{Heldetal.} P.J. Held; F. McCormick; A. Ojo; J.P. Roberts. A Cost-Benefit Analysis of Government Compensation of Kidney Donors. \textit{American Journal of Transplantation}. vol. 16. no. 3. pp. 877--885. 2016.
\bibitem{tseZhang} Tse, Yiu Kuen; Zhang, Xibin. A Monte Carlo investigation of some tests for stochastic dominance. \textit{Journal of Statistical Computation; Simulation}. Taylor \&amp; Francis. vol. 74. no. 5. pp. 361--378. 2004.
\bibitem{davidsonDuclos} Davidson, Russell; Duclos, Jean-Yves. Statistical inference for stochastic dominance; for the measurement of poverty; inequality. \textit{Econometrica}. JSTOR. pp. 1435--1464. 2000.
\bibitem{Bernheim.ExAnteExPost} James Andreoni; Deniz Aydin; Blake Barton; B. Douglas Bernheim; Jeffrey Naecker. When Fair Isn't Fair: Sophisticated Time Inconsistency in Social Preferences. \textit{unpublished manuscript}. 2015.
\bibitem{kessler2014getting} Kessler, Judd B; Roth, Alvin E. Getting more organs for transplantation. \textit{The American Economic Review}. American Economic Association. vol. 104. no. 5. pp. 425--430. 2014.
\bibitem{NiederleRoth} Niederle, Muriel; Roth, Alvin E. Philanthropically Funded Heroism Awards for Kidney Donors. \textit{Law \& Contemporary Problems}. HeinOnline. vol. 77. pp. 131--144. 2014.
\bibitem{LeiderRoth} Leider, Stephen; Roth, Alvin E. Kidneys for sale: Who disapproves,; why?. \textit{American Journal of Transplantation}. Wiley Online Library. vol. 10. no. 5. pp. 1221--1227. 2010.
\bibitem{vanDenSteen} Eric van den Steen. Rational overoptimism (and other biases). \textit{American Economic Review}. JSTOR. pp. 1141--1151. 2004.
\bibitem{Posneretal.} Choi, Stephen J; Gulati, Mitu; Posner, Eric A. Altruism Exchanges; the Kidney Shortage. \textit{Law \& Contemporary Problems}. HeinOnline. vol. 77. pp. 289--322. 2014.
\bibitem{Rabin.JEBO} Rabin, Matthew. Cognitive dissonance; social change. \textit{Journal of Economic Behavior \& Organization}. Elsevier. vol. 23. no. 2. pp. 177--194. 1994.
\bibitem{kahnemanKnetschThaler} Kahneman, Daniel; Knetsch, Jack L; Thaler, Richard. Fairness as a constraint on profit seeking: Entitlements in the market. \textit{The American Economic Review}. JSTOR. vol. 76. no. 4. pp. 728--741. 1986.
\bibitem{ERP2013} Economic Report of the President. Reducing Costs; Improving the Quality of Health Care. Government Printing Office. pp. 161-183. 2013.
\bibitem{FatigueLiver} Beavers, Kimberly L; Sandler, Robert S; Fair, Jeffrey H; Johnson, Mark W; Shrestha, Roshan. The living donor experience: donor health assessment; outcomes after living donor liver transplantation. \textit{Liver transplantation}. Wiley Online Library. vol. 7. no. 11. pp. 943--947. 2001.
\bibitem{FatigueKidney} Tellioglu, G; Berber, I; Yatkin, I; Yigit, B; Ozgezer, T; Gulle, S; Isitmangil, G; Caliskan, M; Titiz, I. Quality of life analysis of renal donors. \textit{Transplantation proceedings}. vol. 40. no. 1. pp. 50--52. 2008.
\bibitem{Ibrahim} Ibrahim, Hassan N; Foley, Robert; Tan, LiPing; Rogers, Tyson; Bailey, Robert F; Guo, Hongfei; Gross, Cynthia R; Matas, Arthur J. Long-term consequences of kidney donation. \textit{New England Journal of Medicine}. Mass Medical Soc. vol. 360. no. 5. pp. 459--469. 2009.
\bibitem{kidneLongTermNorway} Mj{\o}en, Geir; Hallan, Stein; Hartmann, Anders; Foss, Aksel; Midtvedt, Karsten; {\O}yen, Ole; Reis{\ae}ter, Anna; Pfeffer, Per; Jenssen, Trond; Leivestad, Torbj{\o}rn. Long-term risks for kidney donors. \textit{Kidney International}. Nature Publishing Group. vol. 86. no. 1. pp. 162--167. 2014.
\bibitem{Bodri} Bodri, Daniel. Risk; complications associated with egg donation. In Principles of Oocyte; Embryo Donation. Springer. pp. 205--219. 2013.
\bibitem{Gottlieb} Gottlieb, Daniel. Imperfect memory; choice under risk. \textit{Games; Economic Behavior}. Elsevier. vol. 85. pp. 127--158. 2014.
\bibitem{QuattroneTversky} Quattrone, George A; Tversky, Amos. Causal versus diagnostic contingencies: On self-deception; on the voter's illusion.. \textit{Journal of personality; social psychology}. American Psychological Association. vol. 46. no. 2. pp. 237. 1984.
\bibitem{churches} Alan C. Clark. The Challenge of Proselytism; the Calling to Common Witness. \textit{Ecumenical Review}. WORLD COUNCIL CHURCHES PUBLICATIONS OFFICE 150 ROUTE DE FERNEY, 1211 GENEVA 20, SWITZERLAND. vol. 48. no. 2. pp. 212--221. 1996.
\bibitem{Cave} Damien Cave. Critics Say It's Time to Overhaul The Army's Bonus System. \textit{New York Times}. 2005.
\bibitem{McCormick} McCormick, Patrick T. Volunteers; Incentives: Buying the Bodies of the Poor. \textit{Journal of the Society of Christian Ethics}. JSTOR. vol. 27. no. 1. pp. 77--93. 2007.
\bibitem{PosnerKidneys} Richard Posner. A Moral Market. \textit{Slate}. 2014.
\bibitem{Svitnev} Svitnev, Konstantin. Legal regulation of assisted reproduction treatment in Russia. \textit{Reproductive Biomedicine Online}. Elsevier. vol. 20. no. 7. pp. 892--894. 2010.
\bibitem{Ashrafetal.AwardsUnbundled} Ashraf, Nava; Bandiera, Oriana; Lee, Scott S. Awards unbundled: Evidence from a natural field experiment. \textit{Journal of Economic Behavior \&amp; Organization}. Elsevier. vol. 100. pp. 44--63. 2014.
\bibitem{Osteretal.Huntingtons} Oster, Emily; Shoulson, Ira; Dorsey, E. Optimal Expectations; Limited Medical Testing: Evidence from Huntington Disease. \textit{The American Economic Review}. American Economic Association. vol. 103. no. 2. pp. 804--830. 2013.
\bibitem{BesleyOnSandel} Besley, Timothy. What's the good of the market? An essay on Michael Sandel's what money can't buy. \textit{Journal of Economic Literature}. American Economic Association. vol. 51. no. 2. pp. 478--495. 2013.
\bibitem{xtdhreg} Engel, Christoph; Moffatt, Peter G. dhreg, xtdhreg,; bootdhreg: Commands to implement double-hurdle regression. \textit{Stata Journal}. StataCorp LP. vol. 14. no. 4. pp. 778--797. 2014.
\bibitem{Kunda} Kunda, Ziva. The case for motivated reasoning.. \textit{Psychological bulletin}. American Psychological Association. vol. 108. no. 3. pp. 480. 1990.
\bibitem{LordRossLepper} Lord, Charles G; Ross, Lee; Lepper, Mark R. Biased assimilation; attitude polarization: the effects of prior theories on subsequently considered evidence.. \textit{Journal of personality; social psychology}. American Psychological Association. vol. 37. no. 11. pp. 2098. 1979.
\bibitem{Saccardoetal.} Uri Gneezy; Silvia Saccardo; Marta Serra-Garcia; Roel van Veldhuizen. Motivated self-deception, identity,; unethical behavior. \textit{unpublished manuscript}. 2015.
\bibitem{GulPesendorfer.Temptation} Gul, Faruk; Pesendorfer, Wolfgang. Temptation; self-control. \textit{Econometrica}. JSTOR. pp. 1403--1435. 2001.
\bibitem{FestingerCarlsmith} Festinger, Leon; Carlsmith, James M. Cognitive consequences of forced compliance.. \textit{The Journal of Abnormal; Social Psychology}. American Psychological Association. vol. 58. no. 2. pp. 203. 1959.
\bibitem{cognitiveDissonance} Festinger, Leon. A theory of cognitive dissonance. Stanford university press. vol. 2. 1962.
\bibitem{BayesianPersuasion} Gentzkow, Matthew; Kamenica, Emir. Bayesian persuasion. \textit{American Economic Review}. vol. 101. no. 6. pp. 2590--2615. 2011.
\bibitem{HuesmanWambach} Huesmann, Katharina; Wambach, Achim. Constraints on Matching Markets Based on Moral Concerns. 2015.
\bibitem{oDonoghueRabin} O'Donoghue, Ted; Rabin, Matthew. Doing it now or later. \textit{American Economic Review}. JSTOR. vol. 89. no. 1. pp. 103--124. 1999.
\bibitem{AugenblickNiederleSprenger} Augenblick, Ned; Niederle, Muriel; Sprenger, Charles. Working over time: Dynamic inconsistency in real effort tasks. \textit{Quarterly Journal of Economics}. vol. 130. no. 3. 2015.
\bibitem{Fredericketal.timeInconsistent} Frederick, Shane; Loewenstein, George; o'Donoghue, Ted. Time discounting; time preference: A critical review. \textit{Journal of Economic Literature}. JSTOR. vol. 40. no. 2. pp. 351--401. 2002.
\bibitem{Laibson.goldenEggs} Laibson, David. Golden eggs; hyperbolic discounting. \textit{Quarterly Journal of Economics}. JSTOR. pp. 443--477. 1997.
\bibitem{Eliasetal.prostitution} Elias, Julio J; Lacetera, Nicola; Macis, Mario. Markets; Morals: An Experimental Survey Study. \textit{PloS one}. Public Library of Science. vol. 10. no. 6. 2015.
\bibitem{Eliasetal.kidneys} Elias, Julio; Lacetera, Nicola; Macis, Mario. Sacred Values? The Effect of Information on Attitudes Toward Payments for Human Organs. \textit{American Economic Review, Papers \& Proceedings}. vol. 105. no. 5. pp. 361--365. 2015.
\bibitem{LargeStakesBigMistakes} Ariely, Dan; Gneezy, Uri; Loewenstein, George; Mazar, Nina. Large stakes; big mistakes. \textit{The Review of Economic Studies}. Oxford University Press. vol. 76. no. 2. pp. 451--469. 2009.
\bibitem{MalmendierSchmidt} Malmendier, Ulrike; Schmidt, Klaus. You owe me. \textit{NBER working paper}. vol. 18543. 2012.
\bibitem{LoewensteinAngner} Loewenstein, George; Angner, Erik. Predicting; indulging changing preferences. \textit{Time; decision: Economic; psychological perspectives on intertemporal choice}. pp. 351--391. 2003.
\bibitem{Alevyetal.Anchoring} Alevy, Jonathan E; Landry, Craig E; List, John A. Field experiments on anchoring of economic valuations. \textit{Available at SSRN 1824400}. 2010.
\bibitem{Sunsteinetal.Anchoring} Sunstein, Cass R; Kahneman, Daniel; Schkade, David; Ritov, Ilana. Predictably incoherent judgments. \textit{Stanford Law Review}. JSTOR. pp. 1153--1215. 2002.
\bibitem{simonsonDrolet} Simonson, Itamar; Drolet, Aimee. Anchoring effects on consumers' willingness-to-pay; willingness-to-accept. \textit{Journal of Consumer Research}. The Oxford University Press. vol. 31. no. 3. pp. 681--690. 2004.
\bibitem{Bergmanetal.Anchoring} Bergman, Oscar; Ellingsen, Tore; Johannesson, Magnus; Svensson, Cicek. Anchoring; cognitive ability. \textit{Economics Letters}. Elsevier. vol. 107. no. 1. pp. 66--68. 2010.
\bibitem{BeggsGraddy.Anchoring} Beggs, Alan; Graddy, Kathryn. Anchoring effects: Evidence from art auctions. \textit{The American Economic Review}. JSTOR. pp. 1027--1039. 2009.
\bibitem{vatican} Vatican Radio. Pope Francis meets a group of transplant surgeons; including the mayor of Rome. 2014.
\bibitem{openLetterObama} Nir Eyal; Julio Frenk; Michele B. Goodwin; Lori Gruen; Gary R. Hall; Douglas W. Hanto; Frances Kissling; Ruth Macklin; Steven Pinker; Lloyd E. Ratner; Harold T. Shapiro; Peter Singer; Andrew W. Torrance; Robert D. Truog; Robert M. Veatch. An Open Letter to President Barack Obama, Secretary of Health; Human Services Sylvia Mathews Burwell, Attorney General Eric Holder; Leaders of Congress. 2014.
\bibitem{SatelCronin} Satel, Sally; Cronin, David C. Time to Test Incentives to Increase Organ Donation. \textit{JAMA internal medicine}. vol. 175. no. 8. pp. 1329-1330. 2015.
\bibitem{MichiganHSAA} Michigan High School Athletic Association. The History, Rationale; Application of the Essential Regulations of High School Athletics in Michigan. 2015.
\bibitem{IndianaHSAA} Indiana High School Athletic Association. By-Laws \& Articles of Incorporation. 2015.
\bibitem{AlabamaHSAA} Alabama High School Athletic Association. Handbook. 2015.
\bibitem{KentuckyHSAA} Kentucky High School Athletic Association. Handbook. 2014.
\bibitem{NCAAbylaws} National Collegiate Athletic Association. Division 1 Manual. 2015.
\bibitem{Selten.QSR} Selten, Reinhard. Axiomatic characterization of the quadratic scoring rule. \textit{Experimental Economics}. Springer. vol. 1. no. 1. pp. 43--62. 1998.
\bibitem{Smith.InducedPreferences} Smith, Vernon L. Experimental economics: Induced value theory. \textit{The American Economic Review}. JSTOR. pp. 274--279. 1976.
\bibitem{Hortonetal.} John J. Horton; David G. Rand; Richard J. Zeckhauser. The online laboratory: Conducting experiments in a real labor market. \textit{Experimental Economics}. vol. 14. pp. 399-425. 2011.
\bibitem{MasonSuri} Winter Mason; Siddarth Suri. Conducting behavioral research on Amazon's Mechanical Turk. \textit{Behavioral Research}. vol. 44. pp. 1-23. 2012.
\bibitem{MarksMiller.falseConsensus} Marks, Gary; Miller, Norman. Ten years of research on the false-consensus effect: An empirical; theoretical review.. \textit{Psychological Bulletin}. American Psychological Association. vol. 102. no. 1. pp. 72. 1987.
\bibitem{Rossetal.FalseConsensus} Ross, Lee; Greene, David; House, Pamela. The ``false consensus effect'': An egocentric bias in social perception; attribution processes. \textit{Journal of Experimental Social Psychology}. Elsevier. vol. 13. no. 3. pp. 279--301. 1977.
\bibitem{Brenneretal.OneSided} Brenner, Lyle A; Koehler, Derek J; Tversky, Amos. On the evaluation of one-sided evidence. \textit{Journal of Behavioral Decision Making}. Citeseer. vol. 9. no. 1. pp. 59--70. 1996.
\bibitem{Topkis} Milgrom, Paul; Shannon, Chris. Monotone comparative statics. \textit{Econometrica}. JSTOR. pp. 157--180. 1994.
\bibitem{Gilletal.} Gill, Jagbir; Dong, Jianghu; Gill, John. Population income; longitudinal trends in living kidney donation in the United States. \textit{J Am Soc Nephrol}. vol. 26. 2014.
\bibitem{Rosenberg2} Tina Rosenberg. It's Time to Compensate Kidney Donors. 2015.
\bibitem{Sickandetal.} Sickand, M; Cuerden, MS; Klarenbach, SW; Ojo, AO; Parikh, CR; Boudville, N; Garg, AX. Reimbursing Live Organ Donors for Incurred Non-Medical Expenses: A Global Perspective on Policies; Programs. \textit{American Journal of Transplantation}. Wiley Online Library. vol. 9. no. 12. pp. 2825--2836. 2009.
\bibitem{Singapore} Singapore Ministry of Health. Human Organ Transplant Act. 2010.
\bibitem{Khaleeli} Homa Khaleeli. The hair trade's dirty secret. \textit{The Guardian}. 2012.
\bibitem{AkerlofDickens} Akerlof, George A; Dickens, William T. The economic consequences of cognitive dissonance. \textit{The American economic review}. JSTOR. pp. 307--319. 1982.
\bibitem{poopWashingtonPost} Rachel Feltman. You can earn \$13,000 a year selling your poop. \textit{Washington Post}. 2015.
\bibitem{CampbellGuardian} Denis Campbell. Egg; sperm donors to be paid more compensation. \textit{The Guardian}. 2011.
\bibitem{EggGermany} Bundesrepublik Deutschland. Gesetz zum Schutz von Embryonen \textsection 1 Missbrauchliche Anwendung von Fortpflanzungstechniken. 1990.
\bibitem{financialIncentivesEggs} American Society for Reproductive Medicine. Financial compensation of oocyte donors. \textit{Fertility; Sterility}. Elsevier. vol. 88. no. 2. pp. 305--309. 2007.
\bibitem{CouncilOfEurope} Council of Europe. Medically assisted procreation; the protection of the human embryo. Comparative study on the situation in 39 states.. 1998.
\bibitem{Gamble} Gamble, Natalie. Crossing the line: the legal; ethical problems of foreign surrogacy. \textit{Reproductive biomedicine online}. Elsevier. vol. 19. no. 2. pp. 151--152. 2009.
\bibitem{Drabiaketal.} Drabiak, Katherine; Wegner, Carole; Fredland, Valita; Helft, Paul R. Ethics, law,; commercial surrogacy: a call for uniformity. \textit{The Journal of Law, Medicine \& Ethics}. Wiley Online Library. vol. 35. no. 2. pp. 300--309. 2007.
\bibitem{Arons} Arons, Jessica. Future choices: Assisted reproductive technologies; the law. \textit{Center for American Progress}. 2007.
\bibitem{NakashHerdiman} Nakash, A; Herdiman, J. Surrogacy. \textit{Journal of obstetrics; gynaecology}. Informa Healthcare. vol. 27. no. 3. pp. 246--251. 2007.
\bibitem{Vohsetal.Money} Vohs, Kathleen D; Mead, Nicole L; Goode, Miranda R. The psychological consequences of money. \textit{science}. American Association for the Advancement of Science. vol. 314. no. 5802. pp. 1154--1156. 2006.
\bibitem{BartlingWeber} Bartling, Bj{\"o}rn; Weber, Roberto A; Yao, Lan. Do Markets Erode Social Responsibility?. \textit{The Quarterly Journal of Economics}. Oxford University Press. vol. 130. no. 1. pp. 219--266. 2015.
\bibitem{FalkSzech} Falk, Armin; Szech, Nora. Morals; markets. \textit{Science}. American Association for the Advancement of Science. vol. 340. no. 6133. pp. 707--711. 2013.
\bibitem{Roth.repugnance} Roth, Alvin E. Repugnance as a Constraint on Markets. \textit{Journal of Economic Perspectives}. American Economic Association. vol. 21. no. 3. pp. 37-58. 2007.
\bibitem{Maniadisetal.Anchoring} Maniadis, Zacharias; Tufano, Fabio; List, John A. One swallow doesn't make a summer: New evidence on anchoring effects. \textit{The American Economic Review}. American Economic Association. vol. 104. no. 1. pp. 277--290. 2014.
\bibitem{Fudenbergetal.Anchoring} Fudenberg, Drew; Levine, David K; Maniadis, Zacharias. On the robustness of anchoring effects in WTP; WTA experiments. \textit{American Economic Journal: Microeconomics}. American Economic Association. vol. 4. no. 2. pp. 131--145. 2012.
\bibitem{BeckerElias} Becker, Gary S; Elias, Julio J. Introducing incentives in the market for live; cadaveric organ donations. \textit{The Journal of Economic Perspectives}. JSTOR. vol. 21. no. 3. pp. 3--24. 2007.
\bibitem{Kanbur} Kanbur, Ravi. On obnoxious markets. In Globalization, Culture; the Limits of the Market: Essays in Economics; Philosophy. Oxford University Press. 2004.
\bibitem{Rozinetal.SympatheticMagic} Rozin, Paul; Millman, Linda; Nemeroff, Carol. Operation of the laws of sympathetic magic in disgust; other domains.. \textit{Journal of personality; social psychology}. American Psychological Association. vol. 50. no. 4. pp. 703. 1986.
\bibitem{Keisneretal.} Keisner, Deborah Kerley; Messer, Kent D; Schulze, William D; Zarghamee, Homa. Testing Social Preferences for an Economic ``Bad'': An Artefactual Field Experiment*. \textit{The Scandinavian Journal of Economics}. Wiley Online Library. vol. 115. no. 1. pp. 27--61. 2013.
\bibitem{DebbieStevens} abc News. N.Y. Mom Fired After Donating Kidney To Help Her Boss. 2012.
\bibitem{Stracketal.DDM} Drew Fudenberg; Philipp Strack; Tomasz Strzalecki. Stochastic Choice; Optimal Sequential Sampling. \textit{Working paper, Harvard University}. 2015.
\bibitem{PinkerOpEd} Steven Pinker. The Moral Imperative For Bioethics. \textit{The Boston Globe}. 2015.
\bibitem{KR2006} K{\H{o}}szegi, Botond; Rabin, Matthew. A model of reference-dependent preferences. \textit{The Quarterly Journal of Economics}. JSTOR. pp. 1133--1165. 2006.
\bibitem{Cragg} Cragg, John G. Some statistical models for limited dependent variables with application to the demand for durable goods. \textit{Econometrica}. JSTOR. vol. 39. no. 5. pp. 829--844. 1971.
\bibitem{LinSchmidt} Lin, Tsai-Fen; Schmidt, Peter. A test of the Tobit specification against an alternative suggested by Cragg. \textit{The Review of Economics; Statistics}. JSTOR. pp. 174--177. 1984.
\bibitem{MatejkaMcKayAER} Mat{\v{e}}jka, Filip; McKay, Alisdair. Rational inattention to discrete choices: A new foundation for the multinomial logit model. \textit{American Economic Review}. American Economic Association. vol. 105. no. 1. pp. 272--98. 2015.
\bibitem{Basu.Coercion} Basu, Kaushik. Coercion, Contract; the Limits of the Market. \textit{Social Choice; Welfare}. Springer. vol. 29. no. 4. pp. 559--579. 2007.
\bibitem{BasuJEP} Basu, Kaushik. The economics; law of sexual harassment in the workplace. \textit{The Journal of Economic Perspectives}. American Economic Association. vol. 17. no. 3. pp. 141--157. 2003.
\bibitem{FreyJegen} Frey, Bruno S; Jegen, Reto. Motivation Crowding Theory. \textit{Journal of Economic Surveys}. Wiley Blackwell. vol. 15. no. 5. pp. 589--611. 2001.
\bibitem{Titmuss} Richard Titmuss. The gift relationship: From human blood to social policy. Allen \& Unwin. 1970.
\bibitem{Fershtmanetal.} Fershtman, Chaim; Gneezy, Uri; Hoffman, Moshe. Taboos; identity: Considering the unthinkable. \textit{American Economic Journal: Microeconomics}. JSTOR. pp. 139--164. 2011.
\bibitem{Raven} Raven, John C. Guide to the standard progressive matrices: sets A, B, C, D; E. Pearson. 1960.
\bibitem{Inbaretal.} Inbar, Yoel; Pizarro, David A; Bloom, Paul. Conservatives are more easily disgusted than liberals. \textit{Cognition; Emotion}. Taylor \& Francis. vol. 23. no. 4. pp. 714--725. 2009.
\bibitem{Carneyetal.} Carney, Dana R; Jost, John T; Gosling, Samuel D; Potter, Jeff. The secret lives of liberals; conservatives: Personality profiles, interaction styles,; the things they leave behind. \textit{Political Psychology}. Wiley Online Library. vol. 29. no. 6. pp. 807--840. 2008.
\bibitem{Rozinetal.} Rozin, Paul; Haidt, Jonathan; McCauley, Clark R. Disgust. Guilford Press. 2008.
\bibitem{UNentomophagy} Food; Agriculture Organization of the United Nations. Edible insects Future prospects for food; feed security. 2013.
\bibitem{Macklin} Macklin, Ruth. `Due'; `Undue' Inducements: On Paying Money to Research Subjects. \textit{IRB}. JSTOR. vol. 3. no. 5. pp. 1--6. 1981.
\bibitem{Kuczewski} Kuczewski, Mark. Is informed consent enough? Monetary incentives for research participation; the integrity of biomedicine. \textit{American Journal of Bioethics}. Taylor \& Francis. vol. 1. no. 2. pp. 49--51. 2001.
\bibitem{Ripley} Ripley, Elizabeth BD. A review of paying research participants: It's time to move beyond the ethical debate. \textit{Journal of Empirical Research on Human Research Ethics}. SAGE Publications. vol. 1. no. 4. pp. 9--19. 2006.
\bibitem{NurembergCode} Nuremberg Code. In Trials of war criminals before the Nuremberg military tribunals under control council law. vol. 10. pp. 181-182. 1949.
\bibitem{BelmontReport} US Department of Health, Education,; Welfare (DHEW). The Belmont report: Ethical principles; guidelines for the protection of human subjects of research. National Commission for the Protection of Human Subjects of Biomedical; Behavioral Research, Washington DC. 1978.
\bibitem{Casarettetal.} Casarett, David; Karlawish, Jason; Asch, David A. Paying hypertension research subjects. \textit{Journal of general internal medicine}. Wiley Online Library. vol. 17. no. 8. pp. 651--653. 2002.
\bibitem{Gradyetal.Empirical} Grady, Christine; Dickert, Neal; Jawetz, Tom; Gensler, Gary; Emanuel, Ezekiel. An analysis of US practices of paying research participants. \textit{Contemporary clinical trials}. Elsevier. vol. 26. no. 3. pp. 365--375. 2005.
\bibitem{BernheimRangelAddiction} B. Douglas Bernheim; Antonio Rangel. Addiction; Cue-Triggered Decision Processes. \textit{American Economic Review}. vol. 94. no. 5. pp. 1558-90. 2004.
\bibitem{BernheimRangelWelfare} B. Douglas Bernheim; Antonio Rangel. Beyond Revealed Preference: Choice-Theoretic Foundations for Behavioral Welfare Economics. \textit{The Quarterly Journal of Economics}. vol. 124. no. 1. pp. 51-104. 2009.
\bibitem{BernheimWelfareReview} B. Douglas Bernheim. Behavioral Welfare Economics. \textit{Journal of the European Economic Association}. vol. 7. no. 2-3. pp. 267-319. 2009.
\bibitem{Slovic} Slovic, Paul. The construction of preference.. \textit{American psychologist}. American Psychological Association. vol. 50. no. 5. pp. 364. 1995.
\bibitem{LichtensteinSlovic} Lichtenstein, Sarah; Slovic, Paul. The construction of preference. Cambridge University Press. 2006.
\bibitem{coherentArbitrariness} Ariely, Dan; Loewenstein, George; Prelec, Drazen. `Coherent Arbitrariness': Stable Demand Curves Without Stable Preferences. \textit{The Quarterly Journal of Economics}. MIT Press. vol. 118. no. 1. pp. 73--105. 2003.
\bibitem{HolmstromMyerson} Holmstr{\"o}m, Bengt; Myerson, Roger B. Efficient; durable decision rules with incomplete information. \textit{Econometrica: Journal of the Econometric Society}. JSTOR. pp. 1799--1819. 1983.
\bibitem{Freakonomics} Levitt, Steven D; Dubner, Stephen J. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. William Morrow. 2005.
\bibitem{McGregor} McGregor, Joan. `Undue Inducement'as Coercive Offers'. \textit{The American Journal of Bioethics}. Taylor \& Francis. vol. 5. no. 5. pp. 24--25. 2005.
\bibitem{EmanuelNonsenseOnStilts} Emanuel, Ezekiel J. Undue inducement: Nonsense on stilts?. \textit{The American Journal of Bioethics}. Taylor \& Francis. vol. 5. no. 5. pp. 9--13. 2005.
\bibitem{EmanuelEndingConcerns} Emanuel, Ezekiel J. Ending concerns about undue inducement. \textit{The Journal of Law, Medicine \& Ethics}. Wiley Online Library. vol. 32. no. 1. pp. 100--105. 2004.
\bibitem{Spital} Spital, Aaron. Living organ donation: Shifting responsibility. \textit{Archives of internal medicine}. American Medical Association. vol. 151. no. 2. pp. 234--235. 1991.
\bibitem{Delmonicoetal.} Delmonico, FL; Martin, D; Dom{\'\i}nguez-Gil, B; Muller, E; Jha, V; Levin, A; Danovitch, GM; Capron, AM. Living; Deceased Organ Donation Should Be Financially Neutral Acts. \textit{American Journal of Transplantation}. Wiley Online Library. 2015.
\bibitem{Salomonetal.} Salomon, DR; Langnas, AN; Reed, AI; Bloom, RD; Magee, JC; Gaston, RS. AST/ASTS workshop on increasing organ donation in the United States: Creating an ``arc of change'' from removing disincentives to testing incentives. \textit{American Journal of Transplantation}. Wiley Online Library. 2015.
\bibitem{KahnemanTversky1974} Tversky, Amos; Kahneman, Daniel. Judgment under uncertainty: Heuristics; biases. \textit{Science}. American Association for the Advancement of Science. vol. 185. no. 4157. pp. 1124--1131. 1974.
\bibitem{Stanovichetal.} Stanovich, Keith E; West, Richard F; Toplak, Maggie E. Myside bias, rational thinking,; intelligence. \textit{Current Directions in Psychological Science}. Sage Publications. vol. 22. no. 4. pp. 259--264. 2013.
\bibitem{Fisheretal.} Fisher, JS; Butt, Z; Friedewald, J; Fry-Revere, S; Hanneman, J; Henderson, ML; Ladin, K; Mysel, H; Preczewski, L; Sherman, LA; others. Between Scylla; Charybdis: Charting an Ethical Course for Research Into Financial Incentives for Living Kidney Donation. \textit{American Journal of Transplantation}. Wiley Online Library. 2015.
\bibitem{NBAC} National Bioethics Advisory Commission. Ethical; policy issues in research involving human participants. 2001.
\bibitem{Grant} Grant, Ruth W. Strings attached: Untangling the ethics of incentives. Princeton University Press. 2011.
\bibitem{Sandel} Sandel, Michael J. What money can't buy: the moral limits of markets. Macmillan. 2012.
\bibitem{Abeleretal.} Johannes Abeler; Anke Becker; Armin Falk. Truth-Telling - A Representative Assessment. \textit{mimeo}. 2012.
\bibitem{Cryderetal.} Cynthia E. Cryder; Alex John London; Kevin G. Volpp; George Loewenstein. Informative inducement: Study payment as a signal of risk. \textit{Social Science \& Medicine}. vol. 70. pp. 455-464. 2010.
\bibitem{EratGneezy} S. Era; Uri Gneezy. White Lies. \textit{Management Science}. vol. 58. no. 4. pp. 723-733. 2012.
\bibitem{FischbacherHeusi} Urs Fischbacher; Franziska Heusi. Lies In Disguise: An Experimental Study on Cheating. \textit{TWI Research Paper Series}. 2008.
\bibitem{Gneezy} Uri Gneezy. Deception: The Role of Consequences. \textit{American Economic Review}. vol. 95. no. 1. pp. 384-394. 2005.
\bibitem{Hannanetal.} R. Lynn Hannan; Frederick W. Rankin; Kristy L. Towry. The Effect of Information Systems on Honesty in Managerial Reporting: A Behavioral Perspective. \textit{Contemporary Accounting Research}. vol. 23. no. 4. pp. 885-918. 2006.
\bibitem{HaoHouser} Li Hao; Daniel Houser. Honest Lies. \textit{mimeo}. 2010.
\bibitem{HurkensKartik} Sjaak Hurkens; Navin Kartik. Would I lie to you? On Social Preferences; Lying Aversion. \textit{Experimental Economics}. vol. 12. pp. 180-192. 2009.
\bibitem{Kline} Paul Kline. Handbook of Psychological Testing. Routledge. 1999.
\bibitem{Loewensteinetal.ProjectionBias} George Loewenstein; Ted O'Donogue; Matthew Rabin. Projection Bias in Predicting Future Utility. \textit{Quarterly Journal of Economics}. vol. 118. no. 4. pp. 1209-1248. 2003.
\bibitem{LopezPerezSpiegelman} Rau Lopez-Perez; Eli Spiegelman. Why do people tell the truth? Experimental evidence for pure lie aversion. \textit{Experimental Economics}. forthcoming.
\bibitem{Lundquistetal.} Tobias Lundquist; Tore Ellingsen; Erik Gribbe; Magnus Johannesson. The Aversion To Lying. \textit{Journal of Economic Behavior; Organization}. vol. 70. pp. 81-92. 2009.
\bibitem{MazarAriely} Nina Mazar; Dan Ariely. The Dishonesty of Honest People: A Theory of Self-Concept Maintenance. \textit{Journal of Marketing Research}. vol. 45. no. 6. pp. 633-644. 2008.
\bibitem{Satz} Debra Satz. Why Some Things Should Not Be For Sale: The Moral Limits of Markets. Oxford University Press. 2010.
\bibitem{Shleiferetal.AERPP} Nicola Gennaioli; Andrei Shleifer; Robert Vishny. Neglected Risks: The Psychology of Financial Crises. \textit{American Economic Review}. vol. 105. no. 5. 2015.
\bibitem{BarberOdean.AllThatGlitters} Barber, Brad M; Odean, Terrance. All that glitters: The effect of attention; news on the buying behavior of individual; institutional investors. \textit{Review of Financial Studies}. Soc Financial Studies. vol. 21. no. 2. pp. 785--818. 2008.
\bibitem{DellaVignaPollet} DellaVigna, Stefano; Pollet, Joshua M. Investor inattention; Friday earnings announcements. \textit{The Journal of Finance}. Wiley Online Library. vol. 64. no. 2. pp. 709--749. 2009.
\bibitem{DoskelandHvide} D{\o}skeland, Trond M; Hvide, Hans K. Do individual investors have asymmetric information based on work experience?. \textit{The Journal of Finance}. Wiley Online Library. vol. 66. no. 3. pp. 1011--1041. 2011.
\bibitem{Daetal.} Da, Zhi; Engelberg, Joseph; Gao, Pengjie. In search of attention. \textit{The Journal of Finance}. Wiley Online Library. vol. 66. no. 5. pp. 1461--1499. 2011.
\bibitem{Huoetal.} Hou, Kewei; Xiong, Wei; Peng, Lin. A tale of two anomalies: The implications of investor attention for price; earnings momentum. \textit{Available at SSRN 976394}. 2009.
\bibitem{Gherzietal.} Gherzi, Svetlana; Egan, Daniel; Stewart, Neil; Haisley, Emily; Ayton, Peter. The meerkat effect: Personality; market returns affect investors' portfolio monitoring behaviour. \textit{Journal of Economic Behavior \& Organization}. Elsevier. vol. 107. pp. 512--526. 2014.
\bibitem{Luetkepohletal.} Argentesi, Elena; L{\"u}tkepohl, Helmut; Motta, Massimo. Acquisition of information; share prices: An empirical investigation of cognitive dissonance. \textit{German Economic Review}. vol. 11. no. 3. pp. 381-396. 2010.
\bibitem{BarberisThaler} Barberis, Nicholas; Thaler, Richard. A survey of behavioral finance. \textit{Handbook of the Economics of Finance}. Elsevier. vol. 1. pp. 1053--1128. 2003.
\bibitem{GrossmanOwens} Grossman, Zachary; Owens, David. An unlucky feeling: Overconfidence; noisy feedback. \textit{Journal of Economic Behavior \& Organization}. Elsevier. vol. 84. no. 2. pp. 510--524. 2012.
\bibitem{CaplinLeahy} Caplin, Andrew; Leahy, John. Psychological expected utility theory; anticipatory feelings. \textit{Quarterly Journal of Economics}. JSTOR. vol. 116. no. 1. pp. 55--79. 2001.
\bibitem{Moutsianaetal.} Moutsiana, Christina; Garrett, Neil; Clarke, Richard C; Lotto, R Beau; Blakemore, Sarah-Jayne; Sharot, Tali. Human development of the ability to learn from bad news. \textit{Proceedings of the National Academy of Sciences}. National Acad Sciences. vol. 110. no. 41. pp. 16396--16401. 2013.
\bibitem{Sims2003} Sims, Christopher A. Implications of rational inattention. \textit{Journal of Monetary Economics}. Elsevier. vol. 50. no. 3. pp. 665--690. 2003.
\bibitem{FehrRangel} Ernst Fehr; Antonio Rangel. Neuroeconomic foundations of economic choice---recent advances. \textit{The Journal of Economic Perspectives}. vol. 25. no. 4. pp. 3-30. 2011.
\bibitem{WaldWolfowitz} Abraham Wald; J. Wolfowitz. Optimum character of the sequential probability ratio test. \textit{Annals of Mathematical Statistics}. vol. 19. pp. 326-339. 1948.
\bibitem{Wald} Abraham Wald. Sequential Analysis. New York: Wiley. 1947.
\bibitem{HirshleiferYu} Hirshleifer, David A; Yu, Jianfeng. Asset pricing in production economies with extrapolative expectations. In AFA 2012 Chicago Meetings Paper. 2013.
\bibitem{Fusteretal.} Fuster, Andreas; Laibson, David; Mendel, Brock. Natural expectations; macroeconomic fluctuations. \textit{The journal of economic perspectives: a journal of the American Economic Association}. NIH Public Access. vol. 24. no. 4. pp. 67. 2010.
\bibitem{GreenwoodShleifer} Greenwood, Robin; Shleifer, Andrei. Expectations of returns; expected returns. \textit{Review of Financial Studies}. Soc Financial Studies. vol. 27. no. 3. 2014.
\bibitem{Nelsonetal.} Nelson, Jonathan D; McKenzie, Craig RM; Cottrell, Garrison W; Sejnowski, Terrence J. Experience matters information acquisition optimizes probability gain. \textit{Psychological science}. Sage Publications. vol. 21. no. 7. pp. 960--969. 2010.
\bibitem{KlaymanHa} Klayman, Joshua; Ha, Young-Won. Confirmation, disconfirmation,; information in hypothesis testing.. \textit{Psychological review}. American Psychological Association. vol. 94. no. 2. pp. 211. 1987.
\bibitem{Roetheli} R{\"o}theli, Tobias F. Pattern-based expectations: International experimental evidence; applications in financial economics. \textit{Review of Economics; Statistics}. The MIT Press. vol. 93. no. 4. pp. 1319--1330. 2011.
\bibitem{Barberisetal.InvestorSentiment} Barberis, Nicholas; Shleifer, Andrei; Vishny, Robert. A model of investor sentiment. \textit{Journal of financial economics}. Elsevier. vol. 49. no. 3. pp. 307--343. 1998.
\bibitem{AmbuehlLi} Sandro Ambuehl; Shengwu Li. Belief Updating; the Demand for Information. \textit{unpublished manuscript, Stanford University}. 2015.
\bibitem{Abdellaouietal.} Mohammed Abdellaoui; Peter Klibanoff; Laetitia Placido. Ambiguity; Compound Risk Attitudes: an Experiment. \textit{mimeo}. 2011.
\bibitem{AbrevayaJiang} Jason Abrevaya; Wei Jiang. A Nonparametric Approach to Measuring; Testing Curvature. \textit{Journal of Business; Economic Statistics}. vol. 23. no. 1. pp. 1-19. 2005.
\bibitem{Allen} Allen, Franklin. Discovering personal probabilities when utility functions are unknown. \textit{Management Science}. vol. 33. no. 4. pp. 307-319. 1987.
\bibitem{AndersenMPL} Steffen Andersen; Glenn W. Harrison; Mortel I. Lau; E. Elisabet Rutstrom. Elicitation using multiple price list formats. \textit{Experimental Economics}. vol. 9. pp. 383-405. 2006.
\bibitem{AndreoniMylovanov} James Andreoni; Tymofiy Mylovanov. Diverging Opinions. \textit{American Economic Journal: Microeconomics}. vol. 4. no. 1. pp. 209-232. 2012.
\bibitem{AndreoniSprengerRiskTime} James Andreoni; Charles Sprenger. Risk Preferences Are Not Time Preferences. \textit{American Economic Review}. vol. 102. no. 7. pp. 3357-3376. 2012.
\bibitem{AndreoniSprengerUncertaintyEquivalents} James Andreoni; Charles Sprenger. Uncertainty Equivalents: Testing the Limits of the Independence Axiom. \textit{NBER working paper}. vol. w17342. 2013.
\bibitem{Antoniouetal.} Constantinos Antoniou,; Glenn W. Harrison,; Morten I. Lau,; Daniel Read. Information Characteristics; Errors in Expectations: Experimental Evidence. \textit{mimeo, University of Warwick}. 2014.
\bibitem{AtheyLevin} Susan Athey; Jonathan Levin. The Value of Information in Monotone Decision Problems. \textit{mimeo. Stanford University.}. 2001.
\bibitem{AugenblickRabin} Ned Augenblick; Matthew Rabin. Testing for Excess Movement in Beliefs. \textit{mimeo}. 2015.
\bibitem{AzrielyLehrer} Yaron Azrieli; Ehud Lehrer. The value of a stochastic information structure. \textit{Games; Economic Behavior}. vol. 63. pp. 679-693. 2008.
\bibitem{barronerev} Greg Barron; Ido Erev. Small feedback-based decisions; their limited correspondence to description-based decisions. \textit{Journal of Behavioral Decision Making}. vol. 16. pp. 215-233. 2003.
\bibitem{BastardiShafir} Anthony Bastardi; Eldar Shafir. On the Pursuit; Misuse of Useless Information. \textit{Journal of Personality; Social Psychology}. vol. 75. no. 1. pp. 19-32. 1998.
\bibitem{BernasconiLoomes} Michele Bernasconi; Graham Loomes. Failures of the Reduction Principle in an Ellsberg-Type Problem. \textit{Theory; Decision}. vol. 32. no. 1. pp. 77-100. 1992.
\bibitem{BrunnermeierParker} Markus K. Brunnermeier; Jonathan A. Parker. Optimal Expectations. \textit{American Economic Review}. vol. 95. no. 4. pp. 1092-1118. 2005.
\bibitem{Burksetal.} Stephen V. Burks; Jeffrey P. Carpenter; Lorenz Goette; Aldo Rustichini. Overconfidence; Social Signaling. \textit{Review of Economic Studies}. vol. 80. no. 3. pp. 949-983. 2013.
\bibitem{Cabralesetal.} Antonio Cabrales; Olivier Gossner; Roberto Serrano. Entropy; the Value of Information for Investors. \textit{American Economic Review}. vol. 103. no. 1. pp. 360-377. 2013.
\bibitem{Cabralesetal.Appeal} Antonio Cabrales; Olivier Gossner; Roberto Serrano. The Appeal of Information Transactions. \textit{Working paper}. 2013.
\bibitem{CamererOverview} Colin Camerer. Individual Decision Making. In Handbook of Experimental Economics. Princeton University Press. pp. 587-683. 1995.
\bibitem{Caplinetal.} Andrew Caplin; Mark Dean; Daniel Martin. Search; Satisficing. \textit{American Economic Review}. vol. 101. no. 7. 2011.
\bibitem{CarlssonVanDamme} Hans Carlsson; Eric van Damme. Global Games; Equilibrium Selection. \textit{Econometrica}. vol. 61. no. 5. pp. 989-1018. 1993.
\bibitem{CharnessLevin} Gary Charness; Dan Levin. When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity,; Affect. \textit{American Economic Review}. vol. 95. no. 4. pp. 1300-1309. 2005.
\bibitem{ChettySaez} Raj Chetty; Emmanuel Saez. Teaching the Tax Code: Earnings Responses to an Experiment with EITC Recipients. \textit{American Economic Journal: Applied Economics}. vol. 5. no. 1. pp. 1-31. 2013.
\bibitem{CopelandFriedman} Copeland, T.; Friedman, D.. The effect of sequential information arrival on asset prices: An experimental study.. \textit{Journal of Finance}. vol. 42. 1987.
\bibitem{CostaGomezetal.} Miguel A. Costa-Gomes; Steffen Huck; Georg Weizsaecker. Beliefs; actions in the trust game: Creating instrumental variables to estimate the causal effect. \textit{Discussion Paper, Wissenschaftszentrum Berlin fuer Sozialforschung}. vol. SP II 2012-302. 2012.
\bibitem{CRT} Shane Frederick. Cognitive Reflection; Decision Making. \textit{The Journal of Economic Perspectives}. vol. 19. no. 4. pp. 25-42. 2005.
\bibitem{Danaetal.} Jason Dana; Roberto A. Weber; Jason Xi Kuang. Exploiting Moral Wiggle Room: Experiments Demonstrating an Illusory Preference for Fairness. \textit{Economic Theory}. vol. 33. pp. 67-80. 2007.
\bibitem{danieletal} Kent Daniel; David Hirshleifer; Avanidhar Subrahmanyam. Investor Psychology; security market under-; overreactions. \textit{The Journal of Finance}. vol. 53. pp. 1839-1885. 1998.
\bibitem{Dekeletal.} Eddie Dekel; Barton B. Lipman; Aldo Rustichini. Standard state-space models preclude unawareness. \textit{Econometrica}. vol. 66. no. 1. pp. 159-173. 1998.
\bibitem{duffyfeltovich} John Duffy; Nick Feltovich. Does observation of others affect learning in strategic environments? An Expermental Study. \textit{International Journal of Game Theory}. vol. 28. no. 1. pp. 131-152. 1999.
\bibitem{Dyson2004} Freeman Dyson. A meeting with \uppercase{E}nrico \uppercase{F}ermi. \textit{Nature}. vol. 427. pp. 297. 2004.
\bibitem{EdererManso} Florian Ederer; Gustavo Manso. Is Pay-for-performance Detrimental to Innovation?. \textit{mimeo}. 2012.
\bibitem{EilRao} David Eil; Justin M. Rao. The Good News-Bad News Effect: Asymmetric Processing of Objective Information about Yourself. \textit{American Economic Journal: Microeconomics}. vol. 3. pp. 114-138. 2011.
\bibitem{ElGamalGrether} Mahmoud A. El-Gamal; David M. Grether. Are People Bayesian? Uncovering Behavioral Strategies. \textit{Journal of the American Statistical Association}. vol. 90. no. 432. 1995.
\bibitem{EliazSchotter} Kfir Eliaz; Andrew Schotter. Paying for confidence: An experimental study of the demand for non-instrumental information. \textit{Games; Economic Behavior}. vol. 70. pp. 304-324. 2010.
\bibitem{EliazSpiegler} Kfir Eliaz; Ran Spiegler. Can anticipatory feelings explain anomalous choices of information sources?. \textit{Games; Economic Behavior}. vol. 56. pp. 87-104. 2006.
\bibitem{Enikopolovetal.} Ruben Enikolopov; Maria Petrova; Ekaterina Zhuravskaya. Media; Political Persuasion: Evidence from Russia. \textit{American Economic Review}. vol. 101. pp. 3253 - 3285. 2011.
\bibitem{EpperFehrDuda} Helga Fehr-Duda; Thomas Epper. Probability; Risk: Foundations; Economic Implications of Probability-Dependent Risk Preferences. \textit{Annual Reviews of Economics}. vol. 4. no. 567-593. 2012.
\bibitem{EpsteinLivingWithRisk} Larry G. Epstein. Living With Risk. \textit{Review of Economic Studies}. vol. 75. pp. 1121-1141. 2008.
\bibitem{ErevHaruvy} Ido Erev; Ernan Haruvy. Learning; the Economics of Small Decisions. In The Handbook of Experimental Economics. vol. 2. 2015.
\bibitem{ErginGul} Haluk Ergin; Faruk Gul. A theory of subjective compound lotteries. \textit{Journal of Economic Theory}. vol. 144. pp. 899-929. 2009.
\bibitem{Ertac} Seda Ertac. Does self-relevance affect information processing? Experimental evidence on the response to performance; non-performance feedback. \textit{Journal of Economic Behavior; Organization}. vol. 80. pp. 532-545. 2011.
\bibitem{FalkZimmermann} Armin Falk; Florian Zimmermann. Beliefs; Utility: Experimental Evidence on Preferences for Information. \textit{Mimeo, University of Zurich}. 2014.
\bibitem{FiedlerBook} Information Sampling; Adaptive Cognition. Cambridge University Press. 2006.
\bibitem{Frederick2005} Frederick, Shane. Cognitive reflection; decision making. \textit{Journal of Economic Perspectives}. JSTOR. pp. 25--42. 2005.
\bibitem{Gabaixetal.} Xavier Gabaix; David Laibson; Guillermo Moloche; Stephen Weinberg. Costly Information Acquisition: Experimental Analysis of a Boundedly Rational Model. \textit{American Economic Review}. vol. 96. no. 4. 2006.
\bibitem{GentzkowShapiroMediaBiasReputation} Matthew Gentzkow; Jesse M. Shapiro. Media Bias; Reputation. \textit{Journal of Political Economy}. vol. 114. no. 2. pp. 280 - 316. 2006.
\bibitem{GentzkowShapiroMediaSlant} MAtthew Gentzkow; Jesse M. Shapiro. What Drives Media Slant? Evidence from U.S. Daily Newspapers. \textit{Econometrica}. vol. 78. no. 1. pp. 35-71. 2010.
\bibitem{GneezyListWu} Uri Gneezy; John A. List; George Wu. The Uncertainty Effect: When a risky prospect is valued less than it's worst possible outcome. \textit{The Quarterly Journal of Economics}. vol. 121. no. 4. pp. 1283-1309. 2006.
\bibitem{GoldsteinEinhorn} W.M. Goldstein; H.J. Einhorn. Expression theory; the preference reversal phenomena. \textit{Psychological Review}. vol. 94. pp. 236-254. 1987.
\bibitem{GonzalesWu} Richard Gonzalez; George Wu. On the Shape of the Probability Weighting Function. \textit{Cognitive Psychology}. vol. 38. pp. 129-166. 1999.
\bibitem{Greene} William H. Greene. Econometric Analysis. Pearson Prentice Hall. 2008.
\bibitem{Grether92} David M. Grether. Testing bayes rule; the representativeness heuristic: Some experimental evidence. \textit{Journal of Economic Behavior; Organization}. vol. 17. no. 1. pp. 31-57. 1992.
\bibitem{GretherQJE} David M. Grether. Bayes Rule as a Descriptive Model: The Representativeness Heuristic. \textit{Quarterly Journal of Economics}. vol. 95. no. 3. pp. 537-557. 1980.
\bibitem{GretschkoRajko} Vitali Gretschko; Alexander Rajko. Excess information acquisition in auctions. \textit{Experimental Economics}. 2014.
\bibitem{GriffinTversky} Dale Griffin; Amos Tversky. The Weighing of Evidence; the Determinants of Confidence. \textit{Cognitive Psychology}. vol. 24. pp. 411-435. 1992.
\bibitem{GriffithsTenenbaum} Thomas L. Griffiths; Joshua B. Tenenbaum. Optimal Predictions in Everyday Cognition. \textit{Psychological Science}. vol. 17. no. 9. pp. 767-773. 2006.
\bibitem{HalevyEllsbergRevisited} Yoram Halevy. Ellsberg revisited: an experimental study. \textit{Econometrica}. vol. 75. no. 2. pp. 503-536. 2007.
\bibitem{HalevyFeltkamp} Yoram Halevy; Vincent Feltkamp. A Bayesian Approach to Uncertainty Aversion. \textit{Review of Economic Studies}. vol. 72. pp. 449-466. 2005.
\bibitem{HastingsWeinstein} Justine Hastings; Jeffrey Weinstein. Information, School Choice; Academic Achievement: Evidence from Two Experiments. \textit{Quarterly Journal of Economics}. vol. 123. no. 4. pp. 1373-1414. 2008.
\bibitem{HertwigErev} Ralph Hertwig; Ido Erev. The description-experience gap in risky choice. \textit{Cell}. 2009.
\bibitem{hertwigetal} Ralph Hertwig; Greg Barron; Elke U. Weber; Ido Erev. Decision from experience; the effect of rare events in risky choice. \textit{Psychological Science}. vol. 15. pp. 534-539. 2004.
\bibitem{HoelzlRustichini} Erik Hoelzl; Aldo Rustichini. Overconfident: Do You Put Your Money On It?. \textit{Economic Journal}. vol. 115. no. 04. pp. 305-318. 2005.
\bibitem{Hoffman} Mitchell Hoffman. How is Information Valued? {E}vidence from Framed Field Experiments. \textit{unpublished manuscript}. 2014.
\bibitem{HoltLaury} Charles A. Holt; Susan K. Laury. Risk Aversion; Incentive Effects. \textit{American Economic Review}. vol. 92(5). pp. 1644 - 1655. 2002.
\bibitem{HoltSmith} Charles A. Holt; Angela M. Smith. An Update on Bayesian Updating. \textit{Journal of Economic Behavior; Organization}. vol. 69. pp. 125-134. 2009.
\bibitem{Jessupetal.} Ryan K. Jessup; Anthony J. Bishara; Jerome R. Busemeyer. Feedback Produces Divergence From Prospect Theory in Descriptive Choice. \textit{Psychological Science}. vol. 19. no. 10. pp. 1015-1022. 2008.
\bibitem{KahnemanTversky1979} Daniel Kahneman; Amos Tversky. Prospect Theory: An Analysis of Decision under Risk. \textit{Econometrica}. vol. 2. pp. 263-292. 1979.
\bibitem{KahnemanTversky1992} Amos Tversky; Daniel Kahneman. Advances in prospect theory: Cumulative representation of uncertainty. \textit{Journal of Risk; Uncertainty}. vol. 5. no. 4. pp. 297-323. 1992.
\bibitem{KahnemanTverskyRepresentativeness} Daniel Kahneman; Amos Tversky. Subjective Probability: A Judgement of Representativeness. \textit{Cognitive Psychology}. vol. 3. pp. 430-454. 1972.
\bibitem{Karni} Edi Karni. A Mechanism for Eliciting Probabilities. \textit{Econometrica}. vol. 77. no. 2. pp. 603-606. 2009.
\bibitem{KarniViero} Edi Karni; Marie-Louise Viero. `Reverse Bayesianism': A choice-based theory of growing awareness. \textit{American Economic Review}. vol. 103. no. 7. pp. 2790-2810. 2013.
\bibitem{kaustiaknupfer} Markku Kaustia; Samuli Kn{\"u}pfer. Do investors overweight personal experience? Evidence from IPO subscriptions. \textit{Journal of Finance}. vol. 63. pp. 2679-2702. 2008.
\bibitem{KerenWillemsen} Keren, Gideon; Martijn C. Willemsen. Decision Anomalies, Experimenter Assumptions; Participants' Comprehension: {R}evaluating the Uncertainty Effect. \textit{Journal of Behavioral Decision Making}. vol. 22. no. 3. 2008.
\bibitem{Kocheretal.} Kocher, Martin; Michal Krawczyk; Frans van Winden. Let Me Dream on! {A}nticipatory Emotions; Preference for Timing in Lotteries. \textit{Working paper}. 2009.
\bibitem{Koszegi.EgoUtility} Botond Koszegi. Ego Utility, Overconfidence,; Task Choice. \textit{Journal of the European Economic Assiciation}. vol. 4. no. 4. pp. 673-707. 2006.
\bibitem{KoszegiRabin2009} Koszegi, Botond; Matthew Rabin. Reference-Dependent Consumption Plans. \textit{American Economic Review}. vol. 99. no. 3. pp. 909-936. 2009.
\bibitem{KraemerWeber} Carlo Kraemer; Martin Weber. How Do People Take into Account Weight, Strength; Quality of Segregated vs. Aggregated Data? Experimental Evidence. \textit{The Journal of Risk; Uncertainty}. vol. 92. no. 2. pp. 113-142. 2004.
\bibitem{KT74} Daniel Kahneman; Amos Tversky. Judgment under Uncertainty: Heuristics; Biases. \textit{Science}. vol. 185. no. 4157. pp. 1124-1131. 1974.
\bibitem{KublerWeizsacker} Dorothea Kubler; Georg Weizsacker. Limited Depth of Reasoning; Failure of Cascade Formation in the Laboratory. \textit{Review of Economic Studies}. vol. 71. 2004.
\bibitem{LOTR} M.F. Scheier; C.S. Carver; M.W. Bridges. Distinguishing Optimism from Neuroticism (and trait anxiety, self-mastery,; self-esteem): A re-evaluation of the Life Orientation Test. \textit{Journal of Personality; Social Psychology}. vol. 67. pp. 1063-1078. 1994.
\bibitem{MalmendierNagel2} Ulrike Malmendier; Stefan Nagel. Learning from Inflation experiences. \textit{mimeo}. 2012.
\bibitem{malmendnagel} Ulrike Malmendier; Stefan Nagel. Depression babies: Do macroeconomic experiences affect risk taking?. \textit{Quarterly Journal of Economics}. vol. 126. pp. 373-416. 2011.
\bibitem{MasseyWu} Cade Massey; George Wu. Detecting Regime Shifts: The Causes of Under-; Overreaction. \textit{Management Science}. vol. 51. no. 6. pp. 932-947. 2005.
\bibitem{McKelveyZavoina} Richard D. McKelvey; William Zavoina. A statistical model for the analysis of ordinal level dependent variables. \textit{The Journal of Mathematical Sociology}. vol. 4. no. 1. pp. 103-120. 1975.
\bibitem{MerloSchotter} Antonio Merlo; Andrew Schotter. Learning by no doing: an experimental investigation of observational learning. \textit{Games; Economic Behavior}. vol. 42. pp. 116-136. 2003.
\bibitem{milgrom1982information} Milgrom, Paul; Stokey, Nancy. Information, trade; common knowledge. \textit{Journal of Economic Theory}. Elsevier. vol. 26. no. 1. pp. 17--27. 1982.
\bibitem{millermaniadis} Joshua Miller; Zacharias Maniadis. The weight of personal experience: an experimental Measurement. \textit{IGI institute for economic research working paper}. vol. 452. 2012.
\bibitem{Moebiusetal.} Markus M. Moebius; Muriel Niederle; Paul Niehaus; Tanya S. Rosenblat. Managing Self-Confidence: Theory; Experimental Evidence. \textit{unpublished manuscript}. 2013.
\bibitem{MorrisShin} Stephen Morris; Hyun Song Shin. Unique Equilibrium in a Model of Self-Fulfilling Currency Attacks. \textit{American Economic Review}. vol. 88. no. 3. pp. 587-597. 1998.
\bibitem{nisbettross} Richard E Nisbett; Lee Ross. Human Inference: Strategies; Shortcomings in Social Judgement. Prentice-Hall, Englewood Cliffs, NJ, 1980. 1980.
\bibitem{PetersonBeach} Cameron R. Peterson; Lee Roy Beach. Man As An Intuitive Statistician. \textit{Psychological Bulletin}. vol. 68. no. 1. pp. 29-46. 1967.
\bibitem{PetersonSchneiderMiller} Cameron R. Peterson; Robert J. Schneider; Alan J. Miller. Sample Size; the Revision of Subjective Probabilities. \textit{Journal of Experimental Psychology}. vol. 69. no. 5. pp. 522-527. 1965.
\bibitem{PrelecPrelec} Danica Mijovic-Prelec; Drazen Prelec. Self-deception as self-signalling: a model; experimental evidence. \textit{Phil. Trans. R. Soc.}. vol. 365. pp. 227 - 240. 2009.
\bibitem{Roth1979} Roth, Alvin E; Malouf, Michael W. Game-theoretic models; the role of information in bargaining.. \textit{Psychological Review}. American Psychological Association. vol. 86. no. 6. pp. 574. 1979.
\bibitem{Rydvaletal.} Rydval, Ondrej; Andreas Ortmann; Sasha Prokosheva; Ralph Hertwig. How Certain is the Uncertainty Effect. \textit{Experimental Economics}. vol. 12. pp. 473-487. 2009.
\bibitem{Savage1954} Leonard J Savage. Foundations of Statistics. Wiley. 1954.
\bibitem{SchlagVanDeWeele} Schlag, Karl; Joel van der Weele. Eliciting Probabilities, Means, Medians, Variances; Covariances without assuming Risk Neutrality. \textit{Technical Report, Universitat Pompeu Fabra}. October 2009.
\bibitem{Schotteretal.} Meta Brown; Christopher J. Flinn; Andrew Schotter. Real-Time Search in the Laboratory; the Market. \textit{American Economic Review}. vol. 101. no. 2. 2011.
\bibitem{SchweizerSzech} Nikolaus Schweizer; Nora Szech. Optimal Revelation of Life-Changing Information. \textit{mimeo}. 2013.
\bibitem{Segal} Uzi Segal. The Ellsberg Paradox; Risk Aversion: An Anticipated Utility Approach. \textit{International Economic Review}. vol. 28. pp. 175-202. 1987.
\bibitem{Seo} Kyoungwon Seo. Ambiguity; Second Order Belief. \textit{Econometrica}. vol. 77. no. 5. pp. 1575 - 1605. 2009.
\bibitem{Sharotetal.} Tali Sharot; Christoph W Korn; Raymond J Dolan. How unrealistic optimism is maintained in the face of reality. \textit{Nature Neuroscience}. vol. 14. no. 11. pp. 1475-1481. 2011.
\bibitem{Shiller} Robert J. Shiller. Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?. \textit{American Economic Review}. vol. 71. no. 3. pp. 421-436. 1981.
\bibitem{Simonsohn} Uri Simonsohn. Direct Risk Aversion: Evidence from Risky Prospects Valued Below Their Worst Outcome. \textit{Psychological Science}. vol. 20. no. 6. pp. 686-692. 2009.
\bibitem{Simonsohnetal} Uri Simonsohn; Niklas Karlsson; George Loewenstein; Dan Ariely. The tree of experience in the forrest of information: Overeighting experienced relative to observed information. \textit{Games; Economic Behavior}. vol. 62. pp. 263-268. 2008.
\bibitem{StanovichWest} Keith E. Stanovich; Richard F. West. On the Relative Independence of Thinking Biases; Cognitive Ability. \textit{Journal of Personality; Social Psychology}. vol. 94. no. 4. pp. 672-695. 2008.
\bibitem{Sunder} Shyam Sunder. Market for Information: Experimental Evidence. \textit{Econometrica}. vol. 60. no. 3. pp. 667-695. 1992.
\bibitem{SzkupTrevino} Michal Szkup; Isabel Trevino. Costly information acquisition in a speculative attack: Theory; experiments. \textit{mimeo, New York University}. 2013.
\bibitem{ThinkingFastAndSlow} Daniel Kahneman. Thinking Fast; Slow. Macmillan. 2011.
\bibitem{TobacykMilford} Jerome Tobacyk; Gary Milford. Belief in Paranormal Phenomena: Assessment Instrument Development; Implications for Personality Functioning. \textit{Journal of Personality; Social Psychology}. vol. 44. no. 5. pp. 1029-1037. 1983.
\bibitem{TverskyFox} Amos Tversky; Craig R. Fox. Weighting Risk; Uncertainty. \textit{Psychological Review}. vol. 102. pp. 269-283. 1995.
\bibitem{YatesZukowski} Frank Yates; Lisa Zukowski. Characterization of Ambiguity in Decision Making. \textit{Behavioral Science}. vol. 21. pp. 19-25. 1976.
\bibitem{Zimmermann} Florian Zimmermann. Clumped or Piecewise? {E}vidence on Preferences for Information. \textit{Management Science (forthcoming)}. 2014.
\bibitem{ztree} Urs Fischbacher. z-Tree: Zurich Toolbox for Ready-made Economic Experiments. \textit{Experimental Economics}. vol. 10. no. 2. pp. 171-178. 2007.
\bibitem{BenabouTirole2002} B{\'e}nabou, Roland; Tirole, Jean. Self-confidence; personal motivation. \textit{Quarterly Journal of Economics}. JSTOR. vol. 117. no. 3. pp. 871--915. 2002.
\bibitem{Ambuehletal.Repugnance} Sandro Ambuehl; Muriel Niederle; Alvin E. Roth. More Money, More Problems? {C}an High Pay be Coercive; Repugnant?. \textit{American Economic Review, Papers \& Proceedings}. vol. 105. no. 5. pp. 357-60. 2015.
\bibitem{hanna2014learning} Hanna, Rema; Mullainathan, Sendhil; Schwartzstein, Joshua. Learning Through Noticing: Theory; Experimental Evidence in Farming⇤. 2014.
\bibitem{schwartzstein2014selective} Schwartzstein, Joshua. Selective attention; learning. \textit{Journal of the European Economic Association}. Wiley Online Library. vol. 12. no. 6. pp. 1423--1452. 2014.
\bibitem{bartos2013attention} Bartos, Vojtech; Bauer, Michal; Chytilov{\'a}, Julie; Mat{\v{e}}jka, Filip. Attention discrimination: Theory; field experiments. \textit{American Economic Review}. vol. forthcoming. 2015.
\bibitem{Sichermanetal.} Sicherman, Nachum; Loewenstein, George; Seppi, Duane J; Utkus, Stephen P. Financial attention. \textit{Available at SSRN 2339287}. 2014.
\bibitem{Karlssonetal.} Karlsson, Niklas; Loewenstein, George; Seppi, Duane. The ostrich effect: Selective attention to information. \textit{Journal of Risk; Uncertainty}. Springer. vol. 38. no. 2. pp. 95--115. 2009.
\bibitem{sundaresan2014inattentive} Sundaresan, Savitar; Turban, S{\'e}bastien. Inattentive Valuation; Belief Polarization. 2014.
\bibitem{Wilson} Wilson, Andrea. Bounded memory; biases in information processing. \textit{Econometrica}. Wiley Online Library. vol. 82. no. 6. pp. 2257--2294. 2014.
\bibitem{BabcockLoewenstein2} Babcock, Linda; Loewenstein, George; Wang, Xianghong. The relationship between uncertainty, the contract zone,; efficiency in a bargaining experiment. \textit{Journal of Economic Behavior \& Organization}. Elsevier. vol. 27. no. 3. pp. 475--485. 1995.
\bibitem{Loewensteinetal.SelfServing} Loewenstein, George; Issacharoff, Samuel; Camerer, Colin; Babcock, Linda. Self-serving assessments of fairness; pretrial bargaining. \textit{The Journal of Legal Studies}. JSTOR. pp. 135--159. 1993.
\bibitem{BabcockLoewenstein3} Babcock, Linda; Loewenstein, George; Issacharoff, Samuel; Camerer, Colin. Biased judgments of fairness in bargaining. \textit{The American Economic Review}. JSTOR. pp. 1337--1343. 1995.
\bibitem{BabcockLoewenstein} Babcock, Linda; Loewenstein, George. Explaining bargaining impasse: The role of self-serving biases. \textit{The Journal of Economic Perspectives}. JSTOR. vol. 11. no. 1. pp. 109--126. 1997.
\bibitem{Yariv} Yariv, Leeat. I'll See It When I Believe It? A Simple Model of Cognitive Consistency. Cowles Foundation Discussion Paper. 2002.
\bibitem{RabinSchrag} Rabin, Matthew; Schrag, Joel L. First impressions matter: A model of confirmatory bias. \textit{Quarterly Journal of Economics}. JSTOR. pp. 37--82. 1999.
\bibitem{baliga2013polarization} Baliga, Sandeep; Hanany, Eran; Klibanoff, Peter. Polarization; ambiguity. \textit{The American Economic Review}. American Economic Association. vol. 103. no. 7. pp. 3071--3083. 2013.
\bibitem{mcfadden2000rationality} McFadden, Daniel; Machina, Mark J; Baron, Jonathan. Rationality for economists?. In Elicitation of Preferences. Springer. pp. 73--110. 2000.
\bibitem{armel2008biasing} Armel, K Carrie; Beaumel, Aurelie; Rangel, Antonio. Biasing simple choices by manipulating relative visual attention. \textit{Judgment; Decision Making}. Society for Judgment; Decision Making. vol. 3. no. 5. pp. 396--403. 2008.
\bibitem{krajbich2011multialternative} Krajbich, Ian; Rangel, Antonio. Multialternative drift-diffusion model predicts the relationship between visual fixations; choice in value-based decisions. \textit{Proceedings of the National Academy of Sciences}. National Acad Sciences. vol. 108. no. 33. pp. 13852--13857. 2011.
\bibitem{krajbich2010visual} Krajbich, Ian; Armel, Carrie; Rangel, Antonio. Visual fixations; the computation; comparison of value in simple choice. \textit{Nature neuroscience}. Nature Publishing Group. vol. 13. no. 10. pp. 1292--1298. 2010.
\bibitem{RatcliffDDM} Ratcliff, Roger. A theory of memory retrieval.. \textit{Psychological review}. American Psychological Association. vol. 85. no. 2. pp. 59. 1978.
\bibitem{Bogagczetal.} Bogacz, Rafal; Brown, Eric; Moehlis, Jeff; Holmes, Philip; Cohen, Jonathan D. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.. \textit{Psychological Review}. American Psychological Association. vol. 113. no. 4. pp. 700--765. 2006.
\bibitem{woodford2014optimizing} Woodford, Michael. An Optimizing Neuroeconomic Model of Discrete Choice. \textit{National Bureau of Economic Research}. vol. 19897. 2014.
\bibitem{woodford2014stochastic} Woodford, Michael. Stochastic choice: An optimizing neuroeconomic model. \textit{The American Economic Review}. American Economic Association. vol. 104. no. 5. pp. 495--500. 2014.
\bibitem{woodford2012inattentive} Woodford, Michael. Inattentive valuation; reference-dependent choice. \textit{Unpublished manuscript}. 2012.
\bibitem{woodford2012prospect} Woodford, Michael. Prospect theory as efficient perceptual distortion. \textit{The American Economic Review}. American Economic Association. vol. 102. no. 3. pp. 41--46. 2012.
\bibitem{YangInfoAcquisition} Yang, Ming. Coordination with flexible information acquisition. \textit{Journal of Economic Theory}. Elsevier. vol. 158. no. B. pp. 721--738. 2014.
\bibitem{MartinJMP} Martin, Daniel. Strategic pricing; rational inattention to quality. \textit{unpublished manuscript}. 2014.
\bibitem{CaplinMartin} Caplin, Andrew; Martin, Daniel. A testable theory of imperfect perception. \textit{The Economic Journal}. Wiley Online Library. vol. 125. no. 582. pp. 184---202. 2014.
\bibitem{CaplinDeanPosteriorBased} Caplin, Andrew; Dean, Mark. Rational Inattention, Entropy,; Choice: The Posterior-Based Approach. \textit{unpublished manuscript, New York University}. 2013.
\bibitem{CaplinDean.StateDependentStochasticChoice} Caplin, Andrew; Dean, Mark. Rational inattention; state dependent stochastic choice. \textit{unpublished manuscript, New York University}. 2013.
\bibitem{CRT2} Toplak, Maggie E; West, Richard F; Stanovich, Keith E. Assessing miserly information processing: An expansion of the Cognitive Reflection Test. \textit{Thinking \& Reasoning}. Taylor \& Francis. vol. 20. no. 2. pp. 147--168. 2014.
\bibitem{Slowiaczeketal.} Slowiaczek, Louisa M; Klayman, Joshua; Sherman, Steven J; Skov, Richard B. Information selection; use in hypothesis testing: What is a good question,; what is a good answer?. \textit{Memory \& Cognition}. Springer. vol. 20. no. 4. pp. 392--405. 1992.
\bibitem{rusconi2013insensitivity} Rusconi, Patrice; McKenzie, Craig RM. Insensitivity; oversensitivity to answer diagnosticity in hypothesis testing. \textit{The Quarterly Journal of Experimental Psychology}. Taylor \& Francis. vol. 66. no. 12. pp. 2443--2464. 2013.
\bibitem{NickersonReview} Nickerson, Raymond S. Confirmation bias: A ubiquitous phenomenon in many guises.. \textit{Review of general psychology}. Educational Publishing Foundation. vol. 2. no. 2. pp. 175. 1998.
\bibitem{McKenzieInsensitivity} McKenzie, Craig RM. Increased sensitivity to differentially diagnostic answers using familiar materials: Implications for confirmation bias. \textit{Memory \& Cognition}. Springer. vol. 34. no. 3. pp. 577--588. 2006.
\bibitem{KuhnenKnutson} Kuhnen, Camelia M; Knutson, Brian. The influence of affect on beliefs, preferences,; financial decisions. \textit{Journal of Financial; Quantitative Analysis}. Cambridge Univ Press. vol. 46. no. 03. pp. 605--626. 2011.
\bibitem{KlaymanReview} Klayman, Joshua. Varieties of confirmation bias. \textit{Psychology of learning; motivation}. Elsevier. vol. 32. pp. 385--418. 1995.
\bibitem{Sims2006} Sims, Christopher A. Rational inattention: Beyond the linear-quadratic case. \textit{American Economic Review, Papers \& Proceedings}. JSTOR. vol. 96. no. 2. pp. 158--163. 2006.
\bibitem{MatejkaMcKayAERPP} Mat{\v{e}}jka, Filip; McKay, Alisdair. Simple market equilibria with rationally inattentive consumers. \textit{The American Economic Review}. American Economic Association. vol. 102. no. 3. pp. 24--29. 2012.
\end{thebibliography}


 \newpage{}


\appendix

\renewcommand\thetable{\thesection.\arabic{table}}
\renewcommand\thefigure{\thesection.\arabic{figure}}
\setcounter{page}{0}

\setcounter{section}{0}

\begin{center}
{\Huge \textbf{ONLINE APPENDIX}}\\
\vspace{1cm}
{\Large   \textbf{\LARGE What Motivates Paternalism? An Experimental Study} \\ \medskip Sandro Ambuehl, B. Douglas Bernheim, Axel Ockenfels }
\end{center}


\pagenumbering{gobble}
\setstretch{1.2}
\addcontentsline{toc}{section}{Appendix}
\part{}
\parttoc
\setstretch{1.4}

\newpage
\pagenumbering{arabic}

\setcounter{table}{0}
\renewcommand{\thetable}{A\arabic{table}}
\setcounter{figure}{0}
\renewcommand{\thefigure}{A\arabic{figure}}

\setcounter{footnote}{0}


\renewcommand{\thesection}{\Alph{section}}
\renewcommand{\thesubsection}{\Alph{section}.\arabic{subsection}}
\renewcommand{\thesubsubsection}{\Alph{section}.\arabic{subsection}.\arabic{subsubsection}}

\makeatletter    \def\p@subsection{}   \makeatother
\makeatletter    \def\p@subsubsection{}   \makeatother


\section{Proofs}

\label{appendixProofs}

\subsection{Proof of Proposition \ref{noUpperBound}}

\paragraph{Mandates and caps}

We let $q$ and $h$ denote the density functions of $Q$ and $H$,
respectively. We let $h_{u}(u)$ denote the density of the marginal
of $H$ with respect to $u$, and we define $f_{u}(u)$ likewise.
Further, we let $h_{m}(m\mid u)$ denote the density of the distribution
of $m$ conditional on $u$ according to $H$. For $\left(\underline{r},\overline{r}\right)\in\mathbb{R}^{2}$,
aggregate welfare is given by

\bmath
W(\underline{r},\overline{r})&=&\gamma\intop_{-\infty}^{\infty}l\left(\max\left\{ \underline{r},\min\left\{ \overline{r},u\right\} \right\} -u\right)q(u)du \nonumber \\
&&+(1-\gamma)\intop_{-\infty}^{\infty}\left\{ \intop_{0}^{\infty}l\left(\max\left\{ \underline{r},\min\left\{ \overline{r},u-m\right\} \right\} -u\right)h_{m}(m\mid u)dm\right\} h_{u}(u)du \nonumber\\
&=&\gamma\left[\intop_{u\leq\underline{r}}l\left(\underline{r}-u\right)q(u)du+\intop_{u\geq\overline{r}}l\left(\overline{r}-u\right)q(u)du\right] \nonumber \\
&&+(1-\gamma)\intop_{-\infty}^{\infty}\left\{ \intop_{0}^{u-\overline{r}}l\left(\overline{r}-u\right)h_{m}(m\mid u)dm+\intop_{u-\overline{r}}^{u-\underline{r}}l\left(-m\right)h_{m}(m\mid u)dm\right.\nonumber \\
&&+\left.\intop_{u-\underline{r}}^{\infty}l\left(\underline{r}-u\right)h_{m}(m\mid u)dm\right\} h_{u}(u)du  \label{eq:Welfare1}
\emath
Recalling that we interpret $\underline{r}=-\infty$ as the absence
of a mandate and $\overline{r}=\infty$ as the absence of a cap, we
extend the definition of $W$ to the boundaries of $[-\infty,\infty)\times(-\infty,\infty]$
as follows:

\bmath
W(\underline{r},\infty)&=&\gamma\intop_{u\leq\underline{r}}l\left(\underline{r}-u\right)q(u)du \nonumber\\
&&+(1-\gamma)\intop_{-\infty}^{\infty}\left\{ \intop_{-\infty}^{u-\underline{r}}l\left(-m\right)h_{m}(m\mid u)dm+\intop_{u-\underline{r}}^{\infty}l\left(\underline{r}-u\right)h_{m}(m\mid u)dm\right\} h_{u}(u)du\label{eq:Welfare2}
\emath
for $\underline{r}\in\mathbb{R}$,
\bmath
W\left(-\infty,\overline{r}\right)&=&\gamma\intop_{u\geq\overline{r}}l\left(\overline{r}-u\right)q(u)du\nonumber \\ &&+(1-\gamma)\intop_{-\infty}^{\infty}\left\{ \intop_{0}^{u-\overline{r}}l\left(\overline{r}-u\right)h_{m}(m\mid u)dm+\intop_{u-\overline{r}}^{\infty}l\left(-m\right)h_{m}(m\mid u)dm\right\} h_{u}(u)du\label{eq:Welfare3}
\emath
for $\overline{r}\in\mathbb{R}$, and

\begin{equation}
W(-\infty,\infty)=(1-\gamma)\intop_{-\infty}^{\infty}\left\{ \intop_{0}^{\infty}l\left(-m\right)h_{m}(m\mid u)dm\right\} h_{u}(u)du\label{eq:Welfare 4}
\end{equation}

Given our assumption that  $|l|$ has a bounded derivative, we can
bound the terms $l\left(\underline{r}-u\right)$ and $l\left(\underline{r}-u\right)$
above and below by linear functions of $u$, and we can bound $l\left(-m\right)$
above and below by linear functions of $m$. Because the means of
$u$ and $m$ are finite for both $G$ and $H$ (Assumption \ref{Ref: Regularity}),
we can then conclude that $W(\underline{r},\overline{r})$ is finite
for all $(\underline{r},\overline{r})\in[-\infty,\infty)\times(-\infty,\infty]$.
Using the preceding expressions and bounds, it is also straightforward
to verify that
\[
\lim_{\overline{r}\rightarrow\infty}W(\underline{r},\overline{r})=W(\underline{r},\infty)
\]
for $\underline{r}\in\mathbb{R}\cup\{-\infty\}$, and
\[
\lim_{\underline{r}\rightarrow-\infty}W(\underline{r},\overline{r})=W(-\infty,\overline{r})
\]
for $\overline{r}\in\mathbb{R}\cup\{\infty\}$.

\subparagraph{Part (i) }

By applying the Leibniz rule to (\ref{eq:Welfare1}) (or to (\ref{eq:Welfare3}),
in case of $\underline{r}=-\infty$), we learn that, for all $\left(\underline{r},\overline{r}\right)\in[-\infty,\infty)\times\mathbb{R}$:
\bmathnn
\frac{\partial W(\underline{r},\overline{r})}{\partial\overline{r}}&=&\gamma\intop_{u\geq\overline{r}}l'\left(\overline{r}-u\right)q(u)du+(1-\gamma)\intop_{-\infty}^{\infty}\left\{ \intop_{0}^{u-\overline{r}}l'\left(\overline{r}-u\right)h_{m}(m\mid u)dm\right\} h_{u}(u)du \\
&\geq&(1-\gamma)\intop_{-\infty}^{\infty}l'\left(\overline{r}-u\right)P_{H}\left(m<u-\overline{r}\mid u\right)h_{u}(u)du,
\emathnn
where the strictness of the inequality follows from $l'\left(\overline{r}-u\right)>0$
for $u>\overline{r}$. If $u<\overline{r}$, then $P_{H}(m\leq u-\overline{r}|u)\leq P_{H}(m\leq0|u)=0$
by Assumption \ref{unidirectionalAssumption}. Moreover, for $u\geq\overline{r}$,
we have $l'\left(\overline{r}-u\right)\geq0$, with strict inequality
in the case of $u>\overline{r}$. Therefore the derivative is strictly
positive. Recalling in addition that $\lim_{\overline{r}\rightarrow\infty}W(\underline{r},\overline{r})=W(\underline{r},\infty)$
for $\underline{r}\in\mathbb{R}\cup\{-\infty\}$, we see that the
Choice Architect does not impose a binding upper bound $\overline{r}$.

\subparagraph{Part (ii)}

We set $\overline{r}=\infty$ (based on part (i)) and, for simplicity,
suppress it in the notation. Applying the Leibniz rule to (\ref{eq:Welfare2}),
we can write $\frac{dW(\underline{r})}{d\underline{r}}$ as follows:
\bmath
\frac{dW(\underline{r})}{d\underline{r}}&=&\gamma\intop_{u\leq\underline{r}}l'\left(\underline{r}-u\right)q(u)du+(1-\gamma)\intop_{-\infty}^{\infty}\left\{ \intop_{u-\underline{r}}^{\infty}l'(\underline{r}-u)h_{m}(m\mid u)dm\right\} h_{u}(u)du\label{eq:welfare derivative} \\
&=&\intop_{u\leq\underline{r}}l'\left(\underline{r}-u\right)\left[\gamma q(u)+\left(1-\gamma\right)h_{u}(u)\right]du \nonumber \\ &&+(1-\gamma)\intop_{u>\underline{r}}l'(\underline{r}-u)P_{H}\left(m\geq u-\underline{r}\mid u\right)h_{u}(u)du \nonumber
\emath
 The first term is strictly negative while the second is strictly
positive. It easy to check that, as $\underline{r}\rightarrow\infty$,
the first term remains bounded away from zero while the second converges
to zero. It follows that there is some finite threshold, $r^{U}$,
above which $W$ is decreasing in $\underline{r}$.

Endowing the extended real half-line, $[-\infty,r^{U}]$, with the
order topology renders it compact. We have seen that $W(\underline{r})$
is differentiable, and hence continuous (in the order topology), on
$\mathbb{R}$. Adding the fact that $\lim_{\underline{r}\rightarrow-\infty}W(\underline{r})=W(-\infty)$,
we see that $W$ is continuous on $[-\infty,r^{U}]$. It therefore
follows that the set of maximizers is a non-empty closed subset of
$[-\infty,r^{U}]$, and consequently that the smallest maximizer is
well-defined.

\paragraph{Surrogate choices (part (iii))}

We begin by writing aggregate welfare as a function of the surrogate
choice, $s\in\mathbb{R}$:

\begin{equation}
W(s)=\intop_{-\infty}^{+\infty}l\left(s-u\right)f_{u}(u)du\label{eq:WelfareS}
\end{equation}
It follows that
\bmathnn
\frac{dW(s)}{ds}&=&\intop_{-\infty}^{+\infty}l'\left(s-u\right)f_{u}(u)du\\
&=&\intop_{u\leq s}l'\left(s-u\right)f_{u}(u)du+\intop_{u\geq s}l'\left(s-u\right)f_{u}(u)du
\emathnn
The first term is strictly negative, while the second is strictly
positive. Because we have assumed that $l'$ is bounded, it follows
that the first term converges to zero as $s\rightarrow-\infty$ while
the second remains bounded away from zero; consequently there exists
some finite value $s_{L}$ such that the derivative is strictly positive
for $s<s_{L}$. Similarly, the second term converges to zero as $s\rightarrow\infty$
while the first remains bounded away from zero; consequently there
exists some finite value $s_{U}$ such that the derivative is strictly
negative for $s>s_{U}$. It follows that any optimum on $[s_{L},s_{U}]$
is a global optimum. Because $W$ is continuous in $s\in\mathbb{R}$,
the set of maximizers is a non-empty closed subset of $[s_{L},s_{U}]$,
and consequently the smallest maximizer is well-defined.

\subsection{Proof of Proposition \ref{mainProp}}


Our claims concerning conventional behavioral welfarism follow directly
from the fact that, within our model, the only connection between
$c^{A}$ on the one hand and either mandates or surrogate choices
on the other runs through the Choice Architect's beliefs about the
distributions of $u$ and $m$ for Choosers. Consequently, the proof
focuses on the implications of ideals-projective and mistakes-projective
paternalism.

\paragraph{Mandates and caps}

For the purpose of this proof, we will use $\Phi(x)$ and $\phi(x)$
to denote, respectively, the standard normal CDF and density at $x$.
The marginal distributions for $u$ and $z$ are normal with means
and standard deviations of $(\mu_{u},\sigma_{u})$ and $(\mu_{m},\sigma_{m})$,
respectively. The conditional distributions for $u$ and $z$ are
normal with means and standard deviations of $\left(\hat{\mu}_{u}(z),\hat{\sigma}_{u}\right)=\left(\mu_{u}+\rho\frac{\sigma_{u}}{\sigma_{m}}(z-\mu_{m}),\sigma_{u}\sqrt{1-\rho^{2}}\right)$
and $\left(\hat{\mu}_{m}(u),\hat{\sigma}_{m}\right)=\left(\mu_{m}+\rho\frac{\sigma_{m}}{\sigma_{u}}(u-\mu_{u}),\sigma_{m}\sqrt{1-\rho^{2}}\right)$.
Because we are holding the standard deviations and correlations fixed,
we will write the optimal mandate as $\underline{r}^{*}(\mu_{u},\mu_{m})$.\\

\begin{lemma}\ \label{originalLemma}
\begin{enumerate}
\item $\underline{r}^{*}(\mu_{u},\mu_{m})$ is increasing in $\mu_{m}$.
\item $\underline{r}^{*}(\mu_{u},\mu_{m})$ is increasing in $\mu_{u}$.
\end{enumerate}
\end{lemma}

\begin{proof}
\ \textbf{Part (i)} We begin by rewriting the objective function (\ref{eq:Welfare2})
to reflect Assumption \ref{Gaussian}. For clarity, we suppress the
dependence of $W$ on $\overline{r}$ (which we know is fixed at $\infty$),
while adding $\mu=\left(\mu_{u},\mu_{m}\right)$ as explicit arguments.
\bmath
W(\underline{r},\mu)&=&\intop_{-\infty}^{\infty}\left\{ \intop_{M}l\left(\max\left\{ \underline{r},u-\max\left\{ z,0\right\} \right\} -u\right)\phi\left(\frac{z-\hat{\mu}_{m}(u)}{\hat{\sigma}_{m}}\right)\frac{1}{\hat{\sigma}_{m}}dz\right\} \phi\left(\frac{u-\mu_{u}}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}du\nonumber \\
&=&\intop_{\underline{r}}^{\infty}\left\{ \intop_{-\infty}^{0}l\left(0\right)\phi\left(\frac{z-\hat{\mu}_{m}(u)}{\hat{\sigma}_{m}}\right)\frac{1}{\hat{\sigma}_{m}}dz+\intop_{0}^{u-\underline{r}}l\left(-z\right)\phi\left(\frac{z-\hat{\mu}_{m}(u)}{\hat{\sigma}_{m}}\right)\frac{1}{\hat{\sigma}_{m}}dz\right. \nonumber \\ &&+\left.\intop_{u-\underline{r}}^{\infty}l\left(\underline{r}-u\right)\phi\left(\frac{z-\hat{\mu}_{m}(u)}{\hat{\sigma}_{m}}\right)\frac{1}{\hat{\sigma}_{m}}dz\right\} \phi\left(\frac{u-\mu_{u}}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}du \nonumber \\ &&+\intop_{-\infty}^{\underline{r}}l\left(\underline{r}-u\right)\phi\left(\frac{u-\mu_{u}}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}du  \label{eq:Welfare5}
\emath
Applying the Leibniz rule, we obtain:
\bmath \nonumber
\frac{\partial W(\underline{r},\mu)}{\partial\underline{r}}&=&\intop_{\underline{r}}^{\infty}l'\left(\underline{r}-u\right)\left[1-\Phi\left(\frac{u-\underline{r}-\hat{\mu}_{m}(u)}{\hat{\sigma}_{m}}\right)\right]\phi\left(\frac{u-\mu_{u}}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}du\\&&+\intop_{-\infty}^{\underline{r}}l'\left(\underline{r}-u\right)\phi\left(\frac{u-\mu_{u}}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}du\label{eq:Special welfare derivative}
\emath
Now consider $\mu=\left(\mu_{u},\mu_{m}\right)$ and $\mu'=\left(\mu_{u},\mu_{m}'\right)$
with $\mu_{m}'>\mu_{m}$. Then

\bmathnn
\frac{\partial W(\underline{r},\mu')}{\partial\underline{r}}-\frac{\partial W(\underline{r},\mu)}{\partial\underline{r}}&=&
\intop_{\underline{r}}^{\infty}l'\left(\underline{r}-u\right)\left[\Phi\left(\frac{u-\underline{r}-\left(\mu_{m}-\rho\frac{\sigma_{m}}{\sigma_{u}}(u-\mu_{u})\right)}{\hat{\sigma}_{m}}\right)\right. \\ && \left.-\Phi\left(\frac{u-\underline{r}-\left(\mu_{m}'+\rho\frac{\sigma_{m}}{\sigma_{u}}(u-\mu_{u})\right)}{\hat{\sigma}_{m}}\right)\right]\phi\left(\frac{u-\mu_{u}}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}du
\emathnn
With $\mu_{m}'>\mu_{m}$, the term in square brackets is strictly
positive for all $u$. With $u>\underline{r}$, we have $l'\left(\underline{r}-u\right)>0$.
Thus, $\frac{\partial W(\underline{r},\mu)}{\partial\underline{r}}$
is strictly increasing in $\mu_{m}$. It then follows from Topkis'
monotone selection theorem that $\underline{r}^{*}(\mu_{u},\mu_{m})\leq\underline{r}^{*}(\mu_{u},\mu_{m}')$,
as claimed.\\

\textbf{Part (ii)} Rewriting (\ref{eq:Welfare1}) for the special case of $\overline{r}=\infty$
based on our distributional assumptions, we see that for any $k\in\mathbb{R}$,
we have

\begin{multline}
W(\underline{r}+k,\mu_{u}+k,\mu_{m})=\gamma\intop_{-\infty}^{\infty}l\left(\max\left\{ \underline{r}+k,u\right\} -u\right)\phi\left(\frac{u-(\mu_{u}+k)}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}du \\
+(1-\gamma)\intop_{-\infty}^{\infty}\left\{ \intop_{0}^{\infty}l\left(\max\left\{ \underline{r}+k,u-m\right\} -u\right)\phi\left(\frac{z-\left(\mu_{m}+\rho\frac{\sigma_{m}}{\sigma_{u}}(u-\mu_{u}-k)\right)}{\hat{\sigma}_{m}}\right)\frac{1}{\hat{\sigma}_{m}}dz\right\} \\ \times \;\phi\left(\frac{u-(\mu_{u}+k)}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}du
\nonumber
\end{multline}
Now we define $v=u-k$. With a change of variables from $u$ to $v$,
we can then write the welfare function as:

\begin{multline}
W(\underline{r}+k,\mu_{u}+k,\mu_{m})=\gamma\intop_{-\infty}^{\infty}l\left(\max\left\{ \underline{r},v\right\} -v\right)\phi\left(\frac{v-\mu_{u}}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}dv\\
+(1-\gamma)\intop_{-\infty}^{\infty}\left\{ \intop_{0}^{\infty}l\left(\max\left\{ \underline{r},v-m\right\} -v\right)\phi\left(\frac{z-\left(\mu_{m}+\rho\frac{\sigma_{m}}{\sigma_{u}}(v-\mu_{u})\right)}{\hat{\sigma}_{m}}\right)\frac{1}{\hat{\sigma}_{m}}dz\right\} \phi\left(\frac{v-\mu_{u}}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}dv
\\
=W(\underline{r},\mu_{u},\mu_{m}) \nonumber
\end{multline}
It follows that increasing $\mu_{u}$ by $k$ simply shifts the entire
welfare function rightward by $k$, which means any maximizer increases
by $k$. The same statement obviously applies to the smallest maximizer.
\end{proof}

Lemma \ref{originalLemma} immediately yields the following corollary:

\begin{corollary} For any $(\mu_{u},\mu_{m})$ and $(\mu_{u}',\mu_{m}')$
with $\mu_{u}'\geq\mu_{u}$ and $\mu_{m}'\geq\mu_{m}$ and at least
one inequality strict, we have $\underline{r}^{*}(\mu_{u}',\mu_{m}')>\underline{r}^{*}(\mu_{u},\mu_{m})$.
\end{corollary}


\begin{proof}

We can decompose a change from $(\mu_{u},\mu_{m})$ to $(\mu_{u}',\mu_{m}')$
into two steps: (a) $(\mu_{u},\mu_{m})$ to $(\mu_{u},\mu_{m}')$;
(b) $(\mu_{u},\mu_{m}')$ and $(\mu_{u}',\mu_{m}')$. Applying
Lemma \ref{originalLemma} to each of these steps delivers the desired
conclusion.

\end{proof}

The proposition concerns the distribution of $\underline{r}^{*}$
conditional on $c^{A}$, the selection Choice Architects make for
themselves. We use the preceding property to show that this distribution
increases (in the sense of FOSD) as we increase the Choice Architects'
consumption level from $c^{A}$ to any $c^{A'}>c^{A}$.

Let $M_{u}$ denote the random variable with distribution $G_{u^{A}}(\cdot|c^{A})$
and $M'_{u}$ denote the random variable with distribution $G_{u^{A}}(\cdot|{c^{A}}')$.
Define $M_{m}$ and $M'_{m}$ likewise. Assumption \ref{CAbehavior}
implies that $G_{u^{A}}(\cdot|{c^{A}}')$ first-order dominates $G_{u^{A}}(\cdot|c^{A})$,
and $G_{m^{A}}(\cdot|c^{A})$ first-order dominates $G_{m^{A}}(\cdot|c^{A'})$.
By Strassen's monotone coupling theorem, there exists an underlying
state space $\Omega$ such that we can write all of these random variables
as functions of the state $\omega$, with $M_{u}'(\omega)\geq M_{u}(\omega)$
and $M_{m}'(\omega)\leq M_{m}(\omega)$, where in each case the inequality
is strict for a set of states with positive measure.

By the definition of ideals-projection, there exists a function $\varphi:\mathbb{R}\rightarrow\mathbb{R}^{2}$
such that $(\mu_{u},\mu_{m})=\varphi(u^{A})$ with $\varphi$ strictly
increasing in the first argument and weakly increasing in the second.
For any given state $\omega$ we have $M_{u}'(\omega)\geq M_{u}(\omega)$,
which according to the Corollary implies $\underline{r}^{*}\left(\varphi\left(M_{u}'\left(\omega\right)\right)\right)\geq\underline{r}^{*}\left(\varphi\left(M_{u}\left(\omega\right)\right)\right)$,
with strict inequality for a set of states with positive measure.
Thus, with ideals-projective paternalism, the distribution of $\underline{r}^{*}$
conditional on $c^{A'}$ first-order dominates the distribution of
$\underline{r}^{*}$ conditional on $c^{A}$.

By the definition of mistakes-projection, there exists a function
$\psi:\mathbb{R}\rightarrow\mathbb{R}^{2}$ such that $(\mu_{u},\mu_{m})=\psi(m^{A})$
with $\psi$ weakly increasing in the first argument and strictly
increasing in the second. For any given state $\omega$ we have $M_{u}(\omega)\geq M_{u}'(\omega)$,
which according to the Corollary implies $\underline{r}^{*}\left(\varphi\left(M_{u}\left(\omega\right)\right)\right)\geq\underline{r}^{*}\left(\varphi\left(M_{u}'\left(\omega\right)\right)\right)$,
with strict inequality for a set of states with positive measure.
Thus, with mistakes-projective paternalism, the distribution of $\underline{r}^{*}$
conditional on $c^{A}$ first-order dominates the distribution of
$\underline{r}^{*}$ conditional on $c^{A'}$.

\paragraph{Surrogate choices}

Because we are holding the standard deviations and correlations fixed,
we will write the optimal surrogate choice as $s^{*}(\mu_{u},\mu_{m})$.

\begin{lemma}\ \label{surrogateLemma}
\begin{enumerate}
\item $s^{*}(\mu_{u},\mu_{m})$ is independent of $\mu_{m}$.
\item $s^{*}(\mu_{u},\mu_{m})$ is increasing in $\mu_{u}$.
\end{enumerate}
\end{lemma}

\begin{proof}

Part (i) follows immediately from equation (\ref{eq:WelfareS}), which
implies that aggregate welfare is independent of $\mu_{m}$.

For part (ii), we begin by writing aggregate welfare as a function
of the surrogate choice, $s\in\mathbb{R}$, under our distributional
assumptions:

\begin{equation}
W(s,\mu)=\intop_{-\infty}^{+\infty}l\left(s-u\right)\phi\left(\frac{u-\mu_{u}}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}du\label{eq:WelfareS-1}
\end{equation}
Note that for any $k\in\mathbb{R}$, we have
\[
W(s+k,\mu_{u}+k,\mu_{m})=\intop_{-\infty}^{+\infty}l\left(s+k-u\right)\phi\left(\frac{u-(\mu_{u}+k)}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}du
\]
Now we define $v=u-k$. With a change of variables from $u$ to $v$,
we can then write the welfare function as:
\[
W(s+k,\mu_{u}+k,\mu_{m})=\intop_{-\infty}^{+\infty}l\left(s-v\right)\phi\left(\frac{v-\mu_{u}}{\sigma_{u}}\right)\frac{1}{\sigma_{u}}dv=W(s,\mu_{u},\mu_{m})
\]
It follows that increasing $\mu_{u}$ by $k$ simply shifts the entire
welfare function rightward by $k$, which means any maximizer increases
by $k$. The same statement obviously applies to the smallest maximizer.

\end{proof}

The rest of the proof is essentially the same as that of Proposition
\ref{mainProp}, with $s^{*}(\mu_{u},\mu_{m})$ replacing $\underline{r}^{*}(\mu_{u},\mu_{m})$.
The one material difference is that $s^{*}(\mu_{u},\mu_{m})$ is independent
of $\mu_{m}$. As a result, with mistakes-projective paternalism,
we can only say that the distribution of $s^{*}$ conditional on $c^{A}$
\emph{weakly} first-order dominates the distribution of $\underline{r}^{*}$
conditional on $c^{A'}>c^{A}$.


\newpage
\section{Additional Analysis}


\subsection{The Induced Chooser Preferences and Exogenous Removal conditions}\label{additionalConditions}

\label{exogenousRemovalCondition}
\label{influencers}\label{inducedPrefs}


Here, we detail the Induced Chooser Preferences and Exogenous Removal conditions summarized in Section \ref{otherMotives} of the main text.


\paragraph{The Induced Chooser Preferences condition}


The induced preference parameter $\hat{\delta}_i$ equals 0, 0.5, or 1, each with equal probability. These parameter values are chosen to minimize complexity. Choice Architects are aware of this distribution, but no Choice Architect knows the specific value of $\hat{\delta}_i$ that the Chooser assigned to her will face. We describe options as bundles $(X,Y)$ of `gold tokens' and `silver tokens,' respectively, to be exchanged for Euros the day of the experiment. Choice Architects know that the Chooser will be able to exchange each gold token for one Euro, while he will receive $\hat{\delta}_i$  for each silver token.

In the Induced Chooser
Preferences condition, the frequency with which Choice Architects exclude at least one option from a menu equals 22.6\%
(s.e.\ 2.1 percentage points). The corresponding number for decisions in the Main condition, by comparison, is much higher,  39.8\% (s.e.\ 2.3 percentage points). As mentioned in the main text, the monotonic relationship between larger values of $X$
and removal frequencies that characterizes the Main condition vanishes
entirely. Removal rates for the options in the Induced
Chooser Preferences condition with the largest, middle, and smallest
values of $X$ are 12.9\% (s.e.\ 1.6\%), 5.1\% (s.e.\ 1.1\%), and
7.8\% (s.e.\ 1.3\%), respectively.\footnote{The higher removal rate for the first option may reflect a variety
of motives, including fairness concerns. A Chooser facing $\hat{\delta}_i=0$ can
obtain no more than \EURdig4 in the Induced Chooser Preferences condition.
A Chooser facing $\hat{\delta}_i=1$, in contrast, obtains \EURdig15 from choosing
the first option. By removing that option, the Choice Architect can
reduce inequality across Choosers.} Moreover, across all rounds and
Choice Architects in the Main condition, subjects indicate in 29.6\%
(s.e.\ 2.2\%) of cases that the unrestricted opportunity set is worse
for the Chooser. This number drops to 15.3\% (s.e.\ 1.8\%) in the
Induced Chooser Preferences condition. Similarly, the elicited compensating
variations are more than twice as large in the Main condition as in
the Induced Chooser Preferences condition: $0.079$ (s.e. 0.015) versus
0.038 (s.e.\ 0.011).

\paragraph{The Exogenous Restriction condition }


As explained in the main text, in one round of this condition, Choice Architects decide between making all options available, removing the least patient option, and removing the two least patient options, whereas in another round of this condition, we exogenously remove the least patient option. We find that the availability frequency for the middle option falls slightly from 89.7\% when the least restrictive opportunity set contains three options, to  85.8\% when we remove the least patient option exogenously.
One would expect to see some decline merely as a consequence of noisy choice.  For example, if Choice Architects randomize between all their options with equal probabilities, then they would make the middle option available two-thirds of the time when all three options are available, versus one-half of the time when only two options are available.  As explained in the main text, we control for the effects of noisy behavior by including two additional rounds that are identical to the first pair, except that the default is the most restrictive opportunity set, consisting of just the most patient option. By taking action, the Choice Architect adds rather than removes options. Random choosers will add options, but subjects who are motivated by the desire to restrict others' opportunity sets will not. In these two rounds, we find that the exogenous removal of the least patient option causes a similar decline in the availability of the middle option (76.2\% and 73.9\%). The difference-in-differences is far from statistically significant in a regression with session and order fixed effects $(p>0.6)$.

The foregoing analysis is based on within-subject variation. The results do not change qualitatively if we restrict attention to cross-subject variation by only including each Choice Architect's first decision from among these four rounds. The resulting estimates of the decline in the frequency with which Choice Architects offer the middle option are virtually the same regardless of whether we specify the most restrictive set or the least restrictive set as the default option: it falls from 89.0\% to 81.7\% in the first instance, and from 78.3\% to 71.1\% in the second ($p>0.9$ for the difference-in-differences).

We conclude that there is no evidence indicating that Choice Architects are primarily motivated by a desire to exert control, even when they can potentially rationalize such action as beneficial.


\subsection{Assessing anchoring and demand for consistency}\label{anchoringDemandForConsistency}

Mechanisms such as anchoring or a demand for consistency potentially create an artificial relation between the Choice Architect's own choices in stage 2 of the laboratory component and the opportunity sets she has previously constructed for Choosers. We test for such mechanisms using the following strategy. We consider the variable $d_i$, defined as the difference between a Choice Architect's percentile rank of her own choices in stage 2 and the inverse of her patience rank in the online component.\footnote{Specifically, we first calculate the mean amount of money they choose to receive immediately in stage 2, averaging across the three-option menus used in the Main condition (without front-end delay). We obtain each Choice Architect's percentile rank according to that variable. We define the variable $d_i$ as the difference between the percentile rank just defined and the inverse percentile rank of a Choice Architect's patience elicited in the online component.} If the confounding mechanisms are quantitatively important, then we should observe a positive relation between mandates and $d_i$: if mandates influence a Choice Architect's own choices in stage 2 (through anchoring or a demand for consistency), then subjects who impose stricter mandates should exhibit larger discrepancies between their patience rank in the online component and Stage 2 of the laboratory component. (This inference rests on the assumption that the confounding mechanisms do not apply to the relation between mandates and choices in the online component.)

Formally, we regress $d_i$ on the mean mandate a Choice Architect imposes in the Main condition (without front-end delay). We obtain a coefficient estimate of 0.03 (s.e. 0.06). This relation is far from statistically significant ($p = 0.62$). We conclude that there are no quantitatively important confounding mechanisms that artificially relate Choice Architects' mandates to the choices they make for themselves in stage 2.


\subsection{Attention test} \label{attentionTest}

Table \ref{incentivizationTest} displays the attention test administered in Stage 2 of the online component. For each statement, the subject indicates whether it is true or false. The table also lists the percentage of correct responses next to each statement. The mean test score is 6.40 out of 8, with a standard deviation of 1.23. The lowest performance is on question 5, which was answered correctly by 58\% of participants. This question concerns the Induced Chooser Preference condition. Question 2, on front-end delay, was also answered incorrectly relatively often, in spite of the fact that the information on front-end delay was presented  prominently (though only in a single round of Stage 1). While some subjects might have forgotten this detail when taking this test, the fact that the introduction of a front-end delay leads to significant behavioral differences suggests they did pay attention. Indeed, if we only include subjects who correctly answered the test question on front-end delay, the coefficients of the regressions corresponding to columns 1 to 3 of Table \ref{frontEndDelay} are given by -0.28 (s.e. 0.12), -0.20 (s.e. 0.07), and -0.11 (s.e. 0.13), respectively. If we only include subjects who did not answer that question correctly, the coefficients barely change; they are given by -0.35 (s.e. 0.15), -0.28 (s.e. 0.09), and -0.12 (s.e. 0.15).

\begin{table}[H]
\caption{\textbf{Attention test concerning decisions made for Choosers.}\label{incentivizationTest}}
\setstretch{1}
\hrulefill
\begin{compactenum}[1.]
\item In some rounds I could remove options, but I could never add options for the future experiment participant. (False, 85\%)
\item For all options which the future experiment participant could receive, the early payment will always be on the day of the experiment. (False, 60\%)
\item For some options that the future experiment participant could have received, the late payment will occur up to 7 months after the day of the study. (False, 86\%)
\item In some rounds, I had to make a single option available to the future experiment participant; I could not make multiple options available, even if I wanted to. (True, 77\%)
\item Some rounds concerned gold tokens and silver tokens. These rounds only concerned money that would be paid soon after the experiment, not money that would be paid only months after the experiment. (True, 58\%)
\item Other experiment participants could receive \EURdig0.40, \EURdig0.50, or \EURdig0.60 per silver token, each with the same probability. (False, 98\%)
\item In some lines of the decision lists, the base payment of the future participant could be increased. In other rounds it could be decreased. (True, 93\%)
\item If I made some options unavailable, this means that the other experiment participant cannot see that option, and therefore does not need to think about these options. (False, 83\%)
\end{compactenum}
\hrulefill  \\
\scriptsize{\textbf{Note:}} Fractions of correct answers are given in parentheses.
\end{table}


\subsection{Projective Paternalism and Information treatments}\label{projPatInfoAppendix}

Here, we address the question whether informational interventions can affect the extent of projective paternalism. Ex ante, the answer is not obvious. On the one hand, Section \ref{paternalismInfo} shows that interventions respond substantially to Chooser-specific information. On the other hand, previous research on the false consensus effect documents its resistance to informational interventions \citep{KruegerClement, EngelmannStrobel.FalseConsensusInfo}.

We examine two different informational interventions. First, we test whether the information provided in the Chooser Information condition affects the extent of projective paternalism. To this end, we regress mandates on Choice Architects' patience percentiles using data from the Chooser Information Treatment. We allow the coefficient of Choice Architects' patience percentile to
vary freely across the four Chooser statements. As in the main text, we focus on non-libertarian Choice Architects. We control for session, order, and menu fixed effects, and we cluster standard errors by subject. Column 1 of Table \ref{projPatInfoTreatments} displays the results. The estimated coefficients are all similar, and we cannot reject the hypothesis that they are identical, either in pairwise comparisons or in a joint test ($p>0.8$ for the joint test, $p>0.4$ for each pairwise test). In column 2, we use beliefs as dependent variable. This regression allows us to examine the effect of the Chooser statements on the false consensus effect. We find that neither of the Chooser statements significantly affect the magnitude of the false consensus effect  ($p>0.5$ for the joint test, $p>0.15$ for each pairwise test).

There are two explanations for the foregoing finding. First, it is possible that projective paternalism is a robust phenomenon with respect to the provision of information. Second, the verbal statements about Chooser's inclinations and subjective experience require interpretation, which might be influenced by Choice Architects' own preferences in a way that leads to projective paternalism.

\begin{table}[htb!]
\caption{\textbf{Projective paternalism and information provision}\label{projPatInfoTreatments}}
\begin{centering}
\setlength{\tabcolsep}{4pt}
\setstretch{1}

\begin{tabular}{lcccccccc}
\toprule
 & (1)  & (2)  & (3)  & (4)  \\
VARIABLES & Mandate   & Belief & Mandate  & Belief  \\
\midrule
Patience \%-ile  &  &  \\
\hspace{0.4cm} \textit{$\times$ Chooser} \\
\hspace{0.8cm} \textit{patient, happy} & -0.915** & -1.313*** &  &  \\
 & (0.358) & (0.165) &  &  \\
\hspace{0.8cm} \textit{impatient, happy}  & -0.798** & -0.947*** &  &  \\
 & (0.353) & (0.235) &  &  \\
\hspace{0.8cm} \textit{patient, unhappy} & -0.675** & -1.132*** &  &  \\
 & (0.332) & (0.193) &  &  \\
\hspace{0.8cm} \textit{impatient, unhappy}   & -1.022*** & -1.027*** &  &  \\
 & (0.387) & (0.210) &  &  \\
 \hspace{0.4cm} \textit{$\times$ Choice dist. info.}&  &  & -0.290 & -1.022*** \\
 &  &  & (0.374) & (0.253) \\
  \hspace{0.4cm} \textit{$\times$ Not in Choice Dist. info.} &  &  & -1.048*** & -1.280*** \\
 &  &  & (0.269) & (0.170) \\
 Choice dist. info  &  &  & -0.334 & -0.183 \\
 &  &  & (0.327) & (0.200) \\
Mean of dep. var. & 2.825 & 1.505 & 3.085 & 1.442 \\
  & (0.082) & (0.059) & (0.075) & (0.051)  \medskip
\\
$p$ of joint test for differences in slopes & 0.860 & 0.581 & 0.107 & 0.393 \medskip \\
Observations & 716 & 716 & 732 & 729 \\
Number of subjects & 179 & 179 & 244 & 243 \\
\midrule
\end{tabular}
\par\end{centering}

 \medskip \scriptsize \textbf{Notes:} {Method}: OLS.
Unit of observation: subject-round pairs. Dependent variables: \emph{Mandate} is the
maximum amount the Choice Architect allows the Chooser receive immediately
(as in Section \ref{situationalDeterminants}).  \emph{Belief} is the Choice Architect's beliefs about the mean amount a Chooser will receive early if allowed to choose without restrictions.
 Controls: \emph{Patience \%-ile} is the Choice
Architect's percentile rank according to the average number of months
she is willing to delay the receipt of the larger payment in the online
tasks. Other controls include session, order, and menu fixed effects.
Samples: All regressions limited
to non-libertarian subjects who responded monotonically to all multiple-decision lists
in the online component. Columns 1 and 2 are based on the Chooser Information condition using subjects who are not in the Choice Distribution Information treatment. Columns 3 and 4 are based on the Main condition (without front-end delay) adding subjects from the Choice Distribution Information treatment.
Standard errors: clustered
at the subject level. $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$.
\end{table}

To evaluate the second interpretation, we study data from 100 additional
Choice Architects who participated in the \emph{Choice Distribution Information}
condition. The experiment for these Choice Architects proceeded in
the same fashion as for all other Choice Architects, with the exception
that, in each round, these Choice Architects could click a button
to view the distribution of previous selections Choosers made when
all options were available. Upon clicking the button they observed, for each of the three options
in the menu, a line of text of the following form: \textit{Out of
100 previous Choosers, $N$ choose $X$ today and $Y$ in six months}.\footnote{We calibrated the numbers \textit{$N$} based on online pilot studies.
Each Choice Architect in each session session faced a 25\% chance
of being assigned to the Choice Distribution Information condition.}

In this analysis, we focus again on non-libertarian Choice Architects. The fraction of libertarians in the Choice Distribution Information treatment is 32\%, compared to 38.3\% libertarians amongst subjects not in that treatment ($p>0.25$).

Choice Architects' decisions to reveal the information are instructive: 39.7\% of non-libertarian subjects in the Choice Distribution information treatment never viewed the information provided, even though they could do so costlessly, at the click of a button. The remaining subjects viewed the information in 46\% of all rounds, on average.

To study the effect of distributional information formally, we regress mandates on Choice Architects' patience percentile using data from the Main condition (without front-end delay). We allow the coefficient to vary freely depending on whether a Choice Architect is in the Choice Distribution Information treatment, and we include an indicator for that treatment. We control for session, order, and menu fixed effects, and we cluster standard errors by subject.

Column 3 of Table \ref{projPatInfoTreatments} displays the results. Projective paternalism appears to be attenuated in the Choice Distribution Information treatment. However, given the high frequency with which subjects choose not to view the information, we interpret this result with caution. Two further results suggest a cautious interpretation. First, the difference in estimated coefficients across the two treatments is not statistically significant ($p>0.1$). Second, column 4 shows the estimated coefficients when we replace the dependent variable with Choice Architects' beliefs about unrestricted Choosers' choices. We find that the effect of the Choice Distribution Information treatment is much weaker and statistically insignificant ($p>0.39$). Because the Choice Distribution information treatment does not alter beliefs, we consider it unlikely that the treatment has a genuine effect on mandates.

Overall, we conclude that ideals-projective paternalism is robust to the kind of information we have provided.


\subsection{Choosers' choices}\label{ChooserChoices}

Table \ref{chooserChoices} lists the choices by Choosers. Each Chooser ranked the three options associated with single round of the experiment. The modal Chooser ranks the most patient option first and the least patient option last, though each option is ranked first by some Chooser. Choosers participate merely to incentivize the Choice Architects; we do not draw inferences from their behavior. We recorded the minimum data required to determine each Chooser's payment.

\begin{table}[H]
\setstretch{1}
	\caption{\textbf{Choosers' choices.}\label{chooserChoices}}
	\vspace{-0.5cm}
	\begin{center}
	\begin{tabular}{lcccc}
		\toprule
		& \multicolumn{3}{c}{{Preference rank}}  \\
		\cmidrule(r){2-4}
		& {First}	& {Second} & {Third} \\
		\midrule
		\textit{Money over time} ($n=103$) \\
		\hspace{0.4cm}{Most patient choice}		& 79.61\% & 11.65\% & 8.74\% \\

		\hspace{0.4cm}{Middle Choice} 		&14.56\% & 82.52\% & 2.91\%    \\


		\hspace{0.4cm}{Least Patient Choice}		& 5.83\% & 5.83\% & 88.35\%   \medskip \\


		\textit{Induced preferences} ($n=21$) \\
		\hspace{0.4cm}{Most patient choice} 	& 57.14\% & 23.81\% & 19.05\%  \\

		\hspace{0.4cm}{Middle Choice} 		& 23.81\% & 71.43\% & 4.76\%    \\

		\hspace{0.4cm}{Least Patient Choice} 	& 19.05\% & 4.76\% & 76.19\%  \\

		\bottomrule
	\end{tabular}
	\end{center}
	\scriptsize{\textbf{Notes:}} The table shows, for each option, the fraction of Choosers who placed that option at the indicated preference rank position.	\emph{Money over time} concerns choices corresponding to the Main, Exogenous Restriction, and Chooser Information conditions. \emph{Induced preference} concerns choices corresponding to the Induced Chooser Preferences condition.
\end{table}


\subsection{Price sensitivity}\label{priceSensitivity}

Here, we document the sensitivity of subjects' interventions to the monetary cost of impatience. We begin by regressing an a binary variable indicating whether an option is withheld on the amount of Euros the Choice Architect forfeits by choosing that option rather than the most patient option (which always pays a total amount of \EURdig15), and on indicators for whether the option is the least patient or middle option in the menu. For this purpose, the unit of observation is a Choice Architects' decision concerning a given option in a given round. We include observations from the Main treatment without front-end delay, we control for session and order fixed effects, and we cluster standard errors on the subject level. Column 1 of Table \ref{tablePriceSensitivity} displays the result. The coefficient estimates indicate that the magnitude of the monetary cost rather than the relative rank of an option in the choice set is the primary driver of decisions to withhold options. As reported in the main text, a \EURdig1 reduction in total payoff increases the probability that an option is withheld by 2.9 percentage points ($p<0.01$). To reinforce this point, Columns 2 performs the regression including only the middle option of each choice set, which leads to a coefficient of 2.1 percentage points. Column 3 includes only the least patient option of each choice set and yields a coefficient of 10.5 percentage points.


\begin{table}[H]
\setstretch{1}
\caption{\textbf{Price sensitivity.}\label{tablePriceSensitivity} }

\begin{centering}
\begin{tabular}{lcccc} \toprule
 & (1) & (2) & (3)\\
VARIABLES & Option withheld & Option withheld & Option withheld \\
Rank of included option  & All & Middle & Least patient \\
\midrule
 &  &  &  &  \\
Cost of impatience  & 0.029*** & 0.021*** & 0.105*** \\
& (0.006) & (0.007) & (0.037) \\
\textit{Option rank} \\
 \hspace{0.4cm} Middle & -0.008 &  &  \\
 &  (0.021) &  &  \\
 \hspace{0.4cm} Least patient &  0.019 &  &  \\
 &  (0.059) &  &  \\
 &  &  &  \\
Observations  & 2,727 & 909 & 909 \\
 \#subj & 303 & 303 & 303 \\ \bottomrule
 \end{tabular}
\par\end{centering}
 \medskip  \scriptsize  \textbf{Notes:} Method: OLS. Unit of observation: subject-round-option triplet. Controls: \emph{Cost of impatience} is the amount of Euros a Chooser forfeits by selecting a given option, compared to the most patient option. \emph{Option rank} refers to the placement of an option within a menu. Other controls include session and order fixed effects. Sample: all rounds from the Main condition without front-end delay.
\end{table}


\subsection{Beliefs about Chooser welfare}\label{welfareBeliefsAppendix}

Table \ref{appendixWelfareBeliefsOtherTreatments} displays the effects of - delay and of Chooser-specific information on judgments about the welfare effects of withholding options, as referenced in Sections \ref{FED} and \ref{paternalismInfo} of the main text, respectively. Table \ref{welfareBeliefsAppendixOProbit} presents regression results concerning beliefs about the welfare effects of removing options using ordered probit specifications. All coefficient estimates of interest retain their signs as well as their statistical significance.


\begin{table}[H]

\setstretch{1}
\caption{\textbf{Subjects' beliefs about the welfare effects of withholding options in the case of front-end delay and in the Chooser Information condition}\label{appendixWelfareBeliefsOtherTreatments}}
\begin{small}
\begin{centering}

\begin{tabular}{lcccccccc}
\toprule
 & (1)  & (2)  & (3)  & (4)   \\
Dependent variable  & \multicolumn{2}{c}{Belief smaller opportunity set } & \multicolumn{2}{c}{Negative of }\\
 & \multicolumn{2}{c}{ better for Chooser} & \multicolumn{2}{c}{Compensating Variation}\\
\cmidrule(r){2-3} \cmidrule(r){4-5} Smaller set  & Chosen  & Chosen  &  Chosen  & Chosen \medskip{} \\
Menus & 1 - 2 & 5  & 1 - 2 & 5 \\
Menu with FED included &  Yes & - & Yes & - \medskip
 \\
 & \multicolumn{4}{c}{\textbf{Summary statistics for dependent variables}} \\
\midrule
Distribution of beliefs  &   \\
\hspace{0.4cm} \emph{Evaluation opportunity set is}  &   \\
\hspace{0.8cm}\emph{ better}  & 0.297  & 0.356   \\
\hspace{0.8cm}\emph{ same}  & 0.611  & 0.540  \\
\hspace{0.8cm}\emph{ worse}  & 0.092  & 0.103  \\

\hspace{0.4cm} \emph{Mean negative CV}  &  &  & 0.021  & -0.224 \medskip \\
Distribution of beliefs if options withheld &  &  \\
\hspace{0.4cm} \emph{Evaluation opportunity set is}  &  &  &  &  &  &  &  & \\
\hspace{0.8cm} \emph{better}  & 0.674 & 0.693 &  &  &  &  \\
\hspace{0.8cm} \emph{same}  & 0.090 & 0.099 &  &  &  &  \\
\hspace{0.8cm} \emph{worse} & 0.236 & 0.207 &  &  &  &  \\
\hspace{0.4cm} \emph{Mean negative CV} &  &  &  0.095 & 0.078 \medskip{}
\\
 & \multicolumn{4}{c}{\textbf{Regression results}} \\
\midrule


Front-end delay   & 0.077{*}{*}  &  &   0.045{*}  & \\
 & (0.037)  &  &   (0.025)  & \\
Chooser information  &  &   \\
\hspace{0.4cm} \textit{Patient, happy}    &  & 0.077{*}  &  &   0.002 \\
 &  & (0.041)  &  &  (0.028) \\
\hspace{0.4cm} \textit{Patient, unhappy}  &  & 0.015  &  & -0.029 \\
 &  & (0.040)  &  &  (0.025) \\
\hspace{0.4cm} \textit{Impatient, unhappy}   &  & 0.116{*}{*}{*}  &  &  0.010 \\
  &  & (0.040)  &  &   (0.025) \medskip \\
Observations   & 606  & 1,212  & 583  & 1,156 \\
Number of subjects  & 303  & 303  &  294  & 298 \\
\bottomrule
\end{tabular}
\par\end{centering}

 \end{small}
  \medskip \scriptsize  \textbf{Notes:} The top half of the table provides
summary statistics for the dependent variables, and the bottom half
presents regression results.
Unit of observation: subject-round pair.
{Method}: Columns 1 and 2, OLS; columns 3 and 4, interval regression.
{Dependent variables}:
The dependent variable
for columns 1 and 2 measures whether the Choice Architect considers
the smaller opportunity set better, equally good, or worse for the
Chooser than the unrestricted set, coded as 1, 0, and -1, respectively.
For columns 3 and 4, it is the negative of the Choice Architect's beliefs
about the compensating variation (CV) of reducing the opportunity
set. The smaller opportunity set is the one
the Choice Architect has constructed.
{Controls}: All regressions control for
altruism and spite, and include session, order, and menu fixed
effects.
{Samples}:  Each of columns 1 and 2, and each of columns 3 and 4, employs data from a different condition, as indicated. In the bottom panel, each column corresponds to a separate regression. Columns 3 and 4 exclude subjects with
multiple switches in the CV elicitation.
{Standard errors}: clustered by subject.
\black $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$.
\end{table}


\begin{table}[H]
\caption{\textbf{Ordered probit regressions corresponding to Tables \ref{welfareBeliefs} and \ref{appendixWelfareBeliefsOtherTreatments}} \label{welfareBeliefsAppendixOProbit}}
\begin{small}
\setstretch{1}
\begin{centering}

\begin{tabular}{lcccccccc}
\toprule
 & (1)  & (2)  & (3)  & (4)  \\

\midrule
Smaller set  & Chosen  & Exogenous   & Chosen  & Chosen \medskip{}
\\
\# options withheld  &  &  &  & \\
\hspace{0.4cm} 1  & 0.634*** &  &  &  \\
 & (0.144) &  &  &  \\
 \hspace{0.4cm} 2  & 1.035*** &  &  &  \\
 & (0.247) &  &  &  \\
Mean \# options withheld  &  & 1.081*** &  &  \\
\hspace{0.4cm} in Main condition  &  & (0.139) &  &  \\
Opportunity set contains &  & -0.308** &  &  \\
\hspace{0.4cm} most patient option only &  & (0.138) &  &  \\
Front-end delay &  &  & 0.155** &  \\
 &  &  & (0.072) &  \\
Chooser information \\
\hspace{0.4cm} \textit{Patient, happy} &  &  &  & 0.142* \\
 &  &  &  & (0.076) \\
\hspace{0.4cm} \textit{Patient, unhappy} &  &  &  & 0.028 \\
 &  &  &  & (0.073) \\
\hspace{0.4cm} \textit{Impatient, unhappy}  &  &  &  & 0.217*** \\
 &  &  &  & (0.074) \\
 \\
Category thresholds oProbit \\
\hspace{0.4cm} \emph{Worse vs. same} & -0.535* & 0.453 & -1.113*** & -0.986*** \\
 & (0.307) & (0.371) & (0.357) & (0.283) \\
\hspace{0.4cm} \emph{Same vs. better} & 1.385*** & 0.801** & 0.815** & 0.691** \\
 & (0.310) & (0.369) & (0.351) & (0.279) \\
\\
Observations  & 909 & 606 & 606 & 1,212 \\
Number of subjects & 303 & 303 & 303 & 303 \\
\bottomrule
\end{tabular}
\par\end{centering}
\end{small}
\scriptsize  \textbf{Notes:} The dependent measures whether the Choice Architect considers
the smaller opportunity set better, equally good, or worse for the
Chooser than the unrestricted set. We assign higher index numbers to more positive beliefs. Unit of observation: subject-round pair.
{Method}: Ordered probit. Otherwise, specifications are the same as in the corresponding OLS regressions in Tables \ref{welfareBeliefs} and \ref{appendixWelfareBeliefsOtherTreatments}
\black $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$. \\
\end{table}


\subsection{Projective paternalism, beliefs, and measurement error}\label{twoSLS}

Here, we examine the possible effects of measurement error in our analysis on the role of beliefs for projective paternalism---specifically Column 2 in Table \ref{projPat2}. We do so by instrumenting for the Choice Architect's patience (measured in the online component) using the choices she makes for herself in stage 2 of the laboratory component using all rounds corresponding to the Main condition. We also instrument for the Choice Architect's beliefs about choices corresponding to the Main condition by the same subjects' beliefs about choices corresponding to the Chooser Information and Exogenous Restriction conditions. Aside from the 2SLS estimation method, our specification is the same as in column 2 of Table \ref{projPat2}.

Table \ref{projPatIVtable} shows the results. For comparison, column 1 replicates the OLS regression featured in the main text. The specification in Column 2 instruments both predictor variables. We find that coefficient on beliefs more than doubles, while the coefficient on the Choice Architects' patience flips sign. The latter coefficient is measured with such a large standard error that its confidence interval ranges from -1.28 to 2.26. This confidence interval includes both the coefficient estimate from a regression without beliefs (column 1 of Table \ref{projPat2}), and the coefficient estimate of the OLS regression in column 1. For completeness, Column 3 presents a version of this regression in which we only instrument for beliefs, but not for patience, while column 4 only instruments for patience but not for beliefs. We find a substantial but noisily estimated effect of own patience in the latter specification.

Overall, we conclude that beliefs about others' choices play an important role in determining mandates. The estimates of the effect of patience conditional on beliefs, in contrast, are too noisy for us to reach definitive conclusions.


\begin{table}[H]
\caption{\textbf{Role of beliefs in projective paternalism: instrumental variables regressions}\label{projPatIVtable}}
\begin{centering}
\setstretch{1}

\begin{tabular}{lcccccccc}
\toprule
 & (1)  & (2)  & (3)  & (4)  \\
VARIABLES &  Mandate  & Mandate  & Mandate & Mandate \\
Method    & OLS  & IV    & IV    & IV  \\
\midrule
 &  &  &  & \\
Patience \%-ile  & -0.540* & 0.491 & 0.008 & -1.149 \\
 & (0.318) & (0.903) & (0.384) & (0.832)   \medskip \\
Belief \%-ile  & -1.136*** & -2.657*** & -2.374*** & -0.781 \\
 & (0.365) & (0.758) & (0.506) & (0.616) \medskip \\
 Instrumented \\
 \hspace{0.4cm} \textit{Patience \%-ile} & No & Yes & No & Yes \\
 \hspace{0.4cm} \textit{Belief \%-ile} & No & Yes & Yes & No \medskip \\
Observations   & 537 & 537 & 537 & 537 \\
Number of subjects  &179  &179  &179  &179 \\
\midrule
\end{tabular}
\par\end{centering}

\scriptsize \textbf{Notes:}
Unit of observation: subject-round
pairs. Dependent variable: \emph{Mandate} is the maximum amount the Choice Architect allows the
Chooser receive immediately (as in Section \ref{situationalDeterminants}).
Controls: \emph{Patience \%-ile}
is the Choice Architect's percentile rank according to the average
number of months she is willing to delay the receipt of the larger
payment in the online tasks. \emph{Beliefs \%-ile} is the Choice Architects'
percentile rank in terms of her beliefs about the Chooser's patience,
as measured by the inverse of the mean amount the Chooser will receive
immediately if allowed to choose without restrictions. Additional
controls include session, order, and menu fixed effects. Samples:
Regressions limited to non-libertarian subjects who responded monotonically
to all multiple-decision lists in the online component.
Rounds from the Main condition without
front-end delay. Standard errors: clustered at
the subject level. $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$.
\end{table}


\subsection{Choice Architects' own choices}\label{caChoicesSection}

In Stage 2, Choice Architects choose one of the options from each menu in Panel A of Table \ref{choiceSets}. Table \ref{caChoices} lists the distribution of these choices for menus corresponding to the Main condition.


\begin{table}[H]
\setstretch{1}
\caption{\textbf{Choices that Choice Architects make for themselves} \label{caChoices}}
\vspace{-0.3cm}
\begin{center}
\setlength{\tabcolsep}{3pt}
\begin{tabular}{cccccc}
\toprule

Menus & \multicolumn{3}{c}{Percent chosen} &Obs. & Subjects \\
\cmidrule(r){2-4}
& Most patient & Middle & Least patient \\
\midrule
 1, 2, 3, 4	 		&	78.3	&	13.0	&	8.4	&			1612	&	403\\
 				&	(1.7)	&	(1.2)	&	(1.2)	&	\medskip\\
1				&	68.7	&	18.5	&	12.3	&			195	&	195\\
				&	(3.3)	&	(2.8)	&	(2.4)	&	\medskip\\
2				&	77.9	&	9.6	&	12.5	&			208	&	208\\
				&	(2.9)	&	(2.0)	&	(2.3)	&	\medskip\\
3				&	70.7	&	23.1	&	6.0	&			403	&	403\\
				&	(2.3)	&	(2.1)	&	(1.2)	&	\medskip\\
4				&	87.6	&	4.7	&	7.4	&			403	&	403\\
				&	(1.6)	&	(1.1)	&	(1.3)	&	\medskip\\
 1, 2 	 			&	81.4	&	10.4	&	7.9	&			403	&	403\\
Front-end delay		&	(1.9)	&	(1.5)	&	(1.3)	&	\medskip\\
 1, 2   			&	73.4	&	13.9	&	12.4	&			403	&	403\\
No front-end delay	&	(2.2)	&	(1.7)	&	(1.6)	&	\medskip\\
\bottomrule
\end{tabular}\\
\scriptsize \textbf{Notes:} Standard errors in parentheses.
\end{center}
\end{table}


\subsection{Judgments about welfare effects of actual paternalistic policies}\label{welfareRealWorld}

Here, we provide material supplementary to our analysis in Section \ref{realWorldPolicy} of the main text concerning judgments about actual paternalistic policies. Panel A of Table \ref{experimentVsRealWorldAppendix}  shows the distribution of welfare judgments by policy. Panel B.1 displays the coefficients of ordered probit versions of the regressions concerning the relation between welfare judgments about actual policies and welfare judgments in the experimental decisions (see Panel B.2 in Table \ref{experimentVsRealWorld}). As in the main text, we find highly statistically significant relations for all tax policies, but not for restrictions on short-term, high-interest lending. Panel B.2 displays corresponding results when we use beliefs about the negative compensating variation for removing options in the experimental tasks. Here, we find statistically significant coefficient estimates for both tobacco taxes and for restrictions on short-term, high-interest lending, but not for the remaining policies. For all policies, we find the expected positive sign. The lack of statistical significance for two of the tax policies is possibly due to the complex elicitation procedure through which subjects reveal beliefs about compensating variations.


\begin{table}[htbp!]

\setstretch{1}
\setlength{\tabcolsep}{4pt}
\caption{\textbf{Experimental decisions and support for real-world paternalistic policies.}\label{experimentVsRealWorldAppendix}}
\begin{centering}
\vspace{-0.3cm}
\begin{small}


\begin{tabular}{lccccc}
\toprule
 & (1)  & (2)  & (3)  & (4)   \\
Policy   & Increase  & Increase  & Introduce sugary  & Tighten restrictions on \\
 &  alcohol tax  & tobacco tax  & drinks tax  & short-term lending\medskip \\
 & \multicolumn{4}{c}{\textbf{A. Summary statistics for dependent variables}}\\
\cmidrule(r){2-5}
Dist. of beliefs about welfare effects\\
\hspace{0.4cm}{\textit{significantly worse off}}    & 0.112  & 0.050  & 0.050  & 0.072 \\
\hspace{0.4cm}{\textit{a little worse off}}   & 0.251  & 0.099  & 0.166  & 0.243 \\
\hspace{0.4cm}{\textit{neither better nor worse off}}  & 0.228  & 0.176  & 0.310  & 0.357 \\
\hspace{0.4cm}{\textit{a little better off}}   & 0.270  & 0.295  & 0.330  & 0.256 \\
\hspace{0.4cm}{\textit{significantly better off}}  & 0.139  & 0.380  & 0.144  & 0.072 \\
\\
 & \multicolumn{4}{c}{\textbf{B. Relation between beliefs about welfare effects}}\\
 & \multicolumn{4}{c}{\textbf{  in the laboratory and concerning actual policies}}\\
\cmidrule(r){2-5}
 & \multicolumn{4}{c}{\textit{B.1 Predictor variable: non-incentivized statement}}\medskip \\
Average belief exog. smaller choice & 0.205*** & 0.230*** & 0.217*** & 0.077 \\
 \hspace{0.4cm} set better for Chooser & (0.078) & (0.083) & (0.076) & (0.076) \\
Category thresholds oProbit \\
\hspace{0.4cm}1 & -1.974*** & -2.511*** & -3.199*** & -0.954 \\
 & (0.685) & (0.697) & (0.714) & (0.689) \\
\hspace{0.4cm}2 & -1.031 & -1.851*** & -2.271*** & 0.099 \\
 & (0.677) & (0.687) & (0.711) & (0.685) \\
\hspace{0.4cm}3 & -0.390 & -1.214* & -1.361* & 1.063 \\
 & (0.674) & (0.679) & (0.708) & (0.687) \\
\hspace{0.4cm}4 & 0.537 & -0.390 & -0.303 & 2.103*** \\
 & (0.675) & (0.678) & (0.708) & (0.695) \medskip \\
 Observations & 403  & 403  & 403  & 403 \\ \\
 & \multicolumn{4}{c}{\textit{B.2 Predictor variable: belief about compensating variation}}\medskip \\
 Mean negative CV & 0.201 & 0.379** & 0.189 & 0.214* \\
 & (0.138) & (0.152) & (0.141) & (0.130) \\
Category thresholds oProbit \\
 \hspace{0.4cm}1 & -1.791** & -2.491*** & -3.178*** & -0.889 \\
 & (0.710) & (0.710) & (0.741) & (0.716) \\
 \hspace{0.4cm}2 & -0.861 & -1.824*** & -2.276*** & 0.189 \\
 & (0.703) & (0.701) & (0.736) & (0.713) \\
 \hspace{0.4cm}3 & -0.214 & -1.191* & -1.383* & 1.129 \\
 & (0.701) & (0.692) & (0.732) & (0.716) \\
 \hspace{0.4cm}4 & 0.698 & -0.375 & -0.319 & 2.181*** \\
 & (0.702) & (0.691) & (0.733) & (0.724) \medskip\\
Observations & 391 & 391 & 391 & 391 \\
\\
\bottomrule
\end{tabular}\end{small}
\par\end{centering}
 \medskip \scriptsize \textbf{Notes.}
Method: ordered probit.
Unit of observation: subject.
Dependent variables:
\emph{Beliefs about welfare effects} is the belief about the welfare effect of various policies.
 Controls:
\emph{Average belief exog. smaller choice set better for Chooser}
(as in Section \ref{benevolence}) encodes
whether the Choice Architect considers
the smaller opportunity set better, equally good, or worse for the
Chooser than the unrestricted set, coded as 1, 0, and -1, respectively, averaged across the four rounds of the Exogenous Restriction Condition.

Controls: \emph{Average belief exog. smaller choice set better for Chooser}  measures whether the Choice Architect considers
the smaller opportunity set better, equally good, or worse for the
Chooser than the unrestricted set, coded as 1, 0, and -1, respectively, averaged across rounds in the Exogenous Restriction condition. \emph{Mean negative CV} is the negative of the Choice Architect's beliefs
about the compensating variation (CV) of reducing the opportunity
set, averaged across rounds in the Exogenous Restriction condition.
All regressions control for gender,
age, self-reported political attitudes, log monthly expenses, high
school GPA, university faculty at which the subject's major field
of study is offered, weekly alcohol consumption, log days of binge
drinking per year (defined as the consumption of at least 4 units
of alcohol for females, 5 for males, within a period of two hours
\citep{NIAAA}), smoking status, number of cigarettes smoked per day,
body mass index, credit card debt, for having taken a short term loan, as well as for session and order fixed effects.
For variables measured with interval precision, we use midpoints for analysis. For control variables that subjects chose not to disclose, the regressions impute population means, and include indicators for whether a variable's value was missing.
Samples: includes 303 subjects who participated in the main experiment, plus 100 subjects
who participated in the Choice Distribution Information Condition (see Appendix Section \ref{projPatInfoAppendix}). Panel B.2 excludes subjects with multiple switches in the elicitation of beliefs about compensating variations.  Standard errors: clustered by subject. $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$. \black
\end{table}


\subsection{Robustness check concerning judgments of actual paternalistic policies}\label{realWorldPoliciesNoControls}

The regressions in Table \ref{experimentVsRealWorld} include an array of control variables. Here, we present estimates of the same regressions in which we only retain session and order fixed effects as control variables. As Panel A.1 of Table \ref{experimentVsRealWorldNoControls} shows, in the case without controls, the relation between mandates in the experimental decisions and judgments of real-world paternalistic policies is slightly weaker for each policy. Even though the sign of the estimated coefficients remains unchanged and the magnitude remains similar in each case, this attenuation causes a loss in statistical significance for three of the four policies. Importantly, the relation to the average of judgments across the four policies remains statistically significant. In Panel A.2 we find a slight attenuation of the estimated coefficients concerning welfare beliefs. Here, the statistical significance of the coefficient estimates remains unchanged for each policy.  Panel B shows the effect of excluding subject-specific control variables on the estimates of projective paternalism concerning real-world policies. Here, we find that the magnitude and levels of statistical significance remain highly similar to the case that includes the controls.


\begin{table}[htbp!]
\setstretch{1}
\setlength{\tabcolsep}{4pt}
\caption{\textbf{Replication of estimates in Table \ref{experimentVsRealWorld} without subject-specific control variables.}\label{experimentVsRealWorldNoControls}}
\begin{centering}
\vspace{-0.3cm}
\begin{small}


\begin{tabular}{lccccc}
\toprule
 & (1)  & (2)  & (3)  & (4)  & (5) \\
Policy  & All  & Increase  & Increase  & Introduce sugary  & Tighten restrictions on \\
 &  & alcohol tax  & tobacco tax  & drinks tax  & short-term lending\medskip \\
 & \multicolumn{5}{c}{\textbf{A. Relation between laboratory choice and policy attitudes}}\\
\cmidrule(r){2-6}
\medskip & \multicolumn{5}{c}{\textit{A.1 Dependent variable: support for policy proposal}}  \\
Average mandate imposed on Chooser   & -0.091** & -0.102 & -0.101** & -0.083 & -0.078 \\
\hspace{0.4cm}{(max. \EURdig\; paid immediately)}  & (0.040) & (0.069) & (0.051) & (0.066) & (0.052) \\
Observations  & 403  & 403  & 403  & 403  & 403 \medskip \\
 & \multicolumn{5}{c}{\textit{A.2 Dependent variable: beliefs about welfare effect of policy
proposal}}\medskip \\
Average belief exog. smaller choice & 0.176*** & 0.200** & 0.222*** & 0.198*** & 0.085 \\
 \hspace{0.4cm} set better for Chooser & (0.052) & (0.084) & (0.080) & (0.072) & (0.075) \\
Observations & 403  & 403  & 403  & 403  & 403 \medskip\\
 & \multicolumn{5}{c}{\textbf{B. Projective paternalism with actual policies}} \\
\cmidrule(r){2-6}
Alcohol consumption  &  &  &  & \\
\hspace{0.4cm} \textit{Alcohol units / week}  && -0.059* &  &  &  \\
 && (0.033) &  &  &  \\
\hspace{0.4cm} \textit{log(days binge drinking / year)} && -0.172*** &  &  &  \\
& & (0.053) &  &  &  \\
Tobacco consumption  &  &  &  & \\
\hspace{0.4cm} \textit{Smoker yes / no}& &  & -0.943*** &  &  \\
 &&  & (0.274) &  &  \\
\hspace{0.4cm} \textit{Cigarettes / day} &&  & -0.034 &  &  \\
 &&  & (0.039) &  &  \\
Body Mass Index&  &&  & -0.064** &  \\
 &&  &  & (0.026) &  \\
Debt  &  &  &  & \\
\hspace{0.4cm} \textit{Credit card debt (in \EURdig1,000)}&&  &  &  & -0.536*** \\
 &&  &  &  & (0.088) \\
\hspace{0.4cm} \textit{Other short-term debt yes / no}& &  &  &  & 0.071 \\
& &  &  &  & (0.361) \medskip\\
Controls  &-& Yes  & Yes  & Yes  & Yes\\
Observations  &-& 403  & 403  & 398  & 351 \\


\bottomrule
\end{tabular}\end{small}
\par\end{centering}
 \medskip \scriptsize \textbf{Notes:}
Method: OLS.
Unit of observation: subject.
Dependent variables: \emph{Support for policy proposal} is the support
expressed for various policies, coded -2 (strong opposition) to 2
(strong support), averaged over policies for ``All.'' A ``neutral''
response was possible only for the question about short-term lending.
Because we asked subjects about \emph{loosening} restrictions on short-term
lending, we reverse-coded these responses for easier comparability
(so that higher values correspond to greater support for tightening
restrictions).
\emph{Beliefs about welfare effects} is the belief about the welfare effect of various policies, coded -2 (significantly worse off) to 2 (significantly better off), averaged over policies for ``All.''
 Controls: \emph{Average mandate imposed on Chooser} is the maximum amount
the Choice Architect allows the Chooser receive immediately (as in
Section \ref{situationalDeterminants}), averaged over rounds involving
menus 3 and 4 in the Main condition.
\emph{Average belief exog. smaller choice set better for Chooser}
(as in Section \ref{benevolence}) encodes
whether the Choice Architect considers
the smaller opportunity set better, equally good, or worse for the
Chooser than the unrestricted set, coded as 1, 0, and -1, respectively, averaged across the four rounds of the Exogenous Restriction Condition.

All regressions control for session and order fixed effects.
For variables measured with interval precision, we use midpoints for analysis.
Samples: includes 303 subjects who participated in the main experiment, plus 100 subjects
who participated in the Choice Distribution Information Condition (see Appendix Section \ref{projPatInfoAppendix}). Regressions in Panel C exclude subjects who chose not to disclose the personal characteristic of interest. Standard errors: clustered
by subject. $^{*}p<0.1,^{**}p<0.05,^{***}p<0.01$. \black
\end{table}


\subsection{Beliefs about reasons for impatient choice}\label{nonIncentivized}

Here, we examine subjects' responses to non-incentivized survey questions regarding the reasons for choosing impatiently. We list six possible reasons a Chooser may select \EURdig4 today and \EURdig0 in half a year over \EURdig0 today and \EURdig10 in half a year. Choice Architects indicate agreement or disagreement on a scale of -2 to 2. Table \ref{impatienceReasons} lists the reasons provided along with the corresponding mean responses. Choice Architects believe that plausible explanations include impatient predispositions and liquidity constraints, with significantly positive mean scores of 0.640 and 0.782, respectively. On average, Choice Architects consider inattention and random choice to be relatively unlikely explanations, and they judge the inability to implement one's objectives as downright implausible. Neither do Choice Architects believe that impatient choices are due to experiment-specific factors such as trust in the experimenter or technical issues with the monetary transaction.


\begin{table}[H]
\setlength{\tabcolsep}{3pt}
\setstretch{1}
\caption{\textbf{Non-incentivized assessment of the plausibility of particular reasons for impatience.\label{impatienceReasons}}}
\begin{center}
\begin{small}

\begin{tabular}{p{0.7\textwidth}ccccc} \toprule
\hspace{-0.4cm}Reason &Mean plausibility rating\\
\midrule
\hspace{-0.4cm}Objectives \\
\textit{They are generally rather impatient.} &
 				  \thead{  0.640***\\ (0.056) } \\
\textit{They have an urgent need for money to pay for things like food or rent. }
& \thead{0.782*** \\ (0.069) }  \\
\hspace{-0.4cm}Implementation \\
\textit{They did not pay attention and chose randomly.}
&\thead{-0.436***\\(0.059) } \\
\textit{They meant to choose \EURdig0 today, \EURdig10 in half a year, but chose something else because some irrelevant event prevented them from choosing what they actually meant to choose (e.g. their hand trembled, or they confused the order of the radio-buttons).}
&\thead{-1.360*** \\ (0.053)} \bigskip\\
\hspace{-0.4cm} Experiment-related factors \\
\textit{They are not sure whether the experimenter will really pay them half a year from now.}
& \thead{-0.838*** \\(0.064)} \\
\textit{They are not sure whether they will be able to receive the money in half a year from now, for instance because they no longer have a PayPal account.}
& \thead{-0.667***\\(0.062)  } \bigskip \\


\bottomrule
\end{tabular}
\end{small}
\end{center}
\scriptsize \textbf{Notes}
Subjects were asked to suppose that a future experiment participant chooses \EURdig4 today and \EURdig0 in half a year over \EURdig0 today and \EURdig10 in half a year, and were asked to indicate how likely they think that each of the reasons listed motivated the participant's choice.  Responses are given on the scale \textit{extremely unlikely, unlikely, possible, likely, extremely likely}, encoded as -2, -1, 0, 1, 2, respectively. Standard errors in parentheses.
\end{table}


\subsection{Performance on memory check} \label{memoryTest}


At the end of the experiment, Choice Architects reproduced their own choices from two of the twelve multiple decision lists about intertemporal choice they completed in the online component (in addition, they reproduced their choices from two lists of lotteries). The first of these lists used early and late payment amounts of \EURdig2 and \EURdig10, respectively, while the second list used \EURdig5 and \EURdig10. A total of 46.9\% of subjects correctly reproduced their responses for both decision lists. This number is potentially inflated due to the fact that 60.1\% of all subjects chose the delayed option on each line of each of the two lists, which simplifies memorization and reconstruction.


\newpage

\section{Vignette experiment}\label{mTurkVignette}

\subsection{Design}

To evaluate the possibility that differential beliefs about the effectiveness of various interventions contribute to our results in Section \ref{realWorldPolicy} of the main text, we conduct a vignette study on Amazon Mechanical Turk. Its structure is similar to the part of our main experiment concerning policy judgments. Appendix \ref{vignetteInstruction} displays the full text of the vignette study.

To control beliefs about the effectiveness of the interventions, we describe a specific individual, ask subjects to assume that the policy will affect that individual in a precisely specified fashion, and ask whether they would support the policy if it affected everyone the same way. In addition, we elicit beliefs about the effectiveness of the policies on potentially heterogenous members of the general population, permitting a test of whether beliefs about effectiveness are related to respondent characteristics in a way that would artificially generate ideals-projective paternalism.

We use four policies, displayed to subjects in random order: Alcohol taxes, sugary drinks taxes, retirement savings mandates, and restrictions on short-term, high-interest lending.\footnote{We do not elicit attitudes to tobacco taxes because we conduct our survey with US subjects. The fraction of smokers in the US is substantially smaller than amongst our German laboratory subjects.} For each policy we describe an individual living in an Anglo-Saxon country other than the US, so respondents' attitude to paying taxes themselves should not influence their responses. Moreover, we describe all taxes as budget-neutral. Regarding sugary drinks taxes, for instance, we employ the following language: \textit{``Anne, [is] a resident of Melbourne, Australia. She is 35 years old, 5 feet and 4 inches tall, and she weighs 190 pounds. ... For this question, assume that if the tax is introduced, Anne will reduce her consumption of sugary drinks so that her weight permanently drops to 145 pounds (from the previous 190 pounds). Given Annes height, this is a normal weight, according to the World Health Organization. If all residents of Melbourne were exactly like Anne, would you support or oppose the introduction of the tax?''}


After subjects provide their judgments, we also elicit respondents' own characteristics that allow for tests of mistakes-projective or ideals-projective paternalism for each of the policies. Specifically, we elicit weekly alcohol consumption, yearly binge drinking frequency, weight and height (to calculate BMI), a subjective assessment of the respondents' body-shape (underweight, healthy, overweight, etc.), the amount of the respondent's credit card debt, whether the subject has ever taken a payday loan, as well as the current stock of retirement savings and the respondent's current retirement savings rate.\footnote{We elicit additional characteristics, see Appendix \ref{vignetteInstruction}.}

\subsection{Analysis}

We conducted the survey on the morning of February 1, 2019, with a total of 250 mTurk workers. Subjects received \$3 plus \$0.25 for each of eight attention check questions they answered correctly (two per policy). We retain the 161 subjects who correctly answered all attention check questions.

We define our independent variable by combining a subjects' responses to the two questions relating to any given policy and extracting the first principal component. We encode the resulting variable such that negative coefficient estimates correspond to ideals-projective paternalism whereas positive coefficient estimates correspond to mistakes-projective paternalism.

For each policy, we perform an ordered probit regression of the support the respondent expresses for the policy under the assumption that all affected individuals are exactly the same as the person we described in the vignette.

Panel A of Table \ref{vignetteResults} displays the results. We find significant ideals-projective paternalism for alcohol taxes and for the retirement savings mandate. For sugary drinks taxes and for limits on short-term, high-interest lending, our coefficient estimates are not significantly different from zero. In no case are our estimates consistent with mistakes-projective paternalism.

Panel B uses as the dependent variable the support respondents express for the policy overall, when we ask them to consider that people are actually heterogeneous and the policy may create externalities. We find a significant negative relation between support and own characteristics for alcohol taxes and for the retirement savings mandate, and a marginally significant negative relation for regulation of payday lending. We continue to find a null effect for sugary drinks taxes.


The similarity of the results in Panels A and B, including for sugary drinks taxes, suggests that the relationship between policy support and the respondents' own characteristics is not contaminated by correlations between own characteristics and views of the policies' efficacy. We demonstrate this point more directly in Panel C, which uses beliefs about the effectiveness of the policies as a dependent variable. There is no significant association between respondents' own characteristics and their beliefs about the effectiveness of any of the three policies for which we elicited these beliefs.

Overall we conclude that our evidence of ideals-projective paternalism regarding real-world paternalistic policies is not contaminated by beliefs about efficacy systematically varying with respondent characteristics. Moreover, while our results show that ideals-projective paternalism is present for some but not all paternalistic policies among our US subjects, we find no indication of mistakes-projective paternalism.

\begin{table}
\setstretch{1}
\begin{center}
\caption{\textbf{Results of the vignette study.}  \label{vignetteResults}}
\begin{tabular}{lcccc}
\toprule
 & (1) & (2) & (3) & (4) \\
Policy & Alcohol & Sugar & Retirement & Loans \\
\midrule
 \multicolumn{5}{c}{\textbf{A. Policy support regarding person described in vignette}} \\
\midrule
Own characteristics & -0.226** & 0.031 & -0.282*** & 0.029 \\
 & (0.095) & (0.081) & (0.101) & (0.070) \\
oProbit thresholds \\
\hspace{0.4cm} 1 & -0.464 & -1.922*** & -1.654** & -0.822 \\
 & (0.691) & (0.736) & (0.716) & (0.666) \\
\hspace{0.4cm} 2 & 0.000 & -1.458** & -1.123 & 0.402 \\
 & (0.694) & (0.722) & (0.708) & (0.653) \\
\hspace{0.4cm} 3 & 0.673 & -0.933 & 0.097 & 1.338** \\
 & (0.697) & (0.716) & (0.695) & (0.658) \\
Observations & 161 & 161 & 146 & 158 \\
\midrule
 \multicolumn{5}{c}{\textbf{B. Overall policy support}} \\
\midrule
Own characteristics  & -0.269*** & 0.072 & -0.273*** & -0.148* \\
 & (0.087) & (0.082) & (0.104) & (0.084) \\
 oProbit thresholds \\
\hspace{0.4cm} 1 & -0.341 & -1.982*** & -2.189*** & -1.131* \\
 & (0.702) & (0.719) & (0.759) & (0.656) \\
\hspace{0.4cm} 2 & 0.164 & -1.534** & -1.511** & -0.041 \\
 & (0.709) & (0.711) & (0.740) & (0.654) \\
\hspace{0.4cm} 3 & 0.913 & -0.882 & -0.500 & 0.926 \\
 & (0.712) & (0.701) & (0.721) & (0.659) \\
 &  &  &  &  \\
Observations & 161 & 161 & 146 & 158 \\
\midrule
 \multicolumn{5}{c}{\textbf{C. Effectiveness beliefs}} \\
\midrule
 &  &  &  \\
Own characteristics & -0.029 & -0.055 &-& -0.023 \\
 & (0.068) & (0.063) && (0.089) \\


 &  &  &  \\
Observations & 161 & 161 &-& 158 \\
 \bottomrule
\end{tabular}
\end{center}
\scriptsize \textbf{Notes:}
Each column in each panel is a separate ordered probit regression that controls for the stage at which each policy was displayed, whether arguments in favor of or opposed to the interventions were mentioned first, the logarithm of the duration the subjects took to complete the sample, age, gender, and a dummy for each level of education. We did not elicit beliefs about the effectiveness of the retirement savings mandate. Standard errors clustered by subjects.
\end{table}


\newpage
\section{Experiment design and implementation details}\label{instructions}

\subsection{Experiment design: comprehensive description}\label{experimentFullDetails}\label{additionalElicitations}


The experiment consists of an online component and a laboratory component. Each encompasses multiple stages. Table \ref{timingOverview} provides an overview, with stages listed in the order they were presented to subjects. Appendix sections \ref{instructionsOnline} and \ref{labInstructions} present verbatim translations of all instructions and questions, which we presented in German.


\begin{table}[htbp!]
\caption{\textbf{Stages of the experiment.}\label{timingOverview}}\setstretch{1}
\centering
\begin{tabular}{lc}
\toprule


\textbf{Online component} \\
1. Decision lists on intertemporal and risky choice		\\
2. Non-incentivized questions 			\medskip\\

\textbf{Laboratory component} \\
1. Constructing Chooser's opportunity sets	\\
2. \textit{Additional decisions}				\\
\quad 2.1  Surrogate choices 				\\
\quad 2.2  Attention test						\\
\quad 2.3  Belief elicitation					\\
\quad 2.4  Choice for oneself					\\
\quad 2.5  Altruism / spite elicitation			\\
3. \textit{Real-world policies survey}			\\
\quad 3.1 Policy judgments	 				\\
\quad 3.2 Questions about reasons for impatient choice \\
\quad 3.3 Elicitation of characteristics related to real-world policies 			\\
4. Memory test	 					\\
\bottomrule
\end{tabular}
\end{table}


\paragraph{Online component}
The main part of the online component presents the six decision lists to elicit Choice Architects' own intertemporal choices, as  described in Section \ref{paradigm} of the main text. These are intermingled with six decision lists concerning risk taking. In these lists, subjects decide between winning amount $y$ with probability $p$ and 0 otherwise, or a sure amount $z\in\{1,2,3,4,5,6\}$. We use $(y,p)\in\{(10,0.5),(6,0.7),(8,0.5),(5,0.8)$, $(20,0.2),(13,0.3)\}$.\label{fnOnlineRisk} The online component concludes with a battery of non-incentivized questions concerning age, gender, number of credit cards, and monthly expenses. Next, subjects select one of the four self-descriptive statements in panel B of Table \ref{chooserInfo}, and make a selection from four similar statements concerning risk preferences. Subjects then provide answers to the non-numerical questions about risk and time preferences described in \citet{Falketal.Validated}. Finally, subjects provide information about the faculty at which their main course of study is offered, about the Bundesland in which they obtained their Abitur,\footnote{The \emph{Abitur} is the German university entrance qualification.} about their GPA in the Abitur exam, about their Abitur grade in mathematics, about their Abitur grade in German language, as well as about whether they had enrolled in honors classes in mathematics and / or German.


\paragraph{Laboratory component}

After reading general instructions about the experiment and passing a comprehension check, subjects make decisions in the following stages.

\textit{1. Constructing opportunity sets.} Subjects read instructions about this stage of the experiment and complete two comprehension checks. Subsequently, in each of eleven rounds, Choice Architects first construct an opportunity set for the Chooser, and then reveal their beliefs about the extent to which receiving a specific subset rather than the full menu affects the Chooser's well-being, as described in Section \ref{paradigm} of the main text.

\textit{2.1 Surrogate choices.} In each of eleven rounds, Choice Architects make surrogate choices, and subsequently reveal their beliefs about the extent to which receiving the chosen option rather than the full menu affects the Chooser's well-being. The rounds correspond to the six menus used for intertemporal decisions, and the two menus used for the Induced Chooser Preferences condition. Subjects make surrogate choices concerning menu 5 four times, once for each of the Chooser self-descriptions in Table \ref{chooserInfo}. Subjects do not see separate instructions for this part. Instead, in each round, subjects read ``in this round you can make exactly one option available to the future experiment participant.'' The options are shown in a list from which the subject selects one. This part also removes the possibility of providing advise to the Chooser by the click of a button.

\textit{2.2 Attention test.} Subjects answer the eight attention test questions reproduced and analyzed in Appendix Section \ref{attentionTest}. The instructions at the beginning of the online component inform subjects that their performance could completely determine their payment from the study. The instructions do not reveal any information about the test's content or focus, except that it will be about ``what happened during this part.''

\textit{2.3 Belief elicitation.} Subjects read instructions concerning this stage and complete a comprehension check. In each of 11 rounds, they then reveal their beliefs about the distribution of the choices ten previous experiment participants have made when allowed to choose from the menus without restrictions, as described in Section \ref{paradigm} of the main text. These are the same 11 rounds as in stage 2.1.

\textit{2.4 Choice for oneself.} Subjects proceed through 8 rounds in which they select an option for themselves from each of the six menus in Table \ref{choiceSets}. Either menu 1 or menu 2 is subject to front-end delay (the same menu for which the choice set construction decision was subject to front-end delay). Subjects also make choices for themselves for each of the two rounds of the Induced Chooser Preferences condition. For these rounds, each subject is assigned one of the induced exchange rates $\hat{\delta}_i \in \{0, 0.5, 1\}$, which applies to both rounds.

\textit{2.5 Altruism / spite.} Subjects decide whether to costlessly increase another Chooser's payment by \EURdig1, leave it unchanged, or decrease it by \EURdig1.

\textit{3.1 Policy judgments.} Subjects rate four policy proposals concerning taxes on sugary drinks, alcohol, and tobacco, as well as about restrictions on short-term, high-interest loans. The tax policies concern Switzerland. We ask subjects to assume that the tax policies would be budget neutral. For each policy, we elicit the extent to which the subject supports or opposes its implementation in Switzerland. We also elicit beliefs about how the policy would change the welfare of the average citizen. The question about the welfare effects of alcohol taxes concerns adolescents and young adults rather than the average citizen, but is otherwise identical.

\textit{3.2 Questions about reasons for impatient choice and about reasons for restricting choice.} Subjects first answer four questions about whether Choosers may have difficulties making (good) choices.\footnote{The questions are the following. Suppose an experiment participant can choose between the following options: Option A: Get \EURdig0 today, \EURdig10 in half a year, Option B: Get \EURdig4 today, \EURdig0 in half a year. 1. How easy do you think it is for the future experiment participant to know which of these two options is best for him? 2. How many out of 10 participants will choose the option that is really best for them? 3. How many out of 10 experiment participants will choose an option other than the one they actually wanted to choose?
Suppose an experiment participant chooses ``Option A: \EurDig0 today, \EurDig10 in 6 months from today''. Given the experiment participant chooses this option, is this a good or bad choice for this experiment participant? 4. Suppose the experiment participant chooses ``\EurDig4 today, \EurDig0 in 6 months from today''. Given the experiment participant chooses this option, is this a good or bad choice for this experiment participant?} Second, subjects provide  judgments about the plausibility of six potential reasons for impatient choice listed and analyzed in Appendix Section \ref{nonIncentivized}. Third, in each of five rounds, subjects are shown their own decisions from the choice set construction stage and are asked an open-ended question as to why they constructed the choice set the way they did. Four of these rounds correspond to the four Chooser types of Table \ref{chooserInfo} (subjects are shown the corresponding Chooser statement). The remaining round corresponds to a decision Choice Architects have made for a Chooser who would be selected at random.


\textit{3.3 Elicitation of characteristics related to real-world policies.} Subjects provide information on their body mass index, their average alcohol consumption, their frequency of binge drinking, their smoking status and cigarette consumption, as well as whether they have ever taken out a short-term, high-interest loan, and if so, how quickly they repaid it. Subjects also rate their own political orientation on a left-right spectrum.


\textit{4. Memory test.} Subjects are asked to reproduce their answers for four of the multiple-decision lists from the online component. Subjects learn of this test immediately before it begins. They receive \EURdig2 for exact replication.  See Appendix Section \ref{memoryTest} for questions and performance.

\label{timingAppendix}\label{implementationAppendix}


\paragraph{Order}

Half of Choice Architects, selected at random, proceed through the laboratory component in the order listed above, while the other half complete Stage 2.3 first.\footnote{Only Choice Architects who reveal beliefs before constructing opportunity sets are in the Choice Distribution Information treatment, because the value of belief elicitation after information provision is debatable.} Within each of these stages, the order in which subjects proceed through the conditions is individually randomized. Questions regarding real world policies, in contrast, are presented in the following order for all subjects: 1. sugary drinks taxes, 2. alcohol taxes, 3. tobacco taxes, 4. restrictions on short-term, high-interest lending.


\paragraph{Implementation} All instructions are displayed on-screen. We intersperse them with four comprehension checks which subjects must pass in order to continue with the study, concerning (i) the incentive scheme and Induced Chooser Preferences condition, (ii) the manner in which Choosers rank and obtain outcomes, (iii) choices concerning Choosers in Stages 1 and 2.1, and (iv) the belief elicitation in Stage 2.3. Each comprehension check consists of at least five statements with at least two answer options each. The subject must select the correct response to each statement. In case of a mistake, the subject only learns that one or more of her responses were incorrect, but not which ones.  Accordingly, it is exceedingly unlikely that subjects pass the comprehension checks by luck or by trial and error.  Subjects who do not pass are referred back to the instructions (all subjects eventually passed).

All incentive payments are processed through PayPal; all transaction fees were paid by the researchers. The invitation email informs subjects of this fact and asks them to open a PayPal account if they do not already possess one.\footnote{PayPal accounts are free.} Subjects received individualized links to ensure they would continue with the correct survey (implemented in Qualtrics) even though they completed the online component on a different machine than in the laboratory component.

Prior to fielding our laboratory experiment, we conducted pilot experiments on Amazon Mechanical Turk. Data from each pilot are consistent with our conclusions concerning projective paternalism.


\subsection{Sessions}
\label{experimentImplementation}
We ran the experiment in 16 sessions at the Cologne Economics Research Lab. All materials were presented in German. The same head research assistant was present in every session. In each session, two additional research assistants were present to help. After the main sessions were concluded, we conducted five sessions to implement the Choice Architects' decisions that affected a Chooser. Each Chooser was presented with a single menu. Payments were determined according to Choosers' rankings of the options and the decisions the matched Choice Architects had made for the corresponding menu. While we do not use any data from the Chooser sessions in our analysis, we report the distribution of Choosers' choices in Appendix Section \ref{ChooserChoices}. Table \ref{sessionDates} lists the details of each session. Dates and times reflect availability of the lab and the research assistant.


\begin{table}[H]
\setstretch{1}
\begin{center}
\caption{\textbf{Session details.} \label{sessionDates}}
\begin{tabular}{ccr}
\toprule
\multicolumn{3}{c}{Choice Architect sessions}\\
Session       & Date    & Time    \\
\midrule
1              & 6/14/18 & 4 pm     \\
2              & 6/15/18 & 4 pm     \\
3              & 6/19/18 & 10 am    \\
4              & 6/19/18 & 1 pm     \\
5              & 6/19/18 & 3.30 pm \\
6              & 6/20/18 & 10 am    \\
7              & 6/20/18 & 1 pm     \\
8              & 6/20/18 & 3.30 pm  \\
9              & 6/26/18 & 10 am    \\
10             & 6/26/18 & 1 pm     \\
11             & 6/26/18 & 3.30 pm \\
12             & 6/27/18 & 1 pm     \\
13             & 6/27/18 & 3.30 pm \\
14             & 7/3/18  & 1 pm     \\
15             & 7/3/18  & 3.30 pm  \\
16             & 7/4/18  & 2 pm     \\
\bottomrule \\
\\
\toprule
\multicolumn{3}{c}{Chooser sessions}\\
Session       & Date    & Time    \\
\midrule
1              & 7/10/18 & 2 pm     \\
2              & 7/10/18 & 3.30 pm \\
3              & 7/11/18 & 2 pm     \\
4              & 7/11/18 & 3.30 pm \\
5		& 10/1/18 & 2pm \\
\bottomrule
\end{tabular}
\end{center}
\end{table}


\newpage
\subsection{Instructions for the online component} \label{instructionsOnline}
\setstretch{1}

\textrm{\textit{[Horizontal lines represent screen breaks.]}}


{\sffamily \footnotesize  {\setlength{\parindent}{0pt}

\begin{center}

\noindent \hrulefill

\textbf{\normalsize Technical Check}\bigskip

To test whether your computer can display the study correctly, please copy the following number into the field below.\bigskip

[Number]

\emptybox[0.5\textwidth]{0.5cm}
\end{center}

If you do not see the number above, make sure Javascript is enabled on your web browser. Alternatively, use a different web browser.

\noindent \hrulefill
\begin{center}

This is a research study by the University of Cologne and the University of Toronto.\bigskip

\textbf{\normalsize Parts}

This study has two parts.\bigskip

\textbf{Online Part}

Finish this part until [Date] (by midnight). This will only take a few minutes. \bigskip

If you do not complete the online part, you will not be allowed to participate in the laboratory part.\bigskip


\textbf{Laboratory Part}

Please appear in the laboratory at the time mentioned in the invitation email. This part will take between 1 and 2 hours.\bigskip

\textbf{\normalsize PAYMENT}

The payment for your participation in this study consists of two parts:\bigskip

\textbf{Base payment \EURdig 12,50}\bigskip

\textit{[For sessions 1 and 2, the base payment was 9.50]}

(\EURdig4.50 for the appearance at the laboratory part, and \EURdig8 for the completion of the laboratory part)\bigskip

\textbf{Bonus payment between \EURdig0 and \EURdig15}\bigskip

You will receive your base payment as soon as you have completed the laboratory part of the study.\bigskip

The exact amount of your bonus payment, as well as the time at which you will receive it, depends on your decisions, as well as on chance.\bigskip

The bonus payment and \EURdig5 of the base payment will be transferred via PayPal. For this purpose, you will provide your email address for the referral to the laboratory staff.\bigskip

{\small If you finish the online part but do not appear in the laboratory, you are entitled to pick up \EurDig2 at Prof. Ockenfels's office on weekdays between 10 a.m. and 12 noon until one week after the laboratory part's date.}

\noindent\hrulefill

\textrm{\textit{At this stage, the subject gives informed consent. If no consent is given, the study is terminated.}}


\end{center}
}}


{\sffamily \footnotesize

\noindent\hrulefill
\begin{center}
\textbf{\normalsize Instructions for the online part}\bigskip

\textbf{IMPORTANT: What happens in the online part of this study has no influence on what will happen in the laboratory part of the study.}\bigskip

In this part of the study you will participate in 12 decision rounds. Then you will answer some questions about yourself.\bigskip

In each round you will see a list of six decisions, for example as follows:

	\noindent\fbox{
	\parbox{0.45\textwidth}{
		\setlength{\tabcolsep}{10pt}
		\begin{tabular}{cccc}
Option A1	& $\bigcirc$ & $\bigcirc$ & Option B1 \\
Option A2	& $\bigcirc$ & $\bigcirc$ & Option B2 \\
Option A3	& $\bigcirc$ & $\bigcirc$ & Option B3 \\
Option A4	& $\bigcirc$ & $\bigcirc$ & Option B4 \\
Option A5	& $\bigcirc$ & $\bigcirc$ & Option B5 \\
Option A6	& $\bigcirc$ & $\bigcirc$ & Option B6 \\
		\end{tabular}\vspace{-0cm}
	}
}\bigskip

These options will be replaced by specific amounts of money.\bigskip

Your task is to select the option you truly prefer on each line. \bigskip

With 25\% probability, your total bonus payment will be determined by a single decision in one such list.  In this case you will receive exactly what you have chosen on the corresponding line. (Which list and which decision this will be will be randomly decided by the computer at the end of this study.)
\bigskip

You should make every decision as if it were the one that counts - because it might be.
\end{center}
\noindent\hrulefill
\\
\noindent\textbf{Bonus payment for this part}

\noindent There is a 25\% chance that your bonus payment from this study will be determined exclusively by this part of the study. (With the remaining probability of 75\% it will be determined by a decision you will make in the laboratory part.)\bigskip

\noindent In this case, at the end of the whole study, the computer will randomly select one of the decision rounds, and one decision from that round. You will receive exactly what you have chosen in this decision.\bigskip

\noindent So it is in your interest to make every decision as if it were the one that counts because it may be!

\noindent\hrulefill

\noindent \textbf{Payments that depend on chance}

\noindent Some options you can choose depend on chance (such as ``Get \EurDig X with p\% probability").\bigskip

\noindent If you choose such an option, and it is randomly selected to determine your bonus payment, the following will happen. At the end of the laboratory part of this study, the study director will provide you with a die. You will be able to throw it once. The resulting number determines your final payout.\bigskip

\noindent \textbf{Late Payments}

\noindent Some options you may choose involve receiving a certain amount of money at a given future date. This date can be up to six months after the laboratory part.\bigskip

\noindent On behalf of the Ockenfels research group, we guarantee that if you decide for such an option, we will transfer exactly the specified amount at the specified time by PayPal.

\noindent\hrulefill

\begin{center}
{\normalsize\textbf{The online part of this study starts now.}

Your decisions and answers \textbf{will not} affect what will happen in the laboratory part of this study.\bigskip

Please click NEXT.}
\end{center}


\noindent\hrulefill

\textrm{\textit{[Decisions regarding time. The parameters $X$ and $Y$ are listed in footnote \ref{fnOnlineTime} of the main text. ]}}\\ \bigskip

\noindent Please select an option on each line according to your real preference.\bigskip

\noindent  I would rather have \ldots
\begin{center}
	\noindent\fbox{
	\parbox{0.9\textwidth}{

		\setlength{\tabcolsep}{2pt}
		\begin{tabular}{p{0.4\textwidth}ccp{0.4\textwidth}}
			\ldots \EurDig$X$ on the day of the laboratory part & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 1 month after the laboratory part \\
			\ldots \EurDig$X$ on the day of the laboratory part & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 2 months after the laboratory part \\
			\ldots \EurDig$X$ on the day of the laboratory part & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 3 months after the laboratory part \\
			\ldots \EurDig$X$ on the day of the laboratory part & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 4 months after the laboratory part \\
			\ldots \EurDig$X$ on the day of the laboratory part& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 5 months after the laboratory part \\
			\ldots \EurDig$X$ on the day of the laboratory part& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 6 months after the laboratory part \\
		\end{tabular}\vspace{-0cm}
	}
}\bigskip
\end{center}


\noindent\hrulefill

\noindent \textrm{\textit{[Decisions regarding risk. The parameters $X$ and $Y$ are listed in Appendix Section \ref{fnOnlineRisk}.]}}\\


\noindent Please choose one option on each line, depending on what you really prefer (all payments for this round will be made on the day of the lab experiment)

\noindent I would rather have \ldots


\begin{center}
	\noindent\fbox{
	\parbox{0.9\textwidth}{

		\setlength{\tabcolsep}{2pt}
		\begin{tabular}{lccl}
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\%
			& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $6$ with certainty \\
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\%
			& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $5$ with certainty \\
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\% & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $4$ with certainty \\
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\% & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $3$ with certainty \\
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\%& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $2$ with certainty \\
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\%& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $1$ with certainty \\
		\end{tabular}\vspace{-0cm}
	}
}\bigskip
\end{center}


\noindent\hrulefill

\begin{center}
\textbf{\normalsize Questions about yourself.}\bigskip

\noindent To conclude the online part, we would like to ask you some questions about yourself.\bigskip

\textbf{Please answer truthfully.}\bigskip

Your answers will not affect your payment or what will happen in the laboratory part of this study.\bigskip

\noindent\hrulefill
\end{center}

\noindent What is your gender?\\
\noindent \textit{[male; female; not listed above (e.g. genderqueer); prefer not to say]} \\

\noindent How old are you?\\
\noindent \textit{[18 - 90]}

\noindent\hrulefill

\noindent How many credit cards do you hold?\\
\noindent \textit{[1;\;2;\;3;\;4;\;5;\;6;\;7;\;8;\;9;\;10;\;$>$10;\;prefer not to say]} \\

\noindent How much credit card debt do you currently have (in total, in Euros, across all your credit cards)?\\
\noindent \textit{[;\;0;\;1--250;\;251--500;\;501--750;\;751--1000;\;1001--1500;\;1501--2000;\;2001--3000;\;3001--5000;\;5001--10000;\;$>$10000]} \\

\noindent How much money do you spend on average per month (including rent, food, mobility, etc.)?\\
\noindent \textit{[\EURdig\;0 - \EURdig\;50; \EURdig\;50 - \EURdig\;100; \EURdig\;100 - \EURdig\;150; \EURdig\;150 - \EURdig\;200; \EURdig\;200 - \EURdig\;250; \EURdig\;250 - \EURdig\;300; \EURdig\;300 - \EURdig\;350; \EURdig\;350 - \EURdig\;400; \EURdig\;400 - \EURdig\;450; \EURdig\;450 - \EURdig\;500; \EURdig\;500 - \EURdig\;600; \EURdig\;600 - \EURdig\;700; \EURdig\;700 - \EURdig\;800; \EURdig\;800 - \EURdig\;900; \EURdig\;900 - \EURdig\;1000; \EURdig\;1000 - \EURdig\;1250; \EURdig\;1250 - \EURdig\;1500; \EURdig\;1500 - \EURdig\;1750; \EURdig\;1750 - \EURdig\;2000; \EURdig\;2000 - \EURdig\;2500; \EURdig\;2500 - \EURdig\;3000; more than \EURdig\;3000; I prefer not to say] }

\noindent\hrulefill

\noindent Please select the statement that describes you best \textrm{\textit{[Choices were displayed in individually randomized order.]}} \\
\noindent \textit{[I am a patient person. I am happy with this. (I often forego things in the present with regard to the future.); I am a patient person. I often regret my decisions. (Perhaps too often, I forego things in the present with regard to the future.); I am an impatient person. I am happy with this. (I rarely forego things in the present with regard to the future.); I am an impatient person. I often regret my decisions. (Perhaps too rarely, I forego things in the present with regard to the future.)]}\\

\noindent Please select the statement that describes you best \\
\noindent \textit{[I am a risk-taker. I am happy with that. (I like to take risks.); I am a risk-averse person. I am happy with that. (I try to avoid risks if possible.); I am a risk-taker. I often regret my decisions. (I might be taking too many risks.); I am a risk-averse person. I often regret my decisions. (I might be trying to avoid risks too much.)]}

\noindent\hrulefill

\noindent Please select the statement that describes you best: I often do without things so that I can afford more later.\\
\noindent \textit{[Absolutely not like me; Very little like me; Not really like me; Neutral; A little like me; Very similar to me; Absolutely like me]}\\

\noindent Are you generally a person who takes risks or do you try to avoid them?\\
\noindent \textit{[I try extremely hard to avoid risks; I try pretty hard to avoid risks; I try a little bit to avoid risk; I don't dislike taking risks; I'm quite willing to take risks; I'm extremly willing to take risks]}\\

\noindent Please select the statement that describes you best: I tend to put things off until later, although it would be better to do them right away.\\
\noindent \textit{[Absolutely not like me; Very little like me; Not really like me; Neutral; A little like me; Very similar to me; Absolutely like me]}\\

\noindent\hrulefill

\noindent At which faculty do you study?\\
\noindent \textit{[Faculty of Economics, Management and Social Science; Faculty of Law; Faculty of Medicine; Faculty of Philosophy; Faculty of Mathematics and Natural Sciences; Faculty of the Humanities; I am not a student]} \\

\noindent Which state conferred your Abitur?  \\
\noindent \textit{[Baden-W\"urttemberg; Bayern; Berlin; Brandenburg; Bremen; Hamburg; Hesse; Mecklenburg-Vorpommern; Niedersaxen; Nordrhein-Westfalen; Rheinland-Pfalz; Saarland; Sachsen; Sachsen-Anhalt; Schleswig-Holstein; Th\"uringen; I received the International Baccalaureate; I do not have an Abitur; I prefer not to say]} \\

\noindent What was your Grade Point Average in the Abitur? \\
\noindent \textit{[1.0, 1.1, 1.2, \ldots, 3.9, 4.0; I do not have an Abitur; I do not remember; I prefer not to say]} \\

\noindent What was your Abitur grade in Mathematics? \\
\noindent \textit{[15 points (1+), 14 points (1), 13 points (1-), 12 points (2+), 11 points (2), 10 points, (2-), \ldots, 3 points (5+), 2 points (2), 1 point (2-), 0 points; I do not have an Abitur; I do not remember; I prefer not to say]} \\

\noindent What was your Abitur grade in German? \\
\noindent \textit{[15 points (1+), 14 points (1), 13 points (1-), 12 points (2+), 11 points (2), 10 points, (2-), \ldots, 3 points (5+), 2 points (2), 1 point (2-), 0 points; I do not have an Abitur; I do not remember; I prefer not to say] }\\

\noindent Have you taken an honors class in Mathematics in high school (Leistungskurs im Abitur)?\\
\noindent \textit{[Yes; No; I do not have an Abitur] }\\

\noindent Have you taken an honors class in German in high school (Leistungskurs im Abitur)?\\
\noindent \textit{[Yes; No; I do not have an Abitur]} \\

\noindent\hrulefill


\begin{center}
\textbf{\normalsize This is the end of the online part of this study}\bigskip

\textbf{\normalsize Please arrive at the laboratory on time.}\bigskip

This is a study of individual decision making. Therefore, please do not discuss this study with other people. \bigskip

{\normalsize  \textbf{Please close this browser window.}}\bigskip

(If you leave this window open, the laboratory part of this study will not start for you.)
\end{center}
\noindent\hrulefill

\newpage
\subsection{Instructions for the laboratory component}\label{labInstructions}

\begin{center}
	\textbf{\normalsize Laboratory Part}\bigskip


Please enter your personal experiment code to ensure that you are proceeding with the correct questionnaire.\footnote{Each participant received an individual number in the invitation email. They were asked to sit at the computer terminal corresponding to their number. That computer contained the individual-specific link to the qualtrics survey that the subject had started in the online part. This mechanism ensures that subjects continue with their own survey, without requiring the storage of any identifiable data within the qualtrics survey.}\\ \medskip

\emptybox[0.5\textwidth]{0.5cm}

 \noindent\hrulefill


Please enter the password provided by the experiment staff to start the laboratory part of this study.\footnote{Subjects could begin the laboratory part only after every subject was seated at the correct terminal. At that stage, the experiment staff supplied the password required to continue.}\bigskip

\emptybox[0.5\textwidth]{0.5cm}

\noindent\hrulefill

	\textbf{\normalsize Instructions}\bigskip

Please read carefully.\\

This study contains multiple comprehension tests.\\

For simplicity, this study uses male pronouns throughout. They refer to both genders.

\noindent\hrulefill
\end{center}

\noindent \textbf{\normalsize Payment for this study}\\

\noindent The laboratory component of this study consists of 3 parts. Your decisions influence not only your own pay, but also that of future experiment participants. The study ends with some opinion questions, and some questions about yourself.\\

\noindent \textbf{Affecting your own payment}\\
\noindent There is a 75\% chance that the lab component of the study will determine your bonus payment. (With the remaining 25\% probability, your bonus payment will be determined by the online component.)\\

\noindent In this case your payment will be determined by exactly one of the three parts of the lab component. At the end of the study, the computer will randomly select a part and a decision you made in that part. This decision will be the only one that determines your bonus payment. \bigskip

{\centering So you should make every decision as if it's the one that counts - because it may be! \par}

\noindent\hrulefill

\noindent \textbf{Affecting the payment of other experiment participants}\\

\noindent A part of this study consists of decisions that affect a future experiment participant. You will be able to influence that person's decision options and bonus payment.\\

\noindent At the end of this study, the computer selects \textit{exactly one} decision you have made in this part. With a 1 in 4 chance we will match you with a future experiment participant. Your decisions in this study will then affect that person exactly as you have determined.\\

\noindent The future experiment participant's options for the bonus payment are determined entirely by the single decision of yours that the computer has randomly selected to be carried out.\\

\noindent You are the only person who determines the options of this future experiment participant. The future experiment participant will not make decisions that affect others; all his decisions will only affect himself.\\

\noindent None of the options that may determine your own payment in this study have been influenced by anyone else.\\

\noindent IMPORTANT: There is NO DECEPTION in this study. We will conduct the partner studies with the future experiment participants within the next 30 days and your decisions will affect future experiment participants with exactly the stated probability.\\

\noindent Anything else would violate the Ethics Protocol (UT36180) under which this study is conducted.\\

\noindent\hrulefill

\noindent Some rounds are about money that you or the other experiment participant can receive at different times. Other rounds are about gold and silver coins. We now explain these one after the other.\bigskip

{\centering\textbf{\normalsize Money at different points in time}\par}

\noindent In every decision concerning money at different points in time, the following choice will be made available (both for the future experiment participant, and for you):\bigskip

{\centering	Standard option:\\
	Receive a bonus of \EURdig0 today and \EurDig15 in 6 months.\par}\bigskip

\noindent Alternative options such as the following may also be available:

{\centering Receive a bonus of \EURdig X today and \EurDig Y in 6 months.\par}\bigskip

	(X and Y will be replaced by concrete amounts) \bigskip

\noindent On behalf of the Ockenfels research group, we guarantee that if an experiment participant chooses such an option, we will transfer exactly the specified amount at the specified time via PayPal.

\noindent\hrulefill


{\centering \textbf{\normalsize Gold and silver tokens}\par}\bigskip

\noindent Some decisions in this study concern gold and silver tokens. If an experiment participant is paid with gold and silver tokens, the following will happen.\bigskip

\noindent We will exchange all tokens into Euros and transfer them to the PayPal account of the experiment participant on the day of the study.\bigskip

\noindent \textbf{Value of gold and silver tokens}\bigskip

 {\centering Each \textbf{gold token} is worth exactly \textbf{\EURdig1}.\par}\bigskip

\noindent The value of the silver tokens varies for different experiment participants.\bigskip

{\centering For \textbf{1/3} of the participants the value of a \textbf{silver token is \EURdig1}. \\For another \textbf{1/3} it is \textbf{\EURdig0.5}.\\ And for the last \textbf{1/3} it is \textbf{\EURdig0}.\par}\bigskip

\noindent Before the future experiment participant makes a choice, he learns exactly how many Euros he will receive per silver token. If you make a choice regarding the tokens for your own bonus, you will also know exactly how many Euros you will receive per silver token.\bigskip

\noindent However, you will \textbf{not} know how many euros the future experiment participant will receive per silver token. He could get \EurDig1,\EurDig0.5, or \EurDig0 per silver token. All you know is that for the future experiment participant each of these cases is equally likely.

\noindent\hrulefill

\noindent To ensure that you have understood these elements of the study correctly, please click on all true statements (and only those).\footnote{All statements were displayed in individually randomized order. A subject could continue only if he had correctly marked all of the statements. In case of an error, the participant did not receive feedback about which of the statements was marked incorrectly.}

\begin{itemize}
	\item[$\square$] For 1/3 of the experiment participants the value of a silver token is \EurDig1. For another 1/3 it is \EurDig0.5. And for the last 1/3 it is \EurDig0. I will know which of them the future experiment participant will get, but the future experiment participant will not know.
	\item[$\square$] For 1/3 of the experiment participants the value of a silver token is \EurDig1. For another 1/3 it is \EurDig0.5. And for the last 1/3 it is \EurDig0. The future experiment participant will know which one of these he will get before making a choice, but I don't know which one he will get.
	\item[$\square$] My bonus payment is determined by three randomly selected decisions, one from each part of the study.
	\item[$\square$] My bonus payment is determined by exactly one decision from one part of the study.
	\item[$\square$] The options of the future experiment participant who is assigned to me with a 1 in 4 chance are completely determined by a single randomly selected decision of mine.
	\item[$\square$] All the decisions I make in the study do not affect anyone else, but determine my own payment.
\end{itemize}
{\centering If you feel that you have understood the instructions but still cannot continue, please raise your hand. \par}

\noindent\hrulefill

{\centering\textbf{\normalsize The three parts of the laboratory component of this study begin now.}\bigskip

\noindent You will receive the instructions for each part just before the corresponding part begins.\par}


\noindent\hrulefill
\begin{center}
\textbf{\normalsize Part 1 of 3}
\end{center}

{\centering
The decisions you make in this part will affect another experiment participant's bonus payment.\bigskip

This is the longest part of the study, and will take about twice as long as the other two parts.\bigskip

 Please make all decisions in this section carefully.\par}

\noindent\hrulefill

\noindent \textbf{How the future experiment participant is affected by your decisions in this part}\bigskip

\noindent This part has 25 rounds. Each round has two halves.\bigskip

\noindent The computer randomly selects one round from this part and one of the two halves within this round. Every round and every half is equally likely.\bigskip

\noindent The bonus payments and decision options of the future experiment participant will be determined by exactly this decision of yours.\bigskip

\noindent So you should make every decision as if it were the decision that will affect the future experiment participant. Because it may be!

\noindent\hrulefill

\noindent \textbf{How this part affects your own bonus payment}\bigskip

\noindent It is in your own interest to be attentive. At the end of this part there will be a test of what happened during this part. It is possible that your bonus payment for this study will be determined entirely by your performance on this test.\bigskip

\noindent We now explain what will happen in each of the two halves of each round.

\noindent\hrulefill

\begin{center}
\textbf{\normalsize First half of each round}
\end{center}

\noindent \textbf{Available and unavailable choice options}

\noindent The future experiment participant will be able to choose his bonus payment from a set of options as follows:\footnote{There is no natural-sounding translation of the expression `opportunity set' in German. We have used the semantically closest expression `Warenkorb'.  Its literal translation is `basket of goods'.}

\begin{center}
\textbf{Opportunity set X} \\ \medskip
		\begin{tabular}{|c|c|}
		\hline
			$\square$ &  Option A \\ \hline
			$\square$ &  Option B \\  \hline
			$\square$ &  Option C \\ \hline
		\end{tabular}\\ \medskip
(Options A, B, and C will be replaced by concrete payments.)
\end{center}

\noindent You decide whether all options should be available to the future experiment participant or whether one or more of them will be unavailable. (You must make at least one option available in each round.)\bigskip

\noindent We ask you to make these decisions with care. There are no right or wrong decisions. These decisions do not affect your own bonus payment.\bigskip

\noindent The details by which you will make the choice options of the future experiment participant available or unavailable will vary across the  rounds.

\noindent\hrulefill

\noindent \textbf{Why such decisions?}\bigskip

\noindent People have different views about influencing other people's choices and decisions.\bigskip

{\centering
	There are no right or wrong answers. We ask you to make exactly those decisions that reflect your genuine views.\par }

\noindent\hrulefill

\begin{center}
\textbf{\normalsize Messages to the future experiment participant}
\end{center}
\noindent In addition to making options available or unavailable, you will be able to send messages to future experiment participants if you wish. There are two types of messages in this study.\bigskip

\noindent \textbf{\normalsize Click messages}\\

\noindent In some rounds you will be able to click a button to advise the future experiment participant not to rank an option highly. If you do, the future experiment participant will see the following when making decisions about his options:

\begin{center}
\includegraphics[width=0.3\textwidth]{publicationFigures/comClick.png}
\end{center}
\textbf{\normalsize Free-form messages}\\

\noindent If you wish, you can also send a freely formulated message to the future experiment participant. In this case, the future experiment participant will see the following when deciding between his options:

\begin{center}
\includegraphics[width=0.4\textwidth]{publicationFigures/comMain.png}
\end{center}

\noindent\hrulefill

\noindent \textrm{\textit{[This screen is only displayed to participants in the Choice Distribution Information treatment]}}

\noindent \textbf{\normalsize How previous experiment participants have chosen from the options}\bigskip

\noindent In addition to making options available or unavailable, in each round you can click a button to view a table like the following.

\begin{center}
\begin{tabular}{|c|c|}
\hline
N1 of 100 previous experiment participants chose & \EURdig X1 today and \EURdig Y1 in six months \\ \hline
N2 of 100 previous experiment participants chose & \EURdig X2 today and \EURdig Y2 in six months \\ \hline
N3 of 100 previous experiment participants chose & \EURdig X3 today and \EURdig Y3 in six months \\ \hline
\end{tabular}
\end{center}

\noindent This table displays how 100 previous experiment participants have chosen between the options when all options were available (X1, Y1, N1, etc. will be replaced by numbers).


\noindent\hrulefill

\noindent \textbf{\normalsize How the future experiment participant will choose}\\

\noindent The future experiment participant will \textbf{not} see which of the options are available in an opportunity set and which are not.\\

\noindent Instead, he will see all options and rank them according to his preference, regardless of whether they are available or not. The participant then receives the available option that he has ranked the highest.\\

\noindent The experiment-participant will know that not all options may be available, and that he will receive the option he has ranked the highest  among those options that are available. This is all he knows about how his payments are determined.

\noindent\hrulefill

\noindent Here you can see how the future experiment participant can rank his options. We will ask him to put the option he most wants first and the option he least wants third (To try this, drag the options into the box on the right.)\\

\begin{center}
	\includegraphics[width=0.3\textwidth]{publicationFigures/Rank2.png}\\
\end{center}


\noindent \textbf{Example}\\

\noindent Suppose there are three options, A, B and C.\\

\noindent Also suppose you make option A unavailable for the future experiment participant.\\

\noindent For example, the future experiment participant might rank A first, then B, and C last. In this case, the experiment participant will receive B. The reason is that A is not available, and the participant ranks B higher than C.

\noindent\hrulefill

\noindent To ensure that you understand how your decisions affect the other experiment participant, please answer the following questions.\\

\noindent Each question concerns three options, A, B and C.\\

\noindent Assume that the future experiment participant ranks B at the top, followed by C, and that he ranks A lowest.\\

\noindent If all options are available, which will the future experiment participant receive? \\
\noindent \textit{[Option A, Option B, Option C.]} \\

\noindent If Option A is not available, which option will the future experiment participant receive? \\
\noindent \textit{[Option A, Option B, Option C.]} \\

\noindent If Option B is not available, which option will the future experiment participant receive? \\
\noindent \textit{[Option A, Option B, Option C.]} \\

\noindent If Option C is not available, which option will the future experiment participant receive? \\
\noindent \textit{[Option A, Option B, Option C.]} \\

\noindent If Option B and C are not available, which option will the future experiment participant receive? \\
\noindent \textit{[Option A, Option B, Option C.]} \\

\noindent\hrulefill
\begin{center}
\textbf{\normalsize Second Half of Each Round}
\end{center}
\noindent In the second half of each round you will see two opportunity sets according to which the choices of future experiment participants could be determined, such as in the following example. \\

\begin{center}
\begin{tabular}{cc}
Opportunity Set Right
&
Opportunity Set Left \\
	\begin{tabular}{|c|}
	\hline
	Option A \\
	Option B \\
	Option C \\
	\hline
	\end{tabular}
&
	\begin{tabular}{|c|}
	\hline
	Option A \\
	\st{Option B} \\
	Option C \\
	\hline
	\end{tabular}
\end{tabular}
\end{center}

\noindent In this example, all options are available in the opportunity set on the left. Option B is not available in the opportunity set on the right.\\

\noindent Your decision is
\begin{itemize}
	\item whether the future experiment participant should receive the option that he has ranked highest from the opportunity set on the left, or
	\item whether the future experiment participant should receive the option that he has ranked the highest from the opportunity set on the right; AND additionally the amount \EurDig Z should be added to / deducted from his base payment for the experiment.
\end{itemize}


\noindent You make several such decisions, for different values of Z, in a list such as this one:

\begin{center}
	\noindent\fbox{
	\parbox{0.8\textwidth}{
		The bonus payment of the future participant should be determined by \ldots \\
		\setlength{\tabcolsep}{2pt}
		\begin{tabular}{p{0.35\textwidth}ccp{0.35\textwidth}}
			\ldots opportunity set Left and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots opportunity set Right and his base payment  is raised by \EURdig 0.5. \\
			\ldots opportunity set Left and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots opportunity set Right and his base payment  is raised by \EURdig 0.3. \\
			\ldots opportunity set Left and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots opportunity set Right and his base payment  is raised by \EURdig 0.2. \\
			\ldots opportunity set Left and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots opportunity set Right and his base payment  is raised by \EURdig 0.1. \\
			\ldots &&& \ldots \\


		\end{tabular}
	}
}\bigskip
\end{center}

\noindent\hrulefill

\noindent \textbf{Implementation}\bigskip

\noindent If this half of a round is randomly selected for the implementation, the following will happen:\bigskip

\noindent The computer randomly selects one of the lines in the list. The decision you made in this line is carried out. \bigskip

\begin{center}
{Therefore, you should make every decision on each line as if it were the one that counts. Because it may be!}
\end{center}\bigskip

\noindent In some rounds Opportunity Set Left and Opportunity Set Right may be the same. This is intentional. In these cases too, please make a careful choice in each line.

\noindent\hrulefill

\noindent In the second half of each round we will also ask you:

\begin{center}
In your opinion, which opportunity set is better for the future experiment participants' own good?
\end{center}

\noindent This question refers to \textbf{your opinion} about which opportunity set is better for the future experiment participant's own good -- \textbf{regardless of whether the future experiment participant would agree with you or not!}\bigskip

\noindent\hrulefill

\noindent To ensure that you have understood these elements of the study correctly, please click on all true statements (and only those).\footnote{All statements were displayed in individually randomized order. A subject could continue only if he had correctly marked all of the statements. In case of an error, the participant did not receive feedback about which of the statements was marked incorrectly.}


\begin{itemize}
\item[$\square$] The computer will carry out all decisions I make in this part.
\item[$\square$] I can make some options available and others not, if I think that is right.
\item[$\square$] I can make all the options available if I think that is right.
\item[$\square$] I can make all but one option unavailable if I think that's right.
\item[$\square$] There are NO right or wrong decisions, I can make available or unavailable whatever I think is right (as long as at least one option is available for the future experiment participant).
\item[$\square$] There ARE right or wrong decisions, I can NOT just make available or unavailable whatever I think is right.
\item[$\square$] The future experiment participant will only see the options I make available and will not even see the options I make  unavailable.
\item [$\square$] The future experiment participant will rank all options. He will not know which ones are available or unavailable. His bonus payment is based on the option he has ranked most highly amongst those that are available.
\item[$\square$] The computer carries out ONE randomly selected decision. This decision is equally likely from the first half of a round as from the second half of a round. And each round is equally likely.
\end{itemize}

{\centering \small If you feel that you have understood the instructions but still cannot continue, please raise your hand. \par}
\noindent\hrulefill


\vspace{0.6cm}
\noindent \textbf{\normalsize First half of round $X$}\bigskip

\textrm{\textit{(Note: The subject is informed that they have been matched with another participant that's either impatient and unhappy, impatient and happy, patient and unhappy, patient and happy, or they are informed that they have been matched randomly.)}\\~\\}
\begin{center}
 If this round is implemented, it will concern an experimental participant who says about himself: \medskip \\

 I'm an impatient person. I often regret my decisions. (Perhaps too often, I forego things in the present with regard to the future.)
 \end{center}
 \medskip
    	\centering
\textsf{Which of the choice options will be available to the future participant?} \\
\textsf{(You must make at least one option available)}\\
\includegraphics[width = 0.6\textwidth]{publicationFigures/basicTreatment}\\
\bigskip
\textsf{If you have a message for the future participant, enter it here:\\}
\emptybox[0.65\textwidth]{1cm}


\textrm{\textit{(Note: For surrogate choices, subjects are shown  .)}} \\ \medskip


In this round you can make exactly one option available to the future experiment participant. Which option will that be?

 $\bigcirc$ \textbf{\EURdig X1} today \textbf{\EURdig Y1} in 6 months from today \\
 $\bigcirc$ \textbf{\EURdig X2} today \textbf{\EURdig Y2} in 6 months from today \\
 $\bigcirc$ \textbf{\EURdig X3} today \textbf{\EURdig Y3} in 6 months from today


\noindent\hrulefill
\\ \medskip
	\textbf{\normalsize  Second half of round $X$}

\textrm{\textit{(Note: The subject is informed that they have been matched with another participant that's either impatient and unhappy, impatient and happy, patient and unhappy, patient and happy, or they are informed that they have been matched randomly.)}} \\ \medskip

	If this round is implemented, it will concern an experimental participant who says about himself:\medskip

{I'm an impatient person. I often regret my decisions. (Perhaps too often, I forego things in the present with regard to the future.)}

 \includegraphics[width = 0.5\textwidth]{publicationFigures/welfareSelfStated}\\

\begin{center}
	\noindent\fbox{
	\parbox{0.8\textwidth}{
		The bonus payment of the future participant should be determined by \ldots \\ \bigskip
		\setlength{\tabcolsep}{2pt}
		\begin{tabular}{p{0.35\textwidth}ccp{0.35\textwidth}}
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is raised by \EURdig 1. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is raised by \EURdig 0.5. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is raised by \EURdig 0.2. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is raised by \EURdig 0.1. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is lowered by \EURdig 0.1. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is lowered by \EURdig 0.2. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is lowered by \EURdig 0.5. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is lowered by \EURdig 1.
		\end{tabular}\vspace{-0.5cm}
	}
}
\end{center}


\noindent\hrulefill \\
\textrm{\textit{[The subject proceeds through the remaining rounds of part 1 in the same fashion]}}\\
\noindent\hrulefill


\vspace{0.6cm}
\noindent \textbf{\normalsize First half of round $X$}\medskip

\begin{center}
This round concerns a random future experiment participant.
 \end{center}
 \medskip
    	\centering


In this round you can make exactly one option available to the future experiment participant. Which option will that be?

The bonus payment of the future experiment participant will be \ldots \medskip \\
 $\bigcirc$ \textbf{\EURdig X1} today \textbf{\EURdig Y1} in 6 months from today \\
 $\bigcirc$ \textbf{\EURdig X2} today \textbf{\EURdig Y2} in 6 months from today \\
 $\bigcirc$ \textbf{\EURdig X3} today \textbf{\EURdig Y3} in 6 months from today


\bigskip
\textsf{If you have a message for the future participant, enter it here:\\}
\emptybox[0.65\textwidth]{1cm}


\noindent\hrulefill
\\ \medskip
	\textbf{\normalsize  Second half of round $X$}


\begin{center}
This round concerns a random future experiment participant.
 \end{center}

  \includegraphics[width = 0.5\textwidth]{publicationFigures/welfareSelfStatedSurrogate}\\

\begin{center}
	\noindent\fbox{
	\parbox{0.8\textwidth}{
		The bonus payment of the future participant should be determined by \ldots \\ \bigskip
		\setlength{\tabcolsep}{2pt}
		\begin{tabular}{p{0.35\textwidth}ccp{0.35\textwidth}}
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is raised by \EURdig 1. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is raised by \EURdig 0.5. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is raised by \EURdig 0.2. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is raised by \EURdig 0.1. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is lowered by \EURdig 0.1. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is lowered by \EURdig 0.2. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is lowered by \EURdig 0.5. \\
			\ldots choice set Left, and his base payment remains unchanged
			& $\bigcirc$ & $\bigcirc$ & \ldots choice set Right and his base payment is lowered by \EURdig 1.
		\end{tabular}\vspace{-0.5cm}
	}
}
\end{center}


\noindent\hrulefill \\
\textrm{\textit{[The subject proceeds through the remaining rounds of part 2 in the same fashion]}}\\
\noindent\hrulefill


\flushleft
\noindent \textbf{Please answer all questions about this part} \\

\noindent If the computer selects this part to determine your payment, your bonus is calculated as follows: You receive \EURdig1 for each correct answer, and \EURdig0 for each incorrect answer.\\ \medskip

\noindent In some rounds I could remove options, but I could never add options for the future experiment participant.\\
\textit{[True, False]} \\ \medskip

For all options which the future experiment participant could receive, the early payment will always be on the day of the experiment. \\ \textit{[True, False]} \\ \medskip

For some options that the future experiment participant could have received, the late payment will occur up to 7 months after the day of the study.\\  \textit{[True, False]} \\ \medskip

In some rounds, I had to make a single option available to the future experiment participant; I could not make multiple options available, even if I wanted to.  \\
\textit{[True, False]} \\ \medskip

Some rounds concerned gold tokens and silver tokens. These rounds only concerned money that would be paid soon after the experiment, not money that would be paid only months after the experiment.\\
 \textit{[True, False]} \\ \medskip

Other experiment participants could receive \EURdig0.40, \EURdig0.50, or \EURdig0.60 per silver token, each with the same probability. \\\textit{[True, False]} \\ \medskip

In some lines of the decision lists, the base payment of the future participant could be increased. In other rounds it could be decreased.  \\\textit{[True, False]} \\ \medskip

If I made some options unavailable, this means that the other experiment participant cannot see that option, and therefore does not need to think about these options. \\
 \textit{[True, False]} \\ \medskip


\noindent\hrulefill

\begin{center}
\textbf{\normalsize  Part 2 of 3 }
\end{center}
This part has 12 rounds. Each round follows the same structure.\bigskip

Previous experiment participants decided between different options from shopping baskets of three options each as follows:\bigskip

\begin{center}
{ \textbf{Shopping basket X}}\bigskip

		\setlength{\tabcolsep}{2pt}
		\begin{tabular}{|p{0.025\textwidth}|p{0.1\textwidth}|}
		\hline
			$\square$ &  Option A \\ \hline
			$\square$ &  Option B \\  \hline
			$\square$ &  Option C \\ \hline
		\end{tabular}\vspace{0cm}
\end{center}

(Options A, B, and C were concrete payments.)\bigskip

In each round we show you a different shopping basket.
Your task is to estimate how previous participants chose between the options in the given shopping basket.\bigskip

(In one of the 12 rounds we ask you instead to estimate which of 4 descriptions each of the previous participants considered the best description of themselves.)\bigskip

\noindent\hrulefill

You will make these estimates as follows:\bigskip

On the left side of the graph below you see ``Participant'' written 10 times. Each represents one of the 10 experiment participants. You can move them using Drag \& Drop.\bigskip

On the right side you see three fields. Each of them is a choice option.\bigskip

Your task is to sort each of the 10 experiment participants on the left into one of the boxes on the right, depending on your estimate of how previous experiment participants actually chose from these options.\bigskip

If you estimate that X of the previous participants had selected option A, that Y had selected option B, and that Z had selected option C, place X of the ``Participants'' in the ``Option A'' container, Y of the ``Participants'' in the ``Option B'' container, and Z of the ``Participants'' in the ``Option C'' container.\bigskip

\begin{center}
\includegraphics[width=0.6\textwidth]{publicationFigures/beliefElicitation.png}
\end{center}

\noindent\hrulefill

\textbf{\normalsize How your estimate will affect your bonus payment}\bigskip

Your payment from this study could be determined solely by this part of the study! (The computer will randomly decide whether you will be paid for this or another part.)\bigskip

If so, the following will happen.\bigskip

The computer randomly draws a round from this part.\bigskip

We have data on how 10 previous experiment participants chose among these choice options. We compare your estimate with what the previous experiment participants actually chose.\bigskip

If your estimate for the selected round is correct, your bonus payment will be \EurDig10, which you will receive today via PayPal.\bigskip

If your estimate is incorrect compared to what previous experiment participants actually did, the following will happen.\bigskip

Suppose you have assigned too many experiment participants to one option and too few to another option (compared to what the previous experiment participants actually chose).\bigskip

We then take one of the fields ``experiment participant'' from one of the containers that has too many fields ``experiment participant'', and place it in one of the containers that has too few. We will do this until there are exactly as many ``experiment participants'' fields in each container as we actually observed with the 10 previous experiment participants.\bigskip

For each ``experiment participant`` field that we put in a different container in this way, we will deduct \EurDig1 from the \EurDig10 that you would receive if you had perfectly estimated the decisions of the previous experiment participants. The rest you will receive as a bonus payment.\bigskip

Therefore, you can expect that you will earn the most with this study if you think carefully about how the previous experiment participants actually decided and place the experiment participants in the containers accordingly.\\
\noindent\hrulefill

\textbf{Gold and silver coins}\bigskip

Some rounds involve decisions on gold and silver coins.\bigskip

Their estimate concerns former \textbf{experiment participants} who knew whether they would receive \EurDig1, \EurDig0.5, or \EurDig0 per silver coin \textbf{before} making their decisions.

\noindent\hrulefill

{ Please select all true statements. You can continue as soon as you have classified all statements correctly.}
\begin{itemize}
	\item[$\square$] I will make the most money in this part if I put all the experiment participants in the same container.
	\item[$\square$] I will make the most money in this part if I put the same number of experiment participants in each container.
	\item[$\square$] I will make the most money in this part by placing the experiment participants in the containers, according to my best estimate of how the previous experiment participants actually decided.
	\item[$\square$] If I put too many experiment participants in one container and too few in another (compared to what the previous experiment participants actually chose), I lose \EurDig1 for each experiment participant that I put in the wrong container.
	\item[$\square$] My answers in this section do not affect my payment.
	\item[$\square$] My payment from this study could be determined solely by this part of the study!
\end{itemize}

{\centering \small If you feel that you have understood the instructions but still cannot continue, raise your hand.\par}

\noindent\hrulefill

\begin{center}\textbf{\normalsize Part 3 of 3}
\end{center}
In this section you make decisions that only affect your own payment.\bigskip

This part has 8 rounds. You will make a selection in each round. At the end of the experiment, the computer randomly selects \textbf{exactly one} of your options. If this part determines your payment for this study, this option is the only one that counts.\bigskip


\begin{center}
So you should make every decision as if it were the one that counts. Because it maybe be!
\end{center}

\noindent\hrulefill
\begin{center}
\textrm{\textit{[In rounds corresponding to the Induced Chooser Preferences condition, subjects see the following screen.]}}
\end{center}
Please choose one of the following options, depending on what you actually prefer.

\begin{itemize}
	\item[$\bigcirc$] X1 gold coins, Y1 silver coins
	\item[$\bigcirc$] X2 gold coins, Y2 silver coins
	\item[$\bigcirc$] X3 gold coins, Y3 silver coins
\end{itemize}\bigskip

\begin{center}
\textbf{ Value of gold and silver coins}

Each gold coin is worth \EurDig1.

The value of the silver coins differs between the experiment participants. For you every silver coin is worth \EurDig 0.5.

This payment will be made today.
\end{center}

\noindent\hrulefill
\begin{center}
\textrm{\textit{[In rounds except those corresponding to the Induced Chooser Preferences condition, subjects see the following screen. In the round with front-end delay, the dates are `1 week from today' and `6 months plus a week from today']}}
\end{center}

Please choose one of the following options, depending on what you actually prefer.

\begin{itemize}
	\item[$\bigcirc$] \EurDig\;X1 today, \EurDig\;Y1 in 6 months
	\item[$\bigcirc$] \EurDig\;X2 today, \EurDig\;Y2 in 6 months
	\item[$\bigcirc$] \EurDig\;X3 today, \EurDig\;Y3 in 6 months
\end{itemize}\bigskip

\noindent\hrulefill

\textbf{Completion payment for future experiment participants}\bigskip

There is a 1 in 4 chance that you will be assigned to a second future experiment participant. This is another experiment participant than the one for which you have decided what choice options they should get.\bigskip

Like yourself, this future experiment participant will receive a base payment of \EurDig\;9.50 for the completion of the study.\bigskip

However, you can decide to change his base payment. The base payment of the future experiment participant will be exactly what you decide.\bigskip

\noindent\hrulefill

\begin{center}\textbf{What completion payment should the second future experiment participant receive?}\bigskip

(This is a person other than the one whose options you have made available or unavailable)
\end{center}

\begin{itemize}
	\item[$\bigcirc$] The future experiment participant should receive a base payment of \EurDig\;10.50 instead of \EurDig\;9.50.
	\item[$\bigcirc$] The future experiment participant should receive the planned base payment of \EurDig\;9.50.
	\item[$\bigcirc$] The future experiment participant should receive a base payment of \EurDig\;8.50 instead of \EurDig\;9.50.
\end{itemize}\bigskip

\noindent\hrulefill

\begin{center}\textbf{\normalsize Questions about your opinion and about yourself}\bigskip

The last part of this study consists of a questionnaire. Please answer the questions honestly. Your answers do not affect your payment or the payment of other people from this study.
\end{center}
\noindent\hrulefill

We would first like to ask your opinion on four policy proposals.\bigskip

The first three proposals concern Switzerland. That country is very similar to Germany in many aspects. Since Switzerland has only one tenth of Germany's population and is not a member of the European Union, policy changes in Switzerland have no direct effect on Germany.

\noindent\hrulefill

\textbf{Proposal 1: Taxes on high-sugar beverages}\bigskip

Several countries around the world levy taxes on beverages with a high sugar content (which is associated with obesity). These countries include Hungary, Ireland, Norway, the Philippines, the United Arab Emirates, Great Britain and others.\bigskip

There are  many overweight people in Switzerland, as in the vast majority of developed countries. \bigskip

What is your attitude towards a tax that would increase the price of sugary beverages in Switzerland by 20\% (income tax would be reduced so that the government would earn the same tax revenue as before)?\bigskip

Switzerland should \ldots

\textit{[definitely not introduce such a tax, probably not introduce such a tax, probably introduce such a tax, definitely introduce such a tax.]}
\bigskip

What do you think the effect of such a tax would be?\\ \bigskip
If such a tax were introduced, people in Switzerland would be on average\ldots

\textit{[significantly worse off, a little worse off, neither better nor worse off, a little better off, significantly better off.]}

\noindent\hrulefill

\textbf{Proposal 2: Taxes on alcoholic beverages}\bigskip

Binge drinking is the excessive consumption of alcoholic beverages with the aim of getting heavily drunk. Binge drinking is sometimes considered problematic. One reason is that binge drinking might be harmful to health.\bigskip

It is therefore being discussed whether  alcohol taxes should be increased in order to make binge drinking more expensive and correspondingly less frequent. Specifically, the price of cheap alcohol (spirits and cheap wines) could be increased disproportionately in percentage terms by charging the tax per liter of pure alcohol in the beverage.\bigskip

What is your attitude towards a tax that would increase the price of spirits and cheap wines in Switzerland by 50\%, on average (income tax would be reduced so that the government would earn the same tax revenue as before)?\bigskip

Switzerland should ...

\textit{[definitely not introduce such a tax, probably not introduce such a tax, probably introduce such a tax, definitely introduce such a tax.]} \\ \bigskip

What do you think the effect of such a tax would be?

If such a tax were introduced, adolescents or young adults in Switzerland would be on average...

\textit{[significantly worse off, a little worse off, neither better nor worse off, a little better off, significantly better off.]}

\noindent\hrulefill

\textbf{Proposal 3: Taxes on tobacco}\bigskip

The harmful effects on health and the addictive potential of smoking cigarettes have been proven scientifically.\bigskip

There is therefore a discussion as to whether taxes on cigarettes and other tobacco products in Switzerland should be further increased in order to reduce consumption and to deter young people more from smoking.\bigskip

What is your attitude towards a tax that would increase the price of cigarettes of other tobacco products in Switzerland by an average of half the current price (income tax would be reduced so that the government would earn the same tax revenue as before)?\bigskip

Switzerland should ...

\textit{[definitely not introduce such a tax, probably not introduce such a tax, probably introduce such a tax, definitely introduce such a tax.]}

What do you think the effect of such a tax would be?\bigskip

If such a tax were introduced, people in Switzerland would be on average...

\textit{[significantly worse off, a little worse off, neither better nor worse off, a little better off, significantly better off.]}

\noindent\hrulefill

\textbf{Proposal 4: Restrictions on short-term loans}\bigskip

Short-term loans have been available in Germany since 2010. Such loans typically finance consumption, ranging from \EurDig50 to \EurDig3000, and must be repaid after 30 or 60 days. \bigskip

The interest that can be charged on such loans is high, but limited by law. Therefore, people with very bad credit cannot obtain such loans, even if banks would be willing to grant such loans at very high interest rates and borrowers with very low credit ratings would be willing to pay very high interest rates for them. \bigskip

It is being discussed whether the restrictions should be loosened. Advocates argue that many people who really need the money will otherwise not get credit. Opponents argue that such loans are very expensive and people can end up debt cycles. \bigskip

How do you feel about that? The market for short-term loans in Germany should...

\textit{[be severely restricted (in this case, far fewer people can receive a short-term loan), be somewhat restricted (in this case, fewer people can get a short-term loan), remain unchanged, be somewhat liberalized (in this case, more people can get a short-term loan), be severely liberalized (in this case, far more people can get a short-term loan)]}

What do you think the effect of such liberalization would be?\bigskip

If this market were strongly liberalized, the average German would be...

\textit{[significantly worse off, a little worse off, neither better nor worse off, a little better off, significantly better off.]}


\begin{flushleft}


\noindent\hrulefill

\begin{center}


We would now like to ask you some questions about the experimental decisions\bigskip

The questions on this page concern the following scenario:\bigskip

Suppose an experiment participant can choose between the following options:
\begin{itemize}
	\item Option A: Get \EURdig0 today, \EURdig10 in half a year
	\item Option B: Get \EURdig4 today, \EURdig0 in half a year
\end{itemize}
\end{center}

How easy do you think it is for the future experiment participant to know which of these two options is best for him?\\
\textit{[Very easy, Rather simple, rather difficult, very difficult]}\\ \medskip

How many out of 10 participants will choose the option that is really best for them?\\
\textit{[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}\\ \medskip


How many out of 10 experiment participants will choose an option other than the one they actually wanted to choose?\\
\textit{[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}\\ \medskip


Suppose an experiment participant chooses ``Option A: \EurDig0 today, \EurDig10 in 6 months from today''. Given the experiment participant chooses this option, is this a good or bad choice for this experiment participant?\\
\textit{[Most likely bad, Very likely bad, Somewhat likely bad, Somewhat likely good, Very likely good, Most likely good]}\bigskip


Suppose the experiment participant chooses ``\EurDig4 today, \EurDig0 in 6 months from today''. Given the experiment participant chooses this option, is this a good or bad choice for this experiment participant?\\
\textit{[Most likely bad, Very likely bad, Somewhat likely bad, Somewhat likely good, Very likely good, Most likely good]}
\bigskip

\noindent\hrulefill

\begin{center}
Suppose an experiment participant can choose between the following options:
\begin{itemize}
	\item Option A: \EurDig0 today, \EurDig10 in 6 months from today, and
	\item Option B: \EurDig4 today, \EurDig0 in 6 months from today
\end{itemize}

Assume the experiment participant chooses the less patient option ``\EurDig4 today, \EurDig0 in 6 months from today''.\bigskip

How likely do you think the following reasons are for someone making such a decision?\bigskip

\begin{tabular}{p{0.4\textwidth} ccccc}

&Very unlikely& Unlikely& Maybe& Likely& Very likely \\

They are generally impatient. & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$ \medskip \\

They are not sure if they can receive the money in half a year, for example, because they will no longer have a PayPal account. & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$ \medskip\\

They actually wanted to choose ``Get \EurDig 0 today, \EurDig10 in half a year,'' but chose the other option because an irrelevant event prevented them from choosing what they actually wanted (e.g. their hand trembled, or they confused the order of radio buttons). & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$ \medskip\\

Today they urgently need money to pay expenses such as food or rent. & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$\medskip \\

They are not sure if the study director would really pay them in half a year. & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$\medskip \\

They were not attentive and chose randomly.  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$  & $\bigcirc$ \\
\end{tabular}
\bigskip
\end{center}

\noindent\hrulefill


\begin{center}
In a previous part of this study, you decided to make certain options available or unavailable.\bigskip

We would like to ask you to explain in your own words: Why did you make the decisions you did?\bigskip

Specifically we ask you about your decisions in the following round:\bigskip

	\noindent\fbox{	\parbox{0.65\textwidth}{
		\centering The round was about an experimental participant who said about himself:\bigskip

I'm an impatient person. I often regret my decisions. (I probably do without the present too seldom with regard to the future.)\bigskip

You have made the following options available: \ldots\bigskip

You have made the following options unavailable: \ldots \bigskip
	\vspace{0cm}
}}\bigskip

Your answer:\\
\emptybox[0.65\textwidth]{0.5cm}

\textrm{\textit{[The subject answers this question five times, once for each of the four Chooser types of Table \ref{chooserInfo}, and once for a randomly selected Chooser.]}}

\end{center}


\noindent\hrulefill


Finally, please answer the following questions about yourself truthfully\bigskip

How many alcoholic beverages did you consume on average per week, calculated over the last 12 months? (1 alcoholic drink = 0.2 liter beer, 0.1 liter wine, 1 shot of spirits or liquor)\\
\textit{[10 or more per week, 5-10 per week, 3-5 per week, 1-3 per week, less than 1 per week but some, none]}\\ \medskip

How often did you have 4 or more alcoholic beverages within a 2-hour period over the last 12 months?\\
\textit{[7 days per week, 5-6 days per week, 3-4 days per week, 2 days per week, 1 day per week, 2-3 days per month, 3-11 days over the last year, 1-2 days over the last year, never]}\\

\noindent\hrulefill

Sometimes people need to borrow money quickly and take out short-term, high-interest loans. Such loans include consumer loans of EUR 5000 or less with less than 1-year maturity, payday loans, pawn shop loans, or rent-to-own loans (but not credit card debt).\bigskip

Do you have experience with short-term, high-interest loans?\\
\textit{[No, I've never taken out a loan like this before; Yes, I have taken such a loan in the past, but I don't do it regularly; Yes, I regularly take out such loans]}\\ \bigskip


If you have taken out a short-term, high-interest loan (for example, payday loan or pawnbroking) in the past:

How did the repayment work? (If you have taken out several such loans or take them out regularly, please indicate what typically happens.)\\
\textit{[I repaid it in full on my next payday without taking out another loan immediately; I repaid it in full or in part on my next payday, but took up another loan to pay it; I held the loan longer than originally planned, but later repaid it without immediately taking out another loan; I held the loan longer than originally planned, but later paid it back and took out another loan to pay the other one; I was unable to pay it back and I went bankrupt with the loan; I've never taken out a loan like this before]}

\noindent\hrulefill

Which of the following categories describes you best? I am...\\
\textit{[severely underweight (BMI $<$16); underweight (BMI between 16 and 18.5); normal weight, on the lighter side (BMI between 18.5 and 21.7); normal weight, on the heavier side (BMI between 21.7 and 24.9); overweight (BMI between 24.9 and 29.9); severely overweight (BMI $>$ 29.9); I prefer not to say]}

\begin{center}
\textit{\textrm{[Subjects can click a button labelled `calculate BMI'. If they do so, a window pops up prompting subjects to enter their weight and height. Upon clicking enter, the subjects can then view their BMI.]}}
\end{center}
\noindent\hrulefill

Are you a smoker, or have you ever been a smoker?
\textit{[I am a non-smoker, and have never been a smoker; I now am a non-smoker, but I used to be a smoker; I am an occasional smoker; I am a smoker]} \\ \medskip

How many cigarettes do you consume per day, based on the last 12 months (excluding e-cigarettes)?\\
\textit{[40 (2 packs) or more per day; Between 20 (1 pack) and 40 (2 packs) per day; Between 10 (1/2 pack) and 20 (1 pack) per day; Between 1 and 10 (1/2 pack) per day; I am a non-smoker]}

\noindent\hrulefill

What is your political orientation?\\
\textit{[left, center-left, left-of-center, centrist, right-of-center, center-right, right]}


\noindent\hrulefill

\begin{center}
\textbf{\normalsize Memory test}
\end{center}\bigskip

As the final part of this study, you have the opportunity to earn an additional \EurDig2. These will be added to all your other payments via PayPal.\bigskip

In the online part of this study you made a number of decisions about your bonus payment.\bigskip

In this section we ask you to reproduce these decisions if you remember them.\bigskip

It is \textbf{not} about what options you actually prefer now. It is about clicking on \textbf{the same} options as in the online part, even if you might prefer a different option now.\bigskip

We will show you 4 lists of such decisions. If for all lists you click on the same options as in the online part of this study, we will add \EurDig2 to your PayPal payment today.


\noindent\hrulefill

In each line, please select the SAME option you selected in the online part of this experiment (regardless of which option you currently prefer). \bigskip

In the online part of this experiment I had chosen the following options:

\noindent  I would rather have \ldots
\begin{center}
	\noindent\fbox{
	\parbox{0.9\textwidth}{

		\setlength{\tabcolsep}{2pt}
		\begin{tabular}{p{0.4\textwidth}ccp{0.4\textwidth}}
			\ldots \EurDig$X$ on the day of the laboratory part & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 1 month after the laboratory part \\
			\ldots \EurDig$X$ on the day of the laboratory part & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 2 months after the laboratory part \\
			\ldots \EurDig$X$ on the day of the laboratory part & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 3 months after the laboratory part \\
			\ldots \EurDig$X$ on the day of the laboratory part & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 4 months after the laboratory part \\
			\ldots \EurDig$X$ on the day of the laboratory part& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 5 months after the laboratory part \\
			\ldots \EurDig$X$ on the day of the laboratory part& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig$Y$ 6 months after the laboratory part \\
		\end{tabular}\vspace{-0cm}
	}
}\bigskip
\end{center}

\noindent\hrulefill

\bigskip
In the online part of this experiment I had chosen the following options:
\noindent I would rather have \ldots

\begin{center}
	\noindent\fbox{
	\parbox{0.9\textwidth}{

		\setlength{\tabcolsep}{2pt}
		\begin{tabular}{lccl}
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\%
			& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $6$ with certainty \\
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\%
			& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $5$ with certainty \\
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\% & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $4$ with certainty \\
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\% & $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $3$ with certainty \\
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\%& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $2$ with certainty \\
			\ldots \EurDig $X$ with probability $p$\% and \EurDig $Y$ with probability $(1-p)$\%& $\bigcirc$ & $\bigcirc$ & \ldots \EURdig $1$ with certainty \\
		\end{tabular}\vspace{-0cm}
	}
}\bigskip
\end{center}

\noindent\hrulefill

\begin{center}
\textbf{\normalsize This is the end of this study}\bigskip

Thank you for your participation!\bigskip

\textbf{\normalsize Your Payment}
\end{center}

You will receive your showup payment of \EurDig4.50 in cash, and your base payment of \EurDig5 via PayPal. You will also receive the bonus payment below.\bigskip

The computer has randomly determined that your bonus payment for this study is determined by \ldots \bigskip

\begin{center}
\textbf{Your bonus payment therefore is \EurDig5}
\end{center}
Your responses to the memory coincide with your choices in the online part. Therefore you will receive an additional \EurDig2.\bigskip

Do you have any comments on this study?\bigskip

\begin{center}
\emptybox[0.65\textwidth]{0.5cm}
\end{center}

\scriptsize{Please click here if you wish to withdraw your data from this study. If you choose to do so, you will receive \EurDig1 for your participation in this study. You will not receive any other payment, and the decisions you have made in this experiment will not affect any future experiment participant.}\\
\begin{itemize}
	\item[$\square$] I want to withdraw my data from the study
\end{itemize}\bigskip
\begin{center}
\textbf{\normalsize Now, please raise your hand.}\bigskip

Someone from the experiment staff will take care of your payment.\bigskip

\noindent\hrulefill
\end{center}
\end{flushleft}
}


\newpage

\subsection{Vignette experiment}\label{vignetteInstruction}

Subjects were shown the four policies in individually randomized order. Subjects were paid \$3 for completion, plus an additional \$0.25 for each of eight attention check questions (two per policy) they answered correctly.

{\sffamily  \footnotesize{\setlength{\parindent}{0pt}

\hrulefill \\

\textrm{\textit{[Horizontal lines represent screen breaks.]}}

\textbf{Policy 1 of 4: Sugary drinks taxes in Melbourne, Australia}

The next questions are all about Anne, a resident of Melbourne, Australia. She is 35 years old, 5 feet and 4 inches tall, and she weighs 190 pounds. According to a classification by the World Health Organization, Anne is thus moderately obese.      Sugary drinks such as Coca Cola are a major contributor to the obesity epidemic, according to a study by Harvard University.      Currently, Australia does not have a sugary drinks tax. Suppose the city of Melbourne therefore considers introducing a sugary drinks tax. If passed, the price of all sugar-sweetened beverages would double. The value added tax on all other goods would be lowered, so that the state would collect the same overall amount of taxes as without the change.  \\

With the sugary drinks tax, people would consume fewer sugar-sweetened beverages. On the one hand, this would lower their body weight. On the other hand, they would less frequently enjoy the consumption of a good that gives them satisfaction and enjoyment. \\
\textrm{\textit{[For a random half of subjects, the previous two sentences are presented in reverse order]}}\\


For this question, assume that if the tax is introduced, Anne will reduce her consumption of sugary drinks so that her weight permanently drops to 145 pounds (from the previous 190 pounds). Given Anne's height, this is a ``normal weight'', according to the World Health Organization. \\

If all residents of Melbourne were exactly like Anne, would you support or oppose the introduction of the tax?\\
\textit{[I would strongly support the tax, I would weakly support the tax, I would weakly oppose the tax, I would strongly oppose the tax]}\\

How do you think Anne is affected overall if the tax is introduced?\\
\textit{[Anne will be much better off with the tax than without, Anne will be a little better off with the tax than without, Anne will be equally well off with the tax as without, Anne will be a little worse off with the tax than without, Anne will be much worse off with the tax than without]}

\hrulefill \\


Science does not have a definitive answer about how much the introduction of a sugary drinks tax will change Melbourne residents' body weight. What do you believe are likely effects?   \\

What do you believe is the chance that the tax will cause a typical moderately obese person to lose enough weight to be classified as merely overweight? I believe this is \\
\textit{[Extremely likely, Very likely, Somewhat likely, Somewhat unlikely, Very unlikely, Extremely unlikely]} \\

A sugary drinks tax will not affect everyone equally, because some people are richer than others, some are heavier than others, some like sugary drinks more than others, and so on.  On the left you see 10 tags labelled ``Random resident''.  Please sort the labels into the bins to show, from amongst ten randomly selected Melbourne residents, how many people you believe would be affected in what way. \\
\textrm{\textit{[Subjects drag and drop ten tags labelled ``Random resident'' into three bins labeled ``Better off'', ``Neither better nor worse off'', ``Worse off'']}}


\hrulefill \\

\textbf{Effect on whole society} \\
If residents like Anne have a healthier weight, this affects the well-being of these residents. It also affects the general public. The reason is that people who have a healthier weight are less likely to get ill and cause medical costs. Health insurance in Australia is partly publicly provided. Hence, the less a person relies on it, the lower the costs she causes to the public. \\

Suppose the city of Melbourne asked for your opinion on the proposed sugary drinks tax. Please indicate your opinion. I would \\
\textit{[Strongly support the proposed sugary drinks  tax, Weakly support the proposed sugary drinks  tax, Weakly oppose the proposed sugary drinks  tax, Strongly oppose the proposed sugary drinks  tax]}\\

What determined your answer in the previous question? The well-being of those who change their sugary drinks consumption, or the effects on society at large (lower health costs)?\\
\textit{[Exclusively the change in well-being of those who change their sugary drinks consumption; Mostly the change in well-being of those who change their sugary drinks consumption; Both, but a little more the change in well-being of those who change their sugary drinks consumption; Both, but a little more the effects on society at large (lower health costs); Mostly the effects on society at large (lower health costs); Exclusively the effects on society at large (lower health costs)]}

\hrulefill \\

\textbf{Questions for bonus payment}
About how tall is Anne?\\
\textit{[5 feet 0 inches, 5 feet 4 inches, 5 feet 8 inches, 6 feet 2 inches]}\\

What effect would the tax have on the overall price of sugary drinks? \\
\textit{[It would increase one-and-a-half fold, It would increase two fold, It would increase two-and-a-half fold, It would increase three fold, It would increase three-and-a-half fold]}

\hrulefill \\


\textbf{Policy 2 of 4: Alcohol taxes in London, UK}\\

The next questions are all about Peter, an undergraduate student at Queen Mary University, London, UK.     Like many undergraduate students, on one day of each weekend, Peter consumes five or more alcoholic drinks within two hours or less, a practice known as binge-drinking.  The price of alcohol has a large influence on how much alcohol people consume. Suppose the United Kingdom considers increasing the alcohol tax, such that each unit of alcohol would become twice as expensive as it is today.  The value added tax on all other goods would be lowered, so that the state would collect the same overall amount of taxes as without the change.

If the increased tax reduces alcohol consumption, this will have two effects. On the one hand, it will limit the unfavorable health effects of alcohol consumption. On the other hand, people will less frequently enjoy consuming something they like.\\
\textrm{\textit{[For a random half of subjects, the previous two sentences are presented in reverse order]}}\\

For the following questions, assume that if the tax is introduced, Peter reduces his binge drinking from once a week to once a month.\\

If all residents of London were exactly like Peter, would you support or oppose the tax? \\
\textit{[I would strongly support the tax, I would weakly support the tax, I would weakly oppose the tax, I would strongly oppose the tax]}\\

How do you think Peter is affected overall if the tax is introduced|
\textit{[Peter will be much better off with the tax than without, Peter will be a little better off with the tax than without, Peter will be equally well off with the tax as without, Peter will be a little worse off with the tax than without, Peter will be much worse off with the tax than without]}

\hrulefill \\


Science does not have a definitive answer about how much the introduction of an alcohol tax will affect the frequency of binge drinking. What do you believe is the chance that the tax will reduce the frequency by which people like Peter binge-drink by at least half? I believe this is \\
\textit{[Extremely likely, Very likely, Somewhat likely, Somewhat unlikely, Very unlikely, Extremely unlikely]}\\

An alcohol tax will not affect everyone equally, because some people are richer than others, some like to drink alcoholic drinks more than others, and so on.  On the left you see 10 tags labelled ``random college student''. Please sort the labels into the bins to show how many college students you believe would be affected in what way.\\
\textrm{\textit{[Subjects drag and drop ten tags labelled ``Random resident'' into three bins labeled ``Better off'', ``Neither better nor worse off'', ``Worse off'']}}

\hrulefill \\

\textbf{Effect on whole society} \\
If college students like Peter binge-drink less frequently, this affects the well-being of these students. It also affects the general public. The reason is that binge-drinking college students sometimes cause trouble to others, for instance, by vomiting in public places, through vandalism, or harassing bystanders.    \\

Suppose the city of London asked for your opinion on the proposed alcohol tax. Please indicate your opinion. I would \\
\textit{[Strongly support the proposed alcohol tax, Weakly support the proposed alcohol  tax, Weakly oppose the proposed alcohol  tax, Strongly oppose the proposed alcohol  tax]}\\

What determined your answer in the previous question? The well-being of those who change their alcohol consumption, or the effects on society at large (less vomit, vandalism, etc.)?\\
\textit{[Exclusively the change in well-being of those who change their alcohol consumption; Mostly the change in well-being of those who change their alcohol consumption; Both, but a little more the change in well-being of those who change their alcohol consumption; Both, but a little more the effects on society at large (less vomit, vandalism, etc.); Mostly the effects on society at large (less vomit, vandalism, etc.); Exclusively the effects on society at large (less vomit, vandalism, etc.)]}

\hrulefill \\

\textbf{Questions for bonus payment}

How often does Peter binge-drink?\\
\textit{[Twice a week, Once a week, Twice a month, Once a month, Once every other month]}\\

What effect would the tax have on the overall price of alcohol? \\
\textit{[It would increase one-and-a-half fold, It would increase two fold, It would increase two-and-a-half fold, It would increase three fold, It would increase three-and-a-half fold]}

\hrulefill \\

\textbf{Policy 3 of 4: Restrictions on payday loans in Christchurch, New Zealand} \\

The next questions are all about James, a 40 year old janitor at a school in Christchurch, New Zealand. James receives his paycheck monthly. In the middle of the month, James is running very tight on money. Suddenly, a need comes up that James considers very important. James considers taking out \$500 from a payday loan store. If he does so, he will have to repay the \$500 he has taken out, in addition to \$90 in interest and fees, two weeks later.  Currently, New Zealand does not have any restrictions on the interest and fees that payday lenders can charge their clients. New Zealand is considering introducing tight restrictions on payday lending. Those restrictions would consist of upper limits on the interest rates and fees that payday lenders can charge.

Such regulation would have two effects. First, people can get trapped in debt cycles in which they take up loans to pay back previous loans, at ever increasing interest and fee payments. Some experts argue that restrictions on payday lending make debt cycles less likely. Second, fewer people will be able to get a payday loan, because some payday loan shops may go out of business. Hence, some people who would genuinely benefit from a payday loan may no longer be able to obtain them.\\
\textrm{\textit{[For a random half of subjects, the previous two arguments are presented in reverse order]}}\\


Suppose regulation is introduced such that James will be unable to get the payday loan of \$500 for a cost of \$90 in interest and fees two weeks later. Suppose that James will also be unable to borrow that money from anywhere else.\\

If all residents of New Zealand were exactly like James, would you support or oppose the introduction of the restrictions? \\
\textit{[I would strongly support the restrictions (so loans become unavailable), I would weakly support the restrictions (so loans become unavailable), I would weakly oppose the restrictions (so loans remain available), I would strongly oppose the restrictions (so loans remain available)]}\\

How do you think James is affected if the regulation is introduced?\\
\textit{[James will be much better off if he cannot get the loan, James will be a little better off if he cannot get the loan, James will be just as well off if he can get the loan as if he cannot get it, James will be a little better off if he can get the loan, James will be a much better off if he can get the loan]}\\

\hrulefill \\

Science does not have a definitive answer about exactly how much the introduction of payday loan regulation limits individuals' ability to obtain high-cost, short-term credit. The reason is that even people they cannot get a payday loan, they may find alternatives, such as pawn shops, bank overdrafts, or asking friends and family. All of these options may also cost much in interest and fees or damaged social relationships, and bear a risk of trapping people in debt cycles. \\

What do you think are likely effects?  I believe that if James can no longer get a payday loan, the chance that he will get a loan from some other place at similar costs with similar risks is \\
\textit{[Extremely high ($>$90\%), Very high (75\% - 90\%), Somewhat high (50\% - 75\%), Somewhat low (25\%-50\%), Very low (10\%-20\%), Extremely low ($<$ 10\%)]}\\


Restrictions on payday lending will not affect everyone equally, because people differ in their reasons for taking out these loans.  On the left you see 10 tags labelled ``Random resident''. Please sort the labels into the bins to show how many people like James (who would otherwise get a payday loan) you believe would be affected in what way if payday loans were no longer available. \\
\textrm{\textit{[Subjects drag and drop ten tags labelled ``Random resident'' into three bins labeled ``Better off'', ``Neither better nor worse off'', ``Worse off'']}}

\hrulefill \\

\textbf{Effect on whole society}\\
If workers like James can no longer take out payday loans, this affects these workers. It also affects the general public, for instance by changing the probability that people go bankrupt or become homeless.  Suppose the country of New Zealand asked for your opinion on the proposed regulation to limit the availability of payday loans. Please indicate your opinion. I would \\
\textit{[Strongly support the proposed regulation, Weakly support the proposed regulation, Weakly oppose the proposed regulation, Strongly oppose the proposed regulation]}\\

What determined your answer in the previous question? The well-being of those who change their borrowing behavior, or the effects on society at large?\\
\textit{[Exclusively the change in well-being of those who change their borrowing behavior; Mostly the change in well-being of those who change their borrowing behavior; Both, but a little more the change in well-being of those who change their borrowing behavior; Both, but a little more the effects on society at large; Mostly the effects on society at large; Exclusively the effects on society at large]}

\hrulefill \\

\textbf{Questions for bonus payment}\\

If James took out a payday loan of \$500 today, how much would he have to repay in total (including interest and fees) in two weeks?\\
\textit{[\$500 - \$600 in steps of \$10]}\\

What line of work is James in?\\
\textit{[Factory worker, Farmer, Janitor, Truck driver, Server at a restaurant, Musician]}

\hrulefill \\

\textbf{Policy 4 of 4: Minimal retirement savings requirements in Dublin, Ireland}\\
The next questions are all about Frank, a 30 years old resident of Dublin, Ireland. Frank's income is EUR2900 per month, which is about USD3300 per month. Currently, Frank does not save anything for retirement.  Research finds that in countries in which there is no requirement that people save for retirement, such as the US, only about 1 in 5 working age adults have a retirement savings plan, and that many will start retirement with savings far lower than needed to maintain their standard of living when transitioning into retirement.        Ireland currently does not have a minimum retirement savings mandate. For this question, suppose that Ireland considers introducing a law that every working-age adult must save at least 10\% of their income for their retirement. Financial advisors recommend that everyone save between 10\% and 30\% of their income for retirement, depending on their age.     If the policy is introduced, Frank would be forced to pay at least \$330 per month into a retirement savings account. Upon retirement, he would receive a constant monthly pension for the rest of his life. With the policy could expect to experience only a moderate drop in his standard of living when transitioning into retirement.


Some therefore argue that people should be forced to save some minimal amount for retirement to ensure nobody experiences a large drop in their standard of living upon retirement. Others argue that people should be free to decide how and when to spend or save their money. \\
\textrm{\textit{[For a random half of subjects, the previous two arguments are presented in reverse order]}}\\


Suppose the law is passed that requires Frank to save at least 10\% of his income for retirement (\$330 of the \$3300 he earns each month). If all residents of Ireland were exactly like Frank, would you support or oppose the introduction of the minimal retirement savings law? \\
\textit{[I would strongly support the minimal retirement savings law, I would weakly support the minimal retirement savings law, I would weakly oppose the minimal retirement savings law, I would strongly oppose the minimal retirement savings law]}\\

How do you think Frank is affected if the regulation is introduced?\\
\textit{[Overall, Frank will be much better off if he is forced to save; Overall, Frank will be a little better off if  he is forced to save; Overall, Frank will be just as well off if  he is forced to save as if he is not; Overall, Frank will be a little worse off if  he is forced to save; Overall, Frank will be much worse off if  he is forced to save]}

\hrulefill \\

Minimal retirement savings laws will not affect everyone equally, because people differ in their financial backgrounds. Some are in stable jobs, while others may be in a period of their lives in which they earn unusually little or unusually much.  On the left you see 10 tags labelled ``Random resident''. Please sort the labels into the bins to show how many people in Ireland you believe would be affected in what way if a minimal compulsory retirement savings rate of 10\% were introduced. \\
\textrm{\textit{[Subjects drag and drop ten tags labelled ``Random resident'' into three bins labeled ``Better off'', ``Neither better nor worse off'', ``Worse off'']}}


\hrulefill \\


\textbf{Effect on whole society}\\

If workers like Frank save differently for retirement, this affects the well-being of these workers. It also affects the general public, for instance by changing the probability that people go bankrupt or become homeless. Suppose the Republic of Ireland asked for your opinion on the proposed minimal retirement savings law. Please indicate your opinion. I would \\
\textit{[Strongly support the law that forces workers to save 10\% of their income for retirement, Weakly support the law that forces workers to save 10\% of their income for retirement, Weakly oppose the law that forces workers to save 10\% of their income for retirement, Strongly oppose the law that forces workers to save 10\% of their income for retirement]}\\

What determined your answer in the previous question? The well-being of those who change their saving behavior, or the effects on society at large (reliance on social security)?\\
\textit{[Exclusively the change in well-being of those who change their saving behavior; Mostly the change in well-being of those who change their saving behavior; Both, but a little more the change in well-being of those who change their saving behavior; Both, but a little more the effects on society at large; Mostly the effects on society at large; Exclusively the effects on society at large]}

\hrulefill \\


\textbf{Questions for bonus payment}\\

What is Frank's approximate monthly income (in USD)\\
\textit{[\$1000 - \$10,000 in steps of \$1000]}\\

If the minimal retirement savings law is introduced, what percentage of his earnings will each resident have to put away, at least, each month?\\
\textit{[1\%, 2.5\%, 5\%, 7.5\%, 10\%, 12.5\%, 15\%, 17.5\%, 20\%, 25\%, 30\%]}

\hrulefill \\

\textbf{Questions about yourself}  \\

As the last part of this survey, we would like to ask 22 questions about yourself. Please answer truthfully.

\hrulefill \\


What is your age?\\
\textit{[18 - 90 in steps of 1 year]}\\

What is your gender?\\
\textit{[male, female, other (e.g. non-binary)]}

\hrulefill \\

How do you feel about your own alcohol consumption? \\
\textit{[I am perfectly comfortable with my alcohol consumption; I am fairly comfortable with my alcohol consumption; I feel neutral about my alcohol consumption; I am fairly uncomfortable with my alcohol consumption; I am totally uncomfortable with my alcohol consumption]}\\

How do you feel about your use of short-term credit (such as payday loans)?\\
\textit{[I am perfectly comfortable with my use of short-term credit; I am fairly comfortable with my use of short-term credit; I feel neutral about my use of short-term credit; I am fairly uncomfortable with my use of short-term credit; I am totally uncomfortable with my use of short-term credit]}\\

How do you feel about your retirement savings choices? \\
\textit{[I am perfectly comfortable with my retirement savings choices; I am fairly comfortable with my retirement savings choices; I feel neutral about my retirement savings choices; I am fairly uncomfortable with my retirement savings choices; I am totally uncomfortable with my retirement savings choices]}\\

How do you feel about your body weight? \\
\textit{[I am perfectly comfortable with my body weight; I am fairly comfortable with my body weight; I feel neutral about my body weight; I am fairly uncomfortable with my body weight; I am totally uncomfortable with my body weight]}?

\hrulefill \\


How many alcoholic beverages did you consume on average per week, calculated over the last 12 months? (1 alcoholic drink = 0.2 liter beer, 0.1 liter wine, 1 shot of schnapps or liqueur)\\
\textit{[10 or more per week, 5-10 per week, 3-5 per week, 1-3 per week, less than 1 per week but some, none]}\\

How often have you had 4 or more alcoholic beverages within a 2-hour period over the last 12 months?\\
\textrm{\textit{[For males, ``4 or more'' is replaced by ``5 or more'']}}\\
\textit{[7 days per week, 5-6 days per week, 3-4 days per week, 2 days per week, 1 day per week, 2-3 days per month, 3-11 days over the last year, 1-2 days over the last year, never]}

\hrulefill \\


What is your body height?\\
Feet \textit{[Subject can enter any integer]}\\
Inches \textit{[Subject can enter any integer]}\\

What is your body weight (in pounds)? \textit{[Subject can enter any integer]}\\

How would you describe yourself? \\
\textit{[Underweight, Healthy weight, Overweight, Moderately obese, Severely obese, prefer not to answer]}

\hrulefill \\

Please indicate your current household income in U.S. dollars\\
\textit{[Under \$10,000, \$10,000 - \$19,999, \$20,000 - \$29,999, \$30,000 - \$39,999, \$40,000 - \$49,999, \$50,000 - \$74,999, \$75,000 - \$99,999, \$100,000 - \$150,000, Over \$150,000, prefer not to answer]}\\

What is your credit card debt (across all credit cards you have)? \\
\textit{[\$0-\$500, \$500-\$1000, \$1000-\$2500, \$2500-\$5000, \$5000-\$7500, \$7500-\$10,000, \$10,000-\$20,000, \$20,000-\$50,000, \$50,000 or more, prefer not to answer]}\\


Have you ever taken a payday loan?\\
\textit{[No, never; Yes, once; Yes, a couple of times; Yes, often; prefer not to answer]}\\

Have you ever been in a debt cycle (getting into debt in order to repay other debt)?\\
\textit{[No, never; Yes, once; Yes, a couple of times; Yes, often; prefer not to answer]}?

\hrulefill \\


What are your total retirement savings?\\
\textit{[\$0, \$0 - \$10,000, \$10,000-\$25,000, \$25,000-\$50,000, \$50,000-\$100,000, \$100,000-\$200,000, \$200,000-\$300,000, \$300,000-\$400,000, \$400,000-\$500,000, \$500,000-\$750,000, \$750,000-\$1,000,000, more than \$1,000,000, I do not know, prefer not to say]}\\


Approximately what percentage of your income do you currently save for retirement each month?\\
\textit{[0\%, 0\%-2.5\%, 2.5\%-5\%, 5\%-7.5\%, 7.5\%-10\%, 10\%-15\%, 15\%-20\%, 20\%-30\%, more than 30\%]}\\

Do you currently work for a company that offers a retirement savings plan (401k)? \\
\textit{[Yes, No]}

\hrulefill \\


Where do you stand politically?\\
\textit{[Conservative; Leaning conservative; Centrist; Leaning liberal; Liberal]}\\

Please indicate the highest level of education you completed.\\
\textit{[Elementary School, Middle School, High School or equivalent, Vocational/Technical School (2 year), Some College, College Graduate (4 year), Master's Degree (MS), Doctoral Degree (PhD), Professional Degree (MD, JD, etc.)]}\\

Which of the following best describes the area you live in?\\
\textit{[Urban, Suburban, Rural]}\\

Please choose the option that best describes your situation\\
\textit{[I am unemployed, I am employed part-time, I am employed full-time]}


}}


\end{document}
