\documentclass[smallheadings]{article}

\usepackage{booktabs}
\usepackage{float}
\usepackage{placeins}

\usepackage[]{graphicx}
\usepackage{mathrsfs}
\usepackage{mdwlist}
\usepackage{amssymb}
\usepackage{eucal}
\usepackage{afterpage}


\usepackage{soul}

\usepackage{amsmath}
\usepackage{eucal}
\usepackage{amsthm}
\usepackage{epstopdf}
\usepackage{dsfont}
\usepackage{bm}
\usepackage{enumerate}
\usepackage[usenames]{xcolor}
\definecolor{blue}{rgb}{0,0,1}
\definecolor{purple}{rgb}{0.6,0,1}
\usepackage{natbib}
\usepackage{multirow}
\usepackage[margin=1.2in]{geometry}
\usepackage{pdfpages}

\usepackage{float}
\usepackage{setspace}

\usepackage{pdflscape}
\usepackage{marvosym}


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

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


\usepackage[colorlinks = true, citecolor = blue, linkcolor = blue, urlcolor  = blue,]{hyperref}

\usepackage{rotating}
\usepackage[labelfont=bf]{caption}


\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}}

\defcitealias{BelmontReport}{Belmont Report (1978)}


\clubpenalty = 10000
\widowpenalty = 10000

\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{\var}{\mathrm{var}}
\newcommand{\supp}{\mathrm{supp}}
\newcommand{\logit}{\mathrm{logit}}
\newcommand{\sech}{\mathrm{sech}}
\newcommand{\abs}{\mathrm{abs}}
\newcommand{\sgn}{\mathrm{sgn}}
\newcommand{\del}{\partial}
\newcommand{\w}{\omega}


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

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


\setcounter{MaxMatrixCols}{10}

\theoremstyle{plain}
\newtheorem{proposition}{Proposition}
\newtheorem{prediction}{Prediction}
\newtheorem*{example}{Example}
\setcounter{section}{0}
\newtheorem{theorem}[proposition]{Theorem}
\newtheorem{lemma}{Lemma}
\newtheorem{corollary}[proposition]{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{setspace}
\setstretch{1.4}


\setcounter{tocdepth}{3}
\setcounter{secnumdepth}{3}


\begin{document}

\title{Interventionist Preferences and the Welfare State: \\ The Case of In-Kind Nutrition Assistance \\ PRELIMINARY AND INCOMPLETE}
\author{Sandro Ambuehl, B. Douglas Bernheim, Tony Q. Fan, Zach Freitas-Groff\footnote{Ambuehl: University of Zurich, Department of Economics, Bl\"uemlisalpstrasse 10, 8006 Z\"urich, Switzerland, sandro.ambuehl@econ.uzh.ch. Bernheim, Fan, and Freitas-Groff: Stanford University, Department of Economics, 579 Serra Mall, Stanford, CA, 94305, USA, bernheim@stanford.edu, tonyqfan@stanford.edu, zgroff@stanford.edu. We are especially grateful to research dietician Mandy Murphy Carroll (Stanford University School of Medicine) for her generous help with the nutritional assessment of the food baskets and to Andia Kozmai for excellent research assistance. Ambuehl and Bernheim gratefully acknowledge funding from the Alfred P. Sloan Foundation (grant number G-2017-9017). Ambuehl also gratefully acknowledges funding from the Swiss National Science Foundation Starting Grant \#211222.}}


\maketitle


\begin{abstract}

Poverty assistance is often administered in kind even though cash transfers might raise recipients' welfare more effectively. We study the interventionist preferences that may restrict transfers to in-kind forms. In our lab-in-the-field experiment, U.S. citizens decide whether and how to constrain bundles of in-kind or cash-equivalent deliveries to U.S. SNAP (``food stamps'') participants. Citizens intervene strongly and often, suggesting that in-kind restrictions are an intrinsically desired feature of the system rather than a cost incurred to meet some extraneous goal. Yet, interventions do not target or promote the good of the recipients. They are substantially motivated by the selfish utility individuals derive from the act of intervening. They do not seek to ensure sufficient consumption of items beneficial to recipients but to limit consumption of items interveners find offensive. And because they depend on citizens' systematically distorted beliefs about recipients, interventions are far more restrictive than citizens believe. In-experiment behavior correlates strongly with views about government policy. [157 words]


\end{abstract}


\doparttoc
\faketableofcontents
\part{}


\newpage
\parttoc
\newpage


\newpage
\section{Introduction}\label{intro}


Around the world, social welfare systems prominently feature in-kind transfers \citep{CurrieGahvari}. From the viewpoint of standard economics, this fact is puzzling. Under standard rationality assumptions, unrestricted cash transfers would raise the welfare of recipients more effectively than in-kind transfers. A theoretical literature explains the in-kind restriction as a means to an end. The restriction might, for instance, serve as an ordeal mechanism that improves the targeting of benefits. Yet, voters may view in-kind restrictions as an end in itself. Indeed, \citet{CurrieGahvari} conclude that ``paternalism and interdependent preferences are leading overall explanations for the existence of in-kind transfer programs.'' Yet, to date, there is limited direct evidence on this hypothesis, and none delves into the specific motives underlying such desires.

In this paper, we run a lab-in-the-field experiment in which members of a US general population sample make decisions that affect a sample of U.S. welfare recipients. We answer three questions. First, do citizens want to impose in-kind restrictions as an end in itself? An experiment is the tool of choice to answer this question, as it allows us to exclude mechanisms by which the in-kind restriction is a means to some other end, such as those pertaining to self-targeting. Second, what is the structure of such interventionist preferences? To what extent are they driven by welfarist or deontological, altruistic or selfish motives? To what extent do citizens seek to ensure that recipients consume sufficient amounts of items they deem good and to what extent do they focus on preventing the consumption of items they find offensive? Third, do citizens achieve their own objectives when imposing restrictions, or are the interventions misguided? The latter may occur if citizens seek to account for recipient preferences but have mistaken beliefs about these preferences. Additionally, we examine how the answers to these questions depend on recipients' and CAs' demographic attributes and on CAs' political preferences, as well as how they relate to general attitudes about welfare policy.

We focus on a prime example of an in-kind transfer system, the U.S. Supplemental Nutrition Assistance Program (SNAP), from which 41.9 million U.S. individuals (12.5\% of the population) benefitted in 2023 \citep{pew.NumberSNAP}. The existence of this program is not, per se, evidence for interventionist preferences shaping redistribution, because supporting the U.S. agricultural sector has been a prime goal of the program since its inception.\footnote{Introduced during the Great Depression, the program served as ``a practical way to build a bridge across [the] chasm'' of ``farm surpluses on one cliff and under-nourished city folks with outstretched hands on the other'' \citep[Milo Perkins, first administrator of the food stamps program, quoted in][]{DanielsTrebilcock}. After its expiration during the wartime boom, the program was reintroduced in 1964 with the dual aims of ``rais[ing] levels of nutrition among low-income households'' and ``strengthen[ing] our agricultural economy.'' To date, the corporate sector spends nine-figure amounts on SNAP lobbying efforts \citep[e.g.][]{Fisher.SNAPlobbying}. According to \citet{Fisher.SNAPlobbying}, food stamps never evolved into a cash transfer program in part because of the economic benefits provided to the agriculture and food industries.} While citizens in representative democracies such as the U.S. do not directly decide about the specifics of welfare programs, they can express their preferences by voting for candidates whose policy preferences match their own.


In our experiment, we place general population U.S. citizens in the role of \emph{Choice Architects} (CAs, she). CAs make decisions that may affect the consumption opportunities of another U.S. citizen currently enrolled in the SNAP program (the \emph{welfare recipient}, he). The experiment involves deliveries of groceries each month for half a year or monthly deliveries of cash equivalents over that same time period. A single food delivery roughly covers two weeks of food intake at the recommended calorie intake, whereas cash equivalent deliveries can be worth up to \$130 per month.  A single recipient can thus receive up to \$780 from participating in our study. Compared to the average monthly food stamp benefit of \$181.1 per month \citep{pew.NumberSNAP}, these figures are substantial. Our cash equivalent is a wide selection of gift cards from which the recipient can select any combination up to a specified limit. The menu of gift cards has two key features that we emphasize to CAs. First, they do not include groceries. Second, they can be used to purchase sin goods such as alcoholic beverages, tobacco products, and lottery tickets. Moreover, we can elicit and incentivize CAs' beliefs about how a recipient will allocate a given budget across gift card categories. We make all deliveries in addition to, and independently of, any SNAP benefits the welfare recipients are entitled to.

Each CA makes two types of decisions. In \emph{restriction decisions}, she decides whether to give the recipient a choice between the food deliveries and $p$ in cash equivalents or whether to force the recipient to receive the food deliveries. The CA makes such decisions for various amounts $p$ of up to \$130, which yields a sensitive measure of her restrictiveness. Restriction decisions combine two motives: first, the CA's view about how the recipient ought to choose, and second, her willingness to act on that judgment. To isolate the former, CAs also makes \emph{surrogate choices} in which she chooses on behalf of the recipient; she cannot defer the choice to him. The fact that our selection of gift cards cannot purchase groceries renders surrogate choices distinct from restriction decisions. To determine whether intervention decisions rely on mistaken beliefs, we further elicit the CA's belief distribution about the recipient's \emph{reservation price}. To measure the extent to which CAs care about recipients' preferences, we then tell the CA whether her assigned recipients' reservation price for the food deliveries exceeds a threshold. Based on this information, CAs make another set of surrogate choices and reveal their updated beliefs about the recipients' reservation price. We address the concern that food deliveries simply free up cash that the recipient can spend without restrictions by eliciting and statistically controlling for the CA's beliefs about such crowding out. Notably, these beliefs, elicited at the end of the study, have very little predictive power, suggesting that considerations about crowding out did not enter CA's decisions.\footnote{In contrast to the crowding-out hypothesis, \citet{HastingsShapiro} document a substantial flypaper effect of in-kind transfers. They estimate a marginal propensity to consume food of about 50\% if additional `food stamps' become available but close to 0\% when a drop in gasoline prices frees up cash.}

In response to our first question (intrinsic preference for in-kind restrictions), we find that a large majority of citizens (about 80\%) choose to restrict recipients and prevent them from choosing the cash equivalents in at least some decisions. Because our experiment removes the instrumental concerns modeled in the theoretical literature by design, this behavior constitutes evidence that CAs intrinsically desire the in-kind restriction.

We answer our second question (nature of interventionist preferences) by structurally estimating a model of interventionist preferences. Our model measures the extent to which interventions are driven by non-consequentialist motives on the one hand (akin to `warm glow' in the social preferences literature or to a selfish preference for exerting control over the recipient) and consequentialist concerns about recipient welfare on the other hand (akin to altruism in the social preferences literature). The difference in CAs choices across intervention decisions and surrogate choices identifies these motives. We model the non-consequentialist component as a constant utility shock the CA receives if she restricts the participant. If positive, the CA derives utility from controlling the recipient; a negative value captures a Kantian respect for autonomy. The welfarist component depends on two factors. First, the CA has a view about what each recipient ought to do, updated by information about the recipients' reservation price. The extent of updating measures deference to the recipient. Second, because she imposes a one-size-fits-all policy facing uncertainty about the recipient, the CA needs to trade off two errors: restricting recipients who should not be restricted, and failing to restrict recipients who should be restricted. The weight the CA places on these two errors influences her intervention decisions even conditional on her beliefs about what the recipient ought to do.

Our estimates yield two key results about the structure of interventionist preferences. First, non-consequentialist or selfish motives play a large role. To quantify the effect size, we measure a CA's restrictiveness as the largest value of the cash equivalent for which the CA still prevents the recipient from selecting the gift cards. This \emph{enforcement price} equals about \$60 on average. It would drop by a third if CAs did not enjoy selfish utility from intervening with recipient's choices. This result is consistent with the \emph{ward paternalism} hypothesis \citep[][]{Caplan.WardPaternalism} according to which CAs have a right to impose their views on those who depend on the state.\footnote{According to \citet[][]{Caplan.WardPaternalism}, for those depending on aid, ``\textit{The standard moral constraint to leave others alone does not apply.}'' } Second, the welfarist part places substantial weight on recipients' revealed preferences. This feature opens the door to the possibility that biased beliefs about recipient preferences lead to misguided interventions.


To determine the extent to which CAs seek to encourage the consumption of items they deem good and that to which they seek to discourage those they deem bad, we conduct treatments along two dimensions: the nutritional content of the food deliveries, and information about recipients consumption of sin good from unconstrained funds. We vary nutritional content by constructing two different food baskets of which each CA only ever encounters one. The \emph{representative} basket mirrors the distribution of macronutrient intake for the average SNAP recipient. The \emph{healthy} basket is equally expensive but matches the macronutrient intake recommended by the American Heart Association \citep{AmericanHeartAssociation}. If CAs impose restrictions in an attempt to ensure sufficient consumption of healthy nutrition, we should observe stronger interventions when they decide about the healthy food basket than when they decide about the representative basket. Indeed, we find that CAs believe that the healthy deliveries substantially increases recipients' dietary health compared to receiving the representative food deliveries. Yet, intervention behavior is nearly indistinguishable across the healthy and representative baskets. Ensuring dietary health is not a key driver of CAs' intervention decisions. This result contrasts with a prominent hypotheses about poverty assistance, \emph{specific egalitarianism} \citep{Tobin.SpecificEgalitarianism, Harberger.BasicNeeds}, which posits that there are some goods, such as healthy nutrition, for which we accept less inequality than we typically accept in other domains. If CAs were concerned about ensuring sufficient consumption of healthy food, the treatment effect should have been much larger.

The second treatment dimension exogenously varies CAs' beliefs about recipients' consumption of sin goods (which they presumably regard as bad) by presenting appropriately selected truthful information. One conveys that alcohol consumption is common among recipients and reminds CAs that our cash equivalents can be used to purchase alcohol, tobacco, and lottery tickets, whereas the other contains no such reminder and conveys that alcohol consumption is not especially common among recipients. We find that the first treatment significantly increases enforcement and surrogate reservation prices (without altering reservation price beliefs), showing that CAs place substantial weight on preventing the consumption of items they regard as bad. This result, too, is consistent with the \emph{ward paternalism} hypothesis \citep[][]{Caplan.WardPaternalism} according to which the CAs views and preferences rather than the needs of those who depend on the state should determine interventions.\footnote{`\textit{Under Ward Paternalism, anyone who doesnÕt want to be nudged can simply decline to become dependent on the government.  You can spend your own money your own way, no questions asked.  If, however, you ask taxpayers for help, the help comes with strings attached to encourage you to get your life in order.  He who pays the piper, calls the tune---and why shouldnÕt the tune be, ``Get your life in order''?}' \citep[][]{Caplan.WardPaternalism}}


In answer to our third question, the fact that CAs seek to target interventions to recipients' revealed preferences opens the door for misguided interventions if beliefs about recipient preferences are mistaken. It is plausible for CAs to overestimate recipient reservation prices for two key reasons. First, CAs, who are, by construction, less financially constrained than recipients may be used to paying more for similar baskets of groceries. Second, CAs might not consider the fact that a policy target to an average individual will still fail to match many indivduals who differ from the average. In our case, the food basket targeted to the average SNAP recipient might mismatch any individual recipient's preferences. Indeed, we find that CAs grossly overestimate recipient's reservation prices for the food deliveries. As a result, the average CA's interventions restrict more than twice as many recipients as the CA believes. If CAs had correct beliefs about reservation prices, enforcement prices would drop by nearly \$20. Hence, CAs intervention decisions are misguided even according to their own aims.


Finally we examine how the foregoing results depend on CAs' political preferences and recipient demographics. To examine the effect of recipient demographics, we exogenously vary these attributes. We unobtrusively convey skin color through pixelated mugshots. We explicitly mention gender, age, parental status, and the state in which the recipient resides (stereotypically southern or not). The fact that each CA only ever decides about a single recipient makes it harder for CAs to mask racist, sexist, ageist or other discriminatory preferences. Indeed, we find that subjects' reported beliefs about behaviors such as alcohol intake or the causes for why the recipient is poor enough to qualify for SNAP vary significantly with these characteristics. Yet, intervention decisions are virtually unaffected by these characteristics, including among politically conservative CAs (who intervene more strongly across the board). While previous research has found strong effects of recipient demographics on donor generosity \citep[e.g.][]{alesinaGlaeser, Gilens, Akessonetal.}, the decision to restrict welfare recipients' consumption opportunities appears to follow fundamentally different dynamics.

With respect to CA political preferences, three hypotheses appear plausible: that conservative CAs intervene less (in line with the classic association between libertarian attitudes and conservatism), that conservative CAs intervene more (in line with associations between conservatism and authoritarianism, see ), or that CAs further from the center intervene more (in line with the idea that more extreme political attitudes coincide with greater confidence in one's own views). In fact, we find that conservative CAs intervene substantially more.  Compared to left-leaning CAs, they enjoy larger selfish utility from intervention, place less weight on recipients' revealed preferences when assessing what they ought to do, and are more than twice as likely to never let the recipient choose the cash equivalent for any amount in our experiment. CA attributes other than political attitudes play minor roles; age is the only significant predictor with older CAs imposing stricter restrictions. CAs who benefit from welfare programs themselves impose slightly looser restrictions, but the effect is far from statistically significant.

We also compare in-experiment behavior to elicited attitudes about the SNAP program. In-experiment behavior strongly correlates with these attitudes. The strength of this relation increases our confidence in the external validity of our findings.

Overall, our findings paint a picture of CAs for whom maximizing the good of the recipient is not the primary concern, but who are rather motivated by limiting consumption they find offensive, and who intervene for non-consequentialist reasons, especially on the political right. Because interventions rely on dramatically inflated beliefs about recipients' reservation prices, they are far more restrictive than anyone intends, suggesting that SNAP restrictions may be even more harmful than commonly believed.

Our work relates to several strands of literature. Most broadly, it contributes to a literature on positive welfare economics that characterizes the objective functions individuals endorse when acting as social planner \citep[e.g.][]{AmbuehlBernheimOckenfels, AmbuehlBernheim, politiciansProject, andreoniEtAl, Bernheimetal.Choosing}. In a democracy, the empirical measurement of citizen preferences takes on normative relevance since policy decisions arguably ought to represent these preferences.

More specifically, our paper bridges between two broad strands of research, that on paternalistic and interventionist preferences on the one hand, and that on in-kind transfer systems on the other. Our contribution to the strand on paternalistic and interventionist preferences is the following. Several papers show that when subjects can choose how much to spend on a donation to a recipient, spending is higher if they can place in-kind restrictions on the donation. While this evidence primarily derives from contexts of charitable giving \citep{Jones.Paternalism, Gangadharanetal., Helmsetal.}, \citet{Jacobssonetal.} document such evidence relating to a public health context, and \citet{LiscowPershing} find it in survey evidence in a redistributive transfers context. In addition to the findings in those papers, we estimate the extent to which interventions are driven by consequentialist or non-consequentialist motives, distinguish between motives to encourage the good and discourage the bad, address the specific egalitarianism and ward paternalism hypotheses, and document the extent to which  interventions are misguided. Moreover, while the the cited papers only observe restrictions imposed by subjects who volunteer to give, we study the motives for imposing in-kind restrictions even among subjects who would not voluntarily choose to give (these make up a large minority in the papers just cited). Not only is that population relevant for political economy applications, but it is also plausible that the presence of `dark motives' (such as a selfish desire to control recipients) is plausibly different among voluntary and non-voluntary givers. Our paper differs from a literature on paternalism with a public policy focus \citet{Uhl, KrawczykWozny, AmbuehlBernheimOckenfels, Bartlingetal.} in two ways. First, a key distinguishing feature of our study is the status difference between recipients whose livelihood depends on the state, and donors, who, through the tax system, are forced to provide for that livelihood. Second, that literature features stylized settings and convenience samples, whereas we seek to maintain key features of a specific public policy, including recipients subject to that policy. To the extent that the non-consequentialist intervention motives in our study reflect selfish utility gains CAs derive from intervening, our study is related to \cite{PikulinaTergiman}, who show that a substantial fraction of subjects have a positive willingness to pay to exert power over others without any benefits to themselves.\footnote{Research on paternalism by parents regarding their children \citep[e.g.][]{Kiesslingetal.} is more distantly related to this paper.}

Our key contribution to the literature on in-kind transfers \citep[reviewed in]{CurrieGahvari} is to show that voters regard in-kind restriction as intrinsically desirable, not as a means to the types of ends typically considered in the theoretical literature on in-kind transfers. While the surveys by \citet{LiscowPershing} and \citet{CampbellGaddis} also show that the general population overwhelmingly prefers in-kind redistribution to unconstrainted transfers, neither of these studies asks respondents to make choices that have real consequences for real SNAP participants. Moreover, neither distinguishes between deontological and consequentialist CA attitudes, examines the effect of healthy nutrition, examines ward paternalism and specific egalitarianism, or systematically varies respondent characteristics to study the role of stereotypes. Our work informs a literature on \emph{merit goods} \citep{Olsen, Harberger.BasicNeeds, Besley.MeritGoods, Musgrave.Palgrave, MulliganPhilipson, BeckerMulligan}. That literature characterizes optimal transfer systems under specific assumptions about voters' preferences, who are typically assumed to view recipient preferences as normatively valid, but would nonetheless like to distort their consumption bundle for selfish reasons. We provide an empirical characterization of voters' preferences. More distantly related work on in-kind transfers are \citet{LehmannMatarazzo}, who find that Brazilian voters in mayoral elections prefer in-kind to cash redistribution, and \citet{HessamiUebelmesser}, whose cross-country regression shows that conservative-leaning governments more often use in-kind restrictions than progressive-leaning governments.


The remainder of this paper proceeds as follows. Section \ref{sec:design} details the design of our experiment, Section \ref{sec:data} describes our data. Section \ref{sec:anatomy} shows that CAs seek to intervene frequently even though we remove the reasons for intervention highlighted in the theoretical literature. Section \ref{structural} contains the key analysis of this paper. It documents the structure of interventionist preferences and the motives that underlie interventions. Section \ref{accountingForPrefs} shows that CAs interventions are misguided, even according to their own objectives, because they rely on mistaken beliefs about recipient preferences. Section \ref{CAandChooserDemographics} analyzes the effects of recipient demographics and CA attributes and investigates the external validity of our findings by examining the relation between in-experiment behavior and attitudes about the SNAP program. Section \ref{sec:conclusion} concludes.


\section{Design}\label{sec:design}

We first introduce the goods CAs decide between and explain the main decision tasks. We then exposit our treatments and discuss implementation details.

\paragraph{Goods}

Our experiment features two types of goods: monthly food deliveries and monthly gift card deliveries, each made for half a year.

Each CA makes all decisions concerning one of two food baskets without learning about the existence of the other basket. Displayed in Panels A and B of Figure \ref{foodBoxPic}, each basket consists entirely of SNAP-eligible items.  The first basket (called the \emph{Healthy Food Box}) adheres to the nutritional guidelines of the American Heart Association \citep{AmericanHeartAssociation} and meets the lower limit of the recommended macronutrient intake over a 14-day period. While it includes fresh fruits and vegetables, it also contains many shelf-stable items such as frozen spinach and canned tomato sauce. The second basket (called the \emph{Representatitve Food Box}) is reverse-engineered to meet the distribution of nutrients and food groups representative of SNAP participants' average consumption according to \citet{Zhangetal.SNAP}. A Stanford research nutritionist confirmed that the food baskets adhered to our stated goals. Appendix \ref{app:foodBox} presents details. We inform CAs that one monthly delivery costs \$55 to \$75 and that the costs may vary with recipients' location.

The gift card selection (Panel C of Figure \ref{foodBoxPic}) serves as a cash equivalent that cannot be used to purchase groceries.\footnote{An exception is the fact that gas station gift cards can be used to buy sustenace at gas stations, which usually costs substantially more than comparable products in supermarkets.} Crucially, the gas station gift cards we include can be used to purchase alcoholic beverages, tobacco, and lottery tickets. A recipient who receives the gift cards can choose any desired combination of them, freely adjustable at any time, up to a maximum total value.

A receipient will receive the same good for all six months; he cannot switch forth and back between the food box and gift cards across different months. A recipient receiving the gift cards can, however, flexibly change his selection of gift cards at any time and at short notice.


\begin{figure}
\caption{\label{foodBoxPic}\label{fig:giftCards} Deliveries}
\begin{center}
\begin{tabular}{C{0.48\textwidth}C{0.48\textwidth}}
\textbf{A. Healthy basket} & \textbf{B. Representative basket} \\
\includegraphics[width=0.47\textwidth]{pics/boxHealthy}
&
\includegraphics[width=0.47\textwidth]{pics/boxRepresentative} \\


\end{tabular}
\end{center}
\begin{center}
\textbf{C. Gift-card selection}
\setlength{\tabcolsep}{1pt}
\begin{tabular}{ccc}
\includegraphics[width=0.33\textwidth]{pics/giftCards1}
&
\includegraphics[width=0.33\textwidth]{pics/giftCards2}
&
\includegraphics[width=0.33\textwidth]{pics/giftCards3} \\
\end{tabular}
\end{center}
\footnotesize{\textbf{Notes}}: Pictures of the two food baskets and of the gift card selection displayed to the respondents. All items in the baskets, including the sugar-sweetened beverages, are SNAP eligible.
\end{figure}


\paragraph{Decisions}
At the beginning of the experiment, the CA learns that she is matched with a (possibly real) SNAP participant, and that all her decisions concern that participant.

Since general population Americans may be unfamiliar with SNAP and its participants, we inform them that the average SNAP transfer per month is \$263 to men and \$211 to women\citep{TFP92021} and that `it is extremely difficult to eat enough, let alone to eat healthily from the food stamp money alone.'

After proceeding through initial instructions, the CA sees a description of her assigned participant along with a pixellated photo of his face, as in Figure \ref{chooserPic}. The CA learns that one of her may be randomly selected and implemented. Hence, as long as she is not entirely indifferent towards the recipient, she has incentives to reveal her preferences truthfully.


\begin{figure}
\caption{\label{chooserPic} Information about the welfare recipient displayed to subjects (examples)}
\begin{center}
\begin{tabular}{cc}
\includegraphics[width=0.47\textwidth]{pics/chooserPictureBWO} &
\includegraphics[width=0.47\textwidth]{pics/chooserPictureWWY}
\end{tabular}
\end{center}
\footnotesize{\textbf{Notes}}:
\end{figure}

The core of the experiment consists of two types of decisions, which yield our two main dependent variables. In \emph{intervention decisions}, the CA decides whether to leave the choice between food deliveries and gift-card deliveries to the recipient or whether to force the recipient to receive the food deliveries. CAs such decisions for each of 8 total gift-card values $p$, for $p\in \mathcal{P} = \{0, 25, 45, 60, 70, 85, 105, 130\}$. Panel A of Figure \ref{fig:Decisions} displays a screeshot. The lowest $p$ for which the CA defers the choice to the recipient in intervention decisions (the \emph{enforcement price}) serves as a fine-grained measure of the extent of her restrictiveness. In \emph{surrogate choice decisions}, the CA chooses between food and gift card deliveries on behalf of the recipient without the possibility of leaving the choice to him. The CA makes a surrogate choice decision for each gift-card value  $p$, for $p\in \mathcal{P}$, as shown in Panel B of Figure \ref{fig:Decisions}. The lowest $p$ for which she selects the food deliveries in surrogate choice decisions is her \emph{surrogate reservation price}.


\begin{figure}
\caption{Main decisions \label{fig:Decisions} }
\begin{center}
\begin{tabular}{cc}
\textbf{A. Enforcement decision} & \textbf{B. Surrogate choice decision} \\
\includegraphics[width=0.47\textwidth]{pics/paternalismDecisionExplanation} &
\includegraphics[width=0.47\textwidth]{pics/surrogateChoiceExplanation} \medskip \\
\begin{minipage}{0.47\textwidth}
\centering
\textsf{\scriptsize Please decide on each line whether Morgan can choose between the monthly food box and the monthly gift cards, or must receive the monthly food box.}
\end{minipage}
&
\begin{minipage}{0.47\textwidth}
\centering
\textsf{\scriptsize \centering Please decide on each line whether Morgan will receive the monthly food box or the monthly gift cards.}
\end{minipage}
\\
\includegraphics[width=0.47\textwidth]{pics/paternalismDecisionMPL} &
\includegraphics[width=0.47\textwidth]{pics/surrogateChoiceMPL}
\end{tabular}
\end{center}
\footnotesize{\textbf{Notes}}: The decision lists include further lines. CAs make a decision for each gift card value in $\{0, 25, 45, 60, 70, 85, 105, 130\}$.
\end{figure}


In addition, we elicit CAs' beliefs about the distribution of the recipient's reservation price for the food basket using the balls-in-bins method \citep{Delavandeetal.}. The recipient sorts 10 tags into 8 bins labeled  `\textit{[name]'s valuation is more than \$130}' or `\textit{[name]'s valuation is between $p_1$ and $p_2$}' where $p_1$ and $p_2$ are a pair of adjacent values from $\{0, 25, 45, 60, 70, 85, 105, 130\}$. We incentivize this elicitation with the probabilistic quadratic scoring rule.

A potentially important feature of interventionist preferences is the desire to account for recipient preferences. We identify this motive by providing CAs with information about the recipient's reservation price after CAs have completed the foregoing three stages. Each CA learns whether their assigned recipient's reservation price for the food deliveries is higher or lower than \$70 in monthly gift-card deliveries. Armed with this partial information, CAs complete a second round of surrogate decisions and reveal their updated beliefs about the recipient's reservation price distribution.


\paragraph{Treatments}

Our main treatment variation concerns the question of how much CAs seek to encourage the good, and of how much they seek to discourage the bad. By randomly assigning each CA to either the healthy or the representative food box, we address the first question. To answer the second question, we independently randomly assign each CA to one of two \emph{Information Treatments} which serve to identify how interventions respond to beliefs about recipients' spending behavior. Both present truthful information about SNAP recipients. One conveys that alcohol consumption is common among SNAP recipients and that gas station gift cards can be used to purchase alcoholic beverages, tobacco, and lottery tickets. The other conveys that alcohol consumption among SNAP recipients is not more common than among the U.S. general population, that spending on alcohol merely half as large, and it does not highlight the potential uses of gas station gift cards.\footnote{Specifically, CAs in the first treatment read that ``alcohol spending has recently grown more than twice as fast for the poorest fifth of the US population than for the average
American (U.S. Bureau of Labor Statistics, 2020-2021)'' and that ``Grant and Dawson (1996) found that more than one in eight (13.8\%) of SNAP recipients is a heavy drinker'' while participants in the second treatment read that ``the poorest fifth of the US population spends less than half as much money on alcohol as the average American (U.S. Bureau of Labor Statistics, 2020-2021)'' and that  ``The fraction of heavy drinkers among SNAP recipients is no larger than amongst the general US population (Grant and Dawson, 1996).''}

We also seek to identify the effect of respondent attributes. To this end, we systematically vary the demographics of the hypothetical recipients. We unobtrusively convey the recipients' race (Black/White) through pixellated mugshots (see Figure \ref{chooserPic}). We overtly state the recipient's gender (male / female), age (20-29 or 50-69), whether the recipient lives in a rural, suburban, or urban area, and the state in which he lives (we use the three southern states Kentucky, Mississippi, and West Virginia as well as the four northeastern or midwestern states Connecticut, Massachusetts, and Rhode Island). Among young women, we additionally vary child status (none / one two-year-old); all other recipients are described as childless. To ensure that CAs' choices have maximum impact and to avoid the need to describe entire families, we describe each recipient as single, using the words ``no partner present,'' even when the recipient has a child.\footnote{Ignoring age, state, and urbanicity, our hypothetical recipient demographics cover 23.6\% of all SNAP recipients and 34.4\% of those who report their race either as only Black or only White. Amongst individuals aged 20-29, the corresponding numbers are 34.2\% and 49.8\%. Amongst those aged 50-69, they are 37.7\% and 49.3\%. We obtain these numbers from the 2021 1-year American Community Survey Public Use Microdata Sample (PUMS) \citep{ACS} using the PUMS person weights. Those data include SNAP status, household type, gender, number of children, and race. The data only report whether a recipient has a child below 6 years of age, not whether the child is 2 or below or between 3 and 6.}


\paragraph{Additional elicitations}

To examine mechanisms and secondary hypotheses, we conduct additional elicitations. CAs reveal their beliefs about how a recipient allowed to choose \$100 in gift cards would allocate that endowment across supplier categories.\footnote{We incentivize this prediction as follows. If this prediction determines the CA's payment, she loses \$0.05 for each dollar that has to be reassigned across categories to make her prediction match the mean choice of 10 previous recipients.} CAs also indicate the percentage of gas station gift cards the recipient will spend, respectively, on gas, tobacco products, alcoholic beverages, lottery tickets, and other items.\footnote{We do not incentivize this prediction because we cannot observe the corresponding spending decisions.}

The effect of in-kind deliveries may be limited to freeing up cash, and hence may not affect recipients' overall consumption bundles. To address this concern, CAs report their beliefs about the extent of this crowd-out in grocery purchases both as a function of the food deliveries and as a function of \$65 in monthly gift-card deliveries. In-kind deliveries may also leave consumption unchanged if they go to waste. CAs report the percentage of food they believe will spoil.

We also ask CAs several questions about their attitudes concerning the SNAP program, to get an indication of the external validity of behavior in our experiment. We also elicit CAs' own demographic characteristics, welfare program participation status, and political preferences. See Appendix \ref{app:designDetails} for details about these and additional elicitations.


\paragraph{Quality safeguards.} We ensure high-quality responses through two measures. First, the survey includes two comprehension checks that each CAs must pass to finish the survey. One ensures that the CA understands the implications of her intervention decisions. The other ensures that the CA comprehends the implications of the information that the recipient's reservation price is below \$70 (or above, respectively). Second, we nudge respondents towards providing answers in the multiple price lists that exhibit at most two (in the case of intervention decisions) or one (in the case of surrogate choice) switching points. CAs who violate this condition observe a message informing them of the inconsistency. CAs can then freely choose to resolve the inconsistency or to continue with their original choices.


\section{Data} \label{sec:data}
We conducted the study with $n=1,997$ U.S. individuals participating as CAs in January and February 2024 with prolific.com targeting a sample that matches the population distribution in terms of gender, age, and political position. The median subject took 35 minutes to complete the study. Our geographic coverage extends to all 50 US states and the District of Columbia.\footnote{We ran an earlier sample with $n=4,087$ US individuals participating as CAs in August through October 2022 with the market research company Kantar. That sample was compromised by a programming error.\label{fnPilots}} On the recipient side, we consider a sample of 270 US individuals enrolled in the SNAP program at the time of participation, enlisted through InnovateMR. Additional SNAP participants had participated but provided but did not provide pictures in sufficient quality. We excluded the latter participants before running the CA sample to whom the pixellated portraits were displayed. Appendix \ref{app:demographics} shows the demographics of our samples.


Throughout, we use survey weights to render our results representative of the general US population on observables. To construct survey weights for the CA sample, we define 81 categories defined by the intersection of political preference (Republican, Democrat, or neither)\footnote{We place individuals self-describing as `strong democrat', `not very strong democrat,' and `independent, leaning democrat' into the Democrat category, those self-describing as `strong republican', `not very strong republican,' and `independent, leaning republican' into the Republican category, and those self-describing as  `independent, as far from democrats as from republicans' as well as those selecting `other' into the remaining category.}, race (white, black, other), age (18-39, 40-59, 60 or older), and sex (male, female, other). We weight each category to match the frequency of these categories in the 2022 General Social Survey \citep{GSS2021}, a nationally representative benchmark.\footnote{We keep sufficiently broad categories to prevent the weighting from diluting statistical power \citep{Miratrixetal.}. For a total of 14 individuals, there is no data in the 2021 General Social Survey. We set the weight for these participants to the first percentile of the weight distribution.} We also weight our recipient sample to make it more representative of the pool of SNAP participants according to the  2021 1-year American Community Survey Public Use Microdata Sample (PUMS) \citep{ACS} person weights. We weight by bins defined by the intersection of gender (male, female), age (18-39, 40-59, 60 or older), and race (white, black, other). We winsorize the weights for both samples at 1\% and 99\%.

While we include observations with non-monotonic responses in our structural analysis, we exclude them for our reduced-form analysis, following convention \citep[e.g.][]{Harrisonetal.MPL}. Overall, 3.9\% of subjects exhibit more than one switching point in at least one of their decisions. 0.7\% and 0.5\% of CAs exhibit multiple switching points in unconditional and conditional surrogate choice decisions, respectively, and 3.5\% of subjects exhibit multiple (usually more than two) switches in the intervention decisions.

We randomized whether prices in multiple decision lists are shown in ascending or descending order, whether beliefs about reservation prices are elicited before or after intervention and surrogate choice decisions, and whether beliefs about the gift cards recipients would select were elicited at the beginning or at the end of the experiment. In all reduced-form regressions, we control for three indicators, one for each of the three randomization dimensions. Only order in which prices are displayed exerts statistically significant effects on CAs' behavior. See Appendix \ref{appendixOrderEffects} for details.


\section{Interventions as an end in itself}\label{sec:anatomy}

Do CAs intervene if we remove the motives for in-kind transfers traditionally analyzed in the theoretical literature? Panel A of Figure \ref{figResPrices} displays the histograms of enforcement prices, pooled across all treatments. Three features stand out. First, most CAs restrict the recipient for at least some values of the gift cards. The fraction of subjects who never restrict the recipient (\emph{libertarians}) is just under 10\%; together with subjects who enforce food only if the gift card value is zero, it rises to around 20\%. Second, while enforcement prices are highly heterogeneous, the enforcement prices of the majority of CAs are positive but lower than \$130. These CAs restrict the recipient unless the opportunity cost of doing so in terms of foregone gift card consumption is sufficiently high.  Third, an additional 13\% of CAs restrict the recipient even at the highest gift card value of \$130. These \emph{authoritarian} choices are far more common than one would expect if restrictions at \$130 were just an effect of truncating the distribution of enforcement prices we observe at lower values. Instead, they appear to represent a different type of CA.


\begin{figure}
\caption{Interventions \label{figResPrices}}
\begin{center}
\begin{tabular}{cc}
\textbf{A. Enforcement prices}
&
\textbf{B. Enforcement prices vs.} \\
\textbf{ }
&
\textbf{surrogate reservation prices} \\
\includegraphics[width=0.5\textwidth]{graphs/histPat}
&
\includegraphics[width=0.5\textwidth]{graphs/histDiffPatSurr} \\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Both graphs pool across all treatments. Both show the coefficients of WLS regressions on indicator variables (one for each category on the horizontal axis); spikes show 95\% confidence intervals. Graphs exclude individuals whose choices in the corresponding multiple decision lists violate monotonicity.
\end{figure}


A potential concern is the fact that enforcing in-kind consumption is ineffective because it merely frees up cash that can be used without any ties attached. Two pieces of evidence suggest that CAs' decisions do not mainly reflect the belief that enforcement will be ineffective. First, we elicited CAs' beliefs about the extent of crowding out after they made all decisions concerning recipients. These beliefs do not predict enforcement or surrogate reservation prices, suggesting that CAs did not think of crowding out when making these decisions (see Appendix \ref{app:crowdingOut} for details). Second, CAs who impose higher enforcement prices indicate significantly less agreement with the statement that `Giving [name] a choice is the right thing to do' and significantly more with `Making sure [name] consumes food rather than the things he could buy with the gift cards is the right thing to do.' Appendix \ref{app:interventionReasons} presents details.


To what extent do enforcement prices reflect the CAs' views about what recipients should consume, and to what extent do they reflect their willingness to act on that judgment? To isolate the latter motive, we consider the difference between enforcement prices and surrogate reservation prices, shown in Panel B of Figure \ref{figResPrices}. For the modal CA, capturing just over 40\% of subjects, the two prices coincide---she fully forces the recipient to receive whatever she considers appropriate herself, without leaving any discretion to the recipient. This behavior is notable: If a CA places even a small amount of weight on recipient preferences in their assessment of what is good for the recipient, and if the CA has uncertainty about those preferences, she should grant at least some leeway to the recipient, at least absent countervailing motives such as selfish utility from intervention. It is also noteworthy that the coincidence of enforcement and surrogate reservation prices occurs across the entire range of gift-card values, which suggests that it is not simply driven by subjects who set enforcement and surrogate reservation prices equal to the cost of the food deliveries. Among the remaining CAs, enforcement prices predominantly lie below surrogate reservation prices, consistent with the intuition that a CA who seeks to account for recipient preferences but has imperfect information on them should grant the recipient some leeway.


\section{The structure of interventionist preferences}\label{model}\label{structural}


To study CA's motives in detail, we propose and structurally estimate a model of interventionist preferences. The model features two parts. The consequentialist part depends exclusively on the CA's assessment of the outcomes for the recipient. The non-consequentialist part captures motives unrelated to the recipients' outcomes, such as a selfish desire to intervene or control the recipient or a deontological aversion to restricting the recipient's autonomy. Together, these parts determine the CA's expected utility from her choices. Uncertainty enters the utility calculation because the CA may wish to condition her choices on the recipients' revealed preferences but has imperfect information about them.

We model the consequentialist part of the CAs' utility function as follows. The CA has a view about the reservation price a recipient should exhibit, denoted $p_{ought}$. That assessment is a combination of a prior $\alpha$, adjusted according to information about the recipients' revealed reservation price $p_{revealed}$:
\bmathnn
p_{ought} = \alpha + \beta_{respect} \; p_{revealed}
\emathnn
The CAs' view is that the recipient should consume the food deliveries if the value of the gift cards is $p_{ought}$ or lower, otherwise he should opt for the gift cards. $\beta_{respect}$ captures the extent to which the CA defers his normative assessment to the recipient.

Recipients might not consume the good the CA considers best for the recipient, for two reasons. First, a CA who grants a choice to the recipient forfeits control of what the recipient will choose. Second, the CA decides in light of imperfect information about the recipient's actual reservation price. We assume if a recipient's choice between the food deliveries and gift cards of value $p$ is in conflict with the CA's view, she suffers a utility loss. Assuming that the CA is more concerned about larger deviations from what she considers appropriate, we model the loss as  proportional to the distance between $p$ and $p_{ought}$. It is weighted by $w$ if a recipient contradicts the CAs' judgment by consuming gift cards, and by $(1-w)$ if he contradicts the CAs' judgment by consuming the food deliveries, with $0\leq w \leq 1$.


To model non-consequentialist effects such as warm-glow utility from intervening, we assume that the CA obtains utility $A$ from imposing a restriction on the recipient, in addition to the utility she receives through the consequentialist part. Positive values of $A$ indicate a taste for intervening, negative values indicate hesitance to intervene.


The model elements combine to the following utilities in the surrogate choice decision when the value of the gift cards is $p$:
\bmath
U(\textrm{food}) &=& (1-w) E\big[(p-p_{ought})  \left| p_{revealed} \leq p  \right. \big] P(p_{revealed} \leq p) \label{eq:uSurrogateFood} \\
U(\textrm{gift cards}) &=& w E\big[ (p_{ought}-p)  \left| p_{revealed} > p  \right. \big] P(p_{revealed} > p) \label{eq:uSurrogateCards}
\emath
The expectation is taken with respect to the CA's beliefs about the recipient's reservation price, which affects $p_{ought}$.\footnote{Equation (\ref{eq:uSurrogateFood}) is increasing in $p$ whereas equation (\ref{eq:uSurrogateCards}) is decreasing. The value of $p$ that equalizes them is the surrogate reservation price. More formally, Equation (\ref{eq:uSurrogateFood}) approaches 0 as $p$ drops to 0. Equation (\ref{eq:uSurrogateCards}) is positive for $p = 0$ and approaches 0 as $p$ increases. Hence (by the intermediate value theorem, under suitable continuity assumptions on $F$), there is a value of $p$ such that the two expressions are equal.}

The utilities from the alteratives available in intervention decisions differ from those in surrogate choice decisions in two ways. First, restricting the recipient yields non-consequentialist utility $A$ in addition to any consequentialist utility it may bring. Second, only the surrogate choice but not the intervention decision can force a recipientsto receive the gift cards. Accordingly, CAs' utility from her alternatives in the restriction decision corresponding to gift card value $p$ is given as follows.\footnote{Equation (\ref{eq:uPatRestrict}) is increasing in $p$ and equation (\ref{eq:uPatChoice}) is decreasing in $p$. The value of $p$ that equalizes the expressions is the enforcement price.}
\bmath
U(\textrm{restrict}) & = & U(\textrm{food}) + A \label{eq:uPatRestrict} \\
U(\textrm{choice}) & = & w  E\big[ (p_{ought}-p)  \left| p_{revealed} \leq p < p_{ought}  \right. \big] P(p_{revealed} \leq p < p_{ought}) \nonumber  \\
	&& + (1-w) E\big[(p-p_{ought})  \left| p_{ought} \leq p < p_{revealed}  \right. \big] P(p_{ought} \leq p < p_{revealed} ) \label{eq:uPatChoice}
\emath


Utilities map into choice through a softmax specification: In the intervention decision, the CA chooses to restrict the recipient if $U(\textrm{restrict}) - U(\textrm{choice}) \geq \epsilon_1$, and in the surrogate choice decision, the CA chooses food if $U(\textrm{food}) - U(\textrm{gift cards}) \geq \epsilon_2$, where $\epsilon_i$ is a mean zero Gaussian with variance $\sigma_{\epsilon_i}^2$ for each $i$. Surrogate choices after receiving the information whether the recipient's reservation price exceeds \$70 take the same shape, with variances $\sigma_{\epsilon_3}^2$ and $\sigma_{\epsilon_4}^2$ for the two possible pieces of information, respectively.

This model generates a unimodal distribution of enforcement and surrogate reservation prices. As Figure \ref{figResPrices}.A, some subjects impose the maximal possible enforcement price; many also impose the maximal possible surrogate reservation price. These decision are far more common than what one would expect if they reflected truncation of a Gaussian distribution of enforcement prices describing all CAs. To account for the high incidece of such choices, we assume that fraction $q$ of CAs are authoritarian types whose view is that the recipient should consume the food deliveries no matter the value of the gift cards. Our estimation accounts for the fact that other types may display maximal enforcement and surrogate reservation prices due to censoring, as in a Tobit model.


\paragraph{Identification and estimation}

Intuitively, our estimation identifies the model parameters as follows. The non-consequentialist term $A$ affects only intervention decisions but not surrogate choice decisions so a comparison between these two decisions identifies its magnitude.  Higher values of the parameter $\alpha$ that captures the CA's prior views about the reservation price a recipient should exhibit increase both enforcement and surrogate reservation prices. The updating parameter $\beta_{respect}$ reflects the change in the CAs surrogate choice once she receives revealed preference information, with larger changes indicating larger parameter values. The response to such information also contributes to the identification of the parameter $w$ (aversion to gift card consumption that contradicts the CAs normative views); it determines how much the CA changes her surrogate reservation price upon learning that the recipient prefers the food deliveries to the \$70 in gift card compared relative to how much she changes that price upon receiving the opposite information. Finally, we identify the fraction of authoritarian types $q$ from the excess mass at enforcement prices and surrogate reservation prices at the upper limit of \$130, akin to the way a Tobit model employs mass clustered at the upper bound.

The estimation of our model also requires a parametric assumption. To account for the truncation of CAs' beliefs about the distribution of recipients' reservation prices at \$130, we assume that the belief distribution  is Gaussian with mean $\mu_0$ and variance $\sigma_0^2$ (see Appendix \ref{app:beliefUpdating} for their empirical distribution). We also impose the Gaussian form for subjects' beliefs upon learning whether the recipient prefers the food deliveries over \$70 in gift cards. Because subjects' updating may not fully adhere to Bayes' law, we let the posterior distribution (truncated at \$70) take different mean and variance parameters, $\mu_1$ and $\sigma_1^2$, than the prior distribution.\footnote{We assume that $\mu_1$ and $\sigma_1^2$ apply regardless of the type of information received. This assumption describes the data well (see Appendix \ref{app:beliefUpdating}). Letting the mean and variance parameters vary based on the received information results in weak identification.}

We estimate the model using the generalized method of moments with the optimal weighting matrix estimated with the two-step procedure. We use a total of 48 moment conditions from intervention decisions as well as from conditional and unconditional surrogate choice and belief elicitation decisions.\footnote{We use the following moments: the fraction of CAs choosing the food basket at each gift card amount, both unconditionally and conditionally on each possible piece of information received (24 moments), the fraction of CAs restricting the recipient at each gift card amount in the intervention decisions (8 moments), the fraction of CAs who enforce food at every price in every decision (3 moments, both unconditionally and two conditionals), and the fraction of recipients CAs believe prefer the gift cards over the food basket at each nonzero price, both conditionally (3 moments for each of the 2 information conditions) and unconditionally (7 moments). While we elicit beliefs across 8 categories in the unconditional case, the fact that they need to add up to 1 reduces this data to 7 moments. In the conditional case, we elicit beliefs across 4 categories, which, due to the adding-up constraint, are reduced to 3 moments.} We defer technical details to Appendix \ref{sec:structuralEstimationDetails}.


\subsection{Model estimates}

What normative principles underlie our CAs' choices? Column 1 of Table \ref{tab:structuralMain} presents our model estimates. They display two key features. First, we obtain a substantial estimate of the non-consequentialist utility gain from intervention. As the bottom panel shows, the best-fitting enforcement price (excluding authoritarians) of \$61.44 drops by over two thirds (by \$43.46 to \$17.98) once we set the non-consequentialist utility from intervention to zero. Second, the consequentialist part of CAs' utility assigns substantial weight to the recipient's reservation prices ($\beta_{respect} = 0.93$). This fact opens the door to the possibility that interventions are misguided, even according to the CAs' own objectives if her beliefs about recipient reservation prices are off, a possibility we examine in Section \ref{accountingForPrefs}.


\begin{table}[htbp]
\caption{Structural estimation results} \label{tab:structuralMain}
\small
\begin{center}
\setlength{\tabcolsep}{2pt}
\begin{tabular}{lccccccc}
& (1) & (2) & (3) & & (4) & (5) &  \\
\toprule
\textbf{\textit{CA sample}} \\
\quad \textit{Food box} \\
\qquad Healthy 				& \checkmark & \checkmark 	& 			&& \checkmark & \checkmark & \\
\qquad Representative 		& \checkmark & 		 	& \checkmark 	&& \checkmark & \checkmark & \\
\quad \textit{Alcohol information} \\
\qquad Low				& \checkmark & \checkmark & \checkmark && \checkmark 	& 			& \\
\qquad High				& \checkmark & \checkmark & \checkmark &&  				& \checkmark& \\
\midrule
Difference in estimates & &&& (3)-(2) &&&(4)-(5) \\


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.849 ***& 0.856 ***& 0.847 ***& 0.009 & 0.871 ***& 0.858 ***& 0.013 \\
   &(0.017)   &(0.022)   &(0.024)   &(0.033)   &(0.022)   &(0.025)   &(0.034)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 71.581 ***& 73.278 ***& 70.168 ***& 3.109 ***& 69.399 ***& 73.796 ***& -4.397 ***\\
   &(0.428)   &(0.579)   &(0.595)   &(0.830)   &(0.531)   &(0.616)   &(0.814)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 6.785 ***& 7.291 ***& 6.228 ***& 1.062 **& 6.514 ***& 6.906 ***& -0.392 \\
   &(0.249)   &(0.364)   &(0.311)   &(0.479)   &(0.306)   &(0.348)   &(0.463)\\
\quad Fraction of absolutist types, $q$& 0.146 ***& 0.157 ***& 0.139 ***& 0.018 & 0.115 ***& 0.169 ***& -0.054 ***\\
   &(0.007)   &(0.011)   &(0.009)   &(0.014)   &(0.009)   &(0.010)   &(0.014)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 71.815 ***& 72.631 ***& 71.228 ***& 1.403 & 70.660 ***& 72.479 ***& -1.819 **\\
   &(0.463)   &(0.659)   &(0.610)   &(0.899)   &(0.579)   &(0.658)   &(0.877)\\
\quad Prior mean parameter, $\mu_0$& 70.091 ***& 70.296 ***& 69.939 ***& 0.357 & 69.494 ***& 70.311 ***& -0.817 \\
   &(0.478)   &(0.694)   &(0.623)   &(0.932)   &(0.596)   &(0.688)   &(0.910)\\
\quad Prior SD parameter, $\sigma_0$& 34.270 ***& 36.475 ***& 32.449 ***& 4.026 ***& 31.726 ***& 35.932 ***& -4.207 ***\\
   &(0.439)   &(0.674)   &(0.542)   &(0.865)   &(0.564)   &(0.618)   &(0.837)\\
\quad Posterior mean parameter, $\mu_1$& 96.120 ***& 96.357 ***& 95.792 ***& 0.565 & 95.415 ***& 96.423 ***& -1.008 \\
   &(0.409)   &(0.583)   &(0.519)   &(0.780)   &(0.589)   &(0.518)   &(0.784)\\
\quad Posterior SD parameter, $\sigma_1$& 25.958 ***& 26.983 ***& 24.510 ***& 2.473 ***& 24.609 ***& 26.714 ***& -2.105 ***\\
   &(0.359)   &(0.521)   &(0.447)   &(0.687)   &(0.457)   &(0.516)   &(0.689)\\
\\
\textbf{\textit{Best-fitting enforcement price}}& 61.80 ***& 63.22 ***& 60.12 ***& 3.10 **& 59.91 ***& 63.05 ***& -3.14 ***\\
   &(0.62)   &(0.92)   &(0.78)   &(1.21)   &(0.74)   &(0.92)   &(1.18)\\
\textbf{\textit{Counterfactuals}} \\ \quad Counterfactual: $\alpha = 0, \beta=1$ \\ \qquad Enforcement price& 56.87 ***& 57.44 ***& 56.10 ***& 1.35 & 57.04 ***& 56.57 ***& 0.48 \\
   &(0.80)   &(1.15)   &(1.02)   &(1.54)   &(0.92)   &(1.18)   &(1.50)\\
\qquad Difference to actual& 4.94 ***& 5.78 ***& 4.03 ***& 1.75 & 2.87 ***& 6.48 ***& -3.62 ***\\
   &(0.68)   &(0.96)   &(0.91)   &(1.32)   &(0.85)   &(0.98)   &(1.29)\\
\quad Counterfactual: $A=0$ \\ \qquad Enforcement price& 21.13 ***& 21.98 ***& 19.64 ***& 2.34 & 15.83 ***& 23.31 ***& -7.48 *\\
   &(2.28)   &(3.03)   &(3.20)   &(4.41)   &(3.19)   &(3.17)   &(4.50)\\
\qquad Difference to actual& 40.68 ***& 41.24 ***& 40.49 ***& 0.76 & 44.08 ***& 39.74 ***& 4.34 \\
   &(2.15)   &(2.87)   &(3.02)   &(4.17)   &(2.98)   &(3.02)   &(4.24)\\
\quad Counterfactual: correct beliefs \\ \qquad Enforcement price& 49.16 ***& 49.34 ***& 48.59 ***& 0.75 & 46.13 ***& 50.82 ***& -4.69 ***\\
   &(0.74)   &(1.09)   &(0.95)   &(1.45)   &(0.99)   &(1.04)   &(1.43)\\
\qquad Difference to actual& 12.64 ***& 13.88 ***& 11.53 ***& 2.35 ***& 13.78 ***& 12.23 ***& 1.55 **\\
   &(0.39)   &(0.58)   &(0.49)   &(0.76)   &(0.55)   &(0.55)   &(0.78)\\
\\
$N$& 1997 & 976 & 1021 & & 932 & 1065

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Each column represents a separate estimation. Best-fitting and counterfactual enforcement prices concern non-authoritarian CAs only. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Robust standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\begin{table}[htbp]
\caption{Structural estimation results} \label{tab:structuralMain}
\small
\begin{center}
\setlength{\tabcolsep}{2pt}
\begin{tabular}{lccccccc}
& (1) & (2) & (3) & & (4) & (5) &  \\
\toprule
\textbf{\textit{CA sample}} \\
\quad \textit{Food box} \\
\qquad Healthy 				& \checkmark & \checkmark 	& 			&& \checkmark & \checkmark & \\
\qquad Representative 		& \checkmark & 		 	& \checkmark 	&& \checkmark & \checkmark & \\
\quad \textit{Alcohol information} \\
\qquad Low				& \checkmark & \checkmark & \checkmark && \checkmark 	& 			& \\
\qquad High				& \checkmark & \checkmark & \checkmark &&  				& \checkmark& \\
\midrule
Difference in estimates & &&& (3)-(2) &&&(4)-(5) \\


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta_{respect}$& 1.862 ***\\
   &(0.063)\\
\qquad Ideal res. price for mean recipient, $p_{ought}$& 70.173 ***\\
   &(1.301)\\
\qquad Weight on disapp. gift cards cons., $w$& 0.500 ***\\
   &(0.019)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 8.388 ***\\
   &(0.413)\\
\quad Fraction of authoritarian types, $q$& 0.122 ***\\
   &(0.007)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior mean parameter, $\mu_0$& 70.246 ***\\
   &(0.483)\\
\quad Prior SD parameter, $\sigma_0$& 19.493 ***\\
   &(0.273)\\
\quad Posterior mean parameter, $\mu_1$& 82.218 ***\\
   &(0.550)\\
\quad Posterior SD parameter, $\sigma_1$& 15.314 ***\\
   &(0.692)\\
\textbf{\textit{Error term variances}} \\ \quad Enforcement, $\sigma_{\epsilon_1}$& 11.663 ***\\
   &(0.238)\\
\quad Surrogate choice, $\sigma_{\epsilon_2}$& 9.347 ***\\
   &(0.319)\\
\quad \textit{Conditional surrogate choice} \\ \qquad High reservation price, $\sigma_{\epsilon_3}$& 4.944 ***\\
   &(0.182)\\
\qquad Low reservation price, $\sigma_{\epsilon_4}$& 5.045 ***\\
   &(0.190)\\
\\
\textbf{\textit{Counterfactuals}} \\ \quad Best-fitting enforcement price& 58.23 \\
\quad Counterfactuals: $A=0$ \\ \qquad Counterfactual enforcement price& NaN \\
\qquad Difference to actual& NaN \\
\quad Counterfactuals: correct beliefs \\ \qquad Counterfactual enforcement price& 32.37 \\
\qquad Difference to actual& 25.86 \\
\\
$N$

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Each column represents a separate estimation. Best-fitting and counterfactual enforcement prices concern non-authoritarian CAs only. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Robust standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


These results are consistent with a specific version of ward paternalism: CAs behave as if by following the mantra `it's our money and you'll use it as we think best.' They do this not because they think they know better than the recipient what is good for him (as evidenced by the substantial responsiveness to recipient revealed preferences), but because of a non-consequentialist utility gain from controlling him.

Why, intuitively, do we obtain a positive estimate of a non-consequentialist to intervene? Any subject who is uncertain about the recipients' reservation price and whose consequentialist assessment of what he ought to consume depends on that reservation price should grant the recipient some leeway. That leeway should be larger the more the CA accounts for the recipient reservation price, and the more uncertain she is about that price. While our CAs account for recipient reservation prices to a large extent, and display substantial uncertainty about it, they only grant very little leeway to the recipient, as Panel B of Figure \ref{figResPrices} shows---often none at all.\footnote{The equalization of enforcement and surrogate reservation price occurs across the price range, not only at the price corresponding to the cost of the food deliveries, see Appendix \ref{app:structuralEstimatesRobustness}.} This behavior is inconsistent with purely consequentialist motives. Allowing for a non-consequentialist motive for intervention allows us to bridge that gap.


We test the robustness of our result on the non-consequentialist utility from intervention in two ways. First, we estimate the model on the subsample of individuals whose enforcement and surrogate reservation prices differ. We still find a strong non-consequentialist motive to intervene. The effect of counterfactually setting $A$ to zero is to drop the enforcement price by \$32.62 even on that subsample (and by \$60.27 on the subsample of individuals whose enforcement and surrogate reservation coincide). Second, we explore the drivers of our results by exogenously fixing the values of certain parameters. To show that the estimate of the non-consequentialist utility is driven by the desire to account for recipient reservation prices in the consequentialist part, we exogenously fix $\beta_{respect}$. Decreasing that parameter decreases the estimated non-consequentialist intervention motive; fixing it at 0.1 eliminates the motive. The relative weight on disapproved gift card consumption $w$, by contrast, only exerts small effects on the  estimate of the non-consequentialist intervention motive as we vary the parameter from 0.1 to 0.9. Our results are also robust to the critique that subjects may report excessively wide distributions of beliefs about recipient reservation prices. If we exogenously fix the standard deviation of the belief distribution at 10 (which compares to unconstrained estimates of 35.21 and 23.17 for the unconditional and conditional distributions, respectively), we still obtain a very substantial non-consequentialist intervention motive, as we do if we impose Bayesian belief updating.  Appendix \ref{app:structuralEstimatesRobustness} shows details.


\FloatBarrier


\subsection{Encourage the good or discourage the bad?}\label{sec:compStatics}

Next we test to what extent CAs intervene because they seek to encourage a healthy diet (encouraging the good) and to what extent they intervene because they seek to discourage disapproved consumption (discouraging the bad). Observe that both motives can be at play simultaneously.


\begin{table}[htbp]
\caption{Encouraging the good? Discouraging the bad? \label{tab:treatmentEffectsReducedForm}}
\small
\setlength{\tabcolsep}{2pt}
\begin{center}

\begin{tabular}{lcccccccc}
\multicolumn{5}{c}{\textbf{A. Encouraging the good} }\\
 & (1) & (2) & (3) & (4) \\
\toprule


Dependent variable & \multicolumn{2}{c}{Enforcement Price} & Dietary health & Enforcement Price\\ \cmidrule(r){2-3} \cmidrule(r){4-4} \cmidrule(r){5-5}
Method & WLS & WLS & WLS & 2SLS  \\ \midrule
                    &               &               &               &               \\

Beliefs about dietary health (1 to 5)&       1.527   &               &               &       0.504   \\
                    &     (0.995)   &               &               &     (1.419)   \\
\textit{Treatment}\\  \quad Healthy food deliveries&               &       0.785   &       1.531***&               \\
                    &               &     (2.215)   &     (0.048)   &               \\
Observations        &        1928   &        1928   &        1928   &        1928   \\
\bottomrule


\\
\multicolumn{5}{c}{\textbf{B. Discouraging the bad} }\\
 & (1) & (2) & (3) & (4) \\
\toprule


Dependent variable & \multicolumn{2}{c}{Enforcement Price} & Sin goods beliefs & Enforcement Price\\  \cmidrule(r){2-3} \cmidrule(r){4-4} \cmidrule(r){5-5}
Method & WLS & WLS & WLS & 2SLS  \\ \midrule
                    &               &               &               &               \\

Believed spending on sin goods in \%&       0.467***&               &               &       2.369*  \\
                    &     (0.090)   &               &               &     (0.986)   \\
\textit{Treatment}\\  \quad Information about high alc. consumption&               &       6.034** &       2.405***&               \\
                    &               &     (2.163)   &     (0.687)   &               \\
Observations        &        1928   &        1928   &        1928   &        1928   \\
\bottomrule


\end{tabular}

\end{center}
\footnotesize
\textbf{\textit{Notes:}}  In Panel A, dietary health is measured on a five-point Likert scale, encoded as follows: `If [name] receives monthly deliveries of the Food Box, how will his eating change? He will eat' 1=much less healthily, 2=slightly less healthily, 3=just as healthily or unhealthily, 4=slightly more healthily, 5=much more healthily `than usual'. In Panel B, omitted category for beliefs about spending is \textit{necessities}. Controls for the three order variables and for recipient demographics. Standard errors in parentheses. Regressions exclude subjects with multiple switches in the price lists concerning enforcement decisions. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\begin{table}[htbp]
\caption{POLITICAL LEFT ONLY: Encouraging the good? Discouraging the bad? \label{tab:treatmentEffectsReducedForm}}
\small
\setlength{\tabcolsep}{2pt}
\begin{center}

\begin{tabular}{lcccccccc}
\multicolumn{5}{c}{\textbf{A. Encouraging the good} }\\
 & (1) & (2) & (3) & (4) \\
\toprule


Dependent variable & \multicolumn{2}{c}{Enforcement Price} & Dietary health & Enforcement Price\\ \cmidrule(r){2-3} \cmidrule(r){4-4} \cmidrule(r){5-5}
Method & OLS & OLS & OLS & 2SLS  \\ \midrule
                    &               &               &               &               \\

\textit{Treatment}\\  \quad Healthy food deliveries&               &       1.760   &       1.521***&               \\
                    &               &     (2.859)   &     (0.070)   &               \\
Beliefs about dietary health (1 to 5)&       1.893   &               &               &       1.147   \\
                    &     (1.178)   &               &               &     (1.849)   \\
Observations        &         994   &         994   &         994   &         994   \\
\bottomrule


\\
\multicolumn{5}{c}{\textbf{B. Discouraging the bad} }\\
 & (1) & (2) & (3) & (4) \\
\toprule


Dependent variable & \multicolumn{2}{c}{Enforcement Price} & Sin goods beliefs & Enforcement Price\\  \cmidrule(r){2-3} \cmidrule(r){4-4} \cmidrule(r){5-5}
Method & OLS & OLS & OLS & 2SLS  \\ \midrule
                    &               &               &               &               \\

\textit{Treatment}\\  \quad Information about high alc. consumption&               &       8.581** &       2.626** &               \\
                    &               &     (2.716)   &     (0.828)   &               \\
\textit{Believed spending percentage (0 to 100)}\\ \quad Sin goods&       0.304   &               &               &       3.264*  \\
                    &     (0.163)   &               &               &     (1.451)   \\
\quad Luxury goods  &       0.205*  &               &               &       0.827** \\
                    &     (0.103)   &               &               &     (0.301)   \\
\quad Entertainment and fast food&       0.094   &               &               &       0.573*  \\
                    &     (0.091)   &               &               &     (0.253)   \\
\quad Sporting goods&      -0.152   &               &               &       0.422   \\
                    &     (0.352)   &               &               &     (0.413)   \\
\quad Gas stations, other&      -0.255*  &               &               &       0.316   \\
                    &     (0.103)   &               &               &     (0.295)   \\
Observations        &         994   &         994   &         994   &         994   \\
\bottomrule


\end{tabular}

\end{center}
\footnotesize
\textbf{\textit{Notes:}}  In Panel A, dietary health is measured on a five-point Likert scale, encoded as follows: `If [name] receives monthly deliveries of the Food Box, how will his eating change? He will eat' 1=much less healthily, 2=slightly less healthily, 3=just as healthily or unhealthily, 4=slightly more healthily, 5=much more healthily `than usual'. In Panel B, omitted category for beliefs about spending is \textit{necessities}. Controls for the three order variables and for recipient demographics. Standard errors in parentheses. Regressions exclude subjects with multiple switches in the price lists concerning enforcement decisions. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\begin{table}[htbp]
\caption{POLITICAL RIGHT ONLY: Encouraging the good? Discouraging the bad? \label{tab:treatmentEffectsReducedForm}}
\small
\setlength{\tabcolsep}{2pt}
\begin{center}

\begin{tabular}{lcccccccc}
\multicolumn{5}{c}{\textbf{A. Encouraging the good} }\\
 & (1) & (2) & (3) & (4) \\
\toprule


Dependent variable & \multicolumn{2}{c}{Enforcement Price} & Dietary health & Enforcement Price\\ \cmidrule(r){2-3} \cmidrule(r){4-4} \cmidrule(r){5-5}
Method & OLS & OLS & OLS & 2SLS  \\ \midrule
                    &               &               &               &               \\

\textit{Treatment}\\  \quad Healthy food deliveries&               &       0.277   &       1.614***&               \\
                    &               &     (3.709)   &     (0.077)   &               \\
Beliefs about dietary health (1 to 5)&      -0.028   &               &               &       0.170   \\
                    &     (1.771)   &               &               &     (2.263)   \\
Observations        &         676   &         676   &         676   &         676   \\
\bottomrule


\\
\multicolumn{5}{c}{\textbf{B. Discouraging the bad} }\\
 & (1) & (2) & (3) & (4) \\
\toprule


Dependent variable & \multicolumn{2}{c}{Enforcement Price} & Sin goods beliefs & Enforcement Price\\  \cmidrule(r){2-3} \cmidrule(r){4-4} \cmidrule(r){5-5}
Method & OLS & OLS & OLS & 2SLS  \\ \midrule
                    &               &               &               &               \\

\textit{Treatment}\\  \quad Information about high alc. consumption&               &       7.903*  &       2.601*  &               \\
                    &               &     (3.526)   &     (1.240)   &               \\
\textit{Believed spending percentage (0 to 100)}\\ \quad Sin goods&       0.446** &               &               &       3.522   \\
                    &     (0.139)   &               &               &     (2.178)   \\
\quad Luxury goods  &       0.020   &               &               &       1.217   \\
                    &     (0.123)   &               &               &     (0.832)   \\
\quad Entertainment and fast food&       0.132   &               &               &       1.370   \\
                    &     (0.118)   &               &               &     (0.864)   \\
\quad Sporting goods&      -1.004   &               &               &       1.218   \\
                    &     (0.669)   &               &               &     (1.741)   \\
\quad Gas stations, other&      -0.137   &               &               &       1.290   \\
                    &     (0.133)   &               &               &     (0.989)   \\
Observations        &         676   &         676   &         676   &         676   \\
\bottomrule


\end{tabular}

\end{center}
\footnotesize
\textbf{\textit{Notes:}}  In Panel A, dietary health is measured on a five-point Likert scale, encoded as follows: `If [name] receives monthly deliveries of the Food Box, how will his eating change? He will eat' 1=much less healthily, 2=slightly less healthily, 3=just as healthily or unhealthily, 4=slightly more healthily, 5=much more healthily `than usual'. In Panel B, omitted category for beliefs about spending is \textit{necessities}. Controls for the three order variables and for recipient demographics. Standard errors in parentheses. Regressions exclude subjects with multiple switches in the price lists concerning enforcement decisions. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\paragraph{Encouraging the good}\label{foodHealth}


We test the hypothesis that CAs seek to increase recipients' dietary health we check whether CAs who decide about the healthy food box make different choices than those who decide about the representative box, starting with reduced-form analysis. We first regress enforcement prices on CAs beliefs about how receiving the food deliveries will change recipients' dietary health, pooling across the food box types. Column 1 of Table \ref{tab:treatmentEffectsReducedForm}.A shows the result. The coefficient estimate is not only statistically insignificant, it is also small in magnitude. It implies that an increase in the CA's belief about the recipient's dietary healthy by one standard deviation (1.14 scale points) is associated with an enforcement price that is a mere \$1.74 higher. This evidence is correlational. To obtain causal estimates, Column 2 regresses enforcement prices on a treatment indicator for whether the CA decided about the healthy food box. It shows that these CAs impose enforcement prices that are a mere \$0.79 higher than those deciding about the represetative food box. The effect is far from statistically significant. We obtain this finding even though CAs assigned to the healthy food box have substantially higher beliefs about the effect on the recipient's dietary health than those assigned to the representative food box (Column 3). Because the effect of the healthy food box may work through the CA's beliefs about dietary health, we also perform instrumental variable estimation. The first stage in Column 3 is associated with an $F$-statistic exceeding 1000, making the treatment a strong instrument. Yet, as column 4 shows, the causal effect of beliefs about dietary health on interventions is negligible.


In principle, enforcement prices that are unaffected by the treatment could still reflect countervailing effects on other parameters. To test this hypothesis, we estimate our structural model separately on the subsamples of CAs who decided about the healthy and the representative food boxes, respectively. Columns 2 and 3 of Table \ref{tab:structuralMain}). We do find that CAs disapprove of mistaken gift-card consumption to a larger extent when the foregone opportunity is to consume the healthy rather than the representative food box, suggesting that CAs attach a somewhat higher normative weight to the healthy box.

Nonetheless, overall, the data suggest that encouraging a healthy diet simply is not a primary motive for the average CA in our study.


\paragraph{Discouraging the bad}

To measure the extent to which CAs seek to discourage the consumption of items of which they disapprove, we first regress enforcement prices on CAs' beliefs about how the recipient would spend \$100 in gift cards.\footnote{We bin gift cards into categories, both for easier interpretation and to prevent multicollinearity. The \emph{sin goods} category consists of spending on alcohol, tobacco, and lotteries. The \emph{necessities} category, which we use as omitted baseline, contains inexpensive clothing, home improvement, gas, and spending at gas stations not captured in the remaining categories. The \emph{luxury} category includes full-service restaurants, travel, and clothing stores that sell brand-name clothing. These categories may be viewed as inappropriately expensive for welfare recipients. The \emph{laziness} category consists of spending on on-screen entertainment and consumer electronics, fast food, and hot foods purchased at gas stations. These categories either divert the recipient's attention from more productive endeavours, or enable him to save on effort on common household tasks such as cooking. Because spending on sporting goods is antithetical to the lethargy and apathy for which CAs might condemn spending on the laziness category, we interpret it similarly, but with the opposite sign.}  The estimates in Column 1 of Table \ref{tab:treatmentEffectsReducedForm}.B suggest that per additional \$1 the CA believes the recipient would spend on sin goods (alcoholic beverages, tobacco, lotteries) his enforcement price rises by \$0.52. Raising these beliefs by 1 standard deviation (\$13.01; at the cost of the omitted category, necessities) is associated with an enforcement price that is \$5.90 higher. While this is a considerable effect, it is correlational.


To address potential endogeneity, we first regress enforcement prices on an indicator for whether the CA was in the treatment that suggested a high level of sin good consumption among recipients. We find that the treatment raises enforcement prices by  \$6.03 (Column 2). Hypothesizing that the effect of the treatment works by altering the CAs' beliefs about how the recipient would choose to spend the gift card allotment, we use an IV approach. Column 3 shows that the treatment increases beliefs about spending on sin goods, with an $F$ statistic of 12.27.\footnote{ \citet{Kim.SNAP} provides an (imperfect) benchmark for these beliefs. Using an increase in the SNAP benefits in response to the 2008 Great Recession, that paper finds a significant increase in expenditures for food consumed at home (30.8\%), but no increase in expenditures on tobacco and food away from home (she does not provide statistics regarding alcohol consumption). Instead, additional freed-up resources are spent on housing, transportation, and education.} Column 4 shows the effect of the treatment-induced change in beliefs about spending on sin goods. The coefficient of 2.6 implies that a treatment-induced change in sin-goods spending beliefs of one standard deviation increases enforcement prices by \$33.85, which is a large effect.

To test the mechanisms by which the information about sin good consumption affects CAs, we estimate our structural model separately on the subsamples of CAs who received each type of information. The results (columns 4 and 5 of Table \ref{tab:structuralMain}) show that it primarily works through two mechanisms. First, it increases the estimated weight on disapproved gift-card consumption, as one would expect from the change in beliefs our treatment induces. Second, it increases the fraction of authoritarian types by half.

Overall, for the average CA in our study, discouraging the consumption of items she regards as bad is a key motive.


\FloatBarrier


\section{To what extent are interventions misguided?}\label{accountingForPrefs}

Our structural estimates show that many CAs seek to account for recipient preferences. This opens the possibility for mistakes if beliefs about recipients are wrong. A CA who systematically overestimates recipient preferences, for instance, will unintentionally be excessively restrictive.


\begin{figure}
\caption{Believed vs. actual reservation prices and restrictiveness \label{fig:beliefs}}
\begin{center}
\begin{tabular}{cc}
\textbf{A. Recipient reservation prices}  & \textbf{B. Restrictiveness of interventions} \\
\includegraphics[width=0.5\textwidth]{graphs/chooserWTPactualBeliefsBoth1}

&\includegraphics[width=0.5\textwidth]{graphs/restrictivenessActBel} \\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Both graphs pool across both food baskets. Panel A shows cumulative distribution functions basedd on the respective population weights. Panel B shows the coefficients of WLS regressions on indicator variables, one for each category on the horizontal axis. Spikes show 95\% confidence intervals. Panel B excludes individuals whose intervention choices violate monotonicity.
\end{figure}


There are two main potential reasons why CAs would overestimate recipients' reservation prices. First, because SNAP is a means-tested program, CAs are richer than recipients. They may thus be used to paying more for groceries because they may purchase premium options (brand name or organic), because they may not search for the cheapest options as consistently as SNAP recipients, because they may select out of ordeal mechanisms that decrease sticker prices such as coupon collection, and because they may live in higher cost-of-living areas. The false consensus bias \citep{Rossetal.FalseConsensus} then renders it plausible that richter CAs overestimate what recipients would pay to purchase substitutes of our food baskets. Second, consistent with the Flaw of Averages \citep{FlawOfAverages}, CAs might underaccount for preference heterogeneity among recipients. They might believe that a food basket that represents the consumption of the average recipient is a good match to \emph{every} recipients' tastes than just to those of the average recipient. Any mismatch will decrease actual recipient reservation prices.\footnote{Consistent with the preference mismatch interpretation, 68.4\% of recipients in wave 1 reveal a switching point below their own belief about the cost of the food basket. The average recipient values the food deliveries by \$24.1 less than what he believes the food costs in store. We cannot distinguish whether this effect reflects a demand for autonomy and flexibility, or whether the food deliveries are simply a bad match to the recipients' preferences.}$^,$\footnote{A third reason is specific to our experiment. CAs might consider the average value of the food basket to the recipient, whereas the recipient considers the (lower) marginal value because the food basket is received in addition to what the recipient usually consumes.}

As Figure \ref{figResPrices}.A shows, CAs in fact greatly overestimate recipients' reservation prices. While recipients' mean reservation price is \$48.1, CAs' mean belief is \$74.75. CAs overestimate reservation prices by more than half.

Given that CAs place substantive weight on recipient preferences, these inflated beliefs will lead to excessively restrictive interventions, even according to the CAs' own value judgments. How consequential is that inflation in enforcement prices? We consider the fraction of recipients who are unintentionally restricted when the gift card value is \$60, which is the approximate cost of a food delivery. At that level of $p$, the average CA believes that 12.9\% of recipients would have chosen the gift cards but are forced to receive the food basket by her own intervention.\footnote{To calculate this number, we first calculate the fraction of recipients each CA believes have a reservation price lower than \$60. For any CA who does not restrict the recipient at \$60, we set the resulting number to zero. We then take the average across CAs.} In fact, CAs' decisions restrict 32.5\% of the recipients; more than twice the believed amount.\footnote{We calculate this number as the product of the fraction of CAs who restrict the recipient at \$60 and the fraction of recipients whose reservation price is lower than \$60.} Panel B of Figure \ref{figResPrices} shows the results of analogous computations for each value of gift cards. Subjects underestimate their restrictiveness up to a gift card value of \$105, and they do so particularly for lower gift card values.\footnote{A potential concern is the fact that the CA estimates and decisions presented here concern both real and hypothetical recipients whereas we observe WTP only for real recipients. If we focus only on CAs who decided about a real recipient, we obtain very similar numbers.}

As an alternative way to analyze the effect of excessive beliefs about recipient reservation prices, we use our structural model and replace CAs' reservation price beliefs with the actual mean (\$48.1) and standard deviation (\$43.7) of the recipients' reservation prices. We find that the enforcement price drops from \$58.6 to \$45.2 among non-authoritarian CAs, as the bottom rows of column 1 of Table \ref{tab:structuralMain} show. The drop in the mean enforcement price of \$13.5 is substantial.  We observe a similar drop both for the healthy and representative food boxes, as columns 2 and 3 show.

Overall, CAs' interventions are excessively restrictive, even according to the objectives the CAs seek to implement.


\section{The effect of recipients' and CAs' attributes and views}\label{CAandChooserDemographics}

We now turn to heterogeneity analysis. How do interventions and interventionist preferences differ across different recipients and CAs?


\subsection{Recipient attributes}\label{chooserDemographics}

The popular debate suggests that judgments about welfare policy depend on stereotypes about recipients. We test whether the demographics of the recipient a CA was randomly assigned to indeed explain her intervention choices.


\begin{landscape}
\begin{table}[htbp!]
\caption{Structural estimation results: Chooser demographics} \label{tab:structuralStereotypes}
\footnotesize
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccccccc}
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) \\
\toprule
Recipient & Black & White & Difference & Male & Female & Female & Difference & Difference \\
&&&(1)-(2)& Childless & Childless & With child & (4)-(5) & (5) - (6) \\
&&&&& \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.805***& 0.885***& -0.080**& 0.873***& 0.840***& 0.824***& 0.033 & 0.016 \\
   &(0.024)   &(0.023)   &(0.033)   &(0.030)   &(0.024)   &(0.031)   &(0.038)   &(0.039)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 74.570***& 68.310***& 6.260***& 69.500***& 71.036***& 74.217***& -1.536 & -3.181***\\
   &(0.562)   &(0.589)   &(0.814)   &(0.883)   &(0.600)   &(0.697)   &(1.068)   &(0.920)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 6.789***& 6.621***& 0.168 & 7.223***& 6.013***& 6.825***& 1.210**& -0.812 \\
   &(0.333)   &(0.343)   &(0.478)   &(0.450)   &(0.335)   &(0.392)   &(0.561)   &(0.516)\\
\quad Fraction of absolutist types, $q$& 0.169***& 0.126***& 0.044***& 0.194***& 0.130***& 0.114***& 0.064***& 0.016 \\
   &(0.010)   &(0.009)   &(0.014)   &(0.015)   &(0.010)   &(0.012)   &(0.018)   &(0.015)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 74.496***& 69.376***& 5.120***& 70.105***& 71.761***& 73.752***& -1.656 & -1.991*\\
   &(0.686)   &(0.591)   &(0.906)   &(0.867)   &(0.635)   &(0.888)   &(1.074)   &(1.091)\\
\quad Prior mean parameter, $\mu_0$& 72.439***& 67.986***& 4.453***& 67.933***& 70.495***& 71.865***& -2.563**& -1.370 \\
   &(0.706)   &(0.614)   &(0.936)   &(0.928)   &(0.638)   &(0.916)   &(1.126)   &(1.116)\\
\quad Prior SD parameter, $\sigma_0$& 36.416***& 32.129***& 4.287***& 34.976***& 32.561***& 35.581***& 2.416**& -3.021***\\
   &(0.676)   &(0.530)   &(0.859)   &(0.865)   &(0.587)   &(0.790)   &(1.045)   &(0.984)\\
\quad Posterior mean parameter, $\mu_1$& 98.796***& 94.181***& 4.614***& 97.043***& 95.610***& 96.730***& 1.433 & -1.120 \\
   &(0.655)   &(0.487)   &(0.816)   &(0.757)   &(0.637)   &(0.608)   &(0.990)   &(0.881)\\
\quad Posterior SD parameter, $\sigma_1$& 27.166***& 24.612***& 2.555***& 26.406***& 25.280***& 26.990***& 1.127 & -1.710**\\
   &(0.520)   &(0.463)   &(0.696)   &(0.583)   &(0.536)   &(0.627)   &(0.792)   &(0.825)\\
\\
$N$& 1000 & 997 & & 582 & 845 & 570 &

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Standard error on $m$ calculated with the Delta method. Each column represents a separate estimation. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}
\end{landscape}


\begin{landscape}
\begin{table}[htbp!]
\caption{POLITICAL RIGHT ONLY: Structural estimation results: Chooser demographics} \label{tab:structuralStereotypes}
\footnotesize
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccccccc}
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) \\
\toprule
Recipient & Black & White & Difference & Male & Female & Female & Difference & Difference \\
&&&(1)-(2)& Childless & Childless & With child & (4)-(5) & (5) - (6) \\
&&&&& \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta_{respect}$& 0.914 ***& 0.930 ***& -0.016 & 1.862 & 0.966 & 0.649 ***& 0.896 & 0.317 \\
   &(0.040)   &(0.037)   &(0.054)   &(NaN)   &(NaN)   &(0.032)   &(NaN)   &(NaN)\\
\qquad Ideal res. price for mean recipient, $p_{ought}$& 75.816 ***& 69.286 ***& 6.530 ***& -267.644 & 74.662 & 75.631 ***& -342.305 & -0.969 \\
   &(1.237)   &(0.945)   &(1.557)   &(NaN)   &(NaN)   &(0.939)   &(NaN)   &(NaN)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 12.900 ***& 7.783 ***& 5.117 ***& -94.244 & -3.561 & 5.113 ***& -90.683 & -8.674 \\
   &(0.780)   &(0.573)   &(0.967)   &(NaN)   &(NaN)   &(0.423)   &(NaN)   &(NaN)\\
\quad Fraction of authoritarian types, $q$& 0.362 ***& 0.150 ***& 0.212 ***& 0.000 & 0.000 & 0.184 ***& 0.000 & -0.184 \\
   &(0.020)   &(0.016)   &(0.026)   &(NaN)   &(NaN)   &(0.022)   &(NaN)   &(NaN)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior mean parameter, $\mu_0$& 75.149 ***& 69.295 ***& 5.854 ***& 76.422 & 34.432 & 73.821 ***& 41.989 & -39.388 \\
   &(1.497)   &(0.947)   &(1.771)   &(NaN)   &(NaN)   &(1.590)   &(NaN)   &(NaN)\\
\quad Prior SD parameter, $\sigma_0$& 44.085 ***& 34.354 ***& 9.731 ***& 145.582 & 40.474 & 39.665 ***& 105.108 & 0.810 \\
   &(1.239)   &(0.876)   &(1.517)   &(NaN)   &(NaN)   &(1.442)   &(NaN)   &(NaN)\\
\quad Posterior mean parameter, $\mu_1$& 99.925 ***& 93.759 ***& 6.167 ***& 155.982 & 72.739 & 98.219 ***& 83.243 & -25.479 \\
   &(1.208)   &(0.653)   &(1.373)   &(NaN)   &(NaN)   &(0.981)   &(NaN)   &(NaN)\\
\quad Posterior SD parameter, $\sigma_1$& 30.125 ***& 24.425 ***& 5.700 ***& 131.804 & 2.500 & 27.485 ***& 129.304 & -24.985 \\
   &(0.877)   &(0.701)   &(1.122)   &(NaN)   &(NaN)   &(0.882)   &(NaN)   &(NaN)\\
\textbf{\textit{Error term variances}} \\ \quad Enforcement, $\sigma_{\epsilon_1}$& 8.295 ***& 11.913 ***& -3.618 ***& 9.883 & 13.938 & 9.969 ***& -4.055 & 3.969 \\
   &(0.557)   &(0.479)   &(0.734)   &(NaN)   &(NaN)   &(0.563)   &(NaN)   &(NaN)\\
\quad Surrogate choice, $\sigma_{\epsilon_2}$& 5.356 ***& 8.657 ***& -3.301 ***& 0.427 & 8.289 & 9.846 ***& -7.862 & -1.557 \\
   &(0.108)   &(0.124)   &(0.165)   &(NaN)   &(NaN)   &(0.202)   &(NaN)   &(NaN)\\
\quad \textit{Conditional surrogate choice} \\ \qquad High reservation price, $\sigma_{\epsilon_3}$& 4.995 ***& 5.552 ***& -0.557 & 4.690 & 0.145 & 3.391 ***& 4.545 & -3.246 \\
   &(0.446)   &(0.464)   &(0.644)   &(NaN)   &(NaN)   &(0.079)   &(NaN)   &(NaN)\\
\qquad Low reservation price, $\sigma_{\epsilon_4}$& 5.092 ***& 4.010 ***& 1.082 **& 11.394 & 0.012 & 2.845 ***& 11.381 & -2.833 \\
   &(0.334)   &(0.270)   &(0.430)   &(NaN)   &(NaN)   &(0.226)   &(NaN)   &(NaN)\\
\\
\textbf{\textit{Counterfactuals}} \\ \quad Best-fitting enforcement price& 75.86 ***& 61.18 ***& 14.68 ***& NaN & 70.73 & 67.30 ***& NaN & 3.43 \\
   &(1.43)   &(1.17)   &(1.85)   &(NaN)   &(NaN)   &(1.20)   &(NaN)   &(NaN)\\
\quad Counterfactuals: $A=0$ \\ \qquad Counterfactual enforcement price& 14.11 **& 9.74 *& 4.36 & 0.00 & 77.81 & 46.13 ***& -77.81 & 31.68 \\
   &(5.67)   &(5.57)   &(7.95)   &(NaN)   &(NaN)   &(2.26)   &(NaN)   &(NaN)\\
\qquad Difference to actual& 61.75 ***& 51.43 ***& 10.32 & NaN & -7.08 & 21.17 ***& NaN & -28.25 \\
   &(5.62)   &(5.48)   &(7.85)   &(NaN)   &(NaN)   &(2.00)   &(NaN)   &(NaN)\\
\quad Counterfactuals: correct beliefs \\ \qquad Counterfactual enforcement price& 60.64 ***& 45.83 ***& 14.80 ***& NaN & 74.45 & 57.99 ***& NaN & 16.46 \\
   &(1.55)   &(1.40)   &(2.09)   &(NaN)   &(NaN)   &(1.34)   &(NaN)   &(NaN)\\
\qquad Difference to actual& 15.22 ***& 15.34 ***& -0.12 & NaN & -3.73 & 9.31 ***& NaN & -13.04 \\
   &(0.79)   &(0.91)   &(1.21)   &(NaN)   &(NaN)   &(0.67)   &(NaN)   &(NaN)\\
\\
$N$& 333 & 339 & NaN & 205 & 276 & 191 & NaN

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Standard error on $m$ calculated with the Delta method. Each column represents a separate estimation. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}
\end{landscape}


\begin{landscape}
\begin{table}[htbp!]
\caption{POLITICAL LEFT ONLY: Structural estimation results: Chooser demographics} \label{tab:structuralStereotypes}
\footnotesize
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccccccc}
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) \\
\toprule
Recipient & Black & White & Difference & Male & Female & Female & Difference & Difference \\
&&&(1)-(2)& Childless & Childless & With child & (4)-(5) & (5) - (6) \\
&&&&& \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta_{respect}$& 0.916 ***& 0.875 ***& 0.041 & 1.117 & 0.949 ***& 6.309 & 0.167 & -5.359 \\
   &(0.027)   &(0.027)   &(0.038)   &(NaN)   &(0.029)   &(NaN)   &(NaN)   &(NaN)\\
\qquad Ideal res. price for mean recipient, $p_{ought}$& 70.109 ***& 69.160 ***& 0.949 & 113.134 & 69.194 ***& 474.402 & 43.941 & -405.209 \\
   &(0.743)   &(0.736)   &(1.046)   &(NaN)   &(0.876)   &(NaN)   &(NaN)   &(NaN)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 6.254 ***& 5.831 ***& 0.423 & -4.212 & 6.529 ***& -163.403 & -10.741 & 169.932 \\
   &(0.400)   &(0.362)   &(0.540)   &(NaN)   &(0.394)   &(NaN)   &(NaN)   &(NaN)\\
\quad Fraction of authoritarian types, $q$& 0.086 ***& 0.063 ***& 0.022 *& 0.000 & 0.067 ***& 0.000 & -0.067 & 0.067 \\
   &(0.009)   &(0.007)   &(0.012)   &(NaN)   &(0.008)   &(NaN)   &(NaN)   &(NaN)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior mean parameter, $\mu_0$& 71.350 ***& 71.874 ***& -0.525 & 91.767 & 71.050 ***& 62.663 & 20.717 & 8.387 \\
   &(0.823)   &(0.765)   &(1.124)   &(NaN)   &(0.874)   &(NaN)   &(NaN)   &(NaN)\\
\quad Prior SD parameter, $\sigma_0$& 35.618 ***& 33.472 ***& 2.146 **& 7.597 & 35.495 ***& 7.291 & -27.898 & 28.204 \\
   &(0.757)   &(0.685)   &(1.021)   &(NaN)   &(0.743)   &(NaN)   &(NaN)   &(NaN)\\
\quad Posterior mean parameter, $\mu_1$& 96.381 ***& 96.263 ***& 0.118 & 101.921 & 94.863 ***& 71.899 & 7.058 & 22.964 \\
   &(0.580)   &(0.612)   &(0.843)   &(NaN)   &(0.560)   &(NaN)   &(NaN)   &(NaN)\\
\quad Posterior SD parameter, $\sigma_1$& 24.145 ***& 24.137 ***& 0.008 & 26.355 & 22.972 ***& 10.887 & 3.383 & 12.085 \\
   &(0.598)   &(0.546)   &(0.810)   &(NaN)   &(0.505)   &(NaN)   &(NaN)   &(NaN)\\
\textbf{\textit{Error term variances}} \\ \quad Enforcement, $\sigma_{\epsilon_1}$& 9.463 ***& 11.347 ***& -1.883 ***& 7.856 & 10.986 ***& 22.758 & -3.130 & -11.773 \\
   &(0.347)   &(0.354)   &(0.495)   &(NaN)   &(0.412)   &(NaN)   &(NaN)   &(NaN)\\
\quad Surrogate choice, $\sigma_{\epsilon_2}$& 7.558 ***& 8.127 ***& -0.570 & 4.019 & 8.577 ***& 10.140 & -4.558 & -1.563 \\
   &(0.356)   &(0.103)   &(0.371)   &(NaN)   &(0.128)   &(NaN)   &(NaN)   &(NaN)\\
\quad \textit{Conditional surrogate choice} \\ \qquad High reservation price, $\sigma_{\epsilon_3}$& 4.836 ***& 5.242 ***& -0.406 & 0.275 & 5.328 ***& 1.594 & -5.054 & 3.734 \\
   &(0.189)   &(0.318)   &(0.370)   &(NaN)   &(0.269)   &(NaN)   &(NaN)   &(NaN)\\
\qquad Low reservation price, $\sigma_{\epsilon_4}$& 3.766 ***& 3.227 ***& 0.539 **& 0.132 & 3.831 ***& 3.115 & -3.698 & 0.716 \\
   &(0.202)   &(0.166)   &(0.261)   &(NaN)   &(0.212)   &(NaN)   &(NaN)   &(NaN)\\
\\
\textbf{\textit{Counterfactuals}} \\ \quad Best-fitting enforcement price& 57.22 ***& 57.36 ***& -0.14 & 87.10 & 56.12 ***& 61.56 & 30.98 & -5.44 \\
   &(1.09)   &(0.86)   &(1.39)   &(NaN)   &(1.03)   &(NaN)   &(NaN)   &(NaN)\\
\quad Counterfactuals: $A=0$ \\ \qquad Counterfactual enforcement price& 9.45 **& 12.28 ***& -2.83 & 113.07 & 0.00 & 0.00 & 113.07 & -0.00 \\
   &(3.96)   &(3.88)   &(5.54)   &(NaN)   &(1.18)   &(NaN)   &(NaN)   &(NaN)\\
\qquad Difference to actual& 47.77 ***& 45.09 ***& 2.68 & -25.97 & 56.12 ***& 61.56 & -82.09 & -5.44 \\
   &(3.80)   &(3.65)   &(5.27)   &(NaN)   &(1.18)   &(NaN)   &(NaN)   &(NaN)\\
\quad Counterfactuals: correct beliefs \\ \qquad Counterfactual enforcement price& 43.36 ***& 41.02 ***& 2.34 & NaN & 41.86 ***& 71.43 & NaN & -29.57 \\
   &(1.24)   &(1.19)   &(1.72)   &(NaN)   &(1.18)   &(NaN)   &(NaN)   &(NaN)\\
\qquad Difference to actual& 13.86 ***& 16.35 ***& -2.48 **& NaN & 14.26 ***& -9.87 & NaN & 24.13 \\
   &(0.65)   &(0.72)   &(0.97)   &(NaN)   &(0.61)   &(NaN)   &(NaN)   &(NaN)\\
\\
$N$& 496 & 472 & NaN & 270 & 421 & 277 & NaN

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Standard error on $m$ calculated with the Delta method. Each column represents a separate estimation. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}
\end{landscape}


As a manipulation check, we regress spending beliefs on recipient demographics, using weighted regression and controlling for the CA's own race, gender, age, and child status, as well as for her political attitudes, income, and participation in welfare programs, the order variables, and the information treatment.\footnote{We elicited CAs' beliefs about the amount of money used for gas station gift cards in an incentivized manner. We then asked (unincentivized) what percentage of those gift cards the recipient would spend on alcoholic beverages, tobacco products, hot foods, gas, and other items. We combine the two questions to infer the dollar amount the CA believes the recipient will spend on each of these products. Due to the unpacking effect \citep{TverskyKoehler}, we expect that estimated monetary spending on alcohol and tobacco will thus be biased downwards compared to categories that are not wrapped up in the single `gas stations' category in the first question.} We find that CAs' beliefs respond sensitively to information about recipient backgrounds. We observe effects for each demographic characteristic with the exception of recipient race and hailing from a southern state.\footnote{The unexpected absence of racial effects might arise because we are asking about beliefs concerning individuals who are already SNAP participants. CAs' beliefs about becoming a SNAP participant do show pronounced racial effects (see Section \ref{sec:extValidity}). We also see significant racial effects in responses to the poverty attribution questionnaire. CAs attribute the cause of black recipients' poverty much more strongly situational factors than that of white respondents.} Nonetheless, recipient demographics do not predict enforcement prices in a WLS regression. Appendix \ref{app:manipulationCheckSterotypes} presents details.


Enforcement prices aggregate several motives. To investigate them separately, we estimate our structural model separately for each respondent demographic. Table \ref{tab:structuralStereotypes} shows the results.  As columns 1 and 2 show, recipient race  reveals interesting comparative statics: CAs respect black recipients' revealed preferences less than those of white recipients ($p<0.01$), and they display a higher prior reservation price ($p<0.01$). Moreover, they are slightly likely more authoritarian ($p<0.05$).\footnote{The small race effects are consistent with \citet{FongLuttmer} who do not find an effect of recipient race on charitable giving with a representative sample of US individuals assigned to the role of donors.}
 With respect to the gender of childless recipients, we observe a higher fraction of authoritarian decisions concerning male recipients ($p<0.05$) and a greater non-consequentialist utility gain from intervention ($p<0.05$). Focusing on women, we find that the presence of a child causes CAs to place use lower priors about the reservation price the recipient should have ($p<0.01$) and to worry less about disapproved gift-card consumption.

Overall, while demographics do not predict enforcement prices, our structural analysis shows that men and black recipients are afforded less autonomy or less deference to their own preferences.


\subsection{CA attributes}

Next, we turn to the effect of CA attributes on interventionist preferences. Our main focus lies on the CA's political attitudes.

\begin{figure}
\caption{Enforcement prices by CA political attitudes \label{fig:patPolitical}}
\begin{center}
\begin{tabular}{cc}
\includegraphics[width=0.5\textwidth]{graphs/patByPartyWeighted} &
\includegraphics[width=0.5\textwidth]{graphs/patBySNAPrestrictionsWeighted}
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}}

\end{figure}


Ex ante, three relations between CA political attitudes and interventions appear plausible. First, the classical association between libertarian attitudes and conservatism suggests that conservative CAs will intervene less than liberal CAs. Second, the more recent association between conservatism and authoritarianism suggests that conservative CAs will intervene more than liberal CAs. Third, a factor such as confidence in one's own views might cause CAs both to intervene more and to locate further from the center on the political spectrum. Panel A of Figure \ref{fig:patPolitical} displays enforcement prices by CA political attitudes. We observe a strong association between conservatism and enforcement prices.

To examine the drivers underlying this pattern, we estimate our model for the subsamples of left-leaning and right-leaning CAs, respectively. Columns 1 and 2 of Table \ref{tab:structuralTreatmentsSNAP} show the results. The parameters describing the consequentialist part of CA utility are strikingly similar across the two groups; no pair of parameter estimates shows a statistically significant difference. The difference in restrictions due to political attitudes is entirely attributable to non-consequentialist motives.

Compared to liberal CAs, conservative CAs are motivated by greater non-consequentialist utility from intervention, and are more than twice as likely to never let the recipient choose the cash equivalent for any amount in our experiment. CAs on the political right are more than two-and-a-half times as likely authoritarian types as CAs on the political left, and the estimated non-consequentialist utility parameter $A$ is significantly larger. (Yet, the effect of setting $A=0$ on enforcement prices is larger for CAs on the left, presumably due to the nonlinear nature of the model.)

To check whether other CA attributes explain intervention behavior Appendix \ref{app:CAattributes} shows in reduced-form analyses that older CAs set higher enforcement and surrogate reservation prices, whereas CAs who are themselves SNAP participants (but not recipients of other welfare programs) set lower enforcement and surrogate reservation prices. Other CA characteristics do not predict intervention behavior in reduced form analysis, even if interacted with recipient characteristics (see Appendix \ref{app:chooserCAinteractions}).


\begin{landscape}
\begin{table}[htbp]
\caption{Structural estimation results by CA characteristics and views} \label{tab:structuralTreatmentsSNAP}
\vspace{-0.4cm}
\footnotesize
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lcccccccccc}
 & (1) & (2) & (3) & (4) & (5) \\
\toprule
 &  &  & Difference &  &   & Difference  &   \\
&&&(1)-(2)&  &  & (4)-(5) &  &  \\
\textit{\textbf{CA sample}} \\
\quad Political orientation & Left & Right \\
\quad Views about SNAP restrictions &&&& Tighten & Loosen  \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.881 ***& 0.860 ***& 0.021 & 0.794 ***& 0.891 ***& -0.096 ***\\
   &(0.020)   &(0.030)   &(0.036)   &(0.024)   &(0.023)   &(0.033)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 71.366 ***& 73.668 ***& -2.303 **& 75.263 ***& 68.811 ***& 6.451 ***\\
   &(0.536)   &(0.733)   &(0.909)   &(0.668)   &(0.524)   &(0.849)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 5.755 ***& 7.832 ***& -2.077 ***& 7.926 ***& 5.516 ***& 2.411 ***\\
   &(0.289)   &(0.441)   &(0.527)   &(0.393)   &(0.271)   &(0.477)\\
\quad Fraction of absolutist types, $q$& 0.076 ***& 0.204 ***& -0.127 ***& 0.226 ***& 0.068 ***& 0.158 ***\\
   &(0.006)   &(0.013)   &(0.014)   &(0.012)   &(0.007)   &(0.014)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 73.583 ***& 72.644 ***& 0.939 & 75.243 ***& 69.194 ***& 6.049 ***\\
   &(0.581)   &(0.784)   &(0.976)   &(0.752)   &(0.528)   &(0.919)\\
\quad Prior mean parameter, $\mu_0$& 71.970 ***& 70.300 ***& 1.670 & 72.609 ***& 68.214 ***& 4.395 ***\\
   &(0.601)   &(0.819)   &(1.016)   &(0.821)   &(0.534)   &(0.980)\\
\quad Prior SD parameter, $\sigma_0$& 34.583 ***& 36.507 ***& -1.923 **& 38.379 ***& 30.348 ***& 8.031 ***\\
   &(0.537)   &(0.741)   &(0.915)   &(0.730)   &(0.478)   &(0.872)\\
\quad Posterior mean parameter, $\mu_1$& 97.489 ***& 95.257 ***& 2.232 ***& 96.989 ***& 94.777 ***& 2.212 ***\\
   &(0.477)   &(0.567)   &(0.741)   &(0.578)   &(0.533)   &(0.787)\\
\quad Posterior SD parameter, $\sigma_1$& 24.898 ***& 26.027 ***& -1.129 & 26.811 ***& 24.854 ***& 1.957 ***\\
   &(0.460)   &(0.563)   &(0.727)   &(0.515)   &(0.457)   &(0.689)\\
\\
$N$& 1031 & 700 & & 915 & 1082

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Standard error on $m$ calculated with the Delta method. Each column represents a separate estimation. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}
\end{landscape}


\subsection{In-experiment behavior and views about the SNAP program}

Finally, we relate in-experiment choices to CAs' views about the SNAP program. The results inform us about the  external validity of our earlier findings.

We find a very strong relationship between enforcement prices and CAs' answers to the question of how the restrictiveness of the SNAP program in terms of eligible items should be changed, as Panel B of Figure \ref{fig:patPolitical} shows. Mean enforcement prices of CAs who prefer to drastically loosen restrictions lie below \$50 whereas those of CAs who wish to drastically tighten them exceed \$100.

To examine in more detail of how the motives of CAs on opposite halves of this spectrum differ, columns 4 and 5 of Table \ref{tab:structuralTreatmentsSNAP} estimate our structural model on the corresponding subsamples. We find that nearly all model parameters vary, and they do so in the direction one would expect. Those in favor of tightening restrictions display less respect for recipients' revealed preferences, place more weight on disapproved gift-card consumption, derive higher non-consequentialist utility from interventions and are more than three times as likely authoritarian. These results suggest that in-experiment behavior is informative about how CAs will make political decisions outside the experiment.


\FloatBarrier


\section{Conclusion}\label{sec:conclusion}

[To be written]


\newpage


\begin{thebibliography}{99}
\bibitem{TverskyKoehler} Tversky, Amos; Koehler, Derek J. Support theory: A nonextensional representation of subjective probability.. \textit{Psychological review}. American Psychological Association. vol. 101. no. 4. pp. 547. 1994.
\bibitem{HastingsKesslerShapiro} Hastings, Justine; Kessler, Ryan; Shapiro, Jesse M. The effect of SNAP on the composition of purchased foods: Evidence; implications. \textit{American Economic Journal: Economic Policy}. American Economic Association 2014 Broadway, Suite 305, Nashville, TN 37203-2425. vol. 13. no. 3. pp. 277--315. 2021.
\bibitem{Kim.SNAP} Kim, Jiyoon. Do SNAP participants expand non-food spending when they receive more SNAP Benefits?---Evidence from the 2009 SNAP benefits increase. \textit{Food Policy}. Elsevier. vol. 65. pp. 9--20. 2016.
\bibitem{Andreoni.WarmGlow} Andreoni, James. Giving with impure altruism: Applications to charity; Ricardian equivalence. \textit{Journal of political Economy}. The University of Chicago Press. vol. 97. no. 6. pp. 1447--1458. 1989.
\bibitem{Bernheimetal.Choosing} Bernheim, B Douglas; Kim, Kristy; Taubinsky, Dmitry. Welfare; the Act of Choosing. 2024.
\bibitem{Kiesslingetal.} Kiessling, Lukas; Chowdhury, Shyamal K; Schildberg-H{\"o}risch, Hannah; Sutter, Matthias. Parental paternalism; patience. DICE Discussion Paper. no. 358. 2021.
\bibitem{Bartlingetal.} Bartling, Bj{\"o}rn; Cappelen, Alexander W; Hermes, Henning; Tungodden, Bertil; Skivenes, Marit. Free to fail? Paternalistic preferences in the United States. \textit{NHH Dept. of Economics Discussion Paper}. no. 09. 2023.
\bibitem{PikulinaTergiman} Pikulina, Elena S; Tergiman, Chloe. Preferences for power. \textit{Journal of Public Economics}. Elsevier. vol. 185. pp. 104173. 2020.
\bibitem{Zeisbergeretal.} Heeb, Florian; K{\"o}lbel, Julian F; Paetzold, Falko; Zeisberger, Stefan. Do investors care about impact?. \textit{The Review of Financial Studies}. Oxford University Press. vol. 36. no. 5. pp. 1737--1787. 2023.
\bibitem{Haidt.SocialIntuitionism} Haidt, Jonathan. The emotional dog; its rational tail: a social intuitionist approach to moral judgment.. \textit{Psychological review}. American Psychological Association. vol. 108. no. 4. pp. 814. 2001.
\bibitem{politiciansProject} Sandro Ambuehl; Sebastian Blesse; Philip D\"orrenberg; Christoph Feldhaus; Axel Ockenfels. Politicians' Social Welfare Criteria: An Experiment With German Legislators. \textit{unpublished}. 2021.
\bibitem{pew.NumberSNAP} Pew Research Center. What the data says about food stamps in the U.S.. 2021.
\bibitem{Websteretal.} Webster, Steven W; Glynn, Adam N; Motta, Matthew P. Partisan schadenfreude; candidate cruelty. \textit{Political Psychology}. Wiley Online Library. vol. 45. no. 2. pp. 259--277. 2024.
\bibitem{TenenbaumGriffithsEveryday} Griffiths, Thomas L; Tenenbaum, Joshua B. Optimal predictions in everyday cognition. \textit{Psychological science}. SAGE Publications Sage CA: Los Angeles, CA. vol. 17. no. 9. pp. 767--773. 2006.
\bibitem{FlawOfAverages} Savage, Sam L; Markowitz, Harry M. The flaw of averages: Why we underestimate risk in the face of uncertainty. John Wiley \& Sons. 2009.
\bibitem{Miratrixetal.} Miratrix, Luke W; Sekhon, Jasjeet S; Theodoridis, Alexander G; Campos, Luis F. Worth weighting? How to think about; use weights in survey experiments. \textit{Political Analysis}. Cambridge University Press. vol. 26. no. 3. pp. 275--291. 2018.
\bibitem{GSS2021} Davern, Michael; Bautista, Rene; Freese, Jeremy; Morgan, Stephen L.; Smith, Tom W.. General Social Survey 2021 Cross-section. NORC. 2021.
\bibitem{Fisher.SNAPlobbying} Andrew Fisher. The Identity Crisis of America's Largest Anti-Hunger Program. 2019.
\bibitem{foodStampAct64} Food Stamps Act. Pub. L. No. 88-525. 1964.
\bibitem{DanielsTrebilcock} Daniels, Ronald J; Trebilcock, Michael J. Rethinking the welfare state: the prospects for government by voucher. Routledge. 2004.
\bibitem{Prattoetal.SocialDominanceOrientation} Pratto, Felicia; Sidanius, Jim; Stallworth, Lisa M; Malle, Bertram F. Social dominance orientation: A personality variable predicting social; political attitudes.. \textit{Journal of personality; social psychology}. American Psychological Association. vol. 67. no. 4. pp. 741. 1994.
\bibitem{ACS} U.S. Census Bureau. American Community Survey Public Use Microdata Sample. 2021.
\bibitem{Bennettetal.PovertyAttribution} Bennett, Robert M; Raiz, Lisa; Davis, Tamara S. Development; validation of the poverty attributions survey. \textit{Journal of Social Work Education}. Taylor \& Francis. vol. 52. no. 3. pp. 347--359. 2016.
\bibitem{equivalenceScale} Organization for Economic Co-operation; Development. What are equivalence scales. 2022.
\bibitem{AmbuehlBernheim} Sandro Ambuehl; B. Douglas Bernheim. Interpreting the Will Of The People. A positive analysis of ordinal preference aggregation.. \textit{Unpublished manuscript}. 2022.
\bibitem{LehmannMatarazzo} Lehmann, M Christian; Matarazzo, Hellen. Voters' response to in-kind transfers: Quasi-experimental evidence from prescription drug cost-sharing in Brazil. \textit{Economics Letters}. Elsevier. vol. 184. pp. 108614. 2019.
\bibitem{HessamiUebelmesser} Hessami, Zohal; Uebelmesser, Silke. Empirical determinants of in-kind redistribution: Partisan biases; the role of inflation. \textit{Economics Letters}. Elsevier. vol. 118. no. 2. pp. 318--320. 2013.
\bibitem{Buhmannetal.} Buhmann, Brigitte; Rainwater, Lee; Schmaus, Guenther; Smeeding, Timothy M. Equivalence scales, well-being, inequality,; poverty: sensitivity estimates across ten countries using the Luxembourg Income Study (LIS) database. \textit{Review of income; wealth}. Wiley Online Library. vol. 34. no. 2. pp. 115--142. 1988.
\bibitem{Coulteretal.} Coulter, Fiona AE; Cowell, Frank A; Jenkins, Stephen P. Equivalence scale relativities; the extent of inequality; poverty. \textit{The Economic Journal}. JSTOR. pp. 1067--1082. 1992.
\bibitem{Caplan.WardPaternalism} Bryan Caplan. Nudge: Welfare State Edition. 2020.
\bibitem{Kivikangasetal.} Kivikangas, J Matias; Fern{\'a}ndez-Castilla, Bel{\'e}n; J{\"a}rvel{\"a}, Simo; Ravaja, Niklas; L{\"o}nnqvist, Jan-Erik. Moral foundations; political orientation: Systematic review; meta-analysis.. \textit{Psychological Bulletin}. American Psychological Association. vol. 147. no. 1. pp. 55. 2021.
\bibitem{Grahametal.MFQ} Graham, Jesse; Haidt, Jonathan; Nosek, Brian A. Liberals; conservatives rely on different sets of moral foundations.. \textit{Journal of personality; social psychology}. American Psychological Association. vol. 96. no. 5. pp. 1029. 2009.
\bibitem{Harrisonetal.MPL} Harrison, Glenn W; Lau, Morten Igel; Rutstr{\"o}m, Elisabet E; Sullivan, Melonie B. Eliciting risk; time preferences using field experiments: Some methodological issues. In Field experiments in economics. Emerald Group Publishing Limited. 2005.
\bibitem{Marinescu} Marinescu, Ioana. No strings attached: The behavioral effects of US unconditional cash transfer programs. \textit{NBER working paper}. National Bureau of Economic Research. 2018.
\bibitem{Nestle.SNAPhistory} Nestle, Marion. The Supplemental Nutrition Assistance Program (SNAP): history, politics,; public health implications. \textit{American journal of public health}. American Public Health Association. vol. 109. no. 12. pp. 1631--1635. 2019.
\bibitem{SalaiMartin} Sala-i-Martin, Xavier X. A positive theory of social security. \textit{Journal of Economic Growth}. Springer. vol. 1. pp. 277--304. 1996.
\bibitem{Harberger.BasicNeeds} Harberger, Arnold C. Basic needs versus distributional weights in social cost-benefit analysis. \textit{Economic Development; Cultural Change}. University of Chicago Press. vol. 32. no. 3. pp. 455--474. 1984.
\bibitem{BeckerMulligan} Becker, Gary S; Mulligan, Casey B. Accounting for the Growth of Government. In Essays on Government Growth. Springer. vol. 40. pp. 1--40. 2021.
\bibitem{BrennanWalsh} Brennan, Geoffrey; Walsh, Cliff. Pareto-desirable redistribution in kind: an impossibility theorem. \textit{The American Economic Review}. JSTOR. vol. 67. no. 5. pp. 987--990. 1977.
\bibitem{Helmsetal.} Helms, Sara E; Scott, Brian L; Thornton, Jeremy P. Choosing to give more: experimental evidence on restricted gifts; charitable behaviour. \textit{Applied Economics Letters}. Taylor \& Francis. vol. 19. no. 8. pp. 745--748. 2012.
\bibitem{Jones.Paternalism} Jones, Kristy. Paternalism; ethnicity in giving. \textit{Economic Record}. Wiley Online Library. vol. 93. no. 302. pp. 420--433. 2017.
\bibitem{Gangadharanetal.} Gangadharan, Lata; Grossman, Philip J; Jones, Kristy; Leister, C Matthew. Paternalistic giving: Restricting recipient choice. \textit{Journal of Economic Behavior \& Organization}. Elsevier. vol. 151. pp. 143--170. 2018.
\bibitem{Musgrave.Palgrave} Richard A. Musgrave. Merit Goods. In The New Palgrave: A dictionary of economics. Macmillan. 1998.
\bibitem{Olsen} Olsen, Edgar O. Pareto-Desirable Redistribution in Kind: Comment. \textit{The American Economic Review}. JSTOR. vol. 70. no. 5. pp. 1028--1031. 1980.
\bibitem{LiscowPershing} Liscow, Zachary; Pershing, Abigail. Why is so much redistribution in-kind; not in cash? Evidence from a survey experiment. \textit{National Tax Journal}. The University of Chicago Press. vol. 75. no. 2. pp. 000--000. 2022.
\bibitem{SNAPcharacteristics} USDA. Characteristics of supplemental nutrition assistance program households: fiscal year 2019. 2021.
\bibitem{Banerjeeetal.} Banerjee, Abhijit; Hanna, Rema; Olken, Benjamin A; Satriawan, Elan; Sumarto, Sudarno. Food vs. Food Stamps: Evidence from an At-Scale Experiment in Indonesia. 2021.
\bibitem{FryerLevitt} Fryer Jr, Roland G; Levitt, Steven D. The causes; consequences of distinctively black names. \textit{The Quarterly Journal of Economics}. MIT Press. vol. 119. no. 3. pp. 767--805. 2004.
\bibitem{Simonsohn.Names} Uri Simonsohn. Greg vs. Jamal: Why Didn't Bertrand; Mullainathan (2004) Replicate?. 2016.
\bibitem{ParkLabPictures} Minear, Meredith; Park, Denise C. A lifespan database of adult facial stimuli. \textit{Behavior research methods, instruments, \& computers}. Springer. vol. 36. no. 4. pp. 630--633. 2004.
\bibitem{Tzioumis} Tzioumis, Konstantinos. Demographic aspects of first names. \textit{Scientific data}. Nature Publishing Group. vol. 5. no. 1. pp. 1--9. 2018.
\bibitem{Zhangetal.SNAP} Zhang, Fang Fang; Liu, Junxiu; Rehm, Colin D; Wilde, Parke; Mande, Jerold R; Mozaffarian, Dariush. Trends; disparities in diet quality among US adults by Supplemental Nutrition Assistance Program participation status. \textit{JAMA network open}. American Medical Association. vol. 1. no. 2. pp. e180237--e180237. 2018.
\bibitem{AmericanHeartAssociation} Lloyd-Jones, Donald M; Hong, Yuling; Labarthe, Darwin; Mozaffarian, Dariush; Appel, Lawrence J; Van Horn, Linda; Greenlund, Kurt; Daniels, Stephen; Nichol, Graham; Tomaselli, Gordon F; others. Defining; setting national goals for cardiovascular health promotion; disease reduction: the American Heart Association's strategic Impact Goal through 2020; beyond. \textit{Circulation}. Am Heart Assoc. vol. 121. no. 4. pp. 586--613. 2010.
\bibitem{Akessonetal.} Jesper Akesson; Robert W. Hahn; Robert D. Metcalfe; Itzhak Rasooly. Race; Redistribution in the United States: An Experimental Analysis. \textit{Unpublished manuscript}. 2022.
\bibitem{AmbuehlBernheimOckenfels} Ambuehl, Sandro; Bernheim, B Douglas; Ockenfels, Axel. What motivates paternalism? An experimental study. \textit{American economic review}. vol. 111. no. 3. pp. 787--830. 2021.
\bibitem{Beattyetal.} Beatty, Timothy KM; Blow, Laura; Crossley, Thomas F; O'Dea, Cormac. Cash by any other name? Evidence on labeling from the UK Winter Fuel Payment. \textit{Journal of Public Economics}. Elsevier. vol. 118. pp. 86--96. 2014.
\bibitem{HastingsShapiro} Hastings, Justine; Shapiro, Jesse M. How are SNAP benefits spent? Evidence from a retail panel. \textit{American Economic Review}. vol. 108. no. 12. pp. 3493--3540. 2018.
\bibitem{AbelerMarklein} Abeler, Johannes; Marklein, Felix. Fungibility, labels,; consumption. \textit{Journal of the European Economic Association}. Oxford University Press. vol. 15. no. 1. pp. 99--127. 2017.
\bibitem{IyengarWestwood} Iyengar, Shanto; Westwood, Sean J. Fear; loathing across party lines: New evidence on group polarization. \textit{American Journal of Political Science}. Wiley Online Library. vol. 59. no. 3. pp. 690--707. 2015.
\bibitem{HoyMager} Hoy, Christopher; Mager, Franziska. Why Are Relatively Poor People Not More Supportive of Redistribution? Evidence from a Randomized Survey Experiment across 10 Countries. \textit{American Economic Journal: Economic Policy}. 2019.
\bibitem{Duranteetal.} Durante, Ruben; Putterman, Louis; Van der Weele, Jo{\"e}l. Preferences for redistribution; perception of fairness: An experimental study. \textit{Journal of the European Economic Association}. Oxford University Press. vol. 12. no. 4. pp. 1059--1086. 2014.
\bibitem{TFP92021} U.S. Department of Agriculture. Official USDA Thrifty Food Plan: U.S. Average, September 2021. 2021.
\bibitem{HagertyBarasz} Hagerty, Serena F; Barasz, Kate. Inequality in socially permissible consumption. \textit{Proceedings of the National Academy of Sciences}. National Acad Sciences. vol. 117. no. 25. pp. 14084--14093. 2020.
\bibitem{Brownbacketa.BehavioralFoodSubsidies} Brownback, Andy; Imas, Alex; Kuhn, Michael. Behavioral Food Subsidies. \textit{Available at SSRN 3422272}. 2020.
\bibitem{SadoffSamek.RecipientContributionRequirements} Sadoff, Sally; Samek, Anya. The effect of recipient contribution requirements on support for social programs. \textit{Journal of Public Economics}. Elsevier. vol. 169. pp. 1--16. 2019.
\bibitem{Sadoffetal.FoodDeserts} Sadoff, Sally; Samek, Anya; Sprenger, Charles. Dynamic inconsistency in food choice: Experimental evidence from two food deserts. \textit{The Review of Economic Studies}. Oxford University Press. vol. 87. no. 4. pp. 1954--1988. 2020.
\bibitem{SadoffSamek} Sadoff, Sally; Samek, Anya. Can interventions affect commitment demand? A field experiment on food choice. \textit{Journal of Economic Behavior \& Organization}. Elsevier. vol. 158. pp. 90--109. 2019.
\bibitem{Alesinaetal.IntergenerationalMobility} Alesina, Alberto; Stantcheva, Stefanie; Teso, Edoardo. Intergenerational mobility; preferences for redistribution. \textit{American Economic Review}. vol. 108. no. 2. pp. 521--54. 2018.
\bibitem{Gilens} Gilens, Martin. Why Americans hate welfare: Race, media,; the politics of antipoverty policy. University of Chicago Press. 1999.
\bibitem{EllisFaricy} Ellis, Christopher; Faricy, Christopher. Race,``Deservingness,''; Social Spending Attitudes: The Role of Policy Delivery Mechanism. \textit{Political Behavior}. Springer. vol. 42. no. 3. pp. 819--843. 2020.
\bibitem{Fongetal.Handbook} Fong, Christina M; Bowles, Samuel; Gintis, Herbert. Strong reciprocity; the welfare state. \textit{Handbook of the economics of giving, altruism; reciprocity}. Elsevier. vol. 2. pp. 1439--1464. 2006.
\bibitem{AaroePetersen} Aar{\o}e, Lene; Petersen, Michael Bang. Crowding out culture: Scandinavians; Americans agree on social welfare in the face of deservingness cues. \textit{The Journal of Politics}. Cambridge University Press New York, USA. vol. 76. no. 3. pp. 684--697. 2014.
\bibitem{Dahlbergetal.} Dahlberg, Matz; Edmark, Karin; Lundqvist, Hel{\'e}ne. Ethnic diversity; preferences for redistribution. \textit{Journal of Political Economy}. University of Chicago Press Chicago, IL. vol. 120. no. 1. pp. 41--76. 2012.
\bibitem{BenabouTirole.JustWorld} Benabou, Roland; Tirole, Jean. Belief in a just world; redistributive politics. \textit{The Quarterly journal of economics}. MIT Press. vol. 121. no. 2. pp. 699--746. 2006.
\bibitem{Cavaille} Cavaill{\'e}, Charlotte. Deservingness, self-interest; the welfare state: Why some care more about deservingness than others; why It matters. \textit{Unpublished manuscript}. 2015.
\bibitem{White.Kant} White, Mark D. Can homo economicus follow Kant's categorical imperative?. \textit{The Journal of Socio-Economics}. Elsevier. vol. 33. no. 1. pp. 89--106. 2004.
\bibitem{Meckel} Meckel, Katherine. Is the Cure Worse than the Disease? Unintended Effects of Payment Reform in a Quantity-based Transfer Program. \textit{American Economic Review}. vol. 110. no. 6. pp. 1821--65. 2020.
\bibitem{Luttmer} Luttmer, Erzo FP. Group loyalty; the taste for redistribution. \textit{Journal of Political Economy}. The University of Chicago Press. vol. 109. no. 3. pp. 500--528. 2001.
\bibitem{Mead} Mead, Lawrence M. Government matters: Welfare reform in Wisconsin. Princeton University Press. 2005.
\bibitem{andreoniEtAl} Andreoni, James; Aydin, Deniz; Barton, Blake; Bernheim, B Douglas; Naecker, Jeffrey. When Fair Isn't Fair: Understanding Choice Reversals Involving Social Preferences. \textit{Journal of Political Economy}. The University of Chicago Press Chicago, IL. vol. 128. no. 5. pp. 1673--1711. 2020.
\bibitem{alesinaGlaeser} Alesina, Alberto; Glaeser, Edward. Fighting poverty in the US; Europe: A world of difference. Oxford University Press. 2004.
\bibitem{wettsWiller} Wetts, R.; R. Willer. Privilege on the precipice: Perceived racial status threats lead White Americans to oppose welfare programs. \textit{Social Forces}. vol. 97. no. 2. pp. 793-822. 2018.
\bibitem{Sabia} Sabia, J.. Blacks; the family cap: pregnancy, abortion,; spillovers. \textit{Journal of Population Economics}. vol. 21. no. 1. pp. 111-134. 2008.
\bibitem{Quadagno} Quadagno, J.. The color of welfare: How racism undermined the war on poverty. Oxford University Press. 1994.
\bibitem{PiersonBalik} Pierson-Balik, D.. Race, class,; gender in punitive welfare reform: social eugenics; welfare policy. \textit{Race, Gender \& Class}. pp. 11-30. 2003.
\bibitem{neubeckCazenave} Neubeck, K.; N. Cazenave. Welfare racism: Playing the race card against America's poor. Routledge. 2002.
\bibitem{Mulligan} Mulligan, C.. Work incentives, the Recovery Act,; the economy. In Testimony for the Committee on Oversight; Government Reform, U.S. House of Representatives Subcommittee on Economic Growth, Job Creation; Regulatory Affairs. 2013.
\bibitem{Jagannathan} Jagannathan, R.; Camasso, M.. Family cap; nonmarital fertility: The racial conditioning of policy effects. \textit{Journal of Marriage; Family}. vol. 65. no. 1. pp. 52-71. 2003.
\bibitem{Hancock} Hancock, A. M.. Intersectionality as a normative; empirical paradigm. \textit{Politics; Gender}. vol. 3. no. 2. pp. 248. 2008.
\bibitem{groggerEtAl} Grogger, J.; Karoly, L.; Klerman, J.. Consequences of welfare reform: A research synthesis. \textit{Doc. DRU-2676--DHHS by RAND for Agency for Children; Families, Dept. Health Human Serv. Santa Monica, CA}. 2006.
\bibitem{DeWitt} DeWitt, L.. The decision to exclude agricultural; domestic workers from the 1935 Social Security Act. \textit{Social Security Bulletin}. vol. 70. pp. 49. 2010.
\bibitem{burnham} Burnham, L.. Racism in United States welfare policy. \textit{Race, Poverty,; the Environment}. vol. 14. no. 1. pp. 47-50. 2007.
\bibitem{alstonFerrie} Alston, L.J.; Ferrie, J. P.. Paternalism in agricultural labor contracts in the US South: Implications for the growth of the welfare state. \textit{The American Economic Review}. pp. 852-876. 1993.
\bibitem{Cappelenetal.SecondBestFairness} Cappelen, Alexander W; Cappelen, Cornelius; Tungodden, Bertil. Second-best fairness under limited information: The trade-off between false positives; false negatives. \textit{NHH Dept. of Economics Discussion Paper}. no. 18. 2018.
\bibitem{BertrandMullainathan} Bertrand, Marianne; Mullainathan, Sendhil. Are {E}mily; {G}reg more employable than Lakisha; Jamal? A field experiment on labor market discrimination. \textit{American Economic Review}. vol. 94. no. 4. pp. 991--1013. 2004.
\bibitem{Hungerman} Hungerman, Daniel M. Race; charitable church activity. \textit{Economic inquiry}. Wiley Online Library. vol. 46. no. 3. pp. 380--400. 2008.
\bibitem{FongLuttmer} Fong, Christina M; Luttmer, Erzo FP. Do fairness; race matter in generosity? Evidence from a nationally representative charity experiment. \textit{Journal of Public Economics}. Elsevier. vol. 95. no. 5-6. pp. 372--394. 2011.
\bibitem{Ripken} Ripken, Susanna Kim. Paternalism; Securities Regulation. \textit{Stan. JL Bus. \& Fin.}. HeinOnline. vol. 21. pp. 1. 2015.
\bibitem{ThomasBuckmaster} Thomas, Matthew; Buckmaster, Luke. Paternalism in Social Policy: When Is It Justifiable?. \textit{Parliamentary Library, Parliament of Australia}. Commonwealth of Australia. 2010.
\bibitem{AltreiterLeibetseder} Altreiter, Carina; Leibetseder, Bettina. Constructing inequality: Deserving; undeserving clients in Austrian social assistance offices. \textit{Journal of Social Policy}. Cambridge University Press. vol. 44. no. 1. pp. 127--145. 2015.
\bibitem{DrenikPerezTruglia} Perez-Truglia, Ricardo; Drenik, Andres. Sympathy for the Diligent; the Demand for Workfare. \textit{Journal of Economic Behavior \& Organization}. Elsevier. vol. 153. pp. 77--102. 2018.
\bibitem{Tobin.SpecificEgalitarianism} Tobin, James. On limiting the domain of inequality. \textit{The Journal of Law; Economics}. The University of Chicago Law School. vol. 13. no. 2. pp. 263--277. 1970.
\bibitem{CampbellGaddis} Campbell, Colin; Gaddis, S Michael. ``I Don't Agree with Giving Cash'': A Survey Experiment Examining Support for Public Assistance. \textit{Social Science Quarterly}. Wiley Online Library. vol. 98. no. 5. pp. 1352--1373. 2017.
\bibitem{Jackman} Jackman, Mary R. The velvet glove: Paternalism; conflict in gender, class,; race relations. Univ of California Press. 1994.
\bibitem{Baker.RacePaternalism} Baker, Andy. Race, paternalism,; foreign aid: Evidence from US public opinion. \textit{American Political Science Review}. Cambridge University Press. vol. 109. no. 1. pp. 93--109. 2015.
\bibitem{GlickFiske.AmbivalentSexism} Glick, Peter; Fiske, Susan T. The Ambivalent Sexism Inventory: Differentiating hostile; benevolent sexism.. \textit{Journal of Personality; Social Psychology}. American Psychological Association. vol. 70. no. 3. pp. 491. 1996.
\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. IZA Discussion Paper. 2018.
\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{BernheimTaubinsky} Bernheim, B Douglas; Taubinsky, Dmitry. Behavioral public economics. \textit{NBER working paper}. 2018.
\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{Clemens} Clemens, Michael. Testing for Repugnance in Economic Transactions: Evidence from Guest Work in the Gulf. \textit{unpublished}. 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{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{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{Jacobssonetal.} 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. Efficiency-morality trade-offs in repugnant transactions: A choice experiment. \textit{unpublished}. 2016.
\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{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


\begin{appendix}


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

\begin{center}
{\Huge \textbf{ONLINE APPENDIX}}\\
\vspace{1cm}
{\Large   \textbf{\Large Interventionist Preferences and the Welfare State: \\ The Case of In-Kind Nutrition Assistance}  \\  \bigskip \bigskip Sandro Ambuehl, B. Douglas Bernheim, Tony Q. Fan, Zach Groff}
\end{center}


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

\newpage
\pagenumbering{arabic}

\section{Additional design materials}

\subsection{Food basket content} \label{foodBoxContents}\label{app:foodBox}

The Healthy Basket does not include discouraged nutrients such as added sugar and processed meats. We include low-sodium versions of food items. While we include many fresh fruits and vegetables, we also include items such as frozen spinach and canned tomato sauce to increase the shelf-life of the food deliveries.

At the prices on walmart.com in June 2022 for Sacramento, the contents of the healthy and representative food boxes cost \$51.68 and \$51.73, respectively. Actual deliveries to recipients dispersed across the US varied by location but usually cost between \$60 and \$70.


\begin{table}
\caption{Contents of the healthy food basket \label{contentsBoxHealthy}}
\begin{center}
\begin{tabular}{lccc}
\toprule
                             &Package size & Amount & Unit price \\
\midrule
\textbf{Unsaturated fats}             &            &        &       \\
Canned salmon                & 14 oz      & 1      & 3.24  \\
Olive Oil                    & 8.54 fl oz & 1      & 2.77  \\
                             &            &        &       \\
\textbf{Fiber}                        &            &        &       \\
Rolled oats (whole grain)    & 42 oz      & 1      & 2.58  \\
Whole wheat bread            & 20 oz      & 2      & 1.48  \\
                             &            &        &       \\
\textbf{Legumes, nuts, seeds}         &            &        &       \\
Chickpeas                    & 15.5oz    & 2      & 0.72  \\
Pinto beans                  & 15.5oz     & 2      & 0.72  \\
Walnuts                      & 4oz       & 1      & 2.36  \\
                             &            &        &       \\
\textbf{Vegetables}                   &            &        &       \\
Broccoli Florets, Frozen     & 32oz      & 1      & 2.24  \\
Whole carrots                & 1ct        & 1      & 0.98  \\
Bell peppers                 & 3ct        & 1      & 3.37  \\
Roma tomatoes                & 1ct        & 4      & 0.29  \\
Cucumber                     & 1ct        & 2      & 0.50   \\
Iceberg Lettuce              & 1ct        & 1      & 1.68  \\
Frozen peas                  & 2lb        & 1      & 2.22  \\
Low-salt canned tomato sauce & 8oz        & 1      & 0.38  \\

No salt added whole kernel corn	& 15.25 oz &	2& 0.58 \\
Canned sliced carrots	& 8.25 oz & 1	& 0.50 \\
                            &            &        &       \\
\textbf{Fruit}                        &            &        &       \\
Fuji Apples                  & 3lb        & 1      & 3.98  \\
Bananas                      & 1ct        & 6      & 0.23  \\
Navel oranges                & 2lb        & 1      & 5.23  \\
Frozen berries               & 16oz       & 1      & 2.68  \\
Apple sauce, no sugar added	& 3.9 oz & 6& 0.42 \\
                             &            &        &       \\
\textbf{Other}                        &            &        &       \\
Russet potatoes              & 5lb        & 1      & 2.37  \\
Milk                         & 0.5gal & 1      & 2.06 \\
\bottomrule
\end{tabular}
\end{center}
\end{table}


\begin{table}
\caption{Contents of the representative food basket \label{contentsBoxRepresentative}}

\begin{center}
\begin{tabular}{lccc}
\toprule
                                      & Package size & Amount & Unit price \\
\midrule
\textbf{Soda}                                  &              &        &            \\
Coca cola 2L                          & 2 L          & 2      & 2.14       \\
Mountain Dew 2L                       & 2 L          & 1      & 1.98       \\
Fanta 2L                              & 2 L          & 1      & 1.36       \\
                                      &              &        &            \\
\textbf{Cookies, and snacks}                   &              &        &            \\
Oreo style cookies                    & 14.3 oz      & 1      & 2.28       \\
Honey Roasted Peanuts                 & 2.875 oz     & 2      & 0.98       \\
Pringles                              & 5.2 oz       & 1      & 1.78       \\
Buttery Crackers                      & 13.7 oz      & 1      & 2.18       \\
                                      &              &        &            \\
\textbf{Ready-made meals}                      &              &        &            \\
Canned spaghetti Os                   & 15.8 oz      & 4      & 0.89       \\
Canned beef with vegetable soup       & 18.8 oz      & 1      & 1.46       \\
Canned beef ravioli                   & 15 oz        & 1      & 1.24       \\
Canned pea soup                       & 18.8 oz      & 1      & 1.98       \\
                                      &              &        &            \\
\textbf{Meat and fish}                         &              &        &            \\
Oscar Meyer wieners                   & 8 ct         & 1      & 2.86       \\
Pulled pork in BBQ sauce (pouch)      & 2.6 oz       & 1      & 1.34       \\
Canned tuna                           & 5 oz         & 2      & 0.78       \\
Rotisserie chicken                    & 9 oz         & 1      & 3.48       \\
                                      &              &        &            \\
\textbf{Dairy}                                 &              &        &            \\
2\% Milk                              & 0.5 gal      & 1      & 2.06       \\
Cheddar cheese block                  & 8 oz         & 1      & 1.86       \\
Yoghurt                               & 6 oz         & 1      & 0.64       \\
                                      &              &        &            \\
\textbf{Fruit and vegetable products}     &        &            \\
Dill pickles                          & 24 fl oz     & 1      & 1.76       \\
Jar of roasted red bell pepper slices & 12 oz        & 1      & 1.38       \\
Banana                                & 1 ct         & 6      & 0.23       \\
Orange juice                          & 4 fl oz      & 1      & 2.58       \\
Canned green beans                    & 14.5 oz      & 1      & 0.98       \\
Canned tomato sauce                   & 8 oz         & 1      & 0.29       \\
                                      &              &        &            \\
\textbf{Carbs and proteins}                    &              &        &            \\
Dried spaghetti                       & 16 oz        & 1      & 0.92       \\
White bread loaf                      & 20 oz        & 2      & 0.93       \\
Crunchy honey oats                    & 18 oz        & 1      & 2.72      \\
\bottomrule
\end{tabular}
\end{center}
\end{table}

\FloatBarrier

\subsection{Restrictions on Gas Station Sales} \label{restrictions}\label{app:gasstation}

\begin{table}[H]
    \centering
    \renewcommand{\arraystretch}{1.5}
    \setlength{\tabcolsep}{6pt}
    \caption{Regulations on Alcohol, Tobacco, and Lottery Sales \label{restrictions}}
    \begin{tabular}{p{2.5cm}p{3.5cm}p{5.5cm}p{2.5cm}}
        \hline\hline
        \textbf{States} & \multicolumn{1}{c}{\textbf{Alcohol}} & \multicolumn{1}{c}{\textbf{Tobacco}} & \multicolumn{1}{c}{\textbf{Lottery}} \\
        \toprule
        \textbf{Kentucky} & Permits the sale of beer and wine, subject to local ordinances. & License and age requirements. &  License and age requirements. \\
        \midrule
        \textbf{Mississippi} & Allowed to sell beer and light wine, subject to local ordinances. & License and age requirements. &  License and age requirements (21 and over). \\
        \midrule
        \textbf{West Virginia} & Allowed to sell beer and light wine, subject to local ordinances. &  License and age requirements. &  License and age requirements. \\
        \midrule
        \textbf{Connecticut} & Allowed to sell beer. &  License and age requirements. & License and age requirements. \\
        \midrule
        \textbf{Massachusetts} & Permits the sale of beer and wine, subject to local ordinances. & Cannot sell any flavored e-cigarettes or vaping products. Can only sell non-flavored e-cigarettes with 35mg/ml or less nicotine content. &  License and age requirements. \\
        \midrule
        \textbf{Rhode Island} & Allowed to sell beer, subject to regulations and licensing requirements. & Restrictions on the sale of flavored tobacco products are subject to local ordinances. &  License and age requirements. \\
        \bottomrule
    \end{tabular}
    \vskip 10pt
    \parbox{\textwidth}{\footnotesize
    \textit{\textbf{Notes:}} The table shows the restrictions on what gas stations are allowed to sell in the states of Kentucky, Mississippi, West Virginia, Connecticut, Massachusetts, and Rhode Island. For the sale of alcohol and tobacco, a license must be obtained, and sales are permitted to individuals 21 and over. For the sale of lotteries, the age restriction is 18 and over, unless stated otherwise. A state may have dry (alcohol prohibited), wet (no restrictions beyond age), and/or moist (additional restrictions beyond age) counties. As an exception, Connecticut has no dry counties. Local ordinances may be at the county or city level.}
\end{table}

\subsection{Details}\label{app:designDetails}


Instructions immediately precede each scenario. Throughout, a bar at the top displays the pixellated photo of the recipient, the description of the recipient's characteristics, and two buttons that the user can click to show the contents of the food basket or the selection of gift cards from which the recipient may choose.

Each time, they may also click a button to recommend the food deliveries or to recommend the gift cards. They know that if they do so, the recipient's choice interface will display the message ``A previous study participant recommends that you choose the monthly [food box / gift cards]!'' In addition, subjects can write an open-ended message to the recipient (knowing that that offensive language will be removed). The instructions include a clickable box with the text ``Please click this box to confirm that you are following these instructions.'' Subjects who do not click it are terminated automatically.


We elicit beliefs about crowding out of grocery spending through receiving the food deliveries, in intervals $\{[\infty, -50], [-50, -30], [-30, -15], [-15, -5], [-5, 5], [5, 15], [15, 30], [30, 50], [50, \infty]\}$. We incentivize accuracy; a subject who selects the interval that is correct according to \citet{HastingsShapiro} receives a bonus of \$5, and $0$ otherwise.\footnote{This bonus is paid in the survey company's panel currency. We inform respondents of this fact.} We ask respondents to predict how 10 previous participants have allocated \$50 in gift cards across the various categories. We incentivize their answer by discounting a constant marginal penalty \$0.05 from a baseline payment of \$5 for each dollar that needs to be assigned to a different category to make the subjects' answer match the empirical distribution. In each case, the main screen informs subjects that the accuracy of their answer may determine their study payments, and a button labeled ``details'' reveals an explanation of the specific incentive mechanism.

We elicit beliefs about crowding out of grocery spending through receiving \$50 in gift cards using the same format as for the previous crowding out question. We elicit the CA's belief about the percentage of the food in the food deliveries that will go to waste if sent to the CA, in the following intervals: $\{\{0\}, [0,10], [10-25], [25-50], [50, 100]\}$.

We elicit respondents' beliefs about how recipients will use gas station gift cards across the categories gas, tobacco products, alcoholic beverages, hot foods, and other.


We ask what determined the respondents' choices regarding their paternalism decisions. Subjects rate each of the following three possible reasons as  `completely unimportant', `slightly unimportant', `slightly important', or `very important': (i) `Sending a food box has only a negligible impact on [name]'s consumption, because he will just buy less food in the store.' (ii) `Giving [name] a choice is the right thing to do', (iii) `Making sure [name] consumes food rather the things he could buy with the gift cards is the right thing to do'. They further indicate agreement with each of the following two statements by selecting `completely disagree', `slightly disagree', `slightly agree', or `completely agree': (i) `My choice reflects what I would like if I were the welfare recipient', (ii) `What is right does not depend on the value of the gift cards that the recipient could receive instead of the food box'.

We use a subset of the questions from the poverty attribution literature \citep{Bennettetal.PovertyAttribution}. We ask `Why do you think [name] is poor enough to enroll in SNAP (food stamps)?' For each of six possible reasons, the respondent chooses between \textit{extremely unlikely}, \textit{somewhat unlikely}, \textit{somewhat likely}, and \textit{extremely likely}. The possible reasons are: (i) \textit{Lack of patience and drive}, (i) \textit{Lack of education and skills}, (iii) \textit{Tendency to make bad life choices}, (iv) \textit{Society doesn't give all people an equal chance}, (v) \textit{Discrimination against minorities and the poor}, (vi) \textit{Bad luck (not anybody's fault and not society's fault)}. The first three of these reasons attribute poverty to factors internal to the recipient while the latter three attribute it to factors outside the individual.

To measure the relative weight subjects place on their assigned welfare recipient relative to other citizens, each CA decides how to split a one-time payment of \$50 between the welfare recipient and a randomly selected taxpayer. To measure spite towards the welfare recipient, each CA can reduce the number of shipments by any number of months.


\textit{Attitudes.}

Subjects indicate their attitudes about current SNAP restrictions. They choose from seven answer categories whether restrictions should be tightened drastically, tightened somewhat, left unchanged, loosened somewhat, loosened drastically, abolished, or that recipients should not have a choice at all. Subjects also indicate whether they think benefit levels should be increased by 20\% or more, by 20\% or less, left unchanged, cut by 20\% or less, or cut by 20\% or more.  Additionally, we elicit agreement on a four-point Likert scale about the following statements: (i) \textit{The SNAP (food stamps) program as it is currently administered in the US is a good thing.} (ii) \textit{Welfare recipients make bad choices if they receive cash instead of food.} (iii) \textit{SNAP is a welfare program, hence SNAP participants are taking advantage of others}. In each of these questions, respondents may indicate that they have no opinion on the matter. Finally, respondents predict the share of all SNAP participants that are black women, black men, white women, and white men. For each of these demographic categories, they also estimate the fraction of recipients who have children. We incentivize the latter questions by applying a constant penalty for each percentage point by which the stated response differs from the truth.

\textit{Choice Architect attributes.}

At the very end of the survey, we elicit the CAs' own attributes. We ask about age, gender, ethnicity, how close the respondent feels to their own ethnic group \citep[following][]{FongLuttmer}, household income, number of people in household, education, marital status, number of children, political party closest to own views, agreement with closest party's views. At the very beginning of the survey, we additionally elicit state, citizenship (only U.S. citizens are allowed to participate), and social welfare program participation.


\paragraph{Randomization}

We randomize the following survey elements: (i) A random half of CAs proceed through the decisions in the order listed in Figure \ref{experimentOverview}. The remaining CAs reveal beliefs before making decisions, both before and after learning about the recipient's preferences. Specifically, they proceed through the stages in the following order: 3, 1, 2, 5, 4, 6. (ii) A random half of CAs see the gift card amounts in increasing order throughout, the other half see them in decreasing order throughout.


We randomly vary the color used as a background for the description of the recipient.

We randomize the order in which the gift card categories are displayed, with the exception that gas stations are always displayed second. Respondents are aware that gas stations allow recipients to purchase alcohol and tobacco. Preventing the purchase of addictive goods is an oft-mentioned reason for in-kind transfers in the popular media. We put them into the second position to make them salient while at the same time trying to avoid the demand effect that might occur by placing them first.


\textit{Gift cards.} We construct a selection of gift cards with the intent to create a cash-equivalent that cannot buy groceries. We include gift cards from companies in the following categories: entertainment, travel,  fine clothing, discount clothing, household goods, electronics. It also includes hot foods and restaurants since food stamps does not cover prepared food.  We deliver gift cards electronically where possible but deliver by mail where electronic gift cards are unavailable. Recipients then select, for their assigned budget, how much of the budget to allocate toward each possible brand gift card. While the available amounts are typically not fully flexible, there is a wide range of available amounts.

\textit{Names.} The names for hypothetical recipients come from gender-linked social security data and race-linked mortgage data. We take a list of first names by gender from social security data for the years 1991-2000. For each year, the data include all names with at least five occurrences. For any name with both male and female observations, we keep only the observations with the gender that is more common for that name. We collapse the data to get a list of name-gender pairs with the number of occurrences of each. We then merge this with 2007-2010 mortgage data from \citet{Tzioumis} that links names to the percent that are in each of six racial groups. We multiply the number of occurrences of each name-gender pair in the social security data by the share white and the share black according to the mortgage data to estimate the number of occurrences of each name among black males, black females, white males, and white females born in the 1990s. We then randomly draw 1000 names with replacement according to the empirical frequency, and we randomly assign a name to each recipient from this sample.\footnote{This approach relies on the assumption that the percentage of each name associated with a given race in 2007-2010 mortgage data approximately predicts the share associated with each race among 1990s babies.}

\textit{Pictures.} We draw pictures of hypothetical recipient's faces from the database by \citet{ParkLabPictures}. We display the images with a resolution of 35$\times$35 pixels. Hence, the identity of the individual is hidden, but the skin-color can easily be seen.\footnote{We refrain from conveying race through the use of  stereotypical names because these names are associated with characteristics other than race, such as socioeconomic status \citep{FryerLevitt, Simonsohn.Names}.} We use 80 pixellated pictures, 10 for each category defined by the dimensions age, gender, and race. To each CA, we randomly display one of the 10 images consistent with the assigned recipient characteristics. We ask real clients to take a picture of themselves using their webcam. A pixellated version of this picture is sent to our server, which we display to the CA assigned to that recipient.

\textit{Other.} Our survey displays the full page only once the pixelated image of the recipient is fully loaded. This feature ensures that all see that image regardless of the speed of their internet connection.


 \paragraph{Comprehension and monotonicity check}

CAs must pass a comprehension check after the instructions on her restriction decisions. Specifically, she must select the correct ones out of the following three statements to be able to continue with the survey: (i) `If I decide that [name] will get the food box deliveries, that's what he will get. There will be no choice', `If I decide that [name]  will get the food box deliveries, he will get to choose whether he might rather get gift cards instead', `If I give [name]  a choice, he will get the monthly gift cards. He won't be able to select the food deliveries.'

Two further comprehension checks ensure that subjects correctly process the information about the recipient's preferences. Both checks ask whether, when given the choice, the recipient would choose \$$X$ in gift cards or the food box. Answer options are `[Name] would choose the food box', `I cannot say based on the information given', and `[Name] would choose \$65 in gift cards'. In the first check, $X = 65$. In the second check, $X = 50$ for a subject whose recipient prefers the food box over \$65 in gift cards, and $X = 80$ in case of the reverse preference. The second check thus conveys the implications of preference monotonicity. Subjects must answer correctly to continue. They have as many attempts as required.

In addition, we nudge respondents towards providing answers in the multiple price lists that exhibit at most two (in the case of paternalism decisions) or one (in the case of surrogate choice) switching points. If there is an excess number of switches, the subject sees the message  `\textit{Your choices appear to be inconsistent or random. Most people decide in one of the $X$ ways below. Your choices fit none of those patterns. Please click the Back button and change your choices to make them consistent with one of these $X$ common patterns.}' followed by a list of the admissible patterns ($X$ is five in case of restriction decisions and three in case of surrogate choice). The subject decides herself whether to change her decisions or whether to leave her decisions unchanged and continue with the survey.


\subsection{Recipient surveys and implementation}

137 SNAP participants took the study in August 2022, and the remaining 133 participated in December 2023. There is no noticeable or statistically detectable difference in the distribution of reservation prices across the two waves, neither for each food box separately nor when considering them jointly. All of them were included in the database shown to CAs (the former 137 individuals were shown to CAs in the pilot study mentioned in footnote \ref{fnPilots}). Additional potential recipients had participated but provided unrecognizable pictures. We excluded these participants before running the corresponding CA sample; these participants' information was never shown to a CA. We also exclude them in our analysis of recipient responses.


Welfare recipients participate in two surveys. The first survey serves to elicit their reservation price for each food box, along with demographic information, and to elicit benchmark data required for incentivizing CA beliefs, such as the believed change in grocery spending caused by the receipt of the food box, and the believed fraction of the food that will go to waste. It also requires recipients to take a pixellated photo of themselves. The information from this survey will be displayed to the respondents in the main survey.

In the second survey, recipients make choices from the choice set they were assigned by the CA matched to them. Recipients who choose or who are assigned the gift cards also choose their preferred selection of gift cards (which they can change by email at any point of the six months during which gift cards are sent).


\begin{table}
\caption{Schematic overview of the experiment \label{experimentOverview}}
\begin{tabular}{|p{0.97\textwidth}|}
\hline
\textbf{A. Main decisions}
\begin{enumerate}[1.]
\item Intervention decisions
\item Surrogate choice
\item Elicitation of beliefs about recipient's reservation price
\item Surrogate choice conditional on information about recipient preferences
\item Elicitation of belief about unconstrained recipient choices conditional on information about recipient preferences
\item Distributing money between the recipient and random taxpayer.
\end{enumerate} \\

\textbf{B. Attitudes and beliefs related to decisions}\\
\begin{enumerate}[1.]
\item Beliefs about
	\begin{enumerate}
	\item crowding out grocery spending due to receiving the food deliveries
	\item effect of food box on the recipient's dietary health
	\item food spoilage
	\item crowding out grocery spending due to receiving gift cards
	\item gift card selection and spending of gas station gift cards
	\end{enumerate}
\item Reasons for choices in stage A
\item Poverty attribution
\item Reducing the number of shipments
\item Preference between food boxes
\item Appropriateness of each of the food boxes
\end{enumerate}
\textbf{C. Attitudes and beliefs concerning SNAP}\\
\begin{enumerate}[1.]
\item View on SNAP restrictions
\item View on SNAP amount
\item General views on SNAP
\item Beliefs about demographics of SNAP recipients
\end{enumerate}
\\
\textbf{D. CAs' own characteristics}\\
Demographics \\
\hline
\end{tabular}
\footnotesize
\textbf{\textit{Notes:}} A random half of CAs proceed through the decisions in the order listed. The remaining CAs reveal beliefs before making decisions, both before and after learning about the recipient's preferences. Specifically, they proceed through the stages in the following order: 3, 1, 2, 5, 4, 6. Another, independently drawn random half of CAs reveal beliefs about recipients' gift-card choice and spending before the decisions in part A, for the remaining CAs, this elicitation occurs as listed.
\end{table}


\section{Additional analysis}


\subsection{Demographics}\label{app:demographics}

Table \ref{tab:demoCAs} displays the distribution of demographic characteristics among our CA sample and compares it to the population frequencies according to the 2022 General Social Survey \citep[GSS, ][]{GSS2021} for the characteristics on which data are available. Table \ref{tab:demoChoosers} displays the distribution of demographic characteristics among our welfare recipient sample and compares it to the frequencies in the entire pool of SNAP participants according to the 2021 1-year American Community Survey Public Use Microdata Sample (PUMS) \citep{ACS} for the characteristics on which data are available.


\begin{table}
\scriptsize
\caption{Demographic characteristics of the CAs \label{tab:demoCAs}}
\begin{center}
\begin{tabular}{lccc}
\toprule
Characteristic & \multicolumn{2}{c}{Fraction among} \\
\cmidrule(r){2-3}
&Respondents & US population \\
\midrule


\textbf{Gender}	&		&		\\
Male	&	0.511	&	0.491	\\
Female	&	0.489	&	0.509	\\
\textbf{Age}	&		&		\\
18-19	&	0.009	&	0.017	\\
20-29	&	0.204	&	0.181	\\
30-39	&	0.195	&	0.178	\\
40-49	&	0.182	&	0.164	\\
50-59	&	0.204	&	0.159	\\
60-69	&	0.169	&	0.162	\\
$>70$	&	0.037	&	0.140	\\
\textbf{Race}	&		&		\\
White	&	0.743	&	0.658	\\
Black	&	0.154	&	0.116	\\
Other	&	0.104	&	0.226	\\
\textbf{Marital status}	&		&		\\
Married	&	0.441	&	0.501	\\
Widowed	&	0.022	&	0.052	\\
Divorced	&	0.100	&	0.133	\\
Separated	&	0.016	&	0.026	\\
Never married	&	0.422	&	0.289	\\
\textbf{Children}	&		&		\\
0	&	0.480	&	0.317	\\
1	&	0.146	&	0.154	\\
2	&	0.213	&	0.274	\\
3	&	0.104	&	0.142	\\
4	&	0.035	&	0.068	\\
$>4$	&	0.023	&	0.044	\\
\textbf{Household income}	&		&		\\
\textdollar0-\textdollar10,000	&	0.039	&	0.068	\\
\textdollar10,000-\textdollar20,000	&	0.075	&	0.072	\\
\textdollar20,000-\textdollar30,000	&	0.092	&	0.071	\\
\textdollar30,000-\textdollar40,000	&	0.083	&	0.065	\\
\textdollar40,000-\textdollar50,000	&	0.093	&	0.074	\\
\textdollar50,000-\textdollar60,000	&	0.091	&	0.062	\\
\textdollar60,000-\textdollar90,000	&	0.198	&	0.160	\\
more than \textdollar90,000	&	0.330	&	0.320	\\
\textbf{Household size}	&		&		\\
1	&	0.208	&		\\
2	&	0.316	&		\\
3	&	0.187	&		\\
4	&	0.182	&		\\
5	&	0.062	&		\\
6	&	0.030	&		\\
7	&	0.009	&		\\
$>7$	&	0.005	&		\\
\textbf{Geographic area}	&		&		\\
Urban	&	0.296	&		\\
Suburban	&	0.512	&		\\
Rural	&	0.191	&		\\
\textbf{Political preference}	&		&		\\
Strong democrat	&	0.221	&	0.187	\\
Not very strong democrat	&	0.137	&	0.137	\\
Independent, closer to democrat	&	0.161	&	0.118	\\
Independent, as far from democrats as from republicans	&	0.129	&	0.230	\\
Independent, closer to republican	&	0.120	&	0.078	\\
Not very strong republican	&	0.130	&	0.094	\\
Strong republican	&	0.102	&	0.130	\\
Other	&	0.000	&	0.028	\\
\textbf{Public assistance}	&		&		\\
Medicaid	&	0.213	&		\\
Medicare	&	0.185	&		\\
Veteran Disability Benefits	&	0.014	&		\\
Earned Income Tax Credit	&	0.082	&		\\
Social Security	&	0.181	&		\\
Supplemental Nutrition Assistance Program	&	0.130	&		\\
Temporary Assistance for Needy Families	&	0.006	&		\\
Public Housing	&	0.018	&		\\
Pell Grants	&	0.047	&		\\
None of the above	&	0.506	&		\\
\\ \textbf{N}	&	1997.000	&	4032.000	\\

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Variables for demographic comparison are from the 2022 General Social Survey \citep[GSS, ][]{GSS2021}.
\end{table}


\begin{table}
\caption{Demographic characteristics of the welfare recipients in our sample \label{tab:demoChoosers}}
\begin{center}
\begin{tabular}{lc}
\toprule
Characteristic & Fraction of recipients \\
\midrule


\textbf{Gender}	&		\\
Male	&	0.233	\\
Female	&	0.767	\\
\textbf{Age}	&		\\
20-29	&	0.170	\\
30-39	&	0.304	\\
40-49	&	0.256	\\
50-59	&	0.215	\\
60-69	&	0.033	\\
\textbf{Race}	&		\\
White (non-hispanic)	&	0.681	\\
Black	&	0.181	\\
White (hispanic)	&	0.085	\\
Other	&	0.052	\\
\textbf{Marital status}	&		\\
Married, living with partner	&	0.237	\\
Married but separated	&	0.056	\\
Not married, living with partner	&	0.189	\\
Not married, single	&	0.519	\\
\textbf{Children}	&		\\
0	&	0.515	\\
1	&	0.215	\\
2	&	0.156	\\
3	&	0.085	\\
$>3$	&	0.030	\\
\textbf{Public assistance}	&		\\
Medicaid	&	0.681	\\
Medicare	&	0.248	\\
Veteran Disability Benefits	&	0.011	\\
Earned Income Tax Credit	&	0.126	\\
Social Security	&	0.244	\\
Supplemental Nutrition Assistance Program	&	1.000	\\
Temporary Assistance for Needy Families	&	0.041	\\
Public Housing	&	0.104	\\
Pell Grants	&	0.059	\\
\\ \textbf{N}	&	270.000	\\

 \\
\bottomrule
\end{tabular}
\end{center}
\end{table}


\subsection{Order effects}\label{appendixOrderEffects}

Table \ref{orderEffects} examines the effect of the order in which survey items are displayed to respondents on enforcement price, surrogate reservation price, beliefs about the median reservation price, care about the recipient, and spite. The only randomization dimension that affects behavior in a statistically significant fashion is whether prices in multiple decision lists are shown in ascending or descending order. Even this variable only affects enforcement prices and unconditional beliefs about recipients' mean reservation prices to a statistically significant extent.


\begin{table}[H]
\caption{Order effects \label{orderEffects}}
\begin{center}
\setlength{\tabcolsep}{2pt}
\begin{tabular}{lccccccc} \toprule
 & (1) & (2) & (3) & (4) & (5) & (6) & (7)  \\
VARIABLES & Enforcement & \multicolumn{2}{c}{Surrogate} & \multicolumn{2}{c}{Belief mean}  & Care towards  & Reduce  \\  &  price &  \multicolumn{2}{c}{res. price} &  \multicolumn{2}{c}{res. price} &  recipient &  deliveries \\
\cmidrule(r){3-4}\cmidrule(r){5-6}
Conditional & & & \checkmark & & \checkmark \\
\midrule


            &               &               &               &               &               \\

Dep. var. mean&      59.516***&      77.110***&      80.333***&      74.474***&      76.924***\\
            &     (0.875)   &     (0.711)   &     (0.689)   &     (0.524)   &     (0.627)   \\


                    &               &               &               &               &               \\

Beliefs about gift-card \\ \quad spending elicited first&      -0.194   &      -1.019   &      -0.567   &       1.561   &      -0.369   \\
                    &     (1.746)   &     (1.423)   &     (1.380)   &     (1.034)   &     (1.256)   \\
Beliefs about recipient's \\ \quad WTP elicited first&      -0.648   &      -2.459   &      -0.500   &      -2.189*  &      -0.623   \\
                    &     (1.748)   &     (1.425)   &     (1.382)   &     (1.036)   &     (1.258)   \\
Money amounts shown \\ \quad high to low&      -6.370***&      -0.769   &       1.693   &      -7.219***&       0.775   \\
                    &     (1.747)   &     (1.424)   &     (1.381)   &     (1.035)   &     (1.257)   \\
\medskip
N                   &        1928   &        1983   &        1988   &        1997   &        1997   \\
\bottomrule


\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} OLS regressions. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}

\FloatBarrier


\subsection{Crowding out} \label{app:crowdingOut}

Figure \ref{figCrowdingOut} plots our respondents' beliefs about the effect on grocery expenses from receiving a food box (Panel A, averaged across types of food boxes) or from the receipt of \$65 (Panel B). We see that about a fifth of the respondents believe that grocery spending falls by more than \$50. Given the information that the cost of the food box is between \$55 and \$75, the remaining subjects believe that crowd-out is at most partial.

Using interval midpoints and setting the responses ``over \$50 less'' and ``over \$50 more'' to \$-50 and \$50, respectively, the mean belief about the change in grocery expenses from receiving food boxes is \$-24.8 (s.e. 0.72), averaged across the food boxes.\footnote{The corresponding number for the healthy and representative boxes are \$-26.52 and \$-23.17, respectively. The difference of \$3.36 ($p<0.05$) is puzzling given that food more similar to the recipients' usual consumption basket should cause greater crowding out. The pilot revealed the same directional pattern.} Respondents believe that the receipt of \$65 in gift cards increases grocery spending by \$1.73 (s.e. 0.70), consistent with our interpretation that gift cards cannot be used to purchase groceries.


\begin{figure}
\caption{Beliefs about how receipt of deliveries affects grocery expenses \label{figCrowdingOut}}
\begin{center}
\begin{tabular}{cc}
\textbf{A. Food box} & \textbf{B. Gift cards} \\
\includegraphics[width=0.5\textwidth]{graphs/histCrowdOutCombined}
&
\includegraphics[width=0.5\textwidth]{graphs/histCrowdOutGiftCards}
\\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} WLS regressions on indicator variables (one for each answer category). Frequency of answers to the question ``\textit{If [name] receives the [food box name] in a given month, how much more or less money than usual do you think will he spend on groceries in that month (not including the value of the food box)?} (Panel A) and ``\textit{If, in a given month, [name] receives \$65 in gift cards that she selects from our menu, how much more or less money than usual do you think will he spend on groceries in that month?}'' Graphs show weighted data.
\end{figure}

We elicted beliefs about crowding out at the end of the experiment. It is thus plausible that subjects did not consider crowding out in the decisions that reveal enforcement and surrogate reservation prices. Table \ref{crowdOutEffects} regresses enforcement price, surrogate reservation price, and mean reservation price beliefs on beliefs about the extent of crowding out caused by both the food deliveries and by gift-card deliveries of \$65. We find no economically meaningful effects. There is a statistically significant effect according to which CAs who believe in \$1 more crowding out due to the receipt of the gift cards set surrogate reservation prices that are \$0.07 lower.

The absence of meaningful effects suggests that crowding-out considerations did not play a meaningful role in Choice Architects' decisions in the main part of the experiment.


\begin{table}[H]
\caption{Effects of beliefs about crowding out \label{crowdOutEffects}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lcccc}
\toprule


& \multicolumn{1}{c}{Enforcement}
& \multicolumn{1}{c}{Surrogate}
& \multicolumn{1}{c}{Believed mean}
\\
& \multicolumn{1}{c}{ price}
& \multicolumn{1}{c}{ reservation price}
& \multicolumn{1}{c}{ reservation price}
\\ \midrule
            &               &               &               \\

Dep. var. mean&      61.656***&      78.328***&      74.749***\\
            &     (1.098)   &     (0.888)   &     (0.651)   \\


\textit{Believed change in grocery expenses}\\
                    &               &               &               \\

\quad due to food box&      -0.014   &      -0.044   &       0.050*  \\
                    &     (0.046)   &     (0.039)   &     (0.026)   \\
\quad due to USD65 in gift cards&       0.060   &       0.072** &       0.023   \\
                    &     (0.042)   &     (0.035)   &     (0.023)   \\
\medskip
N                   &        1928   &        1983   &        1997   \\
\bottomrule


\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} WLS regressions. Controls for position of belief elicitation and for order in which cash equivalent amounts were displayed, as well as for beliefs about crowding out and about food waste.
\end{table}


\subsection{Enforcement prices by food box type}\label{app:enforcementFoodType}

Table \ref{tab:enforcementFoodType}
\begin{figure}
\caption{Enforcement prices by food box type \label{tab:enforcementFoodType}}
\begin{center}
\includegraphics[width=0.5\textwidth]{graphs/histPatSeparate}
\end{center}
\footnotesize
\textbf{\textit{Notes:}}
\end{figure}


\subsection{Belief updating}\label{app:beliefUpdating}
Figure \ref{fig:beliefUpdating} plots the mean elicited belief about the fraction of recipients whose reservation price falls in each of the given bins. We seek to measure the extent of deviations from Bayesian updating from the information that the recipients' reservation price is above or below \$65. The Bayesian update of the pdf (without renormalization) is simply the original pdf truncated at \$65. We see that subjects roughly respect this rule, but that updated beliefs tend to be slightly shifted to the right, on average, than the unconditional beliefs in the respective interval.

\begin{figure}
\caption{Belief updating \label{fig:beliefUpdating}}
\begin{center}
\includegraphics[width=0.5\textwidth]{graphs/beliefUpdating.pdf}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} The uninterrupted lines, labeled `uncond.,' show the mean elicited belief about the fraction of recipients whose reservation price falls in each of the given bins before receiving information about the recepient's reservation price. The interrupted lines, labeled `cond.,' show the corresponding estimates CAs made after receiving such information. Solid lines plot averages for CAs who learned that the reservation price exceeds \$70, dashed lines plot them for CAs whose recipient's reservation price was less than \$70.
\end{figure}


\subsection{Response to information about recipient preferences}\label{app:responseInfo}
Figure [X] shows CDFs for CAs' surrogate reservation prices after learning that the recipient has either a high or low reservation price. Panel A (respectively, Panel B) focuses on CAs for whom the enforcement price is strictly less than (respectively, equal to or greater than) the surrogate reservation price before receiving information. Clearly, information concerning recipients' reservation prices for food influences surrogate choices to a similar degree among both groups.

\begin{figure}[H]
\caption{Change in surrogate prices in response to information about recipient preferences}
\label{fig:cdfsSurrogateByInfo}
\begin{center}
\begin{tabular}{cc}
A. Enforcement price $\geq$ surrogate price
&
B. Enforcement price $<$ surrogate price \\
\includegraphics[width=0.48\textwidth]{graphs/cdfSurrCondByInfoSurrLeqEnf}
&
\includegraphics[width=0.48\textwidth]{graphs/cdfSurrCondByInfoSurrGEnf}
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}}
\end{figure}

\FloatBarrier


\subsection{Reasons for interventions}\label{app:interventionReasons}
As Figure \ref{deontologicalTypes} shows, when subjects rate their agreement with the statements that they chose the way they did because `Giving [name] a choice is the right thing to do' and `Making sure [name] consumes food rather than the things he could buy with the gift cards is the right thing to do' we do not see discontinuities for libertarian or authoritarian types.

\begin{figure}[H]
\caption{Types and reasons for choice \label{deontologicalTypes}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\includegraphics[width=0.5\textwidth]{graphs/reasonsByPat}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} WLS regressions on indicators (one for each category on the horizontal axis and answer type). Dependent variable coded as 1 = `completely unimportant', 2 = `slightly unimportant', 3 = `slightly important', 4 = `very important.' Controls for position of belief elicitation and for order in which cash equivalent amounts were displayed, as well as for beliefs about crowding out and about food waste. Omitted category: Tradeoff types. Excludes subjects without well-defined enforcement price.
\end{figure}


\subsection{Robustness of the structural estimates}\label{app:structuralEstimatesRobustness}


\begin{figure}[H]
\caption{Enforcement prices and surrogate reservation prices \label{scatterPatSurr}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\includegraphics[width=0.5\textwidth]{graphs/scatterPatSurr}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Circle area proportional to the number of subjects located at the corresponding location.
\end{figure}


\begin{figure}[H]
\caption{Posterior surrogate reservation prices by information \label{scatterPatSurr}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\includegraphics[width=0.45\textwidth]{graphs/cdfDiffSurrCondSurrByInfo}
\includegraphics[width=0.45\textwidth]{graphs/cdfSurrCondByInfo}
\end{center}
\footnotesize
\textbf{\textit{Notes:}}
\end{figure}


\begin{landscape}
\begin{table}[htbp]
\caption{Robustness of structural estimation results} \label{tab:structural}
\scriptsize
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lcccccccccc}
& (1) & (2) & (3) & (4) & (5)  \\
\toprule
CA sample & All & All & All  & All  & All    \\
Constraint &$\beta_{\textrm{respect}} = 0.8$ & $\beta_{\textrm{respect}} = 0.5$  & $\beta_{\textrm{respect}} = 0.2$ & $\sigma_0 = \sigma_1 = 10$ & $\mu_0 = \mu_1$   \\
	&&&&& $\sigma_0=\sigma_1$  \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.800 \textdagger& 0.500 \textdagger& 0.200 \textdagger& 1.729 ***& 0.806 ***\\
   &    &    &    &(0.036)   &(0.015)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 71.415 ***& 70.722 ***& 72.748 ***& 67.052 ***& 72.954 ***\\
   &(0.419)   &(0.355)   &(0.294)   &(0.311)   &(0.371)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 6.348 ***& 3.689 ***& 1.394 ***& 3.925 ***& 3.980 ***\\
   &(0.207)   &(0.155)   &(0.250)   &(0.172)   &(0.163)\\
\quad Fraction of absolutist types, $q$& 0.149 ***& 0.171 ***& 0.183 ***& 0.137 ***& 0.152 ***\\
   &(0.007)   &(0.007)   &(0.007)   &(0.007)   &(0.007)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 72.119 ***& 74.905 ***& 76.930 ***& 68.735 ***& 71.524 ***\\
   &(0.471)   &(0.552)   &(0.649)   &(0.094)   &(0.408)\\
\quad Prior mean parameter, $\mu_0$& 70.333 ***& 72.706 ***& 69.683 ***& 68.735 ***& 71.096 ***\\
   &(0.489)   &(0.572)   &(0.717)   &(0.094)   &(0.423)\\
\quad Prior SD parameter, $\sigma_0$& 34.600 ***& 37.011 ***& 48.086 ***& 10.000 ***& 27.835 ***\\
   &(0.436)   &(0.491)   &(0.730)   &    &(0.265)\\
\quad Posterior mean parameter, $\mu_1$& 83.617 ***& 89.942 ***& 83.569 ***& 81.145 ***& 71.096 ***\\
   &(0.746)   &(0.877)   &(1.009)   &(0.192)   &(0.423)\\
\quad Posterior SD parameter, $\sigma_1$& 26.769 ***& 31.247 ***& 35.435 ***& 10.000 \textdagger& 27.835 ***\\
   &(0.331)   &(0.371)   &(0.411)   &    &(0.265)\\
\\
$N$& 1997 & 1997 & 1997 & 1997

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Each column represents a separate estimation. Robust standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}
\end{landscape}


\begin{landscape}
\begin{table}[htbp]
\caption{Robustness of structural estimation results} \label{tab:structuralSNAP}
\scriptsize
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccc}
& (1) & (2) & (3) & (4) \\
\toprule
CA sample \\
\quad Tighten SNAP restrictions & Yes & No & both & both \\
\quad SNAP recipients make bad choices with cash & both & both & disagree & agree \\

\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta_{respect}$& 0.940 ***& 0.863 ***& 0.994 ***& 0.946 ***& 0.937 ***\\
   &(0.019)   &(0.025)   &(0.026)   &(0.027)   &(0.028)\\
\qquad Ideal res. price for mean recipient, $\alpha$& 75.726 ***& 75.824 ***& 75.100 ***& 75.617 ***& 74.511 ***\\
   &(0.901)   &(1.193)   &(1.198)   &(1.274)   &(1.099)\\
\qquad Weight on disapproved gift cards consumption, $w$& 0.360 ***& 0.429 ***& 0.329 ***& 0.289 ***& 0.489 ***\\
   &(0.015)   &(0.020)   &(0.019)   &(0.018)   &(0.019)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 3.388 ***& 3.669 ***& 3.040 ***& 2.856 ***& 3.778 ***\\
   &(0.131)   &(0.187)   &(0.158)   &(0.163)   &(0.183)\\
\quad Fraction of authoritarian types, $q$& 0.134 ***& 0.192 ***& 0.077 ***& 0.059 ***& 0.178 ***\\
   &(0.007)   &(0.013)   &(0.007)   &(0.007)   &(0.011)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior mean parameter, $\mu_0$& 71.455 ***& 72.437 ***& 70.470 ***& 71.615 ***& 70.775 ***\\
   &(0.549)   &(0.875)   &(0.644)   &(0.775)   &(0.723)\\
\quad Prior SD parameter, $\sigma_0$& 36.596 ***& 38.686 ***& 34.539 ***& 37.306 ***& 35.757 ***\\
   &(0.499)   &(0.763)   &(0.607)   &(0.715)   &(0.659)\\
\quad Posterior mean parameter, $\mu_1$& 89.230 ***& 89.727 ***& 88.753 ***& 88.508 ***& 89.776 ***\\
   &(20.220)   &(21.008)   &(19.568)   &(18.822)   &(21.335)\\
\quad Posterior SD parameter, $\sigma_1$& 20.220 ***& 21.008 ***& 19.568 ***& 18.822 ***& 21.335 ***\\
   &(0.511)   &(0.720)   &(0.664)   &(0.659)   &(0.734)\\
\textbf{\textit{Error term variances}} \\ \quad Enforcement, $\sigma_{\epsilon_1}$& 10.630 ***& 12.575 ***& 9.429 ***& 8.675 ***& 12.987 ***\\
   &(0.231)   &(0.368)   &(0.314)   &(0.321)   &(0.383)\\
\quad Surrogate choice, $\sigma_{\epsilon_2}$& 8.083 ***& 9.411 ***& 7.427 ***& 6.774 ***& 10.790 ***\\
   &(0.283)   &(0.350)   &(0.327)   &(0.349)   &(0.388)\\
\quad \textit{Conditional surrogate choice} \\ \qquad High reservation price, $\sigma_{\epsilon_3}$& 4.795 ***& 5.013 ***& 4.612 ***& 4.060 ***& 5.692 ***\\
   &(0.196)   &(0.287)   &(0.248)   &(0.207)   &(0.303)\\
\qquad Low reservation price, $\sigma_{\epsilon_4}$& 4.176 ***& 4.206 ***& 3.971 ***& 3.824 ***& 4.244 ***\\
   &(0.141)   &(0.186)   &(0.181)   &(0.181)   &(0.182)\\
\\
\textbf{\textit{Counterfactuals}} \\ \quad Best-fitting enforcement price& 58.63 & 64.09 & 54.43 & 53.00 & 64.51 \\
\quad Counterfactuals: $A=0$ \\ \qquad Counterfactual enforcement price& 15.84 & 25.17 & 9.15 & 13.48 & 17.38 \\
\qquad Difference to actual& 42.79 & 38.92 & 45.28 & 39.52 & 47.13 \\
\quad Counterfactuals: correct beliefs \\ \qquad Counterfactual enforcement price& 43.57 & 50.05 & 38.49 & 38.75 & 48.99 \\
\qquad Difference to actual& 15.06 & 14.04 & 15.94 & 14.25 & 15.52 \\
\\
$N$& 1883 & 864 & 1019 & 866 & 1017

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Each column represents a separate estimation. Robust standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1

\end{table}
\end{landscape}


\subsection{Interventions and poverty attribution}\label{app:interventionsPovertyAttribution}


We next turn to poverty attribution. According to the specific egalitarianism hypothesis, citizen's sole concern is to ensure an adequate consumption level of certain necessities. Therefore, beliefs about the cause of poverty should not affect interventions. According to the ward-paternalism hypothesis, by contrast, interventions reflect a desire to urge the recipient to make allegedly better choices. Therefore, subjects who attribute the cause of poverty to circumstances outside the recipient's control (discrimination, bad luck, or the belief that society does not give everybody an equal chance) should impose significantly laxer restrictions, while those that find fault with the recipient himself (such as lack of patience and drive, lack of education and skills, and bad life choices) should impose stronger constraints. We classify the six poverty attribution questions into these categories. The situation index sums agreement values across the three factors external to the recipient, whereas the person index sums across the three factors internal to him.


\begin{table}[H]
\caption{Effect of Choice Architect beliefs on interventions \label{app:wardVsSpecificPovertyAttribution}}
\small
\setlength{\tabcolsep}{2pt}
\begin{center}


\begin{tabular}{lccccc} \hline
 & (1) & (2) & (3) & (4) & (5) \\
VARIABLES & patSw & surrSwitch & meanBelief & patSw & surrSwitch \\ \hline
 &  &  &  &  &  \\
Constant & 61.656 & 78.328 & 74.749 & 61.656 & 78.328 \\
 & (1.098) & (0.888) & (0.651) & (1.098) & (0.888) \\
 &  &  &  &  &  \\ \hline
\end{tabular}

\\


\begin{tabular}{lccccc} \hline
 & (1) & (2) & (3) & (4) & (5) \\
VARIABLES & patSw & surrSwitch & meanBelief & patSw & surrSwitch \\ \hline
 &  &  &  &  &  \\
povAttPerson & 10.985*** & 6.549*** & -1.481 & 10.985*** & 6.549*** \\
 & (1.614) & (1.305) & (0.973) & (1.614) & (1.305) \\
povAttSituation & -8.143*** & -6.295*** & -0.177 & -8.143*** & -6.295*** \\
 & (1.641) & (1.345) & (0.976) & (1.641) & (1.345) \\
 &  &  &  &  &  \\
 Observations & 1,928 & 1,983 & 1,997 & 1,928 & 1,983 \\ \hline
\end{tabular}

\\
\end{center}

\footnotesize
\textbf{\textit{Notes:}} Controls for the three order variables and for recipient demographics.
\end{table}


\subsection{Distributions of selected variables \label{appendixHistAltruismSpite}}

Figure \ref{histAltruismSpite} shows histograms of the distributions of altruism and spite in our sample (weighted). \red[The standard errors are calculated by the binomial formula, which should be altered to appropriately account for the weighting]\black

\begin{figure}
\caption{Distributions of selected variables \label{histAltruismSpite}}
\begin{center}
\begin{tabular}{cc}
\textbf{A. Care towards recipient}
&
\textbf{B. Spite} \\
\includegraphics[width=0.5\textwidth]{graphs/barsAltruism}
&
\includegraphics[width=0.5\textwidth]{graphs/barsSpite} \\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}}
\end{figure}

\FloatBarrier


\subsection{Self-reported reasons for choice}
 \label{appendixReasonsChoice}


\begin{table}[H]
\caption{Types and reasons for choice \label{tabReasonsChoice}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lC{0.15\textwidth}C{0.15\textwidth}C{0.15\textwidth}C{0.15\textwidth}C{0.15\textwidth}} \hline
 & (1) & (2) & (3) & (4) & (5) \\
 & Sending a food box has only a negligible impact on [name]'s consumption, because he will just buy less food in the store.
&
Giving [name] a choice is the right thing to do
&
Making sure [name] consumes food rather the things he could buy with the gift cards is the right thing to do
&
My choice reflects what I would like if I were the welfare recipient
&
What is right does \textit{not} depend on the value of the gift cards that the recipient could receive instead of the food box.
\\
\end{tabular}


\begin{tabular}{lccccc} \hline
 & (1) & (2) & (3) & (4) & (5) \\
VARIABLES &  &  &  &  &  \\ \hline
 &  &  &  &  &  \\
Constant & 3.250 & 2.812 & 2.232 & 3.331 & 2.504 \\
 & (0.026) & (0.029) & (0.025) & (0.022) & (0.028) \\
 &  &  &  &  &  \\ \hline
\end{tabular}

\\


\begin{tabular}{lccccc} \hline
 & (1) & (2) & (3) & (4) & (5) \\
VARIABLES &  &  &  &  &  \\ \hline
 &  &  &  &  &  \\
libertarian & 0.298*** & -0.374*** & 0.007 & 0.122 & 0.158 \\
 & (0.056) & (0.104) & (0.079) & (0.077) & (0.104) \\
authoritarian & -1.211*** & 0.998*** & 0.088 & -0.219*** & 0.915*** \\
 & (0.098) & (0.056) & (0.097) & (0.081) & (0.068) \\
 &  &  &  &  &  \\
 Observations & 1,997 & 1,997 & 1,997 & 1,997 & 1,997 \\ \hline
\end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Dependent variable in columns 1, 2, and 3 coded as 1 = `completely unimportant', 2 = `slightly unimportant', 3 = `slightly important', 4 = `very important.' Dependent variable in columns 4 and 5 coded as 1 = `completely disagree', 2 = `slightly disagree', 3 = `slightly agree', 4 = `completely agree.' Regressions control for position of belief elicitation and for order in which cash equivalent amounts were displayed, as well as for beliefs about crowding out and about food waste.
\end{table}

\FloatBarrier

\subsection{Effect of respondent demographics on CAs' beliefs}\label{app:manipulationCheckSterotypes}


\begin{landscape}
\begin{table}[htbp]
\caption{Beliefs and attributions by recipient demographics \label{tab:stereotypes}}\
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccccccccc} \toprule
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) \\


& \multicolumn{7}{c}{Believed spending on}
\\
& \multicolumn{1}{c}{Addictive}
& \multicolumn{1}{c}{Lotteries}
& \multicolumn{1}{c}{Luxuries}
& \multicolumn{1}{c}{Laziness}
& \multicolumn{1}{c}{Sporting}
& \multicolumn{1}{c}{Necessities}
& \multicolumn{1}{c}{Gas,}
 \\
& \multicolumn{1}{c}{goods}
& \multicolumn{1}{c}{}
& \multicolumn{1}{c}{}
& \multicolumn{1}{c}{}
& \multicolumn{1}{c}{goods}
& \multicolumn{1}{c}{}
& \multicolumn{1}{c}{other}
& \multicolumn{1}{c}{}
\\ \midrule
            &               &               &               &               &               &               &               \\

Dep. var. mean&       6.590***&       2.146***&      11.726***&      34.340***&       0.968***&      22.151***&      22.080***\\
            &     (0.289)   &     (0.109)   &     (0.469)   &     (0.484)   &     (0.102)   &     (0.488)   &     (0.455)   \\


\textit{Demographics}\\
                    &               &               &               &               &               &               &               \\

\quad Female        &      -3.746***&      -0.253   &       1.216   &      -3.725** &      -1.484***&       7.844***&       0.147   \\
                    &     (0.792)   &     (0.273)   &     (1.114)   &     (1.184)   &     (0.293)   &     (1.038)   &     (1.057)   \\
\quad Black         &       0.734   &       0.367   &       0.382   &       0.300   &      -0.194   &      -1.105   &      -0.485   \\
                    &     (0.562)   &     (0.214)   &     (0.921)   &     (0.954)   &     (0.211)   &     (0.936)   &     (0.908)   \\
\quad Old           &       1.421   &       0.981** &      -2.577*  &      -2.121   &      -0.323   &       1.797   &       0.821   \\
                    &     (0.785)   &     (0.306)   &     (1.159)   &     (1.244)   &     (0.262)   &     (1.110)   &     (1.112)   \\
\quad Has children  &      -1.699** &      -0.064   &      -4.028***&      -0.545   &       0.329   &       5.704***&       0.301   \\
                    &     (0.565)   &     (0.241)   &     (1.167)   &     (1.168)   &     (0.217)   &     (1.235)   &     (1.154)   \\
\quad Rural         &       2.514***&       0.441   &      -0.952   &       0.048   &      -0.032   &       0.095   &      -2.114   \\
                    &     (0.659)   &     (0.265)   &     (1.082)   &     (1.146)   &     (0.272)   &     (1.094)   &     (1.095)   \\
\quad Urban         &       1.709** &       0.261   &       0.693   &       0.062   &       0.111   &       0.141   &      -2.978*  \\
                    &     (0.646)   &     (0.252)   &     (1.165)   &     (1.193)   &     (0.245)   &     (1.157)   &     (1.168)   \\
\medskip
N                   &        1959   &        1959   &        1959   &        1959   &        1959   &        1959   &        1959   \\
\bottomrule


\end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Omitted categories are white, male, young, no children, not from a southern state, lives in a suburban area. Regressions include controls for the order variables, for the alcohol information treatment, and for CAs' own race, gender, age, and child status, as well as for her political attitudes, income, and participation in welfare programs. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1.
\end{table}
\end{landscape}

\subsection{Effect of recipient demographics: Reduced-form analysis \label{app:stereotypesReducedForm}}

Table \ref{stereotypesRestrictions} displays the results of reduced-form analysis of the effect of recipient demographics on enforcement prices, surrogate reservation prices, and beliefs about mean reservation prices. In general, the effects are small and non-significant.


\begin{table}[htbp]
\caption{Intervention behavior by recipient demographics \label{stereotypesRestrictions}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccccccc}
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) \\
\toprule


& \multicolumn{3}{c}{Enforcement price}
& \multicolumn{3}{c}{Surrogate res. price}
\\
\cmidrule(r){2-4}
\cmidrule(r){5-7}
CA political pref.
& \multicolumn{1}{c}{All}
& \multicolumn{1}{c}{Left}
& \multicolumn{1}{c}{Right}
& \multicolumn{1}{c}{All}
& \multicolumn{1}{c}{Left}
& \multicolumn{1}{c}{Right}
\\ \midrule
            &               &               &               &               &               &               \\

Dep. var. mean&      61.656***&      54.587***&      69.140***&      78.328***&      74.059***&      81.713***\\
            &     (1.098)   &     (1.399)   &     (1.858)   &     (0.888)   &     (1.135)   &     (1.544)   \\


\textit{Recipient demo.}\\
                    &               &               &               &               &               &               \\

\quad Female        &       1.516   &      -3.422   &       3.615   &       0.443   &      -4.058   &       0.812   \\
                    &     (2.570)   &     (3.186)   &     (4.444)   &     (2.153)   &     (2.698)   &     (3.565)   \\
\quad Black         &       3.926*  &      -0.004   &       6.688*  &       4.749***&       0.568   &       7.739***\\
                    &     (2.129)   &     (2.762)   &     (3.556)   &     (1.717)   &     (2.208)   &     (2.855)   \\
\quad Old           &      -2.790   &      -6.954** &      -4.168   &       0.400   &       0.934   &       1.167   \\
                    &     (2.685)   &     (3.292)   &     (4.329)   &     (2.157)   &     (2.707)   &     (3.400)   \\
\quad Has children  &       0.008   &       0.329   &      -3.001   &       1.268   &       1.833   &       1.057   \\
                    &     (2.691)   &     (3.571)   &     (4.641)   &     (2.236)   &     (2.902)   &     (3.677)   \\
\quad Rural         &       3.081   &       3.708   &      -2.902   &      -0.344   &      -1.618   &      -4.513   \\
                    &     (2.516)   &     (3.230)   &     (4.068)   &     (2.096)   &     (2.625)   &     (3.254)   \\
\quad Urban         &       0.429   &       0.210   &      -1.865   &      -1.976   &      -1.309   &      -3.613   \\
                    &     (2.626)   &     (3.321)   &     (4.531)   &     (2.131)   &     (2.754)   &     (3.704)   \\
\medskip
N                   &        1890   &         974   &         673   &        1945   &         999   &         695   \\
\bottomrule

 \\
\end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Omitted categories are white, male, young, no children, not from a southern state, and lives in a suburban area.  Regressions include controls for the order variables, for beliefs about waste and crowding out, and for CA attributes. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


Table \ref{stereotypesInteracted} replicates Table \ref{stereotypesRestrictions} with fully interacted recipient demographics, using young white women without children as the omitted category. While we find a statistically significant effect on young white males ($p<0.05$), the relatively large number of statistical tests in this table means that this effect is unlikely to survive a correction for multiple hypothesis testing.


\begin{landscape}
\begin{table}[H]
\caption{Beliefs and attributions by recipient demographics \label{tab:stereotypesMulti}}\
\setlength{\tabcolsep}{2pt}
\begin{center}


\begin{tabular}{lccccccccccc} \hline
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) \\
VARIABLES & gc\_necessity & gc\_lazy & gc\_sporting & gc\_luxury & gc\_addictive & gc\_lotteries & gc\_gasOther & povAttPerson & povAttSituation & altruism & spite \\ \hline
 &  &  &  &  &  &  &  &  &  &  &  \\
Constant & 22.151 & 34.340 & 0.968 & 11.726 & 6.590 & 2.146 & 22.080 & 2.517 & 2.568 & 38.888 & 1.124 \\
 & (0.488) & (0.484) & (0.102) & (0.469) & (0.289) & (0.109) & (0.455) & (0.018) & (0.019) & (0.340) & (0.018) \\
 &  &  &  &  &  &  &  &  &  &  &  \\ \hline
\end{tabular}

 \\


\begin{tabular}{lccccccccccc} \hline
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) \\
VARIABLES & gc\_necessity & gc\_lazy & gc\_sporting & gc\_luxury & gc\_addictive & gc\_lotteries & gc\_gasOther & povAttPerson & povAttSituation & altruism & spite \\ \hline
 &  &  &  &  &  &  &  &  &  &  &  \\
chooserFemale & 9.021*** & -5.577*** & -1.515*** & 2.441* & -4.247*** & -0.383 & 0.261 & -0.026 & 0.097* & 2.108* & -0.004 \\
 & (1.446) & (1.586) & (0.422) & (1.391) & (1.006) & (0.338) & (1.536) & (0.048) & (0.054) & (1.089) & (0.053) \\
chooserBlack & 0.386 & -0.772 & -0.146 & 1.242 & -0.149 & 0.127 & -0.688 & 0.076 & 0.215*** & 1.923 & 0.075 \\
 & (1.856) & (1.974) & (0.759) & (1.940) & (1.554) & (0.503) & (1.976) & (0.064) & (0.073) & (1.466) & (0.104) \\
chooserOld & 1.828* & -2.128* & -0.322 & -2.622** & 1.414* & 0.998*** & 0.830 & -0.133*** & 0.077* & 2.154*** & -0.075 \\
 & (1.105) & (1.241) & (0.271) & (1.150) & (0.786) & (0.305) & (1.110) & (0.039) & (0.043) & (0.803) & (0.049) \\
chooserKids & 5.731*** & -0.580 & 0.329 & -3.997*** & -1.713*** & -0.070 & 0.300 & -0.034 & 0.074* & 5.221*** & -0.094*** \\
 & (1.235) & (1.166) & (0.218) & (1.168) & (0.564) & (0.241) & (1.155) & (0.046) & (0.044) & (0.749) & (0.036) \\
southern & -0.476 & -0.591 & -0.090 & 1.350 & 0.417 & -0.564** & -0.047 & 0.033 & 0.051 & -0.375 & 0.006 \\
 & (0.998) & (1.065) & (0.234) & (1.023) & (0.623) & (0.239) & (1.033) & (0.036) & (0.038) & (0.698) & (0.038) \\
ruralNum & 0.141 & 0.386 & 0.035 & -0.387 & 2.052*** & 0.230 & -2.458** & 0.062 & -0.003 & 0.004 & 0.157*** \\
 & (1.174) & (1.275) & (0.342) & (1.193) & (0.735) & (0.281) & (1.173) & (0.043) & (0.047) & (0.905) & (0.057) \\
urbanNum & 0.058 & 0.831 & 0.166 & 0.469 & 1.535** & 0.166 & -3.226*** & 0.024 & 0.019 & 0.379 & 0.068* \\
 & (1.254) & (1.249) & (0.234) & (1.255) & (0.659) & (0.267) & (1.245) & (0.046) & (0.046) & (0.829) & (0.037) \\
chooserBlackFemale & -2.219 & 1.930 & -0.076 & -1.984 & 1.504 & 0.467 & 0.378 & -0.097 & -0.027 & -0.908 & -0.110 \\
 & (2.109) & (2.226) & (0.739) & (2.184) & (1.645) & (0.540) & (2.165) & (0.076) & (0.084) & (1.637) & (0.111) \\
chooserWhiteSouthRural & -0.293 & -1.417 & -0.297 & -2.548 & 2.087 & 0.944* & 1.525 & 0.107 & 0.015 & 1.269 & -0.060 \\
 & (2.228) & (2.072) & (0.477) & (1.822) & (1.380) & (0.486) & (1.885) & (0.069) & (0.080) & (1.447) & (0.084) \\
chooserUrbanBlackMale & 0.680 & -5.828* & -0.421 & 1.442 & 1.370 & 0.803 & 1.953 & -0.068 & 0.072 & 1.077 & -0.015 \\
 & (2.555) & (3.075) & (0.790) & (2.765) & (2.269) & (0.766) & (2.655) & (0.094) & (0.103) & (1.920) & (0.130) \\
 &  &  &  &  &  &  &  &  &  &  &  \\
 Observations & 1,959 & 1,959 & 1,959 & 1,959 & 1,959 & 1,959 & 1,959 & 1,959 & 1,959 & 1,959 & 1,959 \\ \hline
\end{tabular}

\\
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Omitted categories are white, male, young, no children, not from a southern state, lives in a suburban area. Regressions include controls for the order variables, for the alcohol information treatment, and for Choice Architect's own race, gender, age, and child status, as well as for her political attitudes, income, and participation in welfare programs.
\end{table}
\end{landscape}


\begin{table}[H]
\caption{Intervention behavior by recipient demographics \label{tab:stereotypesRestrictionsMulti}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccccccc} \hline
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) \\


& \multicolumn{3}{c}{Enforcement price}
& \multicolumn{3}{c}{Surrogate res. price}
& \multicolumn{3}{c}{Believed mean res. price}
\\
\cmidrule(r){2-4}
\cmidrule(r){5-7}
\cmidrule(r){8-10}
CA political pref.
& \multicolumn{1}{c}{All}
& \multicolumn{1}{c}{Left}
& \multicolumn{1}{c}{Right}
& \multicolumn{1}{c}{All}
& \multicolumn{1}{c}{Left}
& \multicolumn{1}{c}{Right}
& \multicolumn{1}{c}{All}
& \multicolumn{1}{c}{Left}
& \multicolumn{1}{c}{Right}
\\ \midrule
            &               &               &               &               &               &               &               &               &               \\

Dep. var. mean&      61.656***&      61.656***&      61.656***&      78.328***&      78.328***&      78.328***&      74.749***&      74.749***&      74.749***\\
            &     (1.098)   &     (1.098)   &     (1.098)   &     (0.888)   &     (0.888)   &     (0.888)   &     (0.651)   &     (0.651)   &     (0.651)   \\


\textit{Recipient demo.}\\
                    &               &               &               &               &               &               &               &               &               \\

\quad Female        &       2.911   &      -1.619   &       3.507   &       1.319   &      -4.380   &       2.222   &       4.966** &       4.354   &       5.480*  \\
                    &     (3.587)   &     (4.673)   &     (6.082)   &     (3.085)   &     (4.118)   &     (4.831)   &     (1.973)   &     (2.866)   &     (3.137)   \\
\quad Black         &       5.753   &      -2.184   &       9.059   &       7.281*  &      -2.172   &      13.206** &       6.227** &       7.207** &       3.322   \\
                    &     (4.621)   &     (5.852)   &     (8.178)   &     (3.904)   &     (4.892)   &     (6.379)   &     (2.704)   &     (3.617)   &     (4.515)   \\
\quad Old           &      -2.718   &      -6.755** &      -3.709   &       0.412   &       1.069   &       1.358   &       1.582   &       0.948   &       2.947   \\
                    &     (2.696)   &     (3.293)   &     (4.383)   &     (2.160)   &     (2.718)   &     (3.433)   &     (1.469)   &     (2.117)   &     (2.419)   \\
\quad Has children  &       0.037   &       0.380   &      -2.909   &       1.304   &       2.034   &       1.027   &       2.164   &       0.992   &       3.166   \\
                    &     (2.698)   &     (3.531)   &     (4.649)   &     (2.237)   &     (2.860)   &     (3.703)   &     (1.641)   &     (2.244)   &     (2.798)   \\
\quad Southern      &      -0.765   &       1.449   &      -3.435   &       1.578   &       5.804** &      -0.321   &       1.102   &       1.296   &       1.684   \\
                    &     (2.308)   &     (2.928)   &     (3.929)   &     (1.896)   &     (2.460)   &     (3.286)   &     (1.365)   &     (1.828)   &     (2.324)   \\
\quad Rural         &       2.444   &       3.846   &      -3.370   &      -0.442   &       0.039   &      -4.491   &       0.309   &       2.095   &       0.261   \\
                    &     (2.751)   &     (3.410)   &     (4.544)   &     (2.269)   &     (2.892)   &     (3.494)   &     (1.674)   &     (2.224)   &     (2.780)   \\
\quad Urban         &       0.201   &      -1.720   &      -0.490   &      -1.348   &      -1.632   &      -1.848   &      -0.288   &       3.321   &      -3.383   \\
                    &     (2.835)   &     (3.611)   &     (4.873)   &     (2.298)   &     (2.983)   &     (4.048)   &     (1.623)   &     (2.304)   &     (2.794)   \\
\quad Black female  &      -2.186   &       0.930   &      -1.877   &      -2.996   &       1.560   &      -6.391   &      -7.308** &      -8.276** &      -7.022   \\
                    &     (5.185)   &     (6.529)   &     (9.048)   &     (4.383)   &     (5.451)   &     (7.237)   &     (3.040)   &     (4.108)   &     (5.134)   \\
\quad Rural white southerner&       2.730   &      -1.212   &       3.015   &       0.294   &      -7.208   &      -0.276   &      -0.693   &      -3.568   &      -4.684   \\
                    &     (5.158)   &     (6.714)   &     (8.358)   &     (4.189)   &     (4.861)   &     (6.699)   &     (2.960)   &     (4.699)   &     (4.515)   \\
\quad Urban black male&       1.990   &      13.286*  &      -6.521   &      -4.773   &       2.829   &     -11.671   &      -1.586   &      -5.509   &       2.122   \\
                    &     (6.143)   &     (7.471)   &    (10.543)   &     (4.642)   &     (5.622)   &     (8.263)   &     (3.876)   &     (4.495)   &     (6.493)   \\
\medskip
N                   &        1890   &         974   &         673   &        1945   &         999   &         695   &        1959   &        1011   &         697   \\
\bottomrule


\end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Omitted categories are white, male, young, no children, not from a southern state, and lives in a suburban area.  Regressions include controls for the order variables, for beliefs about waste and crowding out, and for Choice Architect attributes.
\end{table}


\FloatBarrier

\subsection{Intervention behavior by respondent political attitudes \label{appendixRestrPol}}

Table \ref{stereotypesRestrictionsPol} replicates Table \ref{stereotypesRestrictions} separately for subjects who rate themselves as politically left (panel A) and those who rate themselves as politically right (panel B).

\begin{table}[H]
\caption{Intervention behavior by recipient demographics by Choice Architect political preference \label{stereotypesRestrictionsPol}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\textbf{A. Politically left respondents} \\


\begin{tabular}{lccccc} \hline
 & (1) & (2) & (3) & (4) & (5) \\
VARIABLES & patSw & surrSwitch & meanBelief & patSw & surrSwitch \\ \hline
 &  &  &  &  &  \\
ruralNum & 3.914 & -1.559 & 1.140 & 3.587 & -1.841 \\
 & (3.282) & (2.629) & (2.083) & (3.263) & (2.579) \\
urbanNum & 0.767 & -0.887 & 2.463 & 0.237 & -1.606 \\
 & (3.324) & (2.760) & (2.119) & (3.281) & (2.659) \\
southern & 1.431 & 4.649** & 0.608 & 1.383 & 4.436** \\
 & (2.856) & (2.259) & (1.757) & (2.841) & (2.198) \\
chooserBlack & -0.021 & 0.413 & 1.274 & -0.274 & 0.057 \\
 & (2.777) & (2.206) & (1.774) & (2.760) & (2.128) \\
chooserFemale & -3.266 & -3.917 & 1.038 & -3.466 & -4.131 \\
 & (3.232) & (2.722) & (2.167) & (3.197) & (2.569) \\
chooserOld & -6.375* & 1.225 & 0.782 & -6.495* & 1.013 \\
 & (3.355) & (2.771) & (2.114) & (3.377) & (2.659) \\
chooserKids & 0.505 & 1.861 & 0.740 & 0.331 & 1.574 \\
 & (3.588) & (2.886) & (2.213) & (3.568) & (2.861) \\
 &  &  &  &  &  \\
 Observations & 974 & 999 & 1,011 & 974 & 999 \\ \hline
\end{tabular}

 \bigskip\\
\textbf{B. Politically right respondents} \\


\begin{tabular}{lccccc} \hline
 & (1) & (2) & (3) & (4) & (5) \\
VARIABLES & patSw & surrSwitch & meanBelief & patSw & surrSwitch \\ \hline
 &  &  &  &  &  \\
ruralNum & -2.488 & -4.301 & -0.742 & -2.435 & -4.234 \\
 & (4.071) & (3.315) & (2.423) & (4.055) & (3.327) \\
urbanNum & -1.476 & -3.486 & -2.756 & -1.183 & -3.188 \\
 & (4.514) & (3.754) & (2.655) & (4.470) & (3.748) \\
southern & -2.143 & 0.529 & 0.510 & -2.223 & 0.481 \\
 & (3.557) & (2.978) & (2.061) & (3.550) & (2.981) \\
chooserBlack & 6.499* & 7.455** & -0.551 & 6.523* & 7.508** \\
 & (3.591) & (2.928) & (2.044) & (3.576) & (2.926) \\
chooserFemale & 3.501 & 0.933 & 1.679 & 3.318 & 0.727 \\
 & (4.457) & (3.629) & (2.386) & (4.448) & (3.624) \\
chooserOld & -4.570 & 0.262 & 3.400 & -5.015 & -0.108 \\
 & (4.357) & (3.501) & (2.398) & (4.344) & (3.504) \\
chooserKids & -2.840 & 1.389 & 3.339 & -3.291 & 1.012 \\
 & (4.690) & (3.752) & (2.811) & (4.651) & (3.769) \\
 &  &  &  &  &  \\
 Observations & 673 & 695 & 697 & 673 & 695 \\ \hline
\end{tabular}

 \\
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Controls for position of belief elicitation in the survey and for order in which cash equivalents are displayed, as well as for the Choice Architect attributes used as explanatory variables in Table \ref{CAdemographics}.
\end{table}


\begin{table}
\caption{Demographic characteristics of the welfare recipients \red [UPDATE the benchmark column with the data from the American Community survey] \black \label{demoChoosers}}
\begin{center}
\begin{tabular}{lccc}
\toprule
Characteristic & \multicolumn{2}{c}{Fraction among} \\
\cmidrule(r){2-3}
&Respondents & US population \\
\midrule


\textbf{Gender}	&		\\
Male	&	0.233	\\
Female	&	0.767	\\
\textbf{Age}	&		\\
20-29	&	0.170	\\
30-39	&	0.304	\\
40-49	&	0.256	\\
50-59	&	0.215	\\
60-69	&	0.033	\\
\textbf{Race}	&		\\
White (non-hispanic)	&	0.681	\\
Black	&	0.181	\\
White (hispanic)	&	0.085	\\
Other	&	0.052	\\
\textbf{Marital status}	&		\\
Married, living with partner	&	0.237	\\
Married but separated	&	0.056	\\
Not married, living with partner	&	0.189	\\
Not married, single	&	0.519	\\
\textbf{Children}	&		\\
0	&	0.515	\\
1	&	0.215	\\
2	&	0.156	\\
3	&	0.085	\\
$>3$	&	0.030	\\
\textbf{Public assistance}	&		\\
Medicaid	&	0.681	\\
Medicare	&	0.248	\\
Veteran Disability Benefits	&	0.011	\\
Earned Income Tax Credit	&	0.126	\\
Social Security	&	0.244	\\
Supplemental Nutrition Assistance Program	&	1.000	\\
Temporary Assistance for Needy Families	&	0.041	\\
Public Housing	&	0.104	\\
Pell Grants	&	0.059	\\
\\ \textbf{N}	&	270.000	\\

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Variables for demographic comparison are from \red [add cite] \black
\end{table}

\FloatBarrier


\subsection{Interaction between Choice Architect and recipient demographics}\label{app:chooserCAinteractions}


We create indicators for the interaction between Choice Architects' race (black, white, hispanic, other) and the recipient's race (black, white), between the Choice Architect's and the recipient's gender, between the Choice Architect's and the recipient's parental status, and between the Choice Architect's and the recipient's age. We keep controlling for the type of food box, for Choice Architect's political attitudes, as well as for beliefs about crowding out and the believed percentage of the food delivery that will spoil.

\begin{table}[H]
\caption{Interaction between Choice Architect and recipient demographics \label{interactionDemographics}}
\setlength{\tabcolsep}{2pt}
\begin{center}


                    &               &               \\

\textit{White recipient} \\ \quad White (baseline) \\ \quad Black &       0.536   &      -3.254   \\
                    &     (4.121)   &     (3.935)   \\
\quad Other         &       1.775   &       5.138   \\
                    &     (5.476)   &     (3.927)   \\
\textit{Black recipient} \\ \quad White (baseline) \\ \quad Black&       0.088   &      -3.332   \\
                    &     (4.876)   &     (4.000)   \\
\quad Other         &       5.490   &       4.784   \\
                    &     (5.173)   &     (4.222)   \\
\textit{Male recipient} \\ \quad Male (baseline) \\ \quad Female&      -5.332   &      -2.846   \\
                    &     (4.135)   &     (3.501)   \\
\textit{Female recipient} \\ \quad Male (baseline) \\ \quad Female&      -2.098   &      -3.978   \\
                    &     (2.590)   &     (2.058)   \\
\textit{Recipient has children} \\ \quad Has children (baseline) \\ \quad No chi&       2.566   &       4.625   \\
                    &     (4.238)   &     (3.370)   \\
\textit{Recipient has no children} \\ \quad Has children (baseline) \\ \quad No &      -0.090   &       0.173   \\
                    &     (2.833)   &     (2.275)   \\
\textit{Recipient old} \\ \quad Age &       0.217*  &       0.155*  \\
                    &     (0.098)   &     (0.078)   \\
\textit{Recipient young} \\ \quad Age&       0.257** &       0.116   \\
                    &     (0.088)   &     (0.072)   \\
\midrule \textit{Recipient fixed effects} \\ \quad Recipient black &       3.549   &       5.033*  \\
                    &     (2.470)   &     (1.962)   \\
\quad Recipient female&      -0.512   &       0.894   \\
                    &     (3.357)   &     (2.813)   \\
\quad Recipient childless&       0.236   &      -0.050   \\
                    &     (3.750)   &     (3.025)   \\
\medskip
N                   &        1899   &        1954   \\
\bottomrule


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Omitted categories in the format (CA characteristic, receiver characteristic): (white, white), (man, man), (has children, has children). Controls for position of belief elicitation in the survey and for order in which cash equivalents are displayed \red [Add p-values for comparisons between indicators that are both included] \black
\end{table}

Table \ref{interactionDemographics} displays the results. Focusing first on race (omitted category: white CA paired with white recipient), we see few statistically significant effects. There is a marginally statistically significant effect according to which black respondents are more restrictive towards white recipients $(p<0.1)$, and respondents of races neither white, black, or hispanic are significantly less restrictive towards black recipients. We interpret these effects with caution because the former does not survive correction for multiple hypothesis testing, and the latter because it concerns a catchall category that is based on merely 4.8\% (170 subjects) of respondents.   Race does not appear to be a major determinant of interventionism towards welfare recipients neither on the side of Choice Architects nor on the side of recipients. Turning to gender (omitted category: male CA paired with male recipient) we see that both men and women tend to be less interventionist towards female rather than male recipients, though the difference does not reach statistical significance. Neither do we observe effects of parental status, neither on the Choice Architect nor on the recipient side. Finally, while older Choice Architects are more interventionist, the extent of their restrictions does not vary substantially with the recipient's age.

\FloatBarrier


\subsection{Distribution of surrogate prices}\label{appendixDistSurrogatePrices}

\red[Here add table with distribution of surrogate prices]\black
The fact that 10\% of Choice Architects implement a surrogate reservation price of zero is remarkable. When faced with a choice between the food deliveries and zero dollars in gift cards, these individuals select the latter. There are two possible explanations for such behavior. First, these subjects might be malevolent towards welfare recipients. In fact, they are 34.8\%-points more likely to reduce the number of deliveries by at least one month, reduce them on average by one additional month compared to other subjects, allocate \$3.48 less to the recipient when dividing \$50 between him and a random tax payer, and are significantly more in favor of cutting SNAP benefits and less in favor of increasing them than other subjects. (They are, however, more politically left.) The second explanation is that these subjects are confused. Consistent with that interpretation, these subjects are 29.1 \%-points more likely to provide non-monotonic belief distributions than other subjects. ($p<0.001$ for all foregoing comparisons in OLS regressions.)


\subsection{Choice Architect attributes}\label{app:CAattributes}


We now consider the effect of Choice Architects' own attributes. Who is more restrictive and why? Traditionally, libertarian thought has been associated with the political right; and the idea that the government can or should prescribe people what they can do with the political left. Accordingly, one might expect fewer interventions from that side of the political spectrum. On the other hand, it is also the right that pushes for less generous welfare policy, hence they might also be more restrictive in the political domain. Other demographics are also plausible predictors of interventions, with unknown direction of the effects. Older people may have experienced a wider variety of situations people can find themselves in and might thus decide in a more libertarian fashion. Alternatively, having formed stronger opinions and being more set in their ways, they might be more interventionist. Individuals who benefit from in-kind programs themselves might appreciate the guidance that the restrictions provide  and thus impose it on others (consistent with mistakes-projective paternalism as defined in \cite{AmbuehlBernheimOckenfels}), or they might dislike the restrictions and thus free others from them. Men, who have been shown to be more overconfident than women, might be more willing to act on their judgment of what is good for others, and thus impose higher enforcement prices.

To test these hypotheses, we pool across recipient attributes and food boxes. We consider the joint effect of respondent age, gender, race, number of children, equivalent income, and political preference. We use white as the omitted racial category. We include indicators for politically left and politically right attitudes, using political centrists as the omitted category. We also include indicators for participating in social welfare program, which we group into SNAP, a medical category (Medicaid and Medicare), a cash assistance category (Social Security, Veteran Disability Benefits, Earned Income Tax Credit, Temporary Assistance for Needy Families), and a remaining restricted or in-kind transfers category (Public Housing and Pell Grants).\footnote{We combine the programs into categories because participation in many of the specific programs is too low to permit precise estimation of effects.} We control for order effects and the alcohol information treatment as well as for beliefs about the costs of the food box, the fraction of the food that will go to waste, and for crowding out from both receiving groceries and receiving cash equivalents.

Table \ref{CAdemographics} shows the results. We find that politically liberal CAs impose significantly lower enforcement prices ($p<0.01$) whereas politically conservative CAs impose significantly higher enforcement prices ($p<0.05$), relative to politically centrist CAs (column 1). The effect for liberal CAs largely reflects a different judgment about what is appropriate for the CA (lower surrogate prices, $p<0.01$, columns 2, see column 3 for the difference), whereas the effect for conservatives reflects a greater willingness to intervene based on their judgment (column 3, $p<0.05$). Neither effect simply reflects different beliefs about recipient reservation prices (Column 4); controlling the regressions of enforcement and surrogate reservation prices does not greatly affect the forgoing conclusions (columns 5 and 6, respectively). We also observe that while liberal CAs place more weight on welfare recipients than on random taxpayers relative to centrists, conservatives have the opposite preference (column 7). Conservatives are also more spiteful towards recipients (column 8).

Age makes CA significantly more interventionist; each year of age raises mean enforcement prices by \$0.28 (column 1). This effect arises because age changes what CAs regard as appropriate (column 2), rather than their willingness to act on this judgment (column 3). While age also raises beliefs about reservation prices (column 4), controlling for these beliefs decreases the effect of age on enforcement prices (column 5) and surrogate reservation prices (column 6) only slightly. Older individuals also place more weight on recipients relative to random taxpayers.

Black CAs' decisions differ from white CA's decisions mainly in statistically insignificant ways; and these effects disappear once we control for their significantly higher beliefs about recipient reservation prices. Black CAs, however, transfer significantly less money to recipients relative to random taxpayers than white CA ($p<0.01$), and they are more spiteful. The 10.4\% of CAs who classify themselves as neither black nor white do impose significantly higher enforcement and surrogate reservation prices, effects that become more pronounced upon controlling for their lower beliefs about recipient reservation prices. They do not differ from white CAs regarding care about the recipient or spite.

The 13.0\% of CAs who benefit from SNAP themselves impose significantly lower enforcement and surrogate reservation prices. This is neither an effect of household income (for which we control) nor of beliefs about reservation prices (which do not differ for SNAP recipients). Instead, experience as a SNAP recipient appears to directly alter normative stances about the optimal restrictiveness of SNAP. For the 6.3\% of CAs who benefit from in-kind transfers other than SNAP, the 35.7\% who benefit from medical transfers, and the 26.3\% who benefit from financial transfers, we do not observe such effects.

A higher income does not alter views about restrictiveness though it does decrease beliefs about reservation prices and is associated with larger transfers to the welfare recipient relative to random taxpayers.

We also do not see any effects of gender or the number of CAs' children.


\begin{landscape}
\begin{table}[H]
\caption{Effects of Choice Architects' demographics \label{CAdemographics}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lcccccccc}
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) \\
 \toprule


& \multicolumn{1}{c}{Enforcement price}
& \multicolumn{1}{c}{Surrogate price}
\\ \midrule
            &               &               \\

Dep. var. mean&      61.656***&      78.328***\\
            &     (1.098)   &     (0.888)   \\


\textit{CA demographics}\\
                    &               &               \\

Pol. attitude \\ (baseline: center) \\ \quad Left&      -8.911***&      -6.790***\\
                    &     (3.005)   &     (2.390)   \\
\quad Right         &       6.385** &       0.779   \\
                    &     (3.239)   &     (2.606)   \\
Male                &       0.750   &       2.093   \\
                    &     (2.189)   &     (1.792)   \\
Equiv. income       &       0.060   &       0.051   \\
                    &     (0.041)   &     (0.033)   \\
\# children         &      -1.111   &      -0.988   \\
                    &     (0.905)   &     (0.740)   \\
Age                 &       0.282***&       0.203***\\
                    &     (0.085)   &     (0.071)   \\
Race (baseline: white) \\ \quad Black&       6.021*  &       0.218   \\
                    &     (3.232)   &     (2.835)   \\
\quad Other         &       6.831*  &       6.642** \\
                    &     (3.638)   &     (2.945)   \\
Welfare recipient \\ \quad SNAP recipient&      -7.423** &      -8.128***\\
                    &     (3.413)   &     (2.861)   \\
\quad Medicaid/Medicare&      -1.177   &      -1.003   \\
                    &     (2.550)   &     (2.090)   \\
\quad Other cash    &      -0.581   &      -3.362   \\
                    &     (2.578)   &     (2.206)   \\
\quad Other in-kind &      -1.568   &       1.020   \\
                    &     (4.261)   &     (2.980)   \\
\medskip
N                   &        1890   &        1945   \\
\bottomrule


\end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Each column reports the estimate of a separate WLS regression. Omitted category: white. Controls for position of belief elicitation in the survey and for order in which cash equivalents are displayed, beliefs about crowding out, beliefs about the costs of the food box, and beliefs about food waste. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}
\end{landscape}


\FloatBarrier

We also test the hypothesis that displaying a recipient with particular demographic attributes causes Choice Architects to overestimate the fraction of SNAP participants with the same demographic attributes. We regress respondents' beliefs about the fraction of SNAP recipients who are female, black, or have children, respectively, on type of recipient randomly displayed during the main stage of the study. Table \ref{tabCompositionBeliefsArtifact} shows the result. The only effect that is significant at the 5\% level is that respondents presented with a white mother have higher beliefs about the fraction of SNAP respondents with children. The coefficients on white and black mothers are jointly significant at $p=0.04$.


\begin{table}[H]
\caption{Effect of treatments (recipient characteristics) on beliefs about composition of SNAP recipient pool \label{tabCompositionBeliefsArtifact}}
\footnotesize
\setlength{\tabcolsep}{0pt}
\begin{center}


\begin{tabular}{lccc} \hline
 & (1) & (2) & (3) \\
VARIABLES & belDemoC & belDemoB & belDemoF \\ \hline
 &  &  &  \\
chooserType = 2, yWFc & 0.071*** & 0.017 & 0.019 \\
 & (0.023) & (0.022) & (0.023) \\
chooserType = 3, yBFnc & 0.036 & 0.030 & 0.010 \\
 & (0.023) & (0.021) & (0.021) \\
chooserType = 4, yBFc & 0.045** & 0.043** & 0.043** \\
 & (0.021) & (0.019) & (0.020) \\
chooserType = 5, yWM & 0.005 & -0.031 & -0.021 \\
 & (0.027) & (0.023) & (0.022) \\
chooserType = 6, yBM & 0.004 & 0.011 & -0.042 \\
 & (0.028) & (0.025) & (0.030) \\
chooserType = 7, oWF & 0.026 & -0.034 & -0.004 \\
 & (0.025) & (0.024) & (0.023) \\
chooserType = 8, oBF & 0.033 & 0.040* & 0.018 \\
 & (0.030) & (0.023) & (0.025) \\
chooserType = 9, oWM & 0.028 & -0.003 & -0.020 \\
 & (0.025) & (0.024) & (0.026) \\
chooserType = 10, oBM & 0.027 & 0.019 & -0.018 \\
 & (0.025) & (0.024) & (0.022) \\
foodHealthy & 0.008 & -0.008 & 0.013 \\
 & (0.012) & (0.011) & (0.012) \\
Constant & 0.514*** & 0.428*** & 0.227*** \\
 & (0.040) & (0.034) & (0.036) \\
 &  &  &  \\
Observations & 1,994 & 1,994 & 1,994 \\
 R-squared & 0.035 & 0.105 & 0.162 \\ \hline
\end{tabular}


\end{center}
Omitted category: Childless young white female.
\end{table}

\FloatBarrier

\subsection{Recipient preferences and beliefs}\label{appendixRecipients}

Panel A of Figure \ref{figRecipients} plots recipients' answer to the question of how similar or dissimilar the contents of each food box are from the groceries they usually consume. While judgments are dispersed, recipients judge the representative food box clearly as more similar on average.

Panel B displays recipients' answers to the question ``\textit{If you receive the [food box name] food box in one month, how much more or less money than usual do you think you would spend on other groceries in that month?}'' as well as to the question ``If you were to receive \$50 in cash each month without any strings attached, how much more or less money than usual do you think you would spend on groceries in each of these months, on average (including any of the extra cash spent on groceries)?'' Concerning the food deliveries, we observe the expected result that subjects report that some of their usual grocery spending would be crowded out. In fact, close to 40\% state that \$35 or more of their grocery spending would be crowded out. We also observe a mass of recipients who report that there would be no crowding out, and, surprisingly, another tenth of subjects who report that their expenditures on other groceries would increase by \$35 or more. The latter responses might be driven by an insufficient comprehension of the question. In fact, we would expect that an unrestricted cash transfer would increase rather than crowd out grocery spending. Yet, the distribution of answers to the question about cash transfers is rather similar to those concerning food deliveries. This pattern, too, points towards difficulties in comprehending the question. In fact, using interval midpoints for analysis, weighted regressions of statements on food-crowding-out on statements about cash-crowding out show that recipients who report an additional \$1 of food crowding out report more than \$0.50 additional crowding out from cash ($p<0.01$).

Panel C shows the distribution of answers to the question ``\textit{If you receive the deliveries of [food box name], how much of the food in the box do you think you will eventually eat, and how much will spoil or will otherwise go to waste?}.'' With the caveat of experimenter demand effects, recipients report low levels of anticipated waste, which are slightly higher for the healthy than for the representative food box.

Panel D plots recipients' reservation prices. While the mean reservation price is significantly below the cost of the food box of \$52, more than 10\% of recipients have a reservation price in excess of \$100 (censored above).


\begin{figure}
\caption{Recipient beliefs and judgments \label{figRecipients}}
\begin{center}
\begin{tabular}{cc}
\textbf{A. Recipients' judgment of food similarity}
&
\textbf{B. Recipients' beliefs about crowding out} \\
\includegraphics[width=0.5\textwidth]{graphs/foodSimilarity}
&
\includegraphics[width=0.5\textwidth]{graphs/crowdingOutChoosers} \\
\textbf{C. Beliefs about food waste}
&
\textbf{D. Reservation prices } \\
\includegraphics[width=0.5\textwidth]{graphs/wasteChoosers}
&
\includegraphics[width=0.5\textwidth]{graphs/resPricesChoosers} \\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}}
\end{figure}

\subsection{True vs. believed distribution of SNAP demographics}\label{app:snapBeliefs}


\begin{table}[htbp!]
\caption{True vs. believed distribution of SNAP demographics \label{snapBeliefs}}
\begin{center}
\medskip
\begin{tabular}{cc}
	\textit{True}
&
	\textit{Believed}
\\
	\begin{tabular}{lccc}
	\midrule


                   &          \multicolumn{2}{c}{\% with children}
&                                           \\
                   &          Women
&          Men        &          \% race    \\
\cmidrule(r){2-3}

Black              &          0.69
&          0.67       &          0.37       \\
White              &          0.63
&          0.62       &          0.63       \\
\cmidrule(r){2-3}

\% gender          &          0.54
&          0.46       &                     \\

 \\
	\midrule
	\end{tabular}
&
	\begin{tabular}{lccc}
	\midrule


                   &          \multicolumn{2}{c}{\% with children}
&                                           \\
                   &          Women
&          Men        &          \% race    \\
\cmidrule(r){2-3}

Black              &          0.73
&          0.45       &          0.50       \\
White              &          0.68
&          0.44       &          0.50       \\
\cmidrule(r){2-3}

\% gender          &          0.64
&          0.36       &                     \\

 \\
	\midrule
	\end{tabular}
\\
\end{tabular}

\end{center}
\footnotesize
\textit{\textbf{Notes:}} All statistics weighted. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}

\section{Further results on attitudes and beliefs about SNAP}\label{sec:extValidity}

Here we explore how CAs' experimental choices relate to their attitudes and beliefs about the SNAP program and its participant pool. We elicited CAs agreement or disagreement with tightening restrictions on what SNAP participants can purchase with program funds, and on whether sending cash instead of food would cause recipients to make bad choices. Respondents answer on Likert scales whose range we normalize to $[-1, 1]$.


\begin{table}[htbp!]
\caption{Effect on SNAP attitudes \label{tab:snapAttitudes}}
\small

\begin{center}

\begin{tabular}{lccccc} \hline
 & (1) & (2) & (3) & (4) & (5) \\


& \multicolumn{2}{c}{Tighten}
& \multicolumn{2}{c}{Cash}
& \multicolumn{1}{c}{CA political}
\\
& \multicolumn{2}{c}{SNAP}
& \multicolumn{2}{c}{causes}
& \multicolumn{1}{c}{preference}
\\
& \multicolumn{2}{c}{restrictions}
& \multicolumn{2}{c}{bad choices}
\\ \midrule
            &               &               &               &               &               \\

Dep. var. mean&      -0.123***&      -0.123***&       0.031   &       0.031   &       2.909***\\
            &     (0.012)   &     (0.012)   &     (0.018)   &     (0.018)   &     (0.049)   \\


                    &               &               &               &               &               \\

Enforcement price   &       0.004***&               &       0.006***&               &               \\
                    &     (0.000)   &               &     (0.000)   &               &               \\
\textit{CA belief about recipients}\\ \quad Fraction women&               &       0.069   &               &      -0.297   &       1.328** \\
                    &               &     (0.110)   &               &     (0.161)   &     (0.452)   \\
\quad Fraction Black&               &       0.178*  &               &       0.588***&       1.581***\\
                    &               &     (0.073)   &               &     (0.098)   &     (0.279)   \\
\quad Fraction parents&               &       0.029   &               &       0.451***&      -0.180   \\
                    &               &     (0.080)   &               &     (0.122)   &     (0.373)   \\
\textit{CA attributes} \\ \quad Male&               &       0.070** &               &       0.102** &       0.408***\\
                    &               &     (0.023)   &               &     (0.033)   &     (0.097)   \\
\quad White         &               &      -0.019   &               &      -0.135***&       0.942***\\
                    &               &     (0.029)   &               &     (0.040)   &     (0.106)   \\
\quad Equivalent income&               &       0.003***&               &       0.003***&       0.003*  \\
                    &               &     (0.000)   &               &     (0.001)   &     (0.002)   \\
\quad Has children  &               &       0.049   &               &      -0.050   &       0.370***\\
                    &               &     (0.025)   &               &     (0.034)   &     (0.104)   \\
\quad Age           &               &       0.001   &               &       0.006***&      -0.007   \\
                    &               &     (0.001)   &               &     (0.001)   &     (0.004)   \\
\quad Political preference&               &       0.053***&               &       0.101***&               \\
                    &               &     (0.006)   &               &     (0.008)   &               \\
\medskip
N                   &        1894   &        1925   &        1787   &        1815   &        1959   \\
\bottomrule


\end{tabular}


\end{center}
\footnotesize
\textit{\textbf{Notes:}} All dependent variables normalized to the interval $[-1, 1]$. Answer options in columns 1 and 2 are coded as
-1 `Restrictions on the use of the funds should be abolished; all items should be eligible',
$-\frac{2}{3}$ `Restrictions on the use of the funds should be loosened drastically; only addictive goods such as alcohol should be excluded',
$-\frac{1}{3}$ `Restrictions on the use of the funds should be loosened somewhat; certain items such as inexpensive hot foods and non-food items like personal hygiene products should become eligible',
0 `Restrictions should be left unchanged',
$\frac{1}{3}$ `Restrictions on the use of the funds should be tightened somewhat; certain items such as unhealthy foods should be made ineligible',
$\frac{2}{3}$ `Restrictions on the use of the funds should be tightened drastically; only healthy and inexpensive foods should be eligible',
1 `Recipients should not have any choice; the SNAP program should determine which foods the recipients will receive'.
Regressions in any given column exclude subjects who answered `I have no opinion on this' to the question in that column. Regressions in columns 1 and 3 additionally exclude subjects who exhibit multiple switching in the price lists.
Answer options in columns 3 and 4 are coded as -1 `totally disagree', $-\frac{1}{3}$ `disagree somewhat', $\frac{1}{3}$ `agree somewhat', 1 `totally agree.'
Equivalent income is defined as household income divided by the square root of household size, following the OECD square root equivalence scale \citep{equivalenceScale}.
Some respondents stated that they have no opinion about the restrictiveness (1.7\%) of SNAP

or refused to provide judgment on whether SNAP recipients would make bad choices with unconstrained transfers (7.6\%).
These respondents are excluded from the corresponding regressions.


Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


As columns 1 and 3 of Table \ref{tab:snapAttitudes} show, higher enforcement prices in CAs' decisions strongly relate to greater support for tightening SNAP restrictions and more pronounced beliefs that recipients would make bad choices if SNAP transfers were made in cash ($p<0.01$ in both cases).

Given the prominence of stereotypes in the political debate, we also elicited CAs' beliefs about the composition of the SNAP recipient pool in terms of gender, race, and parental status.  While we are interested in the relation between these beliefs and attitudes about SNAP, it is noteworthy that respondents vastly overestimate the fraction of black recipients. They believe that a random SNAP recipient is more likely black than white, while in reality, white recipients are 1.7 times as common as black recipients.\footnote{Appendix \ref{app:snapBeliefs} presents the full comparison between the believed and actual composition of the SNAP recipient pool.} We find beliefs about a greater fraction of Black individuals in the SNAP recipient pool coincide with greater support for tightening SNAP restrictions, though the effect is only significant at the 10\% level when controlling for CA attributes and CA political preferences (column 2).\footnote{Absent the control for CA political preferences, the effect increases to 0.26 and becomes significant at $p<0.01$.} Beliefs about gender and parental status do not affect attitudes about the restrictiveness of SNAP. Beliefs about a greater fraction of Black recipients correlate strongly with the belief that cash would be used unwisely ($p<0.01$, column 4), as do beliefs about a greater fraction of parents. A politicized topic, attitudes are most strongly predicted by CA political preferences. These also relate strongly to beliefs about the composition of the SNAP participant pool.

In conjunction with our previous finding that exogenous variation of recipient demographics does not affect interventions, these results suggest that attitudes about poverty assistance affect beliefs about the recipient pool (for instance through ex-post rationalization) rather than vice versa.


\newpage
\section{Structural estimation: Details} \label{sec:structuralEstimationDetails}


\subsection{Theoretical Framework}

\subsubsection{Primitives}

We write $a = (1 - \gamma)m$ and $b=\gamma$ and estimate $u = a + b \min\{c, \overline{p}\}$. We cap reservation price beliefs  at $\overline{p}$ .

\noindent \textbf{Surrogate choices.} Consider the line on the multiple price list in which the value of the gift cards is $p$. When deciding between choosing the food box and choosing the gift cards of value $p$ for a recipient, the CA trades off the loss $w_i$ from choosing the gift cards for someone who should have received the food box ($u_i > p$) and the loss $1 - w_i$ from choosing the food box for someone who should have received the gift cards ($p > u_i$). All in all, the decision rules can be summarized as follows.

\vskip 3mm

\noindent 1. Consider the (unconditional) surrogate choice where CA $i$ is not provided with any information about the recipient's WTP for the food box. In this choice, CA $i$ \textbf{chooses the food box} if and only if
\begin{equation} \label{focUncondSurrogate1}
    \int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c) + \epsilon_{i1} \geq 0  \nonumber
\end{equation}
where (i) $u_i = \alpha_i + \beta_i \min\{c, \overline{p}\}$ is the CA's normative WTP for a recipient with a WTP of $c$; \\
(ii) $\epsilon_{i1} \sim N(0, \sigma_{\epsilon_1}^2)$ is an unobserved preference shock otherwise not captured by the model.

\vskip 3mm

\noindent 2. Consider the (conditional) surrogate choice where CA $i$ is provided the additional information that the recipient chooses the food box over gift cards of value $\tau$ (i.e., the recipient exhibits a high WTP for the food box). Note that according to the CA's beliefs, the random recipients make this decision with $\frac{1}{2}$ probability, while the attentive recipients make this decision with a $(1-F_i(\tau))$ probability. Therefore, Bayesian updating implies that the posterior probability that the recipient is of the random type is $\frac{\frac{q}{2}}{\frac{q}{2}+(1-q)(1-F_i(\tau))}$, and the posterior probability that the recipient is of the attentive type is $\frac{(1-q)F_i(\tau)}{\frac{q}{2}+(1-q)(1-F_i(\tau))}$. All in all, in this choice, CA $i$ \textbf{chooses the food box} if and only if
\begin{eqnarray} \label{focCondSurrogateUp1}
    && \frac{\frac{q}{2}}{\frac{q}{2}+(1-q)(1-F_i(\tau))} \int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c) \nonumber \\
    + && \frac{(1-q)(1-F_i(\tau))}{\frac{q}{2}+(1-q)(1-F_i(\tau))}\int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c | c \geq \tau) + \epsilon_{i2} \geq 0 \nonumber
\end{eqnarray}
where (i) $u_i = \alpha_i + \beta_i \min\{c, \overline{p}\}$ is the CA's normative WTP for a recipient with a WTP of $c$; \\
(ii) $\epsilon_{i1} \sim N(0, \sigma_{\epsilon_1}^2)$ is an unobserved preference shock otherwise not captured by the model.

\vskip 3mm

\noindent 3. Similarly, consider the (conditional) surrogate choice where CA $i$ is provided the additional information that the recipient chooses gift cards of value $\tau$ over the food box (i.e., the recipient exhibits a low WTP for the food box). Note that according to the CA's beliefs, the random recipients make this decision with $\frac{1}{2}$ probability, while the attentive recipients make this decision with a $F_i(\tau)$ probability. Therefore, Bayesian updating implies that the posterior probability that the recipient is of the random type is $\frac{\frac{q}{2}}{\frac{q}{2}+(1-q)F_i(\tau)}$, and the posterior probability that the recipient is of the attentive type is $\frac{(1-q)F_i(\tau)}{\frac{q}{2}+(1-q)F_i(\tau)}$. All in all, in this choice, CA $i$ \textbf{chooses the food box} if and only if
\begin{eqnarray} \label{focCondSurrogateDown1}
    && \frac{\frac{q}{2}}{\frac{q}{2}+(1-q)F_i(\tau)} \int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c) \nonumber \\
    + && \frac{(1-q)F_i(\tau)}{\frac{q}{2}+(1-q)F_i(\tau)} \int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c | c \leq \tau) + \epsilon_{i3} \geq 0 \nonumber
\end{eqnarray}
where (i) $u_i = \alpha_i + \beta_i \min\{c, \overline{p}\}$ is the CA's normative WTP for a recipient with a WTP of $c$; \\
(ii) $\epsilon_{i1} \sim N(0, \sigma_{\epsilon_1}^2)$ is an unobserved preference shock otherwise not captured by the model.

\vskip 3mm

\noindent \textbf{Paternalistic choices.} On each line of the price list for paternalistic choices, the CA decides between leaving the choice open between the food box and gift cards of value $p$ on the one hand, and forcing the recipient to receive the food box on the other hand. The CA can only alter the outcome of those recipients who, absent an intervention, would have chosen the gift cards of value $p$. These include a half of the random recipients, and the attentive recipients who have a WTP $c < p$. The CA balances the benefit of choosing the food box for those recipients who should receive the food box ($u > p$) but would not choose the food box if deciding themselves ($c < p$) against the loss from recipients who should not receive the food box ($u < p$) but would be forced to receive the food box ($c < p$). Finally, CA $i$ will intervene only if the expected net benefit of intervention is at least $A_i$. All in all, in this choice, CA $i$ \textbf{forces the recipient to receive the food box} instead of \textbf{leaving the choice open} if and only if
\begin{eqnarray} \label{focPaternalism1}
    && \frac{q}{2} \int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c) \nonumber \\
    + && (1-q)F_i(p)\int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c | c < p) + \epsilon_{i4} \geq A_{i} \nonumber
\end{eqnarray}
where (i) $u_i = \alpha_i + \beta_i \min\{c, \overline{p}\}$ is the CA's normative WTP for a recipient with a WTP of $c$; \\
(ii) $\epsilon_{i1} \sim N(0, \sigma_{\epsilon_1}^2)$ is an unobserved preference shock otherwise not captured by the model.

On the left-hand side of the inequality above, the first term corresponds to the net benefit from affecting the random recipients' outcomes, and the second term corresponds to the net benefit from affecting the attentive recipients' outcomes.\footnote{Note that the variance of $\epsilon_{i4}$ can be reinterpreted as providing an upper bound for the variance of $A_i$ in a model with heterogeneity in $A_i$. That is, in that model the variance of $\epsilon_{i4}$ and the variance of $A_i$ cannot be separately identified.}

Finally, note that the model should be interpreted in the context of a particular population of recipients. Therefore, for any given CA, all the parameters are allowed to differ across recipients of different demographic groups.

\subsubsection{Identification} \label{sec:identification}

In this section, we provide a heuristic discussion of how certain parameters in the model are identified in the experimental data:
\begin{equation}
    \alpha,\ \beta,\ w,\ A, \ F(c), \ q, \ q_D,\ \overline{q_N},\ \underline{q_N}, \sigma_{\epsilon_1},\ \sigma_{\epsilon_2},\ \sigma_{\epsilon_3},\ \sigma_{\epsilon_4} \nonumber
\end{equation}

$F(c)$ and $q$ are directly identified by the beliefs elicited from the Choice Architects about recipients' own choices between the food box and gift cards of various values. $q$ also influences the extent of paternalistic interventions.

$q_D$ can be roughly identified by the proportion of ``deontological'' CAs who always choose the food box or force the recipient to receive the food box in the surrogate and paternalism choices. Conditional on the elicited beliefs, $\alpha$ and $w$ are both identified by the switching points in the choice lists for the non-deontological CAs. The switching behavior in the unconditional surrogate choice relative to the conditional surrogate choices also helps to identify $w$.

$\beta$ is identified both by the sensitivity of the conditional surrogate choices to the provided information, and by the prevalence of paternalistic interventions. Intuitively, the lower $\gamma$ is, the less does the CA weight the recipients' own WTP in her normative WTP, and the more does she paternalistically intervene in the recipients' choices. The prevalence of paternalistic interventions also directly identifies the level of $A$. Finally, the $\sigma$'s are identified by the degree of heterogeneity in CAs' behavior (see also Section \ref{sec:parametrization_hetero}).

\subsubsection{Parametrization} \label{sec:parametrization}

We adopt two different approaches to parametrizing the model. We start with assuming that the preferences and beliefs among the economic-type CAs are completely homogeneous. We then consider a more general setup allowing for heterogeneous preferences.

\subsubsection{Homogeneous Preferences} \label{sec:parametrization_homo}

The economic-type CA $i$ has four preference parameters: $\alpha_i$, $\beta_i$, $w_i$, and $A_i$. In the first approach, we assume that there is no across-CA heterogeneity in these parameters:
\begin{equation}
    \alpha_i = \alpha,\ \beta_i = \beta,\ w_i = w,\ A_i = A \nonumber
\end{equation}
We also assume that CA beliefs about recipient WTPs are homogeneous and follow a normal distribution:
\begin{equation}
    F_i(c) = F(c) \sim N(\mu_c, \sigma_c^2) \nonumber
\end{equation}
Heterogeneous behavior across CAs in the data are thus completely attributed to unobserved preference shocks.

\subsubsection{Heterogeneous Preferences} \label{sec:parametrization_hetero}

We also consider a more complex parametrization allowing for heterogeneous preferences among the economic-type CAs. Specifically, we assume that three preference parameters follow normal distributions:
\begin{equation}
    \alpha_i \sim N(\mu_{\alpha}, \sigma_{\alpha}^2),\ \beta_i \sim N(\mu_{\beta}, \sigma_{\beta}^2),\ w_i \sim N(\mu_{w}, \sigma_{w}^2) \nonumber
\end{equation}
As discussed in a footnote at the end of Section \ref{sec:analysis}, to the extent that we assume an unobserved preference shock for the paternalistic choice, its variance $\sigma_{\epsilon_4}^2$ cannot be separately identified from any heterogeneity in $A$. Therefore, $\sigma_{\epsilon_4}$ can be reinterpreted as providing an upper bound for the variance of $A$, $\sigma_A^2$.

Finally, to explore demographic differences in decision-making, we may also allow the (mean) preference parameters $\mu_{\alpha}$, $\mu_{\beta}$, $\mu_{w}$, as well as $A$ to differ across CA demographic groups.

\subsubsection{Estimation}

We plan to estimate the model using (simulated) method of moments, matching moments from choice data and beliefs data from the experiment.

Table \ref{tab:Moments} shows a list of these moments.

Moreover, for the parametrization with heterogeneous preferences, we need to separately identify \textbf{random preference shocks independent across different choices} and \textbf{heterogeneity in preferences that simultaneously affect all choices}. For this purpose, we may need to add additional moments for information about the joint behavior (i.e., correlation) of different choices.


\subsection{Moments}

\begin{table}[H]
\begin{center}
\caption{A list of data moments used in the model estimation (by CA political inclination).}
\vskip 2mm
\label{tab:Moments}
\setlength\extrarowheight{-1.5pt}
\tiny{
\begin{tabular}{l>{\centering\arraybackslash}m{1.8cm}>{\centering\arraybackslash}m{1.8cm}>{\centering\arraybackslash}m{1.8cm}}
\toprule
Moment & Full Sample (Reweighted) & Liberals (Reweighted) & Non-liberals (Reweighted) \\
\hline
\multicolumn{4}{l}{\textbf{8 moments from unconditional surrogate choices}} \\
Fraction Choosing Food Box over \$0 Gift Cards & 91.2\% & 86.9\% & 94.9\% \\
Fraction Choosing Food Box over \$25 Gift Cards & 88.1\% & 83.3\% & 92.3\% \\
Fraction Choosing Food Box over \$45 Gift Cards & 84.3\% & 81.8\% & 86.6\% \\
Fraction Choosing Food Box over \$60 Gift Cards & 76.4\% & 72.9\% & 79.4\% \\
Fraction Choosing Food Box over \$70 Gift Cards & 64.4\% & 57.1\% & 70.7\% \\
Fraction Choosing Food Box over \$85 Gift Cards & 49.7\% & 42.9\% & 55.6\% \\
Fraction Choosing Food Box over \$105 Gift Cards & 37.5\% & 29.5\% & 44.3\% \\
Fraction Choosing Food Box over \$130 Gift Cards & 35.8\% & 25.8\% & 44.3\% \\
\hline
\multicolumn{4}{l}{\textbf{ 4 moments from conditional surrogate choices (knowing that recipient exhibits a WTP $\geq$ \$65)}} \\
Fraction Choosing Food Box over \$70 Gift Cards & 90.6\% & 89.9\% & 91.3\% \\
Fraction Choosing Food Box over \$85 Gift Cards & 65.5\% & 64.4\% & 66.3\% \\
Fraction Choosing Food Box over \$105 Gift Cards & 47.8\% & 46.1\% & 49.2\% \\
Fraction Choosing Food Box over \$130 Gift Cards & 43.0\% & 40.2\% & 45.5\% \\
\hline
\multicolumn{4}{l}{\textbf{ 4 moments from conditional surrogate choices (knowing that recipient exhibits a WTP $\leq$ \$65)}} \\
Fraction Choosing Food Box over \$0 Gift Cards & 92.4\% & 93.9\% & 91.2\% \\
Fraction Choosing Food Box over \$25 Gift Cards & 89.4\% & 89.1\% & 89.8\% \\
Fraction Choosing Food Box over \$45 Gift Cards & 84.4\% & 86.4\% & 82.7\% \\
Fraction Choosing Food Box over \$60 Gift Cards & 67.4\% & 69.6\% & 65.5\% \\
\hline
\multicolumn{4}{l}{\textbf{8 moments from paternalistic choices}} \\
Fraction Forcing Food Box with \$0 Gift Cards & 76.9\% & 72.3\% & 80.8\% \\
Fraction Forcing Food Box with \$25 Gift Cards & 72.4\% & 68.7\% & 75.5\% \\
Fraction Forcing Food Box with \$45 Gift Cards & 64.5\% & 64.1\% & 64.8\% \\
Fraction Forcing Food Box with \$60 Gift Cards & 59.2\% & 59.0\% & 59.3\% \\
Fraction Forcing Food Box with \$70 Gift Cards & 54.6\% & 51.6\% & 57.1\% \\
Fraction Forcing Food Box with \$85 Gift Cards & 43.6\% & 39.2\% & 47.4\% \\
Fraction Forcing Food Box with \$105 Gift Cards & 33.5\% & 31.4\% & 35.3\% \\
Fraction Forcing Food Box with \$130 Gift Cards & 30.6\% & 28.2\% & 32.6\% \\
\hline
\multicolumn{4}{l}{\textbf{2 moments from all (surrogate and paternalistic) choices}} \\
Fraction always choosing / forcing Food Box among those told recipient exhibits a WTP $\geq$ \$65 & 17.7\% & 14.8\% & 20.3\% \\
Fraction always choosing / forcing Food Box among those told recipient exhibits a WTP $\leq$ \$65 & 13.3\% & 6.4\% & 19.1\% \\
\hline
\multicolumn{4}{l}{\textbf{7 moments from beliefs about WTP (without further information)}} \\
Average belief of fraction choosing \$25 Gift Cards over Food Box & 7.4\% & 7.6\% & 7.1\% \\
Average belief of fraction choosing \$45 Gift Cards over Food Box & 15.9\% & 16.1\% & 15.8\% \\
Average belief of fraction choosing \$60 Gift Cards over Food Box & 28.5\% & 25.6\% & 31.0\% \\
Average belief of fraction choosing \$70 Gift Cards over Food Box & 41.4\% & 38.4\% & 44.1\% \\
Average belief of fraction choosing \$85 Gift Cards over Food Box & 54.5\% & 53.7\% & 55.2\% \\
Average belief of fraction choosing \$105 Gift Cards over Food Box & 68.9\% & 68.3\% & 69.5\% \\
Average belief of fraction choosing \$130 Gift Cards over Food Box & 83.7\% & 83.3\% & 84.1\% \\
\hline
\multicolumn{4}{l}{\textbf{4 moments from beliefs about WTP (knowing that recipient exhibits a WTP $\geq$ \$65)}} \\
Average belief of fraction choosing \$70 Gift Cards over Food Box & 3.5\% & 1.0\% & 5.7\% \\
Average belief of fraction choosing \$85 Gift Cards over Food Box & 39.6\% & 39.9\% & 39.4\% \\
Average belief of fraction choosing \$105 Gift Cards over Food Box & 59.4\% & 59.3\% & 59.4\% \\
Average belief of fraction choosing \$130 Gift Cards over Food Box & 81.3\% & 81.1\% & 81.5\% \\
\hline
\multicolumn{4}{l}{\textbf{3 moments from beliefs about WTP (knowing that recipient exhibits a WTP $\leq$ \$65)}} \\
Average belief of fraction choosing \$25 Gift Cards over Food Box & 6.2\% & 6.9\% & 5.7\% \\
Average belief of fraction choosing \$45 Gift Cards over Food Box & 19.7\% & 16.9\% & 22.2\% \\
Average belief of fraction choosing \$60 Gift Cards over Food Box & 42.2\% & 40.8\% & 43.4\% \\
\hline
$N$ & 428 & 220 & 208 \\
\bottomrule
\end{tabular}
}
\end{center}
\end{table}


\newpage
\section{Recipient implementation}\label{chooserImplementation}

Out of the 47 recipients who completed the second-stage survey, 10 received food deliveries, 7 were assigned to the decision in which the Choice Architect split \$50 between them and a random US taxpayer (though one of the recipient had provided invalid Venmo account information and thus could not receive payment). The remaining 30 recipients were assigned to, or chose to receive the gift cards. Out of these, for two recipients, a surrogate choice decision was implemented where \$0 in gift cards would be sent. Amongst the remaining 28, monthly gift card values are the following: $(25, 25, 25, 25, 45, 45, 45, 60, 60, 60, 60, 70, 70, 70, 70, 85, 85, 85, 100, 100, 105, 105, 105, 130, 130, 130, 130, 130)$.\footnote{Values outside the grid used for Choice Architects in the main session derive from the soft-launch that used a different price grid.}  \red [70 and 100 are not on the current grid; those must be from the soft-launch. It seems we have disproportionately many from that soft launch. Why?] \black

Table \ref{giftCardsChosen} shows the fraction of the total value of gift cards disbursed in each category:

\red [ADD THIS TABLE once all the shipments have been made] \black


\newpage
\section{Experiment instructions}

\subsection{Choice Architects}
\includepdf[pages=-, landscape=true]{patPovInstructions}

\subsection{Recipients}
\includepdf[pages=-, landscape=true]{patPovInstructionsChoosers}

\addcontentsline{toc}{section}{References}


\end{appendix}

\end{document}


\documentclass[smallheadings]{article}

\usepackage{booktabs}
\usepackage{float}
\usepackage{placeins}

\usepackage[]{graphicx}
\usepackage{mathrsfs}
\usepackage{mdwlist}
\usepackage{amssymb}
\usepackage{eucal}
\usepackage{afterpage}

\usepackage{nicefrac}


\usepackage{soul}

\usepackage{amsmath}
\usepackage{eucal}
\usepackage{amsthm}
\usepackage{epstopdf}
\usepackage{dsfont}
\usepackage{bm}
\usepackage{enumerate}
\usepackage[usenames]{xcolor}
\definecolor{blue}{rgb}{0,0,1}
\definecolor{purple}{rgb}{0.6,0,1}
\usepackage{natbib}
\usepackage{multirow}
\usepackage[margin=1.2in]{geometry}
\usepackage{pdfpages}

\usepackage{float}
\usepackage{setspace}

\usepackage{pdflscape}
\usepackage{marvosym}


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

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


\usepackage[colorlinks = true, citecolor = blue, linkcolor = blue, urlcolor  = blue,]{hyperref}

\usepackage{rotating}
\usepackage[labelfont=bf]{caption}


\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}}

\defcitealias{BelmontReport}{Belmont Report (1978)}


\clubpenalty = 10000
\widowpenalty = 10000

\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{\var}{\mathrm{var}}
\newcommand{\supp}{\mathrm{supp}}
\newcommand{\logit}{\mathrm{logit}}
\newcommand{\sech}{\mathrm{sech}}
\newcommand{\abs}{\mathrm{abs}}
\newcommand{\sgn}{\mathrm{sgn}}
\newcommand{\del}{\partial}
\newcommand{\w}{\omega}


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

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


\setcounter{MaxMatrixCols}{10}

\theoremstyle{plain}
\newtheorem{proposition}{Proposition}
\newtheorem{prediction}{Prediction}
\newtheorem*{example}{Example}
\setcounter{section}{0}
\newtheorem{theorem}[proposition]{Theorem}
\newtheorem{lemma}{Lemma}
\newtheorem{corollary}[proposition]{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{setspace}
\setstretch{1.4}


\setcounter{tocdepth}{3}
\setcounter{secnumdepth}{3}


\begin{document}

\title{Interventionist Preferences and the Welfare State: \\ The Case of In-Kind Nutrition Assistance \\ PRELIMINARY AND INCOMPLETE}
\author{Sandro Ambuehl, B. Douglas Bernheim, Tony Q. Fan, Zach Freitas-Groff\footnote{Ambuehl: University of Zurich, Department of Economics, Bl\"uemlisalpstrasse 10, 8006 Z\"urich, Switzerland, sandro.ambuehl@econ.uzh.ch. Bernheim, Fan, and Freitas-Groff: Stanford University, Department of Economics, 579 Serra Mall, Stanford, CA, 94305, USA, bernheim@stanford.edu, tonyqfan@stanford.edu, zgroff@stanford.edu. We are especially grateful to research dietician Mandy Murphy Carroll (Stanford University School of Medicine) for her generous help with the nutritional assessment of the food baskets and to Andia Kozmai for excellent research assistance. Ambuehl and Bernheim gratefully acknowledge funding from the Alfred P. Sloan Foundation (grant number G-2017-9017). Ambuehl also gratefully acknowledges funding from the Swiss National Science Foundation Starting Grant \#211222.}}


\maketitle


\begin{abstract}

Poverty assistance is often administered in kind even though cash transfers might raise recipients' welfare more effectively. We study the interventionist preferences that may restrict transfers to in-kind forms. In our lab-in-the-field experiment, U.S. citizens decide whether and how to constrain bundles of in-kind or cash-equivalent deliveries to U.S. SNAP (``food stamps'') participants. Citizens intervene strongly and often, suggesting that in-kind restrictions are an intrinsically desired feature of the system rather than a cost incurred to meet some extraneous goal. Yet, interventions do not target or promote the good of the recipients. They are substantially motivated by the selfish utility individuals derive from the act of intervening. They do not seek to ensure sufficient consumption of items beneficial to recipients but to limit consumption of items interveners find offensive. And because they depend on citizens' systematically distorted beliefs about recipients, interventions are far more restrictive than citizens believe. In-experiment behavior correlates strongly with views about government policy. [157 words]


\end{abstract}


\doparttoc
\faketableofcontents
\part{}


\begin{table}[htbp]
\caption{Structural estimation results} \label{tab:structuralMain}
\small
\begin{center}
\setlength{\tabcolsep}{2pt}
\begin{tabular}{lccccccc}
& (1) & (2) & (3) & & (4) & (5) &  \\
\toprule
\textbf{\textit{CA sample}} \\
\quad \textit{Food basket} \\
\qquad Healthy 				& \checkmark & \checkmark 	& 			&& \checkmark & \checkmark & \\
\qquad Representative 		& \checkmark & 		 	& \checkmark 	&& \checkmark & \checkmark & \\
\quad \textit{Alcohol information} \\
\qquad Low				& \checkmark & \checkmark & \checkmark && \checkmark 	& 			& \\
\qquad High				& \checkmark & \checkmark & \checkmark &&  				& \checkmark& \\
\midrule
Difference in estimates & &&& (3)-(2) &&&(4)-(5) \\


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.849 ***& 0.856 ***& 0.847 ***& 0.009 & 0.871 ***& 0.858 ***& 0.013 \\
   &(0.017)   &(0.022)   &(0.024)   &(0.033)   &(0.022)   &(0.025)   &(0.034)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 71.581 ***& 73.278 ***& 70.168 ***& 3.109 ***& 69.399 ***& 73.796 ***& -4.397 ***\\
   &(0.428)   &(0.579)   &(0.595)   &(0.830)   &(0.531)   &(0.616)   &(0.814)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 6.785 ***& 7.291 ***& 6.228 ***& 1.062 **& 6.514 ***& 6.906 ***& -0.392 \\
   &(0.249)   &(0.364)   &(0.311)   &(0.479)   &(0.306)   &(0.348)   &(0.463)\\
\quad Fraction of absolutist types, $q$& 0.146 ***& 0.157 ***& 0.139 ***& 0.018 & 0.115 ***& 0.169 ***& -0.054 ***\\
   &(0.007)   &(0.011)   &(0.009)   &(0.014)   &(0.009)   &(0.010)   &(0.014)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 71.815 ***& 72.631 ***& 71.228 ***& 1.403 & 70.660 ***& 72.479 ***& -1.819 **\\
   &(0.463)   &(0.659)   &(0.610)   &(0.899)   &(0.579)   &(0.658)   &(0.877)\\
\quad Prior mean parameter, $\mu_0$& 70.091 ***& 70.296 ***& 69.939 ***& 0.357 & 69.494 ***& 70.311 ***& -0.817 \\
   &(0.478)   &(0.694)   &(0.623)   &(0.932)   &(0.596)   &(0.688)   &(0.910)\\
\quad Prior SD parameter, $\sigma_0$& 34.270 ***& 36.475 ***& 32.449 ***& 4.026 ***& 31.726 ***& 35.932 ***& -4.207 ***\\
   &(0.439)   &(0.674)   &(0.542)   &(0.865)   &(0.564)   &(0.618)   &(0.837)\\
\quad Posterior mean parameter, $\mu_1$& 96.120 ***& 96.357 ***& 95.792 ***& 0.565 & 95.415 ***& 96.423 ***& -1.008 \\
   &(0.409)   &(0.583)   &(0.519)   &(0.780)   &(0.589)   &(0.518)   &(0.784)\\
\quad Posterior SD parameter, $\sigma_1$& 25.958 ***& 26.983 ***& 24.510 ***& 2.473 ***& 24.609 ***& 26.714 ***& -2.105 ***\\
   &(0.359)   &(0.521)   &(0.447)   &(0.687)   &(0.457)   &(0.516)   &(0.689)\\
\\
\textbf{\textit{Best-fitting enforcement price}}& 61.80 ***& 63.22 ***& 60.12 ***& 3.10 **& 59.91 ***& 63.05 ***& -3.14 ***\\
   &(0.62)   &(0.92)   &(0.78)   &(1.21)   &(0.74)   &(0.92)   &(1.18)\\
\textbf{\textit{Counterfactuals}} \\ \quad Counterfactual: $\alpha = 0, \beta=1$ \\ \qquad Enforcement price& 56.87 ***& 57.44 ***& 56.10 ***& 1.35 & 57.04 ***& 56.57 ***& 0.48 \\
   &(0.80)   &(1.15)   &(1.02)   &(1.54)   &(0.92)   &(1.18)   &(1.50)\\
\qquad Difference to actual& 4.94 ***& 5.78 ***& 4.03 ***& 1.75 & 2.87 ***& 6.48 ***& -3.62 ***\\
   &(0.68)   &(0.96)   &(0.91)   &(1.32)   &(0.85)   &(0.98)   &(1.29)\\
\quad Counterfactual: $A=0$ \\ \qquad Enforcement price& 21.13 ***& 21.98 ***& 19.64 ***& 2.34 & 15.83 ***& 23.31 ***& -7.48 *\\
   &(2.28)   &(3.03)   &(3.20)   &(4.41)   &(3.19)   &(3.17)   &(4.50)\\
\qquad Difference to actual& 40.68 ***& 41.24 ***& 40.49 ***& 0.76 & 44.08 ***& 39.74 ***& 4.34 \\
   &(2.15)   &(2.87)   &(3.02)   &(4.17)   &(2.98)   &(3.02)   &(4.24)\\
\quad Counterfactual: correct beliefs \\ \qquad Enforcement price& 49.16 ***& 49.34 ***& 48.59 ***& 0.75 & 46.13 ***& 50.82 ***& -4.69 ***\\
   &(0.74)   &(1.09)   &(0.95)   &(1.45)   &(0.99)   &(1.04)   &(1.43)\\
\qquad Difference to actual& 12.64 ***& 13.88 ***& 11.53 ***& 2.35 ***& 13.78 ***& 12.23 ***& 1.55 **\\
   &(0.39)   &(0.58)   &(0.49)   &(0.76)   &(0.55)   &(0.55)   &(0.78)\\
\\
$N$& 1997 & 976 & 1021 & & 932 & 1065

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Each column represents a separate estimation. Best-fitting and counterfactual enforcement prices concern non-absolutist CAs only. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Robust standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\begin{table}[htbp]
\caption{Structural estimation results: ancillary parameters} \label{tab:structuralAncillary}
\small
\begin{center}
\setlength{\tabcolsep}{2pt}
\begin{tabular}{lcccccccc}
& (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8)  \\
\toprule
Table \ref{tab:structuralMain} \\


\quad Surrogate choice, $\sigma_{\epsilon_1}$& 8.122 ***& 8.302 ***& 8.374 ***& -0.073 & 7.062 ***& 9.709 ***& -2.647 ***\\
   &(0.274)   &(0.371)   &(0.323)   &(0.492)   &(0.320)   &(0.424)   &(0.532)\\
\quad Enforcement, $\sigma_{\epsilon_2}$& 10.547 ***& 11.332 ***& 9.899 ***& 1.433 ***& 9.536 ***& 11.457 ***& -1.921 ***\\
   &(0.221)   &(0.311)   &(0.308)   &(0.438)   &(0.301)   &(0.326)   &(0.443)\\
\quad \textit{Conditional surrogate choice} \\ \qquad High reservation price, $\sigma_{\epsilon_3}$& 5.269 ***& 5.037 ***& 5.613 ***& -0.576 *& 5.292 ***& 5.185 ***& 0.107 \\
   &(0.170)   &(0.204)   &(0.250)   &(0.322)   &(0.221)   &(0.230)   &(0.319)\\
\qquad Low reservation price, $\sigma_{\epsilon_4}$& 4.129 ***& 4.381 ***& 3.838 ***& 0.544 **& 3.576 ***& 4.521 ***& -0.945 ***\\
   &(0.128)   &(0.182)   &(0.169)   &(0.248)   &(0.156)   &(0.186)   &(0.243)\\
\\


Table \ref{tab:structuralTreatmentsSNAP} \\


\quad Surrogate choice, $\sigma_{\epsilon_1}$& 7.443 ***& 9.171 ***& -1.728 ***& 8.706 ***& 7.611 ***& 1.095 **\\
   &(0.271)   &(0.127)   &(0.299)   &(0.464)   &(0.290)   &(0.547)\\
\quad Enforcement, $\sigma_{\epsilon_2}$& 10.387 ***& 11.333 ***& -0.945 **& 11.775 ***& 9.460 ***& 2.315 ***\\
   &(0.256)   &(0.379)   &(0.458)   &(0.383)   &(0.265)   &(0.466)\\
\quad \textit{Conditional surrogate choice} \\ \qquad High reservation price, $\sigma_{\epsilon_3}$& 5.023 ***& 5.324 ***& -0.301 & 5.405 ***& 4.948 ***& 0.457 \\
   &(0.203)   &(0.298)   &(0.360)   &(0.283)   &(0.185)   &(0.338)\\
\qquad Low reservation price, $\sigma_{\epsilon_4}$& 3.668 ***& 4.605 ***& -0.936 ***& 4.598 ***& 3.414 ***& 1.184 ***\\
   &(0.147)   &(0.225)   &(0.269)   &(0.206)   &(0.146)   &(0.252)\\
\\


Table \ref{tab:structuralStereotypes} \\


\quad Surrogate choice, $\sigma_{\epsilon_1}$& 8.483***& 7.982***& 0.500 & 8.839***& 7.660***& 8.987***& 1.179***& -1.327**\\
   &(0.469)   &(0.290)   &(0.551)   &(0.183)   &(0.337)   &(0.505)   &(0.383)   &(0.607)\\
\quad Enforcement, $\sigma_{\epsilon_2}$& 9.828***& 11.268***& -1.439***& 11.207***& 10.403***& 10.798***& 0.804 & -0.395 \\
   &(0.278)   &(0.325)   &(0.428)   &(0.444)   &(0.293)   &(0.387)   &(0.531)   &(0.485)\\
\quad \textit{Conditional surrogate choice} \\ \qquad High reservation price, $\sigma_{\epsilon_3}$& 4.807***& 5.661***& -0.854**& 5.647***& 5.026***& 5.284***& 0.621 & -0.258 \\
   &(0.198)   &(0.280)   &(0.343)   &(0.336)   &(0.219)   &(0.325)   &(0.401)   &(0.392)\\
\qquad Low reservation price, $\sigma_{\epsilon_4}$& 4.111***& 4.022***& 0.089 & 4.614***& 3.537***& 4.374***& 1.078***& -0.837***\\
   &(0.168)   &(0.186)   &(0.251)   &(0.227)   &(0.171)   &(0.225)   &(0.285)   &(0.283)\\
\\


Table \ref{tab:structuralEstimatesRobustness} \\


\quad Surrogate choice, $\sigma_{\epsilon_1}$& 7.906 ***& 7.189 ***& 7.182 ***& 8.928 ***& 7.781 ***\\
   &(0.261)   &(0.230)   &(0.197)   &(0.306)   &(0.248)\\
\quad Enforcement, $\sigma_{\epsilon_2}$& 10.441 ***& 9.822 ***& 26.947 ***& 10.737 ***& 10.904 ***\\
   &(0.217)   &(0.217)   &(0.462)   &(0.217)   &(0.224)\\
\quad \textit{Conditional surrogate choice} \\ \qquad High reservation price, $\sigma_{\epsilon_3}$& 5.176 ***& 3.746 ***& 5.820 ***& 4.142 ***& 5.495 ***\\
   &(0.168)   &(0.183)   &(0.166)   &(0.184)   &(0.182)\\
\qquad Low reservation price, $\sigma_{\epsilon_4}$& 3.975 ***& 3.103 ***& 6.020 ***& 3.146 ***& 2.632 ***\\
   &(0.118)   &(0.104)   &(0.177)   &(0.111)   &(0.090)\\
\\


Table \ref{tab:structuralStereotypesCApol} \\


\quad Surrogate choice, $\sigma_{\epsilon_1}$& 7.748 ***& 7.858 ***& -0.110 & 0.351 & 0.276 & 0.075 \\
   &(0.126)   &(0.124)   &(0.177)   &(NaN)   &(NaN)   &(NaN)\\
\quad Enforcement, $\sigma_{\epsilon_2}$& 9.914 ***& 11.473 ***& -1.559 ***& 73.940 & 68.901 & 5.039 \\
   &(0.350)   &(0.383)   &(0.519)   &(NaN)   &(NaN)   &(NaN)\\
\quad \textit{Conditional surrogate choice} \\ \qquad High reservation price, $\sigma_{\epsilon_3}$& 4.780 ***& 5.253 ***& -0.473 & 0.816 & 6.607 & -5.791 \\
   &(0.185)   &(0.332)   &(0.381)   &(NaN)   &(NaN)   &(NaN)\\
\qquad Low reservation price, $\sigma_{\epsilon_4}$& 3.769 ***& 3.444 ***& 0.326 & 0.270 & 99.484 & -99.214 \\
   &(0.215)   &(0.193)   &(0.289)   &(NaN)   &(NaN)   &(NaN)\\
\\

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}}
 Robust standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\newpage


\begin{table}[htbp]
\caption{Structural estimation results by CA characteristics and views} \label{tab:structuralTreatmentsSNAP}

\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lcccccccccc}
 & (1) & (2) & (3) & (4) & (5) \\
\toprule
 &  &  & Difference &  &   & Difference  &   \\
&&&(1)-(2)&  &  & (4)-(5) &  &  \\
\textit{\textbf{CA sample}} \\
\quad Political orientation & Left & Right \\
\quad Views about SNAP restrictions &&&& Tighten & Loosen  \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.881 ***& 0.860 ***& 0.021 & 0.794 ***& 0.891 ***& -0.096 ***\\
   &(0.020)   &(0.030)   &(0.036)   &(0.024)   &(0.023)   &(0.033)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 71.366 ***& 73.668 ***& -2.303 **& 75.263 ***& 68.811 ***& 6.451 ***\\
   &(0.536)   &(0.733)   &(0.909)   &(0.668)   &(0.524)   &(0.849)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 5.755 ***& 7.832 ***& -2.077 ***& 7.926 ***& 5.516 ***& 2.411 ***\\
   &(0.289)   &(0.441)   &(0.527)   &(0.393)   &(0.271)   &(0.477)\\
\quad Fraction of absolutist types, $q$& 0.076 ***& 0.204 ***& -0.127 ***& 0.226 ***& 0.068 ***& 0.158 ***\\
   &(0.006)   &(0.013)   &(0.014)   &(0.012)   &(0.007)   &(0.014)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 73.583 ***& 72.644 ***& 0.939 & 75.243 ***& 69.194 ***& 6.049 ***\\
   &(0.581)   &(0.784)   &(0.976)   &(0.752)   &(0.528)   &(0.919)\\
\quad Prior mean parameter, $\mu_0$& 71.970 ***& 70.300 ***& 1.670 & 72.609 ***& 68.214 ***& 4.395 ***\\
   &(0.601)   &(0.819)   &(1.016)   &(0.821)   &(0.534)   &(0.980)\\
\quad Prior SD parameter, $\sigma_0$& 34.583 ***& 36.507 ***& -1.923 **& 38.379 ***& 30.348 ***& 8.031 ***\\
   &(0.537)   &(0.741)   &(0.915)   &(0.730)   &(0.478)   &(0.872)\\
\quad Posterior mean parameter, $\mu_1$& 97.489 ***& 95.257 ***& 2.232 ***& 96.989 ***& 94.777 ***& 2.212 ***\\
   &(0.477)   &(0.567)   &(0.741)   &(0.578)   &(0.533)   &(0.787)\\
\quad Posterior SD parameter, $\sigma_1$& 24.898 ***& 26.027 ***& -1.129 & 26.811 ***& 24.854 ***& 1.957 ***\\
   &(0.460)   &(0.563)   &(0.727)   &(0.515)   &(0.457)   &(0.689)\\
\\
$N$& 1031 & 700 & & 915 & 1082

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Standard error on $m$ calculated with the Delta method. Each column represents a separate estimation. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\begin{table}[htbp!]
\caption{Structural estimation results: Recipient demographics} \label{tab:structuralStereotypes}
\small
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccccccc}
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) \\
\toprule
Recipient & Black & White & Difference & Male & Female & Female & Difference & Difference \\
&&&(1)-(2)& Childless & Childless & With child & (4)-(5) & (5)-(6) \\
&&&&& \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.805***& 0.885***& -0.080**& 0.873***& 0.840***& 0.824***& 0.033 & 0.016 \\
   &(0.024)   &(0.023)   &(0.033)   &(0.030)   &(0.024)   &(0.031)   &(0.038)   &(0.039)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 74.570***& 68.310***& 6.260***& 69.500***& 71.036***& 74.217***& -1.536 & -3.181***\\
   &(0.562)   &(0.589)   &(0.814)   &(0.883)   &(0.600)   &(0.697)   &(1.068)   &(0.920)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 6.789***& 6.621***& 0.168 & 7.223***& 6.013***& 6.825***& 1.210**& -0.812 \\
   &(0.333)   &(0.343)   &(0.478)   &(0.450)   &(0.335)   &(0.392)   &(0.561)   &(0.516)\\
\quad Fraction of absolutist types, $q$& 0.169***& 0.126***& 0.044***& 0.194***& 0.130***& 0.114***& 0.064***& 0.016 \\
   &(0.010)   &(0.009)   &(0.014)   &(0.015)   &(0.010)   &(0.012)   &(0.018)   &(0.015)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 74.496***& 69.376***& 5.120***& 70.105***& 71.761***& 73.752***& -1.656 & -1.991*\\
   &(0.686)   &(0.591)   &(0.906)   &(0.867)   &(0.635)   &(0.888)   &(1.074)   &(1.091)\\
\quad Prior mean parameter, $\mu_0$& 72.439***& 67.986***& 4.453***& 67.933***& 70.495***& 71.865***& -2.563**& -1.370 \\
   &(0.706)   &(0.614)   &(0.936)   &(0.928)   &(0.638)   &(0.916)   &(1.126)   &(1.116)\\
\quad Prior SD parameter, $\sigma_0$& 36.416***& 32.129***& 4.287***& 34.976***& 32.561***& 35.581***& 2.416**& -3.021***\\
   &(0.676)   &(0.530)   &(0.859)   &(0.865)   &(0.587)   &(0.790)   &(1.045)   &(0.984)\\
\quad Posterior mean parameter, $\mu_1$& 98.796***& 94.181***& 4.614***& 97.043***& 95.610***& 96.730***& 1.433 & -1.120 \\
   &(0.655)   &(0.487)   &(0.816)   &(0.757)   &(0.637)   &(0.608)   &(0.990)   &(0.881)\\
\quad Posterior SD parameter, $\sigma_1$& 27.166***& 24.612***& 2.555***& 26.406***& 25.280***& 26.990***& 1.127 & -1.710**\\
   &(0.520)   &(0.463)   &(0.696)   &(0.583)   &(0.536)   &(0.627)   &(0.792)   &(0.825)\\
\\
$N$& 1000 & 997 & & 582 & 845 & 570 &

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Standard error on $m$ calculated using the Delta method. Each column represents a separate model. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\begin{table}[htbp]
\caption{Drivers of the structural estimates} \label{tab:structural}
\small
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lcccccccccc}
& (1) & (2) & (3) & (4) & (5)  \\
\toprule
CA sample & All & All & All  & All  & All    \\
Constraint &$\beta_{\textrm{respect}} = 0.8$ & $\beta_{\textrm{respect}} = 0.5$  & $\beta_{\textrm{respect}} = 0.2$ & $\sigma_0 = \sigma_1 = 10$ & $\mu_0 = \mu_1$   \\
	&&&&& $\sigma_0=\sigma_1$  \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.800 \textdagger& 0.500 \textdagger& 0.200 \textdagger& 1.729 ***& 0.806 ***\\
   &    &    &    &(0.036)   &(0.015)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 71.415 ***& 70.722 ***& 72.748 ***& 67.052 ***& 72.954 ***\\
   &(0.419)   &(0.355)   &(0.294)   &(0.311)   &(0.371)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 6.348 ***& 3.689 ***& 1.394 ***& 3.925 ***& 3.980 ***\\
   &(0.207)   &(0.155)   &(0.250)   &(0.172)   &(0.163)\\
\quad Fraction of absolutist types, $q$& 0.149 ***& 0.171 ***& 0.183 ***& 0.137 ***& 0.152 ***\\
   &(0.007)   &(0.007)   &(0.007)   &(0.007)   &(0.007)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 72.119 ***& 74.905 ***& 76.930 ***& 68.735 ***& 71.524 ***\\
   &(0.471)   &(0.552)   &(0.649)   &(0.094)   &(0.408)\\
\quad Prior mean parameter, $\mu_0$& 70.333 ***& 72.706 ***& 69.683 ***& 68.735 ***& 71.096 ***\\
   &(0.489)   &(0.572)   &(0.717)   &(0.094)   &(0.423)\\
\quad Prior SD parameter, $\sigma_0$& 34.600 ***& 37.011 ***& 48.086 ***& 10.000 ***& 27.835 ***\\
   &(0.436)   &(0.491)   &(0.730)   &    &(0.265)\\
\quad Posterior mean parameter, $\mu_1$& 83.617 ***& 89.942 ***& 83.569 ***& 81.145 ***& 71.096 ***\\
   &(0.746)   &(0.877)   &(1.009)   &(0.192)   &(0.423)\\
\quad Posterior SD parameter, $\sigma_1$& 26.769 ***& 31.247 ***& 35.435 ***& 10.000 \textdagger& 27.835 ***\\
   &(0.331)   &(0.371)   &(0.411)   &    &(0.265)\\
\\
$N$& 1997 & 1997 & 1997 & 1997

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Each column represents a separate estimation. Robust standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\begin{table}[htbp!]
\caption{Structural estimation results: Recipient demographics and CA political attitudes} \label{tab:structuralStereotypesCApol}
\small
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccccccc}
 & (1) & (2) & (3) & (4) & (5) & (6) \\
\toprule
CA political attitudes & \multicolumn{3}{c}{Left} & \multicolumn{3}{c}{Right}  \\
\cmidrule(r){2-4} \cmidrule(r){5-7}
Recipient & Black & White & Difference  & Black & White & Difference  \\
&&&(1)-(2)&  & & (4)-(5)  \\
&&&&& \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.887 ***& 0.843 ***& 0.044 & 0.916 & 1.843 & -0.927 \\
   &(0.027)   &(0.026)   &(0.038)   &(NaN)   &(NaN)   &(NaN)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 72.434 ***& 70.375 ***& 2.059 **& -8.332 & -49.348 & 41.016 \\
   &(0.721)   &(0.739)   &(1.032)   &(NaN)   &(NaN)   &(NaN)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 5.501 ***& 5.475 ***& 0.026 & 4.216 & 16.525 & -12.309 \\
   &(0.397)   &(0.375)   &(0.546)   &(NaN)   &(NaN)   &(NaN)\\
\quad Fraction of absolutist types, $q$& 0.092 ***& 0.067 ***& 0.025 **& 0.000 & 0.000 & 0.000 \\
   &(0.009)   &(0.007)   &(0.011)   &(NaN)   &(NaN)   &(NaN)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 73.696 ***& 73.228 ***& 0.468 & 97.600 & 91.421 & 6.179 \\
   &(0.811)   &(0.773)   &(1.120)   &(NaN)   &(NaN)   &(NaN)\\
\quad Prior mean parameter, $\mu_0$& 71.746 ***& 71.967 ***& -0.220 & 88.163 & 89.179 & -1.017 \\
   &(0.840)   &(0.792)   &(1.154)   &(NaN)   &(NaN)   &(NaN)\\
\quad Prior SD parameter, $\sigma_0$& 35.760 ***& 33.102 ***& 2.658 **& 61.372 & 43.786 & 17.587 \\
   &(0.782)   &(0.675)   &(1.033)   &(NaN)   &(NaN)   &(NaN)\\
\quad Posterior mean parameter, $\mu_1$& 99.192 ***& 98.708 ***& 0.484 & 120.537 & 115.359 & 5.178 \\
   &(0.716)   &(0.745)   &(1.034)   &(NaN)   &(NaN)   &(NaN)\\
\quad Posterior SD parameter, $\sigma_1$& 26.026 ***& 25.887 ***& 0.139 & 51.187 & 41.119 & 10.067 \\
   &(0.652)   &(0.622)   &(0.901)   &(NaN)   &(NaN)   &(NaN)\\
\\
$N$& 499 & 481 & & 332 & 345

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Standard error on $m$ calculated using the Delta method. Each column represents a separate model. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\FloatBarrier

\begin{appendix}


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

\begin{center}
{\Huge \textbf{ONLINE APPENDIX}}\\
\vspace{1cm}
{\Large   \textbf{\Large Interventionist Preferences and the Welfare State: \\ The Case of In-Kind Nutrition Assistance}  \\  \bigskip \bigskip Sandro Ambuehl, B. Douglas Bernheim, Tony Q. Fan, Zach Groff}
\end{center}


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

\newpage
\pagenumbering{arabic}


\begin{table}
\caption{Cardinal payoff profiles \label{tab:profilesCardinalNEW}}
\begin{center}
    \setlength{\tabcolsep}{16pt}
    \begin{tabular}{ccc}
    \multicolumn{2}{c}{\textbf{A. Swap treatments}} \\
    (1) & (2) \\
    \midrule
    \\
    $S1$: Original cardinal payoff profile &
    Implied ordinal preferences&
    \\
    \setlength{\tabcolsep}{2pt}
	\begin{tabular}{ccccccc}
	\toprule
	Option & \multicolumn{6}{c}{Stakeholder} \\
	\cmidrule(r){2-7}
	  && 1 && 2 && 3 \\
	\midrule
	A &&  6 && 3   &$\overset{\text{\tiny swap}}{\longleftrightarrow}$& 5 \\
	B && 3 &$\overset{\text{\tiny swap}}{\longleftrightarrow}$& 5 & & 6 \\
	C & & 1 &$\overset{\text{\tiny swap}}{\longleftrightarrow}$& 4 & & 2 \\
	\bottomrule
	\end{tabular}
    &
    \setlength{\tabcolsep}{6pt}
	\begin{tabular}{lccc}
	\toprule
	Preference & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	Best&A&B& B\\
	Middle&B& C&A\\
	Worst&C& A&C\\
	\bottomrule
	\end{tabular}
\\ \\
    Altered cardinal payoff profile &
    Implied ordinal preferences&
    \\
    \setlength{\tabcolsep}{2pt}
	\begin{tabular}{ccccccc}
	\toprule
	Option & \multicolumn{6}{c}{Stakeholder} \\
	\cmidrule(r){2-7}
	  && 1 && 2 && 3 \\
	\midrule
	A &&  6 && 5   &\phantom{$\overset{\text{\tiny swap}}{\longleftrightarrow}$}& 3 \\
	B && 5 &\phantom{$\overset{\text{\tiny swap}}{\longleftrightarrow}$}& 3 & & 6 \\
	C & & 4 &\phantom{$\overset{\text{\tiny swap}}{\longleftrightarrow}$}& 1 & & 2 \\
	\bottomrule
	\end{tabular}
    &

\setlength{\tabcolsep}{6pt}
	\begin{tabular}{lccc}
	\toprule
	Preference & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	Best&A&A&B\\
	Middle&B&B&A\\
	Worst&C&C&C\\
	\bottomrule
	\end{tabular} \\
	\\
	\multicolumn{3}{c}{\textbf{B. Deletion treatments}}  \\
    (1) & (2) \\
    \midrule
    \\
	 Original cardinal payoff profile &
     	 Implied ordinal preferences
    	\\
    \setlength{\tabcolsep}{6pt}
	\begin{tabular}{cccc}
	\toprule
	Option & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	A & 3 & 2 & 2 \\
	B & 1 & 3 & 3 \\
	X & 2 & 1 & 1 \\
	\bottomrule
	\end{tabular}
	&
    \setlength{\tabcolsep}{6pt}
	\begin{tabular}{lccc}
	\toprule
	Preference & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	Best & A& B & B\\
	Middle & X& A & A\\
	Worst & B& X & X\\
	\bottomrule
	\end{tabular}
\\ \\
	 Altered cardinal payoff profile &
     	 Implied ordinal preferences
    	\\
    \setlength{\tabcolsep}{6pt}
	\begin{tabular}{cccc}
	\toprule
	Option & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	A & 3 & 2 & 2 \\
	B & 1 & 3 & 3 \\
	\bottomrule
	\end{tabular}
	&

    \setlength{\tabcolsep}{6pt}
	\begin{tabular}{lccc}
	\toprule
	Pref. rank & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	Best & A& B & B\\
	Worst & B& A & A\\
	\bottomrule
	\end{tabular}
	\\
\end{tabular}
\end{center}
\end{table}


\begin{table}
\begin{center}
    \setlength{\tabcolsep}{16pt}
    \begin{tabular}{ccc}
    \multicolumn{3}{c}{\textbf{A. Swap treatments}} \\
    (1) & (2) & (3) \\
    \midrule
    Cardinal payoff profile &
    Implied ordinal preferences&
    Implied ordinal preferences
    \\
    &
    before swap &
    after swap
    \\
    \setlength{\tabcolsep}{2pt}
	\begin{tabular}{ccccccc}
	\toprule
	Option & \multicolumn{6}{c}{Stakeholder} \\
	\cmidrule(r){2-7}
	  && 1 && 2 && 3 \\
	\midrule
	A &&  6 && 3   &$\overset{\text{\tiny swap}}{\longleftrightarrow}$& 5 \\
	B && 3 &$\overset{\text{\tiny swap}}{\longleftrightarrow}$& 5 & & 6 \\
	C & & 1 &$\overset{\text{\tiny swap}}{\longleftrightarrow}$& 4 & & 2 \\
	\bottomrule
	\end{tabular}
    &
    \setlength{\tabcolsep}{6pt}
	\begin{tabular}{cccc}
	\toprule
	Pref. rank & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	1&A&B& B\\
	2&B& C&A\\
	3&C& A&C\\
	\bottomrule
	\end{tabular}
	&
    \setlength{\tabcolsep}{6pt}
	\begin{tabular}{cccc}
	\toprule
	Pref. rank & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	1&A&A&B\\
	2&B&B&A\\
	3&C&C&C\\
	\bottomrule
	\end{tabular} \\
	\\
	\multicolumn{3}{c}{\textbf{B. Deletion treatments}}  \\
    (1) & (2) & (3) \\
    \midrule
	 Cardinal payoff profile &
	 Implied ordinal preferences&
     	 Implied ordinal preferences
    	\\
	&
	when $X$ is available  &
	when $X$ is unavailable \\
    \setlength{\tabcolsep}{6pt}
	\begin{tabular}{cccc}
	\toprule
	Option & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	A & 3 & 2 & 2 \\
	B & 1 & 3 & 3 \\
	X & 2 & 1 & 1 \\
	\bottomrule
	\end{tabular}
	&
    \setlength{\tabcolsep}{6pt}
	\begin{tabular}{cccc}
	\toprule
	Pref. rank & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	1 & A& B & B\\
	2 & X& A & A\\
	3 & B& X & X\\
	\bottomrule
	\end{tabular}
	&
    \setlength{\tabcolsep}{6pt}
	\begin{tabular}{cccc}
	\toprule
	Pref. rank & \multicolumn{3}{c}{Stakeholder} \\
	\cmidrule(r){2-4}
	& 1 & 2 & 3 \\
	\midrule
	1 & A& B & B\\
	2 & B& A & A\\
	\bottomrule
	\end{tabular}
	\\
\end{tabular}
\end{center}
\end{table}


\section{Additional design materials}

\subsection{Food basket content} \label{foodBoxContents}\label{app:foodBox}


Tables \ref{contentsBoxHealthy} and \ref{contentsBoxRepresentative} list the contents of the healthy and representative food baskets, respectively.

At the prices on walmart.com in June 2022 for Sacramento, the contents of the healthy and representative food baskets cost \$51.68 and \$51.73, respectively. Actual deliveries to recipients dispersed across the US varied by location but usually cost between \$55 and \$75. Whenever Walmart does not deliver to a recipient, or when the listed items are unavailable, we select the closest available substitutes.

\begin{table}[H]
\caption{Contents of the healthy food basket \label{contentsBoxHealthy}}
\begin{center}
\begin{tabular}{lccc}
\toprule
                             &Package size & Amount & Unit price \\
\midrule
\textbf{Unsaturated fats}             &            &        &       \\
Canned salmon                & 14 oz      & 1      & 3.24  \\
Olive Oil                    & 8.54 fl oz & 1      & 2.77  \medskip \\
\textbf{Fiber}                        &            &        &       \\
Rolled oats (whole grain)    & 42 oz      & 1      & 2.58  \\
Whole wheat bread            & 20 oz      & 2      & 1.48 \medskip \\
\textbf{Legumes, nuts, seeds}         &            &        &       \\
Chickpeas                    & 15.5oz    & 2      & 0.72  \\
Pinto beans                  & 15.5oz     & 2      & 0.72  \\
Walnuts                      & 4oz       & 1      & 2.36  \medskip\\
\textbf{Vegetables}                   &            &        &       \\
Broccoli Florets, Frozen     & 32oz      & 1      & 2.24  \\
Whole carrots                & 1ct        & 1      & 0.98  \\
Bell peppers                 & 3ct        & 1      & 3.37  \\
Roma tomatoes                & 1ct        & 4      & 0.29  \\
Cucumber                     & 1ct        & 2      & 0.50   \\
Iceberg Lettuce              & 1ct        & 1      & 1.68  \\
Frozen peas                  & 2lb        & 1      & 2.22  \\
Low-salt canned tomato sauce & 8oz        & 1      & 0.38  \\
No salt added whole kernel corn	& 15.25 oz &	2& 0.58 \\
Canned sliced carrots	& 8.25 oz & 1	& 0.50 \medskip\\
\textbf{Fruit}                        &            &        &       \\
Fuji Apples                  & 3lb        & 1      & 3.98  \\
Bananas                      & 1ct        & 6      & 0.23  \\
Navel oranges                & 2lb        & 1      & 5.23  \\
Frozen berries               & 16oz       & 1      & 2.68  \\
Apple sauce, no sugar added	& 3.9 oz & 6& 0.42 \medskip\\
\textbf{Other}                        &            &        &       \\
Russet potatoes              & 5lb        & 1      & 2.37  \\
Milk                         & 0.5gal & 1      & 2.06 \\
\bottomrule
\end{tabular}
\end{center}
\end{table}


\begin{table}
\caption{Contents of the representative food basket \label{contentsBoxRepresentative}}

\begin{center}
\begin{tabular}{lccc}
\toprule
                                      & Package size & Amount & Unit price \\
\midrule
\textbf{Soda}                                  &              &        &            \\
Coca cola 2L                          & 2 L          & 2      & 2.14       \\
Mountain Dew 2L                       & 2 L          & 1      & 1.98       \\
Fanta 2L                              & 2 L          & 1      & 1.36    \medskip   \\
\textbf{Cookies, and snacks}                   &              &        &            \\
Oreo style cookies                    & 14.3 oz      & 1      & 2.28       \\
Honey Roasted Peanuts                 & 2.875 oz     & 2      & 0.98       \\
Pringles                              & 5.2 oz       & 1      & 1.78       \\
Buttery Crackers                      & 13.7 oz      & 1      & 2.18       \medskip\\
\textbf{Ready-made meals}                      &              &        &            \\
Canned spaghetti Os                   & 15.8 oz      & 4      & 0.89       \\
Canned beef with vegetable soup       & 18.8 oz      & 1      & 1.46       \\
Canned beef ravioli                   & 15 oz        & 1      & 1.24       \\
Canned pea soup                       & 18.8 oz      & 1      & 1.98       \medskip\\
\textbf{Meat and fish}                         &              &        &            \\
Oscar Meyer wieners                   & 8 ct         & 1      & 2.86       \\
Pulled pork in BBQ sauce (pouch)      & 2.6 oz       & 1      & 1.34       \\
Canned tuna                           & 5 oz         & 2      & 0.78       \\
Rotisserie chicken                    & 9 oz         & 1      & 3.48       \medskip\\
\textbf{Dairy}                                 &              &        &            \\
2\% Milk                              & 0.5 gal      & 1      & 2.06       \\
Cheddar cheese block                  & 8 oz         & 1      & 1.86       \\
Yoghurt                               & 6 oz         & 1      & 0.64       \medskip\\
\textbf{Fruit and vegetable products}     &        &            \\
Dill pickles                          & 24 fl oz     & 1      & 1.76       \\
Jar of roasted red bell pepper slices & 12 oz        & 1      & 1.38       \\
Banana                                & 1 ct         & 6      & 0.23       \\
Orange juice                          & 4 fl oz      & 1      & 2.58       \\
Canned green beans                    & 14.5 oz      & 1      & 0.98       \\
Canned tomato sauce                   & 8 oz         & 1      & 0.29       \medskip\\
\textbf{Carbs and proteins}                    &              &        &            \\
Dried spaghetti                       & 16 oz        & 1      & 0.92       \\
White bread loaf                      & 20 oz        & 2      & 0.93       \\
Crunchy honey oats                    & 18 oz        & 1      & 2.72      \\
\bottomrule
\end{tabular}
\end{center}
\end{table}

\FloatBarrier
\newpage
\subsection{Restrictions on gas station sales} \label{restrictions}\label{app:gasstation}

Table \ref{tab:gasStationRestrictions} lists the restrictions on gas station sales across the states that we assign to hypothetical recipients. In all states, a license must be obtained for the sale of alcohol and tobacco, and sales are permitted to individuals 21 and over. For the sale of lotteries, the age restriction is 18 and over, unless stated otherwise. A state may have dry (alcohol prohibited), wet (no restrictions beyond age), and/or moist (additional restrictions beyond age) counties. As an exception, Connecticut has no dry counties. Additional local ordinances at the county or city level may apply.

\begin{table}[H]
    \centering
    \renewcommand{\arraystretch}{1.5}
    \setlength{\tabcolsep}{6pt}
    \caption{Regulations on alcohol, tobacco, and lottery sales across states used in the experiment \label{tab:gasStationRestrictions}}
    \begin{tabular}{p{2.5cm}p{3.5cm}p{5.5cm}p{2.5cm}}
    \toprule
        \textbf{State} & \multicolumn{1}{c}{\textbf{Alcohol}} & \multicolumn{1}{c}{\textbf{Tobacco}} & \multicolumn{1}{c}{\textbf{Lottery}} \\
        \midrule
        {Kentucky} & Permits the sale of beer and wine, subject to local ordinances. & License and age requirements. &  License and age requirements. \\
        {Mississippi} & Allowed to sell beer and light wine, subject to local ordinances. & License and age requirements. &  License and age requirements (21 and over). \\
        {West Virginia} & Allowed to sell beer and light wine, subject to local ordinances. &  License and age requirements. &  License and age requirements. \\
        {Connecticut} & Allowed to sell beer. &  License and age requirements. & License and age requirements. \\
        {Massachusetts} & Permits the sale of beer and wine, subject to local ordinances. & Cannot sell any flavored e-cigarettes or vaping products. Can only sell non-flavored e-cigarettes with 35mg/ml or less nicotine content. &  License and age requirements. \\
        {Rhode Island} & Allowed to sell beer, subject to regulations and licensing requirements. & Restrictions on the sale of flavored tobacco products are subject to local ordinances. &  License and age requirements. \\
        \bottomrule
    \end{tabular}
\end{table}

\FloatBarrier

\newpage
\subsection{Design details and additional elicitations}\label{app:designDetails}


\FloatBarrier

\paragraph{Details about the interface}
Instructions immediately precede each scenario. Throughout the study, a bar at the top displays the pixelated photo of the recipient, the description of the recipient's characteristics, and two buttons that the user can click to show the contents of the food basket or the selection of gift cards from which the recipient may choose.

Next to the buttons CAs can click to recommend choosing the food baskets or the gift cards, they can write an open-ended message to the recipient (knowing that that offensive language will be removed).

Our survey displays the full page only once the pixelated image of the recipient is fully loaded. This feature ensures that all see that image regardless of the speed of their internet connection.


\paragraph{Details about hypothetical recipients} The names for hypothetical recipients come from gender-linked social security data and race-linked mortgage data. We take a list of first names by gender from social security data for the years 1991-2000. For each year, the data include all names with at least five occurrences. For any name with both male and female observations, we keep only the observations with the gender that is more common for that name. We collapse the data to get a list of name-gender pairs with the number of occurrences of each. We then merge this with 2007-2010 mortgage data from \citet{Tzioumis} that links names to the percent that are in each of six racial groups. We multiply the number of occurrences of each name-gender pair in the social security data by the share white and the share black according to the mortgage data to estimate the number of occurrences of each name among black males, black females, white males, and white females born in the 1990s. We then randomly draw 1000 names with replacement according to the empirical frequency, and we randomly assign a name to each recipient from this sample.
We refrain from conveying race through the use of  stereotypical names because these names are associated with characteristics other than race, such as socioeconomic status \citep{FryerLevitt, Simonsohn.Names}.

We draw pictures of hypothetical recipient's faces from the database by \citet{ParkLabPictures}. We display the images with a resolution of 35$\times$35 pixels. Hence, the identity of the individual is hidden, but the skin-color can easily be seen.
We use 80 pixellated pictures, 10 for each category defined by the dimensions age, gender, and race. To each CA, we randomly display one of the 10 images consistent with the assigned recipient characteristics. We ask real clients to take a picture of themselves using their webcam. A pixellated version of this picture is sent to our server, which we display to the CA assigned to that recipient.


\paragraph{Details about incentivized elicitations}

We elicit beliefs about crowding out of grocery spending through receiving the food deliveries, in intervals $\{[\infty, -50], [-50, -30], [-30, -15], [-15, -5], [-5, 5],$ $ [5, 15], [15, 30], [30, 50], [50, \infty]\}$. We incentivize accuracy; a subject who selects the interval that is correct according to \citet{HastingsShapiro} receives a bonus of \$5, and $0$ otherwise.

We ask CAs to predict how 10 previous recipients have allocated \$50 in gift cards across the various categories. We incentivize their answer by discounting a constant marginal penalty \$0.05 from a baseline payment of \$5 for each dollar that needs to be assigned to a different category to make the subjects' answer match the empirical distribution.

In each case, the main screen informs subjects that the accuracy of their answer may determine their study payments, and a button labeled ``details'' reveals an explanation of the specific incentive mechanism.

To measure the relative weight subjects place on their assigned welfare recipient relative to other citizens, each CA decides how to split a one-time payment of \$50 between the welfare recipient and a randomly selected taxpayer. To measure spite towards the welfare recipient, each CA can reduce the number of shipments by any number of months.

CAs predict the share of all SNAP participants that are black women, black men, white women, and white men. For each of these demographic categories, they also estimate the fraction of recipients who have children. We incentivize the latter questions by applying a constant penalty for each percentage point by which the stated response differs from the truth.


\paragraph{Details about unincentivized elicitations}

We elicit beliefs about crowding out of grocery spending through receiving \$50 in gift cards using the same format as for the previous crowding out question. We elicit the CA's belief about the percentage of the food in the food deliveries that will go to waste if sent to the CA, in the following intervals: $\{\{0\}, [0,10], [10-25], [25-50], [50, 100]\}$.

We elicit CAs' beliefs about how recipients will use gas station gift cards across the categories gas, tobacco products, alcoholic beverages, hot foods, and other.


We ask what determined the CAs' choices regarding their paternalism decisions. Subjects rate each of the following three possible reasons as  `completely unimportant', `slightly unimportant', `slightly important', or `very important': (i) `Sending a food box has only a negligible impact on [name]'s consumption, because he will just buy less food in the store.' (ii) `Giving [name] a choice is the right thing to do', (iii) `Making sure [name] consumes food rather the things he could buy with the gift cards is the right thing to do'. They further indicate agreement with each of the following two statements by selecting `completely disagree', `slightly disagree', `slightly agree', or `completely agree': (i) `My choice reflects what I would like if I were the welfare recipient', (ii) `What is right does not depend on the value of the gift cards that the recipient could receive instead of the food box'.

We use a subset of the questions from the poverty attribution literature \citep{Bennettetal.PovertyAttribution}. We ask `Why do you think [name] is poor enough to enroll in SNAP (food stamps)?' For each of six possible reasons, the CA chooses between \textit{extremely unlikely}, \textit{somewhat unlikely}, \textit{somewhat likely}, and \textit{extremely likely}. The possible reasons are: (i) \textit{Lack of patience and drive}, (i) \textit{Lack of education and skills}, (iii) \textit{Tendency to make bad life choices}, (iv) \textit{Society doesn't give all people an equal chance}, (v) \textit{Discrimination against minorities and the poor}, (vi) \textit{Bad luck (not anybody's fault and not society's fault)}. The first three of these reasons attribute poverty to factors internal to the recipient while the latter three attribute it to factors outside the individual.


Subjects indicate their attitudes about current SNAP restrictions. They choose from seven answer categories whether restrictions should be tightened drastically, tightened somewhat, left unchanged, loosened somewhat, loosened drastically, abolished, or that recipients should not have a choice at all. Subjects also indicate whether they think benefit levels should be increased by 20\% or more, by 20\% or less, left unchanged, cut by 20\% or less, or cut by 20\% or more.  Additionally, we elicit agreement on a four-point Likert scale about the following statements: (i) \textit{The SNAP (food stamps) program as it is currently administered in the US is a good thing.} (ii) \textit{Welfare recipients make bad choices if they receive cash instead of food.} (iii) \textit{SNAP is a welfare program, hence SNAP participants are taking advantage of others}. In each of these questions, CAs may indicate that they have no opinion on the matter.

At the very end of the survey, we elicit the CAs' own attributes. We ask about age, gender, ethnicity, how close the CA feels to their own ethnic group \citep[following][]{FongLuttmer}, household income, number of people in household, education, marital status, number of children, political party closest to own views, agreement with closest party's views. At the very beginning of the survey, we additionally elicit state, citizenship (only U.S. citizens are allowed to participate), and social welfare program participation.


\paragraph{Details concerning randomization}

We randomize the assignment of recipient characteristics as shown in Table \ref{tab:recipientRandomization}.

\begin{table}[htb]
\caption{Randomization over characteristics of hypothetical recipients}
\label{tab:recipientRandomization}
\begin{center}
   \begin{tabular}{l l l l l l}
        \toprule
        Probability & Gender & Race & Children & Age \\
        \midrule
        $\nicefrac{1}{14}$ & Male   & White & No children & Old (50-69)  \\
        $\nicefrac{1}{14}$ & Female & White & No children & Old (50-69)  \\
        $\nicefrac{1}{14}$ & Male   & Black & No children & Old (50-69)  \\
        $\nicefrac{1}{14}$ & Female & Black & No children & Old (50-69)  \\
        $\nicefrac{1}{14}$ & Male   & White & No children & Young (20-29) \\
        $\nicefrac{1}{14}$ & Male   & Black & No children & Young (20-29) \\
        $\nicefrac{1}{7}$   & Female & White & No children & Young (20-29) \\
        $\nicefrac{1}{7}$   & Female & White & One 2-year old    & Young (20-29) \\
        $\nicefrac{1}{7}$   & Female & Black & No children & Young (20-29) \\
        $\nicefrac{1}{7}$   & Female & Black & One 2-year old    & Young (20-29) \\
        \bottomrule
    \end{tabular}
\end{center}
\end{table}


We randomize the following survey elements: (i) A random half of CAs proceed through the decisions in the order listed in Figure \ref{experimentOverview}. The remaining CAs reveal beliefs before making decisions, both before and after learning about the recipient's preferences. Specifically, they proceed through the stages in the following order: 3, 1, 2, 5, 4, 6. (ii) A random half of CAs see the gift card amounts in increasing order throughout, the other half see them in decreasing order throughout.

CAs assigned to a hypothetical recipient observe recipient characteristics according to the distribution in Table \ref{tab:recipientRandomization}.

We randomly vary the color used as a background for the description of the recipient.

We randomize the order in which the gift card categories are displayed, with the exception that gas stations are always displayed second. CAs are aware that gas stations allow recipients to purchase alcohol and tobacco. Preventing the purchase of addictive goods is an oft-mentioned reason for in-kind transfers in the popular media. We put them into the second position to make them salient while at the same time trying to avoid the demand effect that might occur by placing them first.

The order in which gift cards of different categories are shown to CAs is randomized on the individual level, except that the category \textit{Gas stations and associated convenience stores} always appears second. This placement ensures that CAs will not miss them while limiting demand effects by not placing it first.


 \paragraph{Comprehension and monotonicity check}

CAs must pass a comprehension check after the instructions on her restriction decisions. Specifically, she must select the correct ones out of the following three statements to be able to continue with the survey: (i) `If I decide that [name] will get the food box deliveries, that's what he will get. There will be no choice', `If I decide that [name]  will get the food box deliveries, he will get to choose whether he might rather get gift cards instead', `If I give [name]  a choice, he will get the monthly gift cards. He won't be able to select the food deliveries.'

Two further comprehension checks ensure that subjects correctly process the information about the recipient's preferences. Both checks ask whether, when given the choice, the recipient would choose \$$X$ in gift cards or the food box. Answer options are `[Name] would choose the food box', `I cannot say based on the information given', and `[Name] would choose \$70 in gift cards'. In the first check, $X = 70$. In the second check, $X = 55$ for a subject whose recipient prefers the food box over \$70 in gift cards, and $X = 85$ in case of the reverse preference. The second check thus conveys the implications of preference monotonicity. Subjects must answer correctly to continue. They have as many attempts as required.


In addition, we nudge CAs towards providing answers in the multiple price lists that exhibit at most two (in the case of paternalism decisions) or one (in the case of surrogate choice) switching points. If there is an excess number of switches, the subject sees the message  `\textit{Your choices appear to be inconsistent or random. Most people decide in one of the $X$ ways below. Your choices fit none of those patterns. Please click the Back button and change your choices to make them consistent with one of these $X$ common patterns.}' followed by a list of the admissible patterns ($X$ is five in case of restriction decisions and three in case of surrogate choice). The subject decides herself whether to change her decisions or whether to leave her decisions unchanged and continue with the survey.


\newpage
\subsection{Surveys with SNAP recipients}\label{app:surveysSNAPrecipients}

137 SNAP participants took the study in August 2022, and the remaining 133 participated in December 2023. There is no noticeable or statistically detectable difference in the distribution of reservation prices across the two waves, neither for each food basket separately nor when considering them jointly. All of them were included in the database shown to CAs (the former 137 individuals were shown to CAs in the pilot study mentioned in footnote \ref{fnPilots}). Additional potential recipients had participated but provided unrecognizable pictures. We excluded these participants before running the corresponding CA sample; these participants' information was never shown to a CA. We also exclude them in our analysis of recipient responses.

After completion of the CA sessions, recipients participated in a second survey to make their choice (if the CA provided them with the opportunity). If applicable, recipients decided on their preferred selection of gift cards for the first delivery. While the available gift card amounts are typically not fully flexible (some are available only in increments of \$5, for instance), there is a wide range of available amounts. We delivered gift cards electronically where possible but delivered by postal mail where electronic gift cards were unavailable.


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


\subsection{Demographics}\label{app:demographics}

Table \ref{tab:demoCAs} displays the distribution of demographic characteristics among our CA sample and compares it to the population frequencies according to the 2022 General Social Survey \citep[GSS, ][]{GSS2021} for the characteristics on which data are available. Table \ref{tab:demoChoosers} displays the distribution of demographic characteristics among our welfare recipient sample and compares it to the frequencies in the entire pool of SNAP participants according to the 2021 1-year American Community Survey Public Use Microdata Sample (PUMS) \citep{ACS} for the characteristics on which data are available.


\begin{table}
\scriptsize
\caption{Demographic characteristics of the CAs \label{tab:demoCAs}}
\begin{center}
\begin{tabular}{lccc}
\toprule
Characteristic & \multicolumn{2}{c}{Fraction among} \\
\cmidrule(r){2-3}
&CAs & US population \\
\midrule


\textbf{Gender}	&		&		\\
Male	&	0.511	&	0.491	\\
Female	&	0.489	&	0.509	\\
\textbf{Age}	&		&		\\
18-19	&	0.009	&	0.017	\\
20-29	&	0.204	&	0.181	\\
30-39	&	0.195	&	0.178	\\
40-49	&	0.182	&	0.164	\\
50-59	&	0.204	&	0.159	\\
60-69	&	0.169	&	0.162	\\
$>70$	&	0.037	&	0.140	\\
\textbf{Race}	&		&		\\
White	&	0.743	&	0.658	\\
Black	&	0.154	&	0.116	\\
Other	&	0.104	&	0.226	\\
\textbf{Marital status}	&		&		\\
Married	&	0.441	&	0.501	\\
Widowed	&	0.022	&	0.052	\\
Divorced	&	0.100	&	0.133	\\
Separated	&	0.016	&	0.026	\\
Never married	&	0.422	&	0.289	\\
\textbf{Children}	&		&		\\
0	&	0.480	&	0.317	\\
1	&	0.146	&	0.154	\\
2	&	0.213	&	0.274	\\
3	&	0.104	&	0.142	\\
4	&	0.035	&	0.068	\\
$>4$	&	0.023	&	0.044	\\
\textbf{Household income}	&		&		\\
\textdollar0-\textdollar10,000	&	0.039	&	0.068	\\
\textdollar10,000-\textdollar20,000	&	0.075	&	0.072	\\
\textdollar20,000-\textdollar30,000	&	0.092	&	0.071	\\
\textdollar30,000-\textdollar40,000	&	0.083	&	0.065	\\
\textdollar40,000-\textdollar50,000	&	0.093	&	0.074	\\
\textdollar50,000-\textdollar60,000	&	0.091	&	0.062	\\
\textdollar60,000-\textdollar90,000	&	0.198	&	0.160	\\
more than \textdollar90,000	&	0.330	&	0.320	\\
\textbf{Household size}	&		&		\\
1	&	0.208	&		\\
2	&	0.316	&		\\
3	&	0.187	&		\\
4	&	0.182	&		\\
5	&	0.062	&		\\
6	&	0.030	&		\\
7	&	0.009	&		\\
$>7$	&	0.005	&		\\
\textbf{Geographic area}	&		&		\\
Urban	&	0.296	&		\\
Suburban	&	0.512	&		\\
Rural	&	0.191	&		\\
\textbf{Political preference}	&		&		\\
Strong democrat	&	0.221	&	0.187	\\
Not very strong democrat	&	0.137	&	0.137	\\
Independent, closer to democrat	&	0.161	&	0.118	\\
Independent, as far from democrats as from republicans	&	0.129	&	0.230	\\
Independent, closer to republican	&	0.120	&	0.078	\\
Not very strong republican	&	0.130	&	0.094	\\
Strong republican	&	0.102	&	0.130	\\
Other	&	0.000	&	0.028	\\
\textbf{Public assistance}	&		&		\\
Medicaid	&	0.213	&		\\
Medicare	&	0.185	&		\\
Veteran Disability Benefits	&	0.014	&		\\
Earned Income Tax Credit	&	0.082	&		\\
Social Security	&	0.181	&		\\
Supplemental Nutrition Assistance Program	&	0.130	&		\\
Temporary Assistance for Needy Families	&	0.006	&		\\
Public Housing	&	0.018	&		\\
Pell Grants	&	0.047	&		\\
None of the above	&	0.506	&		\\
\\ \textbf{N}	&	1997.000	&	4032.000	\\

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Variables for demographic comparison are from the 2022 General Social Survey \citep[GSS, ][]{GSS2021}.
\end{table}


\begin{table}
\caption{Demographic characteristics of the welfare recipients in our sample \label{tab:demoChoosers}}
\begin{center}
\begin{tabular}{lc}
\toprule
Characteristic & Fraction of recipients \\
\midrule


\textbf{Gender}	&		\\
Male	&	0.233	\\
Female	&	0.767	\\
\textbf{Age}	&		\\
20-29	&	0.170	\\
30-39	&	0.304	\\
40-49	&	0.256	\\
50-59	&	0.215	\\
60-69	&	0.033	\\
\textbf{Race}	&		\\
White (non-hispanic)	&	0.681	\\
Black	&	0.181	\\
White (hispanic)	&	0.085	\\
Other	&	0.052	\\
\textbf{Marital status}	&		\\
Married, living with partner	&	0.237	\\
Married but separated	&	0.056	\\
Not married, living with partner	&	0.189	\\
Not married, single	&	0.519	\\
\textbf{Children}	&		\\
0	&	0.515	\\
1	&	0.215	\\
2	&	0.156	\\
3	&	0.085	\\
$>3$	&	0.030	\\
\textbf{Public assistance}	&		\\
Medicaid	&	0.681	\\
Medicare	&	0.248	\\
Veteran Disability Benefits	&	0.011	\\
Earned Income Tax Credit	&	0.126	\\
Social Security	&	0.244	\\
Supplemental Nutrition Assistance Program	&	1.000	\\
Temporary Assistance for Needy Families	&	0.041	\\
Public Housing	&	0.104	\\
Pell Grants	&	0.059	\\
\\ \textbf{N}	&	270.000	\\

 \\
\bottomrule
\end{tabular}
\end{center}
\end{table}

\FloatBarrier

\newpage
\subsection{Order effects}\label{appendixOrderEffects}

Table \ref{orderEffects} examines the effect of the order in which survey items are displayed to CAs on enforcement price, surrogate reservation price, and beliefs about the mean reservation price. The only randomization dimension that affects behavior in a statistically significant fashion is whether prices in multiple decision lists are shown in ascending or descending order. Even this variable only affects enforcement prices and unconditional beliefs about recipients' mean reservation prices to a statistically significant extent.


\begin{table}[H]
\caption{Order effects \label{orderEffects}}
\begin{center}
\begin{tabular}{lccccc} \toprule
 & (1) & (2) & (3) & (4) & (5)   \\
VARIABLES & Enforcement & \multicolumn{2}{c}{Surrogate} & \multicolumn{2}{c}{Belief mean} \\
&  price &  \multicolumn{2}{c}{res. price} &  \multicolumn{2}{c}{res. price}\\
\cmidrule(r){3-4}\cmidrule(r){5-6}
After receiving information \\ about recipient WTP & & & \checkmark & & \checkmark \\
\midrule


            &               &               &               &               &               \\

Dep. var. mean&      59.516***&      77.110***&      80.333***&      74.474***&      76.924***\\
            &     (0.875)   &     (0.711)   &     (0.689)   &     (0.524)   &     (0.627)   \\


                    &               &               &               &               &               \\

Beliefs about gift-card \\ \quad spending elicited first&      -0.194   &      -1.019   &      -0.567   &       1.561   &      -0.369   \\
                    &     (1.746)   &     (1.423)   &     (1.380)   &     (1.034)   &     (1.256)   \\
Beliefs about recipient's \\ \quad WTP elicited first&      -0.648   &      -2.459   &      -0.500   &      -2.189*  &      -0.623   \\
                    &     (1.748)   &     (1.425)   &     (1.382)   &     (1.036)   &     (1.258)   \\
Money amounts shown \\ \quad high to low&      -6.370***&      -0.769   &       1.693   &      -7.219***&       0.775   \\
                    &     (1.747)   &     (1.424)   &     (1.381)   &     (1.035)   &     (1.257)   \\
\medskip
N                   &        1928   &        1983   &        1988   &        1997   &        1997   \\
\bottomrule


\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} OLS regressions. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}

\FloatBarrier


\newpage
\subsection{Crowding out} \label{app:crowdingOut}

Figure \ref{figCrowdingOut} plots CAs' beliefs about the effect on grocery expenses from receiving a food basket (Panel A, averaged across types of food baskets) or from the receipt of \$65 (Panel B). About a fifth of the CAs believe that grocery spending falls by more than \$50. Given the information that the cost of the food basket is between \$55 and \$75, the remaining subjects believe that crowd-out is at most partial.

Using interval midpoints and setting the responses ``over \$50 less'' and ``over \$50 more'' to \$-50 and \$50, respectively, the mean belief about the change in grocery expenses from receiving food baskets is \$-24.8 (s.e. 0.72), averaged across the food baskets.\footnote{The corresponding number for the healthy and representative baskets are \$-26.52 and \$-23.17, respectively.}

Assuming, conservatively, a food basket cost of \$50, this figure implies a believed rate of crowd out of 49.6\%. CAs believe that the receipt of \$65 in gift cards increases grocery spending by \$1.73 (s.e. 0.70), consistent with our interpretation that gift cards cannot be used to purchase groceries.


\begin{figure}[hbt]
\caption{Beliefs about how receipt of deliveries affects grocery expenses \label{figCrowdingOut}}
\begin{center}
\begin{tabular}{cc}
\textbf{A. Food basket} & \textbf{B. Gift cards} \\
\includegraphics[width=0.5\textwidth]{graphs/histCrowdOutCombined}
&
\includegraphics[width=0.5\textwidth]{graphs/histCrowdOutGiftCards}
\\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} WLS regressions on indicator variables (one for each answer category). Frequency of answers to the question ``\textit{If [name] receives the [food basket name] in a given month, how much more or less money than usual do you think will he spend on groceries in that month (not including the value of the food box)?} (Panel A) and ``\textit{If, in a given month, [name] receives \$65 in gift cards that she selects from our menu, how much more or less money than usual do you think will he spend on groceries in that month?}''
\end{figure}

We elicted beliefs about crowding out at the end of the experiment. It is thus plausible that subjects did not consider crowding out in the decisions that reveal enforcement and surrogate reservation prices. To test this hypothesis, Table \ref{crowdOutEffects} regresses enforcement price, surrogate reservation price, and mean reservation price beliefs on beliefs about the extent of crowding out caused by both the food deliveries and by gift-card deliveries of \$65. We find no economically meaningful effects. One effect is statistically significant at the 5\% level: If a CAs' believes that the receipt of \$65 in gift cards yields \$1 more in crowd out of grocery expenses, she sets a surrogate reservation prices that is \$0.07 lower. The absence of meaningful effects suggests that crowding-out considerations did not play a meaningful role in Choice Architects' decisions in the main part of the experiment.


\begin{table}[htb]
\caption{Effects of beliefs about crowding out \label{crowdOutEffects}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lcccc}
\toprule


& \multicolumn{1}{c}{Enforcement}
& \multicolumn{1}{c}{Surrogate}
& \multicolumn{1}{c}{Believed mean}
\\
& \multicolumn{1}{c}{ price}
& \multicolumn{1}{c}{ reservation price}
& \multicolumn{1}{c}{ reservation price}
\\ \midrule
            &               &               &               \\

Dep. var. mean&      61.656***&      78.328***&      74.749***\\
            &     (1.098)   &     (0.888)   &     (0.651)   \\


\textit{Believed change in grocery expenses}\\
                    &               &               &               \\

\quad due to food box&      -0.014   &      -0.044   &       0.050*  \\
                    &     (0.046)   &     (0.039)   &     (0.026)   \\
\quad due to USD65 in gift cards&       0.060   &       0.072** &       0.023   \\
                    &     (0.042)   &     (0.035)   &     (0.023)   \\
\medskip
N                   &        1928   &        1983   &        1997   \\
\bottomrule


\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} WLS regressions. Controls for position of belief elicitation and for order in which cash equivalent amounts were displayed, as well as for beliefs about crowding out and about food waste.
\end{table}


\FloatBarrier
\newpage
\subsection{Stated reasons for interventions}\label{app:interventionReasons}
Panel A of Figure \ref{deontologicalTypes} shows agreement with the statements that `Giving [name] a choice is the right thing to do' and `Making sure [name] consumes food rather than the things he could buy with the gift cards is the right thing to do' as a function of the enforcement price they impose.

Panel B shows agreement with the statements `My choice reflects what I would like if I were the welfare recipient,'   `Sending a food box has only a negligible impact on [name]’s consumption, because he will just buy less food in the store,'  and  `What is right does not depend on the value of the gift cards that the recipient could receive instead of the food box' as a function of the enforcement price CAs imposed. While relationships to enforcement prices are generally weak, more interventionist CAs agree less with the statement that their choice reflects what they would like if they were the welfare recipient, and there is a U-shaped relationship with the statement that sending a food box instead of cash has a negligible impact. Consistent with our interpretation that recipients regard the imposition of food deliveries as meaningful, agreement with the statement that food deliveries have a negligible impact is low. CAs who agree more with this statement tend to impose higher enforcement prices, possibly in an attempt to ensure that the impact, though small, nonetheless occurs with a high probability.


\begin{figure}[htb]
\caption{Types and reasons for choice \label{deontologicalTypes}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{cc}
A. Right thing to do & B. Other \\
\includegraphics[width=0.48\textwidth]{graphs/reasonsByPat}  &
\includegraphics[width=0.48\textwidth]{graphs/reasonsByPat2}


\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} WLS regressions on indicators (one for each category on the horizontal axis and answer type). Dependent variable coded as 1 = `completely unimportant', 2 = `slightly unimportant', 3 = `slightly important', 4 = `very important.' Controls for position of belief elicitation and for order in which cash equivalent amounts were displayed, as well as for beliefs about crowding out and about food waste.
\end{figure}


\FloatBarrier
\newpage

\subsection{Joint distribution of surrogate and enforcement prices}\label{app:enforcementVsSurrogatePrices}

Figure \ref{scatterPatSurr} displays the joint distribution of surrogate and enforcement prices. Enforcement prices that equal surrogate reservation prices are common even outside the cost range of the food baskets.

\begin{figure}[H]
\caption{Enforcement prices and surrogate reservation prices \label{scatterPatSurr}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\includegraphics[width=0.5\textwidth]{graphs/scatterPatSurr}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Circle area proportional to the demographically weighted number of CAs located at the corresponding location.
\end{figure}


\FloatBarrier
\newpage


\subsection{Response to information about recipient preferences}\label{app:responseInfo}
Figure \ref{fig:cdfsSurrogateByInfo} shows CDFs for CAs' surrogate reservation prices after learning that the recipient has either a high or low reservation price. Panel A (respectively, Panel B) focuses on CAs for whom the enforcement price is strictly less than (respectively, equal to or greater than) the surrogate reservation price before receiving information. Clearly, information concerning recipients' reservation prices for food influences surrogate choices to a similar degree among both groups.

\begin{figure}[htb]
\caption{Change in surrogate prices in response to information about recipient preferences}
\label{fig:cdfsSurrogateByInfo}
\begin{center}
\begin{tabular}{cc}
A. Enforcement price $\geq$ surrogate price
&
B. Enforcement price $<$ surrogate price \\
\includegraphics[width=0.48\textwidth]{graphs/cdfSurrCondByInfoSurrLeqEnf}
&
\includegraphics[width=0.48\textwidth]{graphs/cdfSurrCondByInfoSurrGEnf}
\end{tabular}
\end{center}


\end{figure}


\FloatBarrier
\newpage


\subsection{Table \ref{tab:treatmentEffectsReducedForm}.B with additional controls}\label{app:goodBadControls}


Table \ref{tabApp:treatmentEffectsReducedForm} replicates table \ref{tab:treatmentEffectsReducedForm} but includes additional controls for believed spending on other categories of goods. We obtain coefficients on beliefs about spending on sin goods that are very close to those in Table \ref{tab:treatmentEffectsReducedForm} both for the WLS and the 2SLS regressions. We observe that the sin goods category has substantially larger effects than beliefs about spending on other categories such as luxury goods or entertainment and fast food.

\begin{table}[htb]
\caption{Encouraging the good? Discouraging the bad? \label{tabApp:treatmentEffectsReducedForm}}
\small
\setlength{\tabcolsep}{4pt}
\begin{center}
\begin{tabular}{lcccccccc}
 & (1) & (2) \\
\toprule


Dependent variable & \multicolumn{2}{c}{Enforcement Price} \\  \cmidrule(r){2-3}
Method & WLS & 2SLS  \\ \midrule
                    &               &               \\

\textit{Believed spending percentage (0 to 100)}\\ \quad Sin goods&       0.454***&       2.602*  \\
                    &     (0.098)   &     (1.114)   \\
\quad Luxury goods  &       0.082   &       0.676*  \\
                    &     (0.078)   &     (0.311)   \\
\quad Entertainment and fast food&       0.120   &       0.681*  \\
                    &     (0.074)   &     (0.297)   \\
\quad Sporting goods&      -0.253   &       0.577   \\
                    &     (0.286)   &     (0.508)   \\
\quad Gas stations, other&      -0.168*  &       0.506   \\
                    &     (0.083)   &     (0.356)   \\
\textit{Treatment}\\  \quad Information about high alc. consumption&               &               \\
                    &               &               \\
Observations        &        1928   &        1928   \\
\bottomrule


\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\FloatBarrier
\newpage


\subsection{Belief updating}\label{app:beliefUpdating}
Figure \ref{fig:beliefUpdating} plots the mean elicited belief about the fraction of recipients whose reservation price falls in each of the given bins. We seek to measure the extent of deviations from Bayesian updating from the information that the recipients' reservation price is above or below \$70. The Bayesian update of the pdf (without renormalization) is simply the original pdf truncated at \$70. We see that subjects roughly respect this rule, but that updated beliefs tend to be slightly shifted to the right, on average, relative to the unconditional beliefs.

\begin{figure}[htb]
\caption{Belief updating \label{fig:beliefUpdating}}
\begin{center}
\includegraphics[width=0.5\textwidth]{graphs/beliefUpdating.pdf}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} The blue, uninterrupted lines, labeled `uncond.,' show the mean elicited belief about the fraction of recipients whose reservation price falls in each of the given bins before receiving information about the recepient's reservation price. The maroon, interrupted lines, labeled `cond.,' show the corresponding estimates CAs made after receiving such information. Solid lines plot averages for CAs who learned that the reservation price exceeds \$70, dashed lines plot them for CAs whose recipient's reservation price was less than \$70.
\end{figure}


\FloatBarrier
\newpage


\subsection{Drivers and robustness of the structural estimates}\label{app:structuralEstimatesRobustness}

Table \ref{tab:structuralEstimatesRobustness} examines the response of our estimates when we exogenously set and vary specific model parameters. Columns 1 to 3 set the value of the deference parameter $\beta$ to 0.8, 0.5, and 0.2, respectively. Greater deference means that welfarist CAs who are uncertain about the recipient's preferences should grant him more leeway.  The model should attribute granting less than the predicted amount of leeway to satisfaction from limiting the recipient's options, parameter $A$. Indeed, as we fix $\beta$ at lower levels, the estimate of $A$ decreases, from \$6.47 in column 1 to \$2.48 in column 3.

Column 4 examines the foregoing hypothesis from a different angle. The more uncertain a welfarist CA is about the recipient's preferences, the more leeway he should grant the recipient. As above, the model should attribute granting less than the predicted amount of leeway to parameter $A$. If we exogenously impose less uncertainty than CAs reveal by exogenously manipulating the prior SD parameter $\sigma_0$, do we observe the predicted effects? Alternatively, could our positive estimates of $A$ be due to the possibility that our belief elicitation artifactually causes CAs to report more uncertainty about receipient preferences than they actually perceive? We answer both questions by exogenously imposing $\sigma_0 = 10$, which is less than a third of the unconstrained estimate of $\sigma_0$ obtained in Table \ref{tab:structuralMain}. We find that, as predicted, imposing $\sigma_0 = 10$ decreases the estimated value of $A$. Yet, its estimate, \$4.00, is still positive to an economically and statistically significant extent.

Column 5 examines the robustness of our results to a Bayesian updating assumption. This exercise addresses the possibility that subjects update beliefs in a close to Bayesian manner but that our belief elicitation after the information revelation might inadvertently and artifactually cause CAs to report beliefs inconsistent with Bayes. We find that the estimates of our main preference parameters remain very close to those estimated in the main specification in Table \ref{tab:structuralMain}. The strongest effect concerns parameter $A$ which decreases to \$4.07 but remains economically and statistically significant.

\begin{table}[htbp]
\caption{Drivers of the structural estimates} \label{tab:structuralEstimatesRobustness}
\scriptsize
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lcccccccccc}
& (1) & (2) & (3) & (4) & (5)  \\
\toprule
CA sample & All & All & All  & All  & All    \\
Constraint &$\beta = 0.8$ & $\beta = 0.5$  & $\beta = 0.2$ & $\sigma_0 = \sigma_1 = 10$ & $\mu_0 = \mu_1$   \\
	&&&&& $\sigma_0=\sigma_1$  \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.800 \textdagger& 0.500 \textdagger& 0.200 \textdagger& 1.729 ***& 0.806 ***\\
   &    &    &    &(0.036)   &(0.015)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 71.415 ***& 70.722 ***& 72.748 ***& 67.052 ***& 72.954 ***\\
   &(0.419)   &(0.355)   &(0.294)   &(0.311)   &(0.371)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 6.348 ***& 3.689 ***& 1.394 ***& 3.925 ***& 3.980 ***\\
   &(0.207)   &(0.155)   &(0.250)   &(0.172)   &(0.163)\\
\quad Fraction of absolutist types, $q$& 0.149 ***& 0.171 ***& 0.183 ***& 0.137 ***& 0.152 ***\\
   &(0.007)   &(0.007)   &(0.007)   &(0.007)   &(0.007)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 72.119 ***& 74.905 ***& 76.930 ***& 68.735 ***& 71.524 ***\\
   &(0.471)   &(0.552)   &(0.649)   &(0.094)   &(0.408)\\
\quad Prior mean parameter, $\mu_0$& 70.333 ***& 72.706 ***& 69.683 ***& 68.735 ***& 71.096 ***\\
   &(0.489)   &(0.572)   &(0.717)   &(0.094)   &(0.423)\\
\quad Prior SD parameter, $\sigma_0$& 34.600 ***& 37.011 ***& 48.086 ***& 10.000 ***& 27.835 ***\\
   &(0.436)   &(0.491)   &(0.730)   &    &(0.265)\\
\quad Posterior mean parameter, $\mu_1$& 83.617 ***& 89.942 ***& 83.569 ***& 81.145 ***& 71.096 ***\\
   &(0.746)   &(0.877)   &(1.009)   &(0.192)   &(0.423)\\
\quad Posterior SD parameter, $\sigma_1$& 26.769 ***& 31.247 ***& 35.435 ***& 10.000 \textdagger& 27.835 ***\\
   &(0.331)   &(0.371)   &(0.411)   &    &(0.265)\\
\\
$N$& 1997 & 1997 & 1997 & 1997

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Each column represents a separate estimation. Robust standard errors in parentheses. Standard errors of 0 indicate imposed values. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\FloatBarrier
\newpage
\subsection{Effect of recipient demographics}

\subsubsection{Reduced-form analysis \label{app:stereotypesReducedForm}}

Table \ref{stereotypesRestrictions} displays the results of reduced-form analysis of the effect of recipient demographics on enforcement prices, surrogate reservation prices, and beliefs about mean reservation prices, using either all CAs (columns 1 and 4), only politically left CAs (columns 2 and 5) or only politically right CAs (columns 3 and 6). The latter exclude politically centrist CAs. Overall, we observe effects for Black and for older recipients. When including all CAs (columns 1 and 4), Black recipients face mildly higher enforcement prices ($p<0.1$) and somewhat higher surrogate prices ($p<0.01$). These effects are entirely driven by politically conservative CAs who impose enfocement and surrogate prices that are \$6.69 and \$7.74 higher for black than for white recipients, respectively (columns 3 and 6). Old recipients face lower enforcement prices, an effect that is driven mainly by left CAs. There are no other significant effects.


\begin{table}[htbp]
\caption{Intervention behavior by recipient demographics \label{stereotypesRestrictions}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lcccccc}
 & (1) & (2) & (3) & (4) & (5) & (6)  \\
\toprule


& \multicolumn{3}{c}{Enforcement price}
& \multicolumn{3}{c}{Surrogate res. price}
\\
\cmidrule(r){2-4}
\cmidrule(r){5-7}
CA political pref.
& \multicolumn{1}{c}{All}
& \multicolumn{1}{c}{Left}
& \multicolumn{1}{c}{Right}
& \multicolumn{1}{c}{All}
& \multicolumn{1}{c}{Left}
& \multicolumn{1}{c}{Right}
\\ \midrule
            &               &               &               &               &               &               \\

Dep. var. mean&      61.656***&      54.587***&      69.140***&      78.328***&      74.059***&      81.713***\\
            &     (1.098)   &     (1.399)   &     (1.858)   &     (0.888)   &     (1.135)   &     (1.544)   \\


\textit{Recipient demo.}\\
                    &               &               &               &               &               &               \\

\quad Female        &       1.516   &      -3.422   &       3.615   &       0.443   &      -4.058   &       0.812   \\
                    &     (2.570)   &     (3.186)   &     (4.444)   &     (2.153)   &     (2.698)   &     (3.565)   \\
\quad Black         &       3.926*  &      -0.004   &       6.688*  &       4.749***&       0.568   &       7.739***\\
                    &     (2.129)   &     (2.762)   &     (3.556)   &     (1.717)   &     (2.208)   &     (2.855)   \\
\quad Old           &      -2.790   &      -6.954** &      -4.168   &       0.400   &       0.934   &       1.167   \\
                    &     (2.685)   &     (3.292)   &     (4.329)   &     (2.157)   &     (2.707)   &     (3.400)   \\
\quad Has children  &       0.008   &       0.329   &      -3.001   &       1.268   &       1.833   &       1.057   \\
                    &     (2.691)   &     (3.571)   &     (4.641)   &     (2.236)   &     (2.902)   &     (3.677)   \\
\quad Rural         &       3.081   &       3.708   &      -2.902   &      -0.344   &      -1.618   &      -4.513   \\
                    &     (2.516)   &     (3.230)   &     (4.068)   &     (2.096)   &     (2.625)   &     (3.254)   \\
\quad Urban         &       0.429   &       0.210   &      -1.865   &      -1.976   &      -1.309   &      -3.613   \\
                    &     (2.626)   &     (3.321)   &     (4.531)   &     (2.131)   &     (2.754)   &     (3.704)   \\
\medskip
N                   &        1890   &         974   &         673   &        1945   &         999   &         695   \\
\bottomrule

 \\
\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Data exclude CAs with multiple switches in the corresponding price lists. Columns 2, 3, 5, and 6 exclude CAs who rate themselves as politically centrist. Omitted categories are white, male, young, no children, and lives in a suburban area.  Regressions include controls for the order variables, for beliefs about waste and crowding out, and for CA attributes. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}


\FloatBarrier
\subsubsection{Structural analysis \label{app:stereotypesStructural}}

Table \ref{tab:structuralStereotypesCApol} replicates columns 1 and 2 fo Table \ref{tab:structuralStereotypes} separately for the subsets of left-leaning and right-leaning CAs. We observe that effects of recipient race are almost exclusively driven by right-leaning CAs.

\begin{landscape}
\begin{table}[htbp!]
\caption{Structural estimation results: Recipient demographics and CA political attitudes} \label{tab:structuralStereotypesCApol}
\footnotesize
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccccccc}
 & (1) & (2) & (3) & (4) & (5) & (6) \\
\toprule
CA political attitudes & \multicolumn{3}{c}{Left} & \multicolumn{3}{c}{Right}  \\
\cmidrule(r){2-4} \cmidrule(r){5-7}
Recipient & Black & White & Difference  & Black & White & Difference  \\
&&&(1)-(2)&  & & (4)-(5)  \\
&&&&& \\
\midrule


\textbf{\textit{Model parameters}} \\ \quad \textit{Consequentialist part} \\ \qquad  Weight on recipient res. price, $\beta$& 0.887 ***& 0.843 ***& 0.044 & 0.916 & 1.843 & -0.927 \\
   &(0.027)   &(0.026)   &(0.038)   &(NaN)   &(NaN)   &(NaN)\\
\qquad Ideal res. price for mean rec., $p_o(\bar{p}_r)$& 72.434 ***& 70.375 ***& 2.059 **& -8.332 & -49.348 & 41.016 \\
   &(0.721)   &(0.739)   &(1.032)   &(NaN)   &(NaN)   &(NaN)\\
\quad \textit{Nonconsequentialist part} \\ \qquad Utility from intervention, $A$& 5.501 ***& 5.475 ***& 0.026 & 4.216 & 16.525 & -12.309 \\
   &(0.397)   &(0.375)   &(0.546)   &(NaN)   &(NaN)   &(NaN)\\
\quad Fraction of absolutist types, $q$& 0.092 ***& 0.067 ***& 0.025 **& 0.000 & 0.000 & 0.000 \\
   &(0.009)   &(0.007)   &(0.011)   &(NaN)   &(NaN)   &(NaN)\\
\textbf{\textit{Beliefs about reservation prices}} \\ \quad Prior belief mean, $\bar{p}_r$& 73.696 ***& 73.228 ***& 0.468 & 97.600 & 91.421 & 6.179 \\
   &(0.811)   &(0.773)   &(1.120)   &(NaN)   &(NaN)   &(NaN)\\
\quad Prior mean parameter, $\mu_0$& 71.746 ***& 71.967 ***& -0.220 & 88.163 & 89.179 & -1.017 \\
   &(0.840)   &(0.792)   &(1.154)   &(NaN)   &(NaN)   &(NaN)\\
\quad Prior SD parameter, $\sigma_0$& 35.760 ***& 33.102 ***& 2.658 **& 61.372 & 43.786 & 17.587 \\
   &(0.782)   &(0.675)   &(1.033)   &(NaN)   &(NaN)   &(NaN)\\
\quad Posterior mean parameter, $\mu_1$& 99.192 ***& 98.708 ***& 0.484 & 120.537 & 115.359 & 5.178 \\
   &(0.716)   &(0.745)   &(1.034)   &(NaN)   &(NaN)   &(NaN)\\
\quad Posterior SD parameter, $\sigma_1$& 26.026 ***& 25.887 ***& 0.139 & 51.187 & 41.119 & 10.067 \\
   &(0.652)   &(0.622)   &(0.901)   &(NaN)   &(NaN)   &(NaN)\\
\\
$N$& 499 & 481 & & 332 & 345

 \\
\bottomrule
\end{tabular}
\end{center}
\footnotesize
\textit{\textbf{Notes:}} Standard error on $m$ calculated using the Delta method. Each column represents a separate model. Columns 2 and 3 exclude subjects who rate themselves politically centrist. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}
\end{landscape}


\subsection{Recipient demographics and CAs' beliefs about recipients' spending}\label{app:manipulationCheckSterotypes}

Here, we examine whether CAs paid attention to recipient characteristics by testing whether their beliefs about recipient spending respond to these demographics.

We calculate believed spending on sin goods (alcohol, tobacco, and lottery tickets) as the percentage of gas station spending the CA believes the recipient allocates to sin goods multiplied with her beliefs about the dollar value of gas station gift cards the recipient will select when given the opportunity to select gift cards of a total value of \$100. Our estimate of spending shares for specific items available in gas stations such as alcohol and tobacco may be biased downwards. The reason is the unpacking effect \citep{TverskyKoehler}, according to which individuals assign more weight to members in a category (in our case gas station spending) when each member (in our case, the items available at gas stations) is presented separately than when asked to assign weight to the category as a whole.

We find that, with the exception of race, each recipient characteristic affects spending beliefs.


\begin{table}[htbp]
\caption{Beliefs about spending by recipient demographics \label{tab:stereotypes}}\
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lccccccccccc} \toprule
 & (1) & (2) & (3) & (4) & (5) & (6) & (7)  \\


& \multicolumn{7}{c}{Believed spending on}
\\
& \multicolumn{1}{c}{Addictive}
& \multicolumn{1}{c}{Lotteries}
& \multicolumn{1}{c}{Luxuries}
& \multicolumn{1}{c}{Laziness}
& \multicolumn{1}{c}{Sporting}
& \multicolumn{1}{c}{Necessities}
& \multicolumn{1}{c}{Gas,}
 \\
& \multicolumn{1}{c}{goods}
& \multicolumn{1}{c}{}
& \multicolumn{1}{c}{}
& \multicolumn{1}{c}{}
& \multicolumn{1}{c}{goods}
& \multicolumn{1}{c}{}
& \multicolumn{1}{c}{other}
& \multicolumn{1}{c}{}
\\ \midrule
            &               &               &               &               &               &               &               \\

Dep. var. mean&       6.590***&       2.146***&      11.726***&      34.340***&       0.968***&      22.151***&      22.080***\\
            &     (0.289)   &     (0.109)   &     (0.469)   &     (0.484)   &     (0.102)   &     (0.488)   &     (0.455)   \\


\textit{Demographics}\\
                    &               &               &               &               &               &               &               \\

\quad Female        &      -3.746***&      -0.253   &       1.216   &      -3.725** &      -1.484***&       7.844***&       0.147   \\
                    &     (0.792)   &     (0.273)   &     (1.114)   &     (1.184)   &     (0.293)   &     (1.038)   &     (1.057)   \\
\quad Black         &       0.734   &       0.367   &       0.382   &       0.300   &      -0.194   &      -1.105   &      -0.485   \\
                    &     (0.562)   &     (0.214)   &     (0.921)   &     (0.954)   &     (0.211)   &     (0.936)   &     (0.908)   \\
\quad Old           &       1.421   &       0.981** &      -2.577*  &      -2.121   &      -0.323   &       1.797   &       0.821   \\
                    &     (0.785)   &     (0.306)   &     (1.159)   &     (1.244)   &     (0.262)   &     (1.110)   &     (1.112)   \\
\quad Has children  &      -1.699** &      -0.064   &      -4.028***&      -0.545   &       0.329   &       5.704***&       0.301   \\
                    &     (0.565)   &     (0.241)   &     (1.167)   &     (1.168)   &     (0.217)   &     (1.235)   &     (1.154)   \\
\quad Rural         &       2.514***&       0.441   &      -0.952   &       0.048   &      -0.032   &       0.095   &      -2.114   \\
                    &     (0.659)   &     (0.265)   &     (1.082)   &     (1.146)   &     (0.272)   &     (1.094)   &     (1.095)   \\
\quad Urban         &       1.709** &       0.261   &       0.693   &       0.062   &       0.111   &       0.141   &      -2.978*  \\
                    &     (0.646)   &     (0.252)   &     (1.165)   &     (1.193)   &     (0.245)   &     (1.157)   &     (1.168)   \\
\medskip
N                   &        1959   &        1959   &        1959   &        1959   &        1959   &        1959   &        1959   \\
\bottomrule


\end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Omitted categories are white, male, young, no children, not from a southern state, lives in a suburban area. Regressions include controls for the order variables, for the alcohol information treatment, and for CAs' own race, gender, age, and child status, as well as for her political attitudes, income, and participation in welfare programs. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1.
\end{table}

\FloatBarrier
\newpage


\subsection{Choice Architect attributes}\label{app:CAattributes}\label{app:chooserCAinteractions}


We now consider the effect of Choice Architects' own attributes. We pool across recipient attributes and food baskets. We consider the joint effect of the CA's age, gender, race, number of children, equivalent income, and political preference. We also include indicators for participating in social welfare program, which we group into SNAP, a medical category (Medicaid and Medicare), a cash assistance category (Social Security, Veteran Disability Benefits, Earned Income Tax Credit, Temporary Assistance for Needy Families), and a remaining restricted or in-kind transfers category (Public Housing and Pell Grants). (We combine the programs into categories because participation in many of the specific programs is too low to permit precise estimation of effects.) We control for order effects and the alcohol information treatment as well as for beliefs about the costs of the food basket, the fraction of the food that will go to waste, and for crowding out from both receiving groceries and receiving cash equivalents.

Table \ref{CAdemographics} shows the results. Next to the effects of CAs' political attitudes examined in Subsection \ref{sec:caPolitical}, we find that older CAs set higher enforcement and surrogate prices whereas CAs who are themselves SNAP recipients set them lower ($p<0.05$ in each case). Moreover, CAs whose ethnicity is neither black nor white set higher surrogate reservation prices ($p<0.05$). No other predictors are significant at the 5\% level.


\begin{table}[htb]
\caption{Effects of Choice Architects' demographics \label{CAdemographics}}
\setlength{\tabcolsep}{2pt}
\begin{center}
\begin{tabular}{lcccc}
 & (1) & (2)  \\
 \toprule


& \multicolumn{1}{c}{Enforcement price}
& \multicolumn{1}{c}{Surrogate price}
\\ \midrule
            &               &               \\

Dep. var. mean&      61.656***&      78.328***\\
            &     (1.098)   &     (0.888)   \\


\textit{CA demographics}\\
                    &               &               \\

Pol. attitude \\ (baseline: center) \\ \quad Left&      -8.911***&      -6.790***\\
                    &     (3.005)   &     (2.390)   \\
\quad Right         &       6.385** &       0.779   \\
                    &     (3.239)   &     (2.606)   \\
Male                &       0.750   &       2.093   \\
                    &     (2.189)   &     (1.792)   \\
Equiv. income       &       0.060   &       0.051   \\
                    &     (0.041)   &     (0.033)   \\
\# children         &      -1.111   &      -0.988   \\
                    &     (0.905)   &     (0.740)   \\
Age                 &       0.282***&       0.203***\\
                    &     (0.085)   &     (0.071)   \\
Race (baseline: white) \\ \quad Black&       6.021*  &       0.218   \\
                    &     (3.232)   &     (2.835)   \\
\quad Other         &       6.831*  &       6.642** \\
                    &     (3.638)   &     (2.945)   \\
Welfare recipient \\ \quad SNAP recipient&      -7.423** &      -8.128***\\
                    &     (3.413)   &     (2.861)   \\
\quad Medicaid/Medicare&      -1.177   &      -1.003   \\
                    &     (2.550)   &     (2.090)   \\
\quad Other cash    &      -0.581   &      -3.362   \\
                    &     (2.578)   &     (2.206)   \\
\quad Other in-kind &      -1.568   &       1.020   \\
                    &     (4.261)   &     (2.980)   \\
\medskip
N                   &        1890   &        1945   \\
\bottomrule


\end{tabular}


\end{center}
\footnotesize
\textbf{\textit{Notes:}} Each column reports the estimate of a separate WLS regression. Controls for position of belief elicitation in the survey and for order in which cash equivalents are displayed, beliefs about crowding out, beliefs about the costs of the food basket, and beliefs about food waste. Standard errors in parentheses. *** p$<$0.01, ** p$<$0.05, * p$<$0.1
\end{table}

Next, we examine whether effects of CA attributes emerge if we let them depend on recipient attributes. Specifically, we create indicators for the interaction between CA's race (black, white, other) and the recipient's race (black, white), between the CA's and the recipient's gender, between the CA's and the recipient's parental status, and between the CA's and the recipient's age. We control for the type of food basket, for CA's political attitudes, as well as for beliefs about crowding out and the believed percentage of the food delivery that will spoil.

Table \ref{interactionDemographics} displays the results. For none of the attributes race, gender, or child status do we find an effect on enforcement or surrogate reservation prices when we vary the attribute for the CA but fix it at any level for the recipient.


\begin{table}[htb]
\caption{Interaction between Choice Architect and recipient demographics \label{interactionDemographics}}
\begin{center}
\begin{tabular}{lccc}
& (1)&(2)\\
\toprule
& Enforcement price & Surrogate price \\
\midrule
{CA characteristic} \\


                    &               &               \\

\textit{White recipient} \\ \quad White (baseline) \\ \quad Black &       0.536   &      -3.254   \\
                    &     (4.121)   &     (3.935)   \\
\quad Other         &       1.775   &       5.138   \\
                    &     (5.476)   &     (3.927)   \\
\textit{Black recipient} \\ \quad White (baseline) \\ \quad Black&       0.088   &      -3.332   \\
                    &     (4.876)   &     (4.000)   \\
\quad Other         &       5.490   &       4.784   \\
                    &     (5.173)   &     (4.222)   \\
\textit{Male recipient} \\ \quad Male (baseline) \\ \quad Female&      -5.332   &      -2.846   \\
                    &     (4.135)   &     (3.501)   \\
\textit{Female recipient} \\ \quad Male (baseline) \\ \quad Female&      -2.098   &      -3.978   \\
                    &     (2.590)   &     (2.058)   \\
\textit{Recipient has children} \\ \quad Has children (baseline) \\ \quad No chi&       2.566   &       4.625   \\
                    &     (4.238)   &     (3.370)   \\
\textit{Recipient has no children} \\ \quad Has children (baseline) \\ \quad No &      -0.090   &       0.173   \\
                    &     (2.833)   &     (2.275)   \\
\textit{Recipient old} \\ \quad Age &       0.217*  &       0.155*  \\
                    &     (0.098)   &     (0.078)   \\
\textit{Recipient young} \\ \quad Age&       0.257** &       0.116   \\
                    &     (0.088)   &     (0.072)   \\
\midrule \textit{Recipient fixed effects} \\ \quad Recipient black &       3.549   &       5.033*  \\
                    &     (2.470)   &     (1.962)   \\
\quad Recipient female&      -0.512   &       0.894   \\
                    &     (3.357)   &     (2.813)   \\
\quad Recipient childless&       0.236   &      -0.050   \\
                    &     (3.750)   &     (3.025)   \\
\medskip
N                   &        1899   &        1954   \\
\bottomrule


\end{tabular}
\end{center}
\footnotesize
\textbf{\textit{Notes:}} Weighted regressions. Rows without estimates indicate the baseline category. Each column reflects a single WLS estimation. Predictor variables are the interactions of CA and recipient characteristics listed in the first column. Regressions control for position of belief elicitation in the survey and for order in which cash equivalents are displayed
\end{table}


\FloatBarrier
\newpage


\section{Structural estimation details} \label{app:structuralEstimationDetails}
\setcounter{table}{0}
\setcounter{figure}{0}


\subsection{Calculations}


We write $a = (1 - \gamma)m$ and $b=\gamma$ and estimate $u = a + b \min\{c, \overline{p}\}$. We cap reservation price beliefs  at $\overline{p}$ .

\noindent \textbf{Surrogate choices.} Consider the line on the multiple price list in which the value of the gift cards is $p$. When deciding between choosing the food basket and choosing the gift cards of value $p$ for a recipient, the CA trades off the loss $w_i$ from choosing the gift cards for someone who should have received the food basket ($u_i > p$) and the loss $1 - w_i$ from choosing the food basket for someone who should have received the gift cards ($p > u_i$). All in all, the decision rules can be summarized as follows.

\vskip 3mm

\noindent 1. Consider the (unconditional) surrogate choice where CA $i$ is not provided with any information about the recipient's WTP for the food basket. In this choice, CA $i$ \textbf{chooses the food basket} if and only if
\begin{equation} \label{focUncondSurrogate1}
    \int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c) + \epsilon_{i1} \geq 0  \nonumber
\end{equation}
where (i) $u_i = \alpha_i + \beta_i \min\{c, \overline{p}\}$ is the CA's normative WTP for a recipient with a WTP of $c$; \\
(ii) $\epsilon_{i1} \sim N(0, \sigma_{\epsilon_1}^2)$ is an unobserved preference shock otherwise not captured by the model.

\vskip 3mm

\noindent 2. Consider the (conditional) surrogate choice where CA $i$ is provided the additional information that the recipient chooses the food basket over gift cards of value $\tau$ (i.e., the recipient exhibits a high WTP for the food basket). Note that according to the CA's beliefs, the random recipients make this decision with $\frac{1}{2}$ probability, while the attentive recipients make this decision with a $(1-F_i(\tau))$ probability. Therefore, Bayesian updating implies that the posterior probability that the recipient is of the random type is $\frac{\frac{q}{2}}{\frac{q}{2}+(1-q)(1-F_i(\tau))}$, and the posterior probability that the recipient is of the attentive type is $\frac{(1-q)F_i(\tau)}{\frac{q}{2}+(1-q)(1-F_i(\tau))}$. All in all, in this choice, CA $i$ \textbf{chooses the food basket} if and only if
\begin{eqnarray} \label{focCondSurrogateUp1}
    && \frac{\frac{q}{2}}{\frac{q}{2}+(1-q)(1-F_i(\tau))} \int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c) \nonumber \\
    + && \frac{(1-q)(1-F_i(\tau))}{\frac{q}{2}+(1-q)(1-F_i(\tau))}\int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c | c \geq \tau) + \epsilon_{i2} \geq 0 \nonumber
\end{eqnarray}
where (i) $u_i = \alpha_i + \beta_i \min\{c, \overline{p}\}$ is the CA's normative WTP for a recipient with a WTP of $c$; \\
(ii) $\epsilon_{i1} \sim N(0, \sigma_{\epsilon_1}^2)$ is an unobserved preference shock otherwise not captured by the model.

\vskip 3mm

\noindent 3. Similarly, consider the (conditional) surrogate choice where CA $i$ is provided the additional information that the recipient chooses gift cards of value $\tau$ over the food basket (i.e., the recipient exhibits a low WTP for the food basket). Note that according to the CA's beliefs, the random recipients make this decision with $\frac{1}{2}$ probability, while the attentive recipients make this decision with a $F_i(\tau)$ probability. Therefore, Bayesian updating implies that the posterior probability that the recipient is of the random type is $\frac{\frac{q}{2}}{\frac{q}{2}+(1-q)F_i(\tau)}$, and the posterior probability that the recipient is of the attentive type is $\frac{(1-q)F_i(\tau)}{\frac{q}{2}+(1-q)F_i(\tau)}$. All in all, in this choice, CA $i$ \textbf{chooses the food basket} if and only if
\begin{eqnarray} \label{focCondSurrogateDown1}
    && \frac{\frac{q}{2}}{\frac{q}{2}+(1-q)F_i(\tau)} \int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c) \nonumber \\
    + && \frac{(1-q)F_i(\tau)}{\frac{q}{2}+(1-q)F_i(\tau)} \int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c | c \leq \tau) + \epsilon_{i3} \geq 0 \nonumber
\end{eqnarray}
where (i) $u_i = \alpha_i + \beta_i \min\{c, \overline{p}\}$ is the CA's normative WTP for a recipient with a WTP of $c$; \\
(ii) $\epsilon_{i1} \sim N(0, \sigma_{\epsilon_1}^2)$ is an unobserved preference shock otherwise not captured by the model.

\vskip 3mm

\noindent \textbf{Paternalistic choices.} On each line of the price list for paternalistic choices, the CA decides between leaving the choice open between the food basket and gift cards of value $p$ on the one hand, and forcing the recipient to receive the food basket on the other hand. The CA can only alter the outcome of those recipients who, absent an intervention, would have chosen the gift cards of value $p$. These include a half of the random recipients, and the attentive recipients who have a WTP $c < p$. The CA balances the benefit of choosing the food basket for those recipients who should receive the food basket ($u > p$) but would not choose the food basket if deciding themselves ($c < p$) against the loss from recipients who should not receive the food basket ($u < p$) but would be forced to receive the food basket ($c < p$). Finally, CA $i$ will intervene only if the expected net benefit of intervention is at least $A_i$. All in all, in this choice, CA $i$ \textbf{forces the recipient to receive the food basket} instead of \textbf{leaving the choice open} if and only if
\begin{eqnarray} \label{focPaternalism1}
    && \frac{q}{2} \int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c) \nonumber \\
    + && (1-q)F_i(p)\int_0^{\infty} w_{i} \max(0, u_{i} - p) - (1 - w_{i}) \max(0, p - u_{i}) d F_{i}(c | c < p) + \epsilon_{i4} \geq A_{i} \nonumber
\end{eqnarray}
where (i) $u_i = \alpha_i + \beta_i \min\{c, \overline{p}\}$ is the CA's normative WTP for a recipient with a WTP of $c$; \\
(ii) $\epsilon_{i1} \sim N(0, \sigma_{\epsilon_1}^2)$ is an unobserved preference shock otherwise not captured by the model.

On the left-hand side of the inequality above, the first term corresponds to the net benefit from affecting the random recipients' outcomes, and the second term corresponds to the net benefit from affecting the attentive recipients' outcomes.\footnote{Note that the variance of $\epsilon_{i4}$ can be reinterpreted as providing an upper bound for the variance of $A_i$ in a model with heterogeneity in $A_i$. That is, in that model the variance of $\epsilon_{i4}$ and the variance of $A_i$ cannot be separately identified.}

Finally, note that the model should be interpreted in the context of a particular population of recipients. Therefore, for any given CA, all the parameters are allowed to differ across recipients of different demographic groups.

\subsection{Estimation}

We use GMM to match the moments in Table \ref{tab:Moments}.

\begin{table}[H]
\begin{center}
\caption{A list of data moments used in the model estimation (by CA political inclination).}
\vskip 2mm
\label{tab:Moments}
\setlength\extrarowheight{-1.5pt}
\tiny{
\begin{tabular}{l>{\centering\arraybackslash}m{1.8cm}>{\centering\arraybackslash}m{1.8cm}>{\centering\arraybackslash}m{1.8cm}}
\toprule
Moment & Full Sample (Reweighted) & Liberals (Reweighted) & Non-liberals (Reweighted) \\
\hline
\multicolumn{4}{l}{\textbf{8 moments from unconditional surrogate choices}} \\
Fraction Choosing Food Box over \$0 Gift Cards & 91.2\% & 86.9\% & 94.9\% \\
Fraction Choosing Food Box over \$25 Gift Cards & 88.1\% & 83.3\% & 92.3\% \\
Fraction Choosing Food Box over \$45 Gift Cards & 84.3\% & 81.8\% & 86.6\% \\
Fraction Choosing Food Box over \$60 Gift Cards & 76.4\% & 72.9\% & 79.4\% \\
Fraction Choosing Food Box over \$70 Gift Cards & 64.4\% & 57.1\% & 70.7\% \\
Fraction Choosing Food Box over \$85 Gift Cards & 49.7\% & 42.9\% & 55.6\% \\
Fraction Choosing Food Box over \$105 Gift Cards & 37.5\% & 29.5\% & 44.3\% \\
Fraction Choosing Food Box over \$130 Gift Cards & 35.8\% & 25.8\% & 44.3\% \\
\hline
\multicolumn{4}{l}{\textbf{ 4 moments from conditional surrogate choices (knowing that recipient exhibits a WTP $\geq$ \$65)}} \\
Fraction Choosing Food Box over \$70 Gift Cards & 90.6\% & 89.9\% & 91.3\% \\
Fraction Choosing Food Box over \$85 Gift Cards & 65.5\% & 64.4\% & 66.3\% \\
Fraction Choosing Food Box over \$105 Gift Cards & 47.8\% & 46.1\% & 49.2\% \\
Fraction Choosing Food Box over \$130 Gift Cards & 43.0\% & 40.2\% & 45.5\% \\
\hline
\multicolumn{4}{l}{\textbf{ 4 moments from conditional surrogate choices (knowing that recipient exhibits a WTP $\leq$ \$65)}} \\
Fraction Choosing Food Box over \$0 Gift Cards & 92.4\% & 93.9\% & 91.2\% \\
Fraction Choosing Food Box over \$25 Gift Cards & 89.4\% & 89.1\% & 89.8\% \\
Fraction Choosing Food Box over \$45 Gift Cards & 84.4\% & 86.4\% & 82.7\% \\
Fraction Choosing Food Box over \$60 Gift Cards & 67.4\% & 69.6\% & 65.5\% \\
\hline
\multicolumn{4}{l}{\textbf{8 moments from paternalistic choices}} \\
Fraction Forcing Food Box with \$0 Gift Cards & 76.9\% & 72.3\% & 80.8\% \\
Fraction Forcing Food Box with \$25 Gift Cards & 72.4\% & 68.7\% & 75.5\% \\
Fraction Forcing Food Box with \$45 Gift Cards & 64.5\% & 64.1\% & 64.8\% \\
Fraction Forcing Food Box with \$60 Gift Cards & 59.2\% & 59.0\% & 59.3\% \\
Fraction Forcing Food Box with \$70 Gift Cards & 54.6\% & 51.6\% & 57.1\% \\
Fraction Forcing Food Box with \$85 Gift Cards & 43.6\% & 39.2\% & 47.4\% \\
Fraction Forcing Food Box with \$105 Gift Cards & 33.5\% & 31.4\% & 35.3\% \\
Fraction Forcing Food Box with \$130 Gift Cards & 30.6\% & 28.2\% & 32.6\% \\
\hline
\multicolumn{4}{l}{\textbf{2 moments from all (surrogate and paternalistic) choices}} \\
Fraction always choosing / forcing Food Box among those told recipient exhibits a WTP $\geq$ \$65 & 17.7\% & 14.8\% & 20.3\% \\
Fraction always choosing / forcing Food Box among those told recipient exhibits a WTP $\leq$ \$65 & 13.3\% & 6.4\% & 19.1\% \\
\hline
\multicolumn{4}{l}{\textbf{7 moments from beliefs about WTP (without further information)}} \\
Average belief of fraction choosing \$25 Gift Cards over Food Box & 7.4\% & 7.6\% & 7.1\% \\
Average belief of fraction choosing \$45 Gift Cards over Food Box & 15.9\% & 16.1\% & 15.8\% \\
Average belief of fraction choosing \$60 Gift Cards over Food Box & 28.5\% & 25.6\% & 31.0\% \\
Average belief of fraction choosing \$70 Gift Cards over Food Box & 41.4\% & 38.4\% & 44.1\% \\
Average belief of fraction choosing \$85 Gift Cards over Food Box & 54.5\% & 53.7\% & 55.2\% \\
Average belief of fraction choosing \$105 Gift Cards over Food Box & 68.9\% & 68.3\% & 69.5\% \\
Average belief of fraction choosing \$130 Gift Cards over Food Box & 83.7\% & 83.3\% & 84.1\% \\
\hline
\multicolumn{4}{l}{\textbf{4 moments from beliefs about WTP (knowing that recipient exhibits a WTP $\geq$ \$65)}} \\
Average belief of fraction choosing \$70 Gift Cards over Food Box & 3.5\% & 1.0\% & 5.7\% \\
Average belief of fraction choosing \$85 Gift Cards over Food Box & 39.6\% & 39.9\% & 39.4\% \\
Average belief of fraction choosing \$105 Gift Cards over Food Box & 59.4\% & 59.3\% & 59.4\% \\
Average belief of fraction choosing \$130 Gift Cards over Food Box & 81.3\% & 81.1\% & 81.5\% \\
\hline
\multicolumn{4}{l}{\textbf{3 moments from beliefs about WTP (knowing that recipient exhibits a WTP $\leq$ \$65)}} \\
Average belief of fraction choosing \$25 Gift Cards over Food Box & 6.2\% & 6.9\% & 5.7\% \\
Average belief of fraction choosing \$45 Gift Cards over Food Box & 19.7\% & 16.9\% & 22.2\% \\
Average belief of fraction choosing \$60 Gift Cards over Food Box & 42.2\% & 40.8\% & 43.4\% \\
\hline
$N$ & 428 & 220 & 208 \\
\bottomrule
\end{tabular}
}
\end{center}
\end{table}


\newpage
\section{Experiment instructions}

\subsection{Choice Architects}
\includepdf[pages=-, landscape=true]{patPovInstructions}

\subsection{Recipients}
\includepdf[pages=-, landscape=true]{patPovInstructionsChoosers}

\addcontentsline{toc}{section}{References}


\end{appendix}

\end{document}
