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Motivated Cognitive Limitations

Christine L. Exley and Judd B. Kessler∗

May 3, 2018

Abstract

Behavioral biases are often blamed on agents’ inherent cognitive limitations. We show that

biases can also arise, or be exacerbated, because agents are motivated to display cognitive

limitations. In a series of experiments involving nearly 3200 participants, agents motivated

to avoid being generous make simple computational errors and respond to the salience of

information known to them, and agents motivated to believe they are smart update on en-

tirely uninformative signals of ability. When we remove self-serving motives, agents appear

completely (or much more) rational. Biases that are due to motivated cognitive limitations

survive standard debiasing interventions including simplifying the decision environment, giving

agents experience, and making sure agents are attentive.

∗Exley: [email protected], Harvard Business School; Kessler: [email protected], The WhartonSchool, University of Pennsylvania.

1 IntroductionOver the past 40 years, psychologists and economists have documented various behavioral bi-

ases and other inconsistencies in human decision making. These biases include anchoring bias,

availability bias, contrast effects, correlation and selection neglect, base-rate neglect, projection

bias, responses to saliency, as well as mental accounting and other forms of narrow bracketing; for

reviews, see Rabin (1998) and DellaVigna (2009).1

A vast literature in behavioral economics has aimed to understand and explain these biases. The

standard explanation is that these biases arise due to humans’ inherent cognitive limitations. Some

of the earliest work in behavioral economics suggested that agents lacked the cognitive capacity to

process all relevant information when making decisions and thus relied on heuristics, which led to

behavioral biases (Simon, 1955; Conlisk, 1996). Similarly, when describing the causes of behavioral

biases in their work, Kahneman and Tversky suggested humans’ cognitive limitations were at play.

They blamed “imperfections of human perception and decision” and drew analogies to the limits of

humans’ visual perception (Tversky and Kahneman, 1981, 1986; Kahneman, 2011).2 More recently,

a rich empirical literature documents, and a rich behavioral theory literature formalizes, how specific

cognitive limitations distort decisions, including work on saliency effects, proportional thinking,

focusing, and relative thinking. Complementary work shows that certain biases can be explained

by assuming agents optimize under cognitive constraints, such as the work on inattention; for a

review, see Caplin, Dean and Leahy (2016).3

Much of the empirical evidence in support of these behavioral biases comes from controlled

laboratory environments that take care to eliminate potential confounds (i.e., so agents respond

only to the financial incentives offered in the experiment). However, one confound that may be of

interest to explore — rather than eliminate — is the motive to hold particular beliefs or to achieve

particular outcomes. In many settings outside of the lab, agents are motivated to hold beliefs that

favor their intelligence, their preferred political party, or their in-group. In addition, agents are

often motivated to avoid costly actions associated with future or social benefits, such as saving for

1In addition to these biases, research has also uncovered a number of non-standard preferences, including lossaversion and inequity aversion. As noted by DellaVigna (2009), however, these non-standard preferences only leadto framing effects and preference reversals due to narrow bracketing. For example, agents might be loss averse, butwithout narrow bracketing, they could not be made to view the same decision in a “loss frame” or a “gain frame”(e.g., without narrow bracketing, saving 200 out of 600 lives or losing 400 out of 600 lives would be interpreted inthe same way, on a base of the entire relevant population).

2The full quote reads: “[R]ational choice requires that the preference between options should not reverse withchanges of frame. Because of imperfections of human perception and decision, however, changes of perspective oftenreverse the relative apparent size of objects and the relative desirability of options” (Tversky and Kahneman, 1981).

3For empirical and theoretical work on these biases and their explanations, see, e.g., Tversky (1972); Tversky andKahneman (1973); Thaler (1985); Ariely, Loewenstein and Prelec (2003); List (2003); Loewenstein, O’Donoghue andRabin (2003); Sims (2003); Simonsohn and Loewenstein (2006); Chetty, Looney and Kroft (2009); Finkelstein (2009);Caplin, Dean and Martin (2011); Bordalo, Gennaioli and Shleifer (2012, 2013); Cunningham (2013); Koszegi andSzeidl (2013); Brocas et al. (2014); Gabaix (2014); Hanna, Mullainathan and Schwartzstein (2014); Schwartzstein(2014); Busse et al. (2015); Taubinsky and Rees-Jones (Forthcoming); Bushong, Rabin and Schwartzstein (2017);Dean, Kıbrıs and Masatlioglu (2017); Enke and Zimmermann (Forthcoming); Enke (2017); Gabaix (2017); Haggagand Pope (Forthcoming); Handel and Schwartzstein (2018).

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retirement, dieting, exercising, investing in education, learning a new technology, or being prosocial.

In this paper, we explore environments in which agents are motivated. We find that agents display

behavioral biases not only because of inherent limitations of their cognitive ability but also because

they are motivated to display cognitive limitations.4

In three studies, including a total of nearly 3200 participants, we show that individuals who

have preferences to act or think in a certain way display behavioral biases in which they: (1) make

computational errors, (2) respond to the saliency of something that is known to them, and (3) update

their beliefs based on uninformative signals. In the first study, participants who want an excuse not

to donate money look like they cannot properly add a zero (i.e., they act as if 4×d 6= 4×d+ 0). In

the second study, participants in a similar setting look like they care about whether or not a charity

that is known to receive a donation of $0 is made salient by being included in a list of potential

recipient charities. In the third study, participants incentivized to correctly guess a measure of their

cognitive ability update favorably on a signal of ability that is known to be entirely uninformative.

These behavioral biases look like limitations of cognitive ability, and one could imagine devel-

oping a behavioral theory based on cognitive constraints to rationalize them. However, in a series

of experimental treatments, we document that once we remove the motivation to be selfish or the

motivation to believe an uninformative signal is “good news,” participants look rational (in the first

and third studies) or much more rational (in the second study). We say that the behavioral biases

we observe are due to “motivated cognitive limitations” because the biases look like they derive

from true limitations of cognitive ability (which we call “unmotivated cognitive limitations”), but

the limitations are motivated in nature.5

The first main contribution of our paper is to document how cognitive limitations operate when

agents are motivated. We show that behavioral biases can arise entirely from motivated cognitive

limitations (as in our first and third studies) and that unmotivated cognitive limitations and mo-

tivated cognitive limitations can simultaneously cause a behavioral bias (as in our second study).

Together, these results suggest that motivated cognitive limitations may cause or contribute to

behavioral biases in many environments.6 Absent jointly considering unmotivated and motivated

4Throughout this paper, we call any systematic response to irrelevant information or to a decision frame a“behavioral bias,” even if arises due to agents being motivated. Our definition contrasts with a potential definitionof “behavioral bias” that requires a behavioral bias be due to an inherent limitation of cognitive ability. However,as we show in our experiments, biased behavior can look identical regardless of whether or not it is motivated innature. Consequently, in settings where agents may be motivated, the latter potential definition would not allow usto call biased behavior a “behavioral bias” until we had ruled out that agents’ motivations might be a contributingcause. See further discussion in footnote 9.

5The distinction between “motivated” and “unmotivated” cognitive limitations seeks to distinguish motives fromthe various ways true limitations on humans’ cognitive ability might affect decisions. In particular, we define “un-motivated cognitive limitations” to include the work on deliberate or rational inattention, in which agents may beunwilling to fully process information in an attempt to save on cognition costs; see, e.g., Taubinsky and Rees-Jones(Forthcoming). As we will show, our results depart from this work in two important ways. First, in our settings,agents systematically exploit their failure to process information correctly in a self-serving direction. Second, when wekeep stakes comparable but remove self-serving motives, agents appear better at processing information, suggestingthat the extent to which they engage in processing itself depends on whether or not they are motivated.

6While not examples we will focus on in this paper, one could imagine motivated cognitive limitations contributing

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cognitive limitations — a combination largely overlooked, since the cognitive limitation and mo-

tivated reasoning literatures have evolved rather separately — one could easily misdiagnose the

underlying driver of biased behavior.

The second main contribution of our paper is highlighting why it is important to diagnose

whether a behavioral bias is caused by an unmotivated cognitive limitation, a motivated cogni-

tive limitation, or both. We test whether debiasing techniques traditionally used to overcome

unmotivated cognitive limitations can also mitigate motivated cognitive limitations. Biases due to

unmotivated cognitive limitations are expected to become less pronounced as decision environments

are made simpler, as agents are made to pay more attention to a decision, or as agents gain expe-

rience with a decision.7 A recent example can be found in Enke and Zimmermann (Forthcoming),

in which unmotivated cognitive limitations prevent agents from making accurate calculations due

to correlation neglect. In that setting — where motivations are not relevant — making sure that

agents pay attention to the correlated nature of signals or simplifying the underlying correlation

structure help agents make fewer mistakes. If cognitive limitations are motivated, however, then

agents may not want to make the correct calculations and these strategies need not be effective.

Indeed, we find thats these techniques do not debias participants displaying motivated cognitive

limitations. Our motivated cognitive limitations arise in exceedingly simple environments and sur-

vive making the environments simpler. Giving participants experience with the decision task does

not mitigate motivated cognitive limitations. Finally, participants who choose to pay attention are,

if anything, more likely to display a motivated cognitive limitation.8 Consequently, determining

how to debias behavior in practice likely requires identifying whether a behavioral bias is driven by

an unmotivated cognitive limitation, a motivated cognitive limitation, or both.

Identifying the driver of a behavioral bias is also relevant for determining whether debiasing

agents will be good for them. Unmotivated cognitive limitations may cause agents to make mistakes

and so debiasing is likely to make them better off; see, e.g., the discussion in Chetty (2015).

Motivated cognitive limitations, on the other hand, may help agents avoid taking actions they

to a number of well-documented behavior biases. For examples, agents may engage in projection bias on a sunny daybecause they want to buy a convertible, even if it is not a prudent purchase; agents may fail to properly calculate atax-inclusive price as an excuse to buy a product that they might otherwise deem too expensive; agents may anchorthe sale price of their house to the purchase price of their house to maintain the belief that they made a goodinvestment; and agents may engage in correlation neglect in their consumption of news to allow them to update toomuch on favorable information from multiple, correlated sources.

7For reviews, see Conlisk (1996); DellaVigna (2009); Madrian (2014); Gabaix (2017); for related examples,see List (2003); Chetty, Looney and Kroft (2009); Finkelstein (2009); Brocas et al. (2014); Hanna, Mullainathanand Schwartzstein (2014); Schwartzstein (2014); Taubinsky and Rees-Jones (Forthcoming); Enke and Zimmermann(Forthcoming). DellaVigna (2009) highlights an exception where failure to Bayesian update can cause experience toexacerbate a bias.

8That attention does not mitigate motivated cognitive limitations underscores that our findings are distinct fromthe work on motivated information avoidance as in Dana, Weber and Kuang (2007). However, we do find evidencethat supports and extends the work on motivated information avoidance. As described in Section 5.2, we test whetherself-serving motives affect information acquisition and find that individuals acquire less information when self-servingmotives are present. We know of no prior literature that facilitates such a direct test.

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prefer not to take, and so debiasing may make them worse off.9 This observation suggests an

important role for new theory on motivated cognitive limitations, including work exploring their

value or cost for agents, which we discuss further in Section 5.1. More generally, our results suggest

the need to explore motivated cognitive limitations alongside, and in conjunction with, the robust

literature on unmotivated cognitive limitations.

The third main contribution of our paper is documenting that the scope for self-serving behavior

and the scope for self-serving beliefs are both much larger than previously thought. Prior work

on motivated reasoning has suggested that it requires some form of “flexibility” to be present

(Gino, Norton and Weber, 2016).10 Prior literature has documented self-serving behavior arising

when payoffs are ambiguous (Haisley and Weber, 2010) or risky (Exley, 2015), or when individuals

maintain uncertainty by avoiding payoff information (Dana, Weber and Kuang, 2007). In our

first two studies, we document that uncertainty is not necessary for self-serving behavior to arise

in response to payoff information; entirely certain payoffs result in self-serving behavior. Similarly,

prior literature has documented self-serving beliefs arising when agents update in response to signals

that are informative but noisy, and thus offer flexibility to non-Bayesian agents (Eil and Rao, 2011;

Mobius et al., 2014; Schwardman and van der Weele, 2017; Zimmermann, 2018). In our third study,

we document that a degree of informativeness is not necessary for self-serving beliefs to arise in

response to signals; entirely uninformative signals generate self-serving beliefs. In all our studies,

motivated cognitive limitations arise when information is certain, unavoidable, and simple — that

is, absent any flexibility. These new findings suggest the broad relevance of self-serving behavior and

self-serving beliefs. They also highlight the potential difficulty in mitigating them, although there

has been some success on that front — see Gneezy et al. (2017) for an example of how self-serving

assessments are reduced when preceded by unbiased assessments.11

9For example, if an agent is donating money to maintain a good self-image, and if a motivated cognitive limitationallows the agent to maintain that self-image without donating, then the agent may value the motivated cognitivelimitation. Note that in this paper, we call any systematic response to irrelevant information or to a decision framea “behavioral bias,” regardless of whether or not displaying the bias is beneficial to the agent. See further discussionin footnote 4.

10Flexibility is also known to arise from subjectivity around which set of actions is fair, appropriate, or plausiblyjustified (Snyder et al., 1979; Kunda, 1990; Babcock et al., 1995; Hsee, 1996; Konow, 2000; Shalvi et al., 2011;Shalvi, Eldar and Bereby-Meyer, 2012; Gino and Ariely, 2012; Gino, Ayal and Ariely, 2013; Di Tella et al., 2015;Pittarello et al., 2015; Danilov and Saccardo, 2016; Exley, 2018; Schwardman and van der Weele, 2017; Zimmermann,2018; Gneezy, Saccardo and van Veldhuizen, Forthcoming) and to arise due to the existence of intermediaries,others, or nature who are seemingly responsible (Hamman, Loewenstein and Weber, 2010; Linardi and McConnell,2011; Coffman, 2011; Bartling and Fischbacher, 2012; Andreoni and Bernheim, 2009; Falk and Szech, 2013). Thatindividuals desire to exploit excuses to avoid giving may also contribute to individuals being less likely to givewhen a donation request is expected or avoidable (Broberg, Ellingsen and Johannesson, 2007; Oberholzer-Gee andEichenberger, 2008; Jacobsen et al., 2011; DellaVigna, List and Malmendier, 2012; Lazear, Malmendier and Weber,2012; Kamdar et al., 2015; Trachtman et al., 2015; Andreoni, Rao and Trachtman, 2016; Lin, Schaumberg and Reich,2016; Exley and Petrie, 2018).

11Relatedly, see also Babcock et al. (1995), Konow (2000) Haisley and Weber (2010), and Gneezy, Saccardo and vanVeldhuizen (Forthcoming). Lin, Zlatev and Miller (2016) further show that the removal of an excuse causes individualsto subsequently engage in more prosocial behavior, and findings in Cialdini (1984), Bazerman, Loewenstein and White(1992), Falk and Zimmermann (2016), Bohnet and Bazerman (2016), and Falk and Zimmermann (Forthcoming)document how a desire for consistency can constrain decisions.

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The rest of the paper proceeds as follows. Section 2 describes the design and results of Study 1.

Section 3 describes design and results of Study 2. Section 4 describes design and results of Study

3. Section 5 summarizes our results and discusses their implications. Section 6 concludes.

2 Study 1: Computational ErrorsIn Study 1, we present results of an experiment in which participants make computational errors.

Their choices suggest that they believe adding a zero to a sum decreases its value. One could imagine

blaming this error on a behavioral bias driven by an unmotivated cognitive limitation. We show that

this behavior is instead due to a motivated cognitive limitation. In particular, when we remove the

possibility that computational errors could be used as an excuse to make a selfish choice, participants

no longer make these computational errors. This pattern reveals that participants are neither unable

nor unwilling to make accurate calculations — they instead avoid accurate calculations when they

are motivated. In additional analysis, we show that the motivated cognitive limitation survives

attempts to debias participants by giving them experience or by further simplifying the (already

simple) decision environment, and we show that the motivated cognitive limitation is present even

among participants who choose to pay full attention to the decision.

2.1 Experimental Design

Study 1 included 1000 participants in one of five versions.12 In all versions, each participant

received $4 for completing the 25-minute study. In addition, one randomly selected decision for

each participant was implemented for bonus payment and resulted in an additional payment for the

participant or a donation to charity.

In all versions, participants make 48 binary choices in which they choose between a “bundle,”

which changes from decision to decision, and an “outside option,” which is fixed for all 48 decisions.

In each decision, the value of the bundle is equal to the sum of 4 or 5 summands. For simplicity,

each summand in a bundle is either 0 or a single positive number that (usually) appears multiple

times. Consequently, the sum of a bundle can always be calculated as n×d (where n is the number

of times the positive number d appears in the bundle, with all remaining summands being 0).

The five versions of Study 1 — Self/Charity, Charity/Charity, Self(150)/Self, Self/Charity-

Choice, and Self/Charity-Sum — vary along three dimensions: (1) the recipient and level of the

outside option, (2) the recipient of the bundle, and (3) what information about the bundle partici-

pants have to learn before making each choice. The differences across the five versions of Study 1

are best visualized in Table 1.

12From January 16-17, 2018, we recruited and randomized 600 participants from Amazon’s Mechanical Turk(MTurk) into one of three study versions: Self/Charity, Charity/Charity, Self(150)/Self, and 599 participants com-pleted the study. On January 18, 2017, we recruited and randomized 401 participants from MTurk into one of twostudy versions: Self/Charity-Choice, Self/Charity-Sum, and all 401 participants completed the study. Overall, 51%of participants are female, the median age is 33 years old, and the median educational attainment is an Associate’sDegree. Across these demographic variables, there is only one significant difference across the Self/Charity, Char-ity/Charity, and Self(150)/Self versions and there are no significant differences across the Self/Charity-Choice andSelf/Charity-Sum versions, demonstrating successful randomization.

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Table 1: Study 1 Versions

Outside Option to... ...Charity ...Self

Information isOptional

Self/Charity-Choice(n = 195)

RequiredCharity/Charity

(n = 199)Self/Charity

(n = 198)Self(150)/Self

(n = 202)Required andSum Shown

Self/Charity-Sum(n = 206)

Bundle to... ...Charity ...Self

We begin by describing the Self/Charity version in depth, since the other four versions are easily

explained as slight variations off of this version.13 In the Self/Charity version, the recipient of the

outside option is the participant and the level of the outside option is calibrated on the participant

level; the recipient of the bundle is the national chapter of the Make-A-Wish Foundation, a charity;

and participants must learn about each summand in the bundle before making their choice. In the

remainder of this section, we explain how the bundles are constructed, we explain how and why

we calibrated the outside option at the participant level, and then we describe how the other four

versions differ from the Self/Charity version.

Bundles in the Self/Charity version

Each bundle in the Self/Charity version of Study 1 includes four or five summands (called

“amounts” to participants) that are either zero or the same non-zero number. Participants are

informed that if the bundle is chosen, the sum of these four or five amounts will be donated (in

cents) to the Make-A-Wish Foundation national chapter. The first amount in a bundle is always

revealed by default (see Figure 1 for an example). Participants are then required to reveal the

remaining three or four amounts in a bundle by clicking on the header above each amount.14 To

ensure participants comprehend this structure, we require participants to correctly answer questions

about how much money would be given to charity in several example bundles before they make

choices in the study.

To facilitate comparisons across each participant’s decisions, we carefully structured the 48

bundles. In particular, we started with 12 “baseline” bundles, which we call n/4-bundles, since

they include four amounts of which n amounts are non-zero (so, if n < 4, then 4 − n amounts are

zero). Each non-zero amount within a bundle equals d. Thus, the total amount going to charity if

13The naming of the versions indicates the recipient of the outside option followed by the recipient of the bundle.For example, in the Self/Charity version, the outside option benefits the participant (thus Self/ ) and the bundlebenefits a charity (thus Charity). Information after a hyphen indicates a difference in information structure. Forexample, as will be described in detail below, in the Self/Charity-Choice version, participants have a choice aboutwhat information to learn about the bundle (thus -Choice).

14We present the bundles to participants in this interactive manner for two reasons. First, we believed this designwould be more engaging for participants whose task is to view and make decisions about 48 bundles. Second, thedesign facilitates a clean comparison with a version of the study, detailed later in this section, in which participantsare not required to become fully informed and can selectively view information (i.e., the Self/Charity-Choice version).

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Figure 1: Example of how a bundle initially appears in Study 1

Clicking on each header reveals the number of cents associated with that amount.

a baseline n/4-bundle is chosen is n× d cents. The n and d parameters for the baseline bundles are

chosen such that n× d varies systematically around 150 cents. We have four baseline bundles with

n = 2, four baseline bundles with n = 3, and four baseline bundles with n = 4. We randomly select

d ∈ {51, 52, 53, 54, 55, 56, 57, 58, 59} at the bundle level, so that n × d is substantially below 150

cents for the bundles with n = 2, slightly above 150 cents for bundles with n = 3, and substantially

above 150 cents for the bundles with n = 4.

Since each of the four or five amounts in a bundle appear in a designated order, in addition to

the amount d and the number of times that amount appears in a bundle, we also vary where the

zeros and the non-zeros are in the bundle, as shown in Appendix Table A.1.15

From each of 12 baseline bundles, we construct an n/5-bundle by “adding a zero” to it. Each

n/5-bundle mirrors the payoff structure of an n/4-bundle except for the addition of a fifth amount

that is zero. From each of these 12 baseline bundles, we additionally construct a (n+1)/5-bundle by

“improving” it. Each (n+1)/5-bundle mirrors the payoffs structure of an n/4-bundle except for the

addition of a fifth amount that is d. We call the 12 baseline bundles and the 24 bundles constructed

from them our “main bundles.”

In addition to our main bundles, we have 12 non-main bundles with four amounts each. We

included these bundles both to balance the number of bundles of each size (i.e., to have 24 bundles

with four amounts along with the 24 bundles with five amounts) and to provide additional data to

perform secondary analyses conducted in Section 5.16 Until then, decisions involving these non-main

bundles are excluded from our analysis.

The order in which participants make their 48 binary decisions varies. Half of participants

make their 24 decisions involving bundles with four amounts first and the other half make their 24

decisions involving bundles with five amounts first. In addition, within each block of 24 decisions,

15Appendix Table A.1 describes the twelve baseline bundles by indicating whether the first, second, third and/orfourth amount was d cents (i.e., a non-zero amount). Note that while the four-amount bundles with n = 4 only varyin terms of which value for d is randomly selected (since there are no zeros in those bundles), the four bundles withn = 2 and the four bundles with n = 3 also vary in terms of which amounts (i.e., the 1st, 2nd, 3rd, and/or 4thamount shown on the decision screen) are zero.

16These bundles are described in Appendix Table A.2.

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the order in which each bundle is shown randomly varies for each participant.

Outside options in the Self/Charity version

We calibrate the outside option for each participant in the Self/Charity version for two reasons.

First, we want each participant to be close to indifferent between the outside option and the bundle

for at least some of the decisions and to be further from indifferent for other decisions, so that

we have a well-controlled measure of how likely the participant is to select the bundle absent any

computational errors.17 Second, the outside option has to be set to something, and the calibration

allows us to keep the value of the outside option similar across study versions with and without

self-serving motives, which we discuss further in Section 2.3 and Section 5.2.18

While the previous paragraph highlights the value of the calibration, it is also worth emphasizing

that the main results of Study 1 and of Study 2 — which also utilizes the calibration — do not rely

on the calibration or on how well it matches the value of the outside option across study versions.

The lack of reliance on the calibration is clear from our within-participant identification strategy:

adding a zero to a bundle should not influence the extent to which a participant prefers it relative

to the outside option, regardless of the value of the outside option. In addition, as discussed in

footnote 17, our results persist in a version of Study 2 in which the calibration is not used to set

the outside option.

How do we implement the participant-level calibration? Before facing the 48 binary decisions,

each participant completes a multiple price list that aims to elicit an X value that makes the

participant indifferent between X cents for themselves and 150 cents for the national chapter of

the Make-A-Wish Foundation. Once we identify the X value, we set each individual participant’s

outside option to this X cents for themselves since, as detailed above, the amount donated by the

main bundles varies systematically around 150 cents.

The multiple price list generates an indifference range for X. We assign participants an X value

equal to the lower bound of their indifference range, unless the lower bound of the indifference range

is 0, in which case we assign X = 5 cents.19 The distribution of X values are displayed in Panel A

17A behavioral bias in response to how a bundle is constructed would be difficult to observe if participants alwayspreferred the outside option benefiting themselves to the bundle benefiting charity. This constant preference for theoutside option might arise in our experiment if we had set the nominal amounts of money in the outside optionand the bundle to be similar, since most individuals value money for themselves more than money for others — ina meta-study on the dictator game, Engel (2011) finds that individuals choose to give nothing approximately 36%of the time. In Study 2, we run an additional study version that does not use this calibration and instead assignsan outside option of 150 cents (i.e., close to the nominal value of donations made by the average bundle) for allparticipants. These results are presented in Section 3.4, after we describe the main results from Study 2. We findthat our main results from Study 2 persist in the absence of the calibration. As expected, however, in this 150-centversion, the rates of choosing the bundle are significantly lower and twice as many participants choose the outsideoption for themselves in all 48 choices than in its calibrated counterpart (51% always choose the outside option in the150-cent version as compared to 25% in its calibrated counterpart), which underscores the value of the calibrationexercise.

18We see the calibration procedure as a valuable methodological contribution to laboratory experiments that aimto make treatments with different outside options comparable, and we have used variants of it in our other work(Exley, 2015, 2018; Exley and Kessler, 2017).

19In particular, the price list contains 31 rows. On each row, the participant must decide between 150 cents being

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of Appendix Figure B.1.20

Additional versions of Study 1

Each of the four other versions of Study 1 have a slight variation off of the Self/Charity version,

and they are described here. Additional details (and screenshots where appropriate) are shown in

the corresponding sections where we discuss the results from these versions.

The Charity/Charity version is like the Self/Charity version, except that the outside option

for all the decisions is 150 cents going to the national chapter of the Make-A-Wish Foundation.

Since the national chapter of the Make-A-Wish Foundation is the recipient of both the bundle

and the outside option, participants who want to maximize donations to the charity should choose

the bundle whenever its sum is greater than 150 cents. This study version allows us to examine

decisions in a setting where stakes are comparable to the Self/Charity version (due to the calibration

procedure) but where self-serving motives are absent. The results of this version are reported in

Section 2.3.

The Self(150)/Self version is like the Self/Charity version, except for two changes. First, the

recipient of the bundle is the participant (i.e., self) and the outside option for all the decisions is 150

cents going to the participant (i.e., self). Since the participant is the recipient of both the bundle

and the outside option, participants who want to maximize earnings in the experiment should choose

the bundle whenever its sum is greater than 150 cents. This study version allows us to consider how

the absence of self-serving motives influences decisions in a setting where participants’ own money

is still at stake. The results of this version are reported in Section 2.3.

The Self/Charity-Choice version is like the Self/Charity version, except for what participants

must learn about each bundle. In particular, in Self/Charity-Choice, participants are shown the

first amount in each bundle by default but do not need to reveal the other three or four amounts

before making a choice about the bundle. This version allows us to examine whether our results

persist among decisions in which participants are known to pay attention to the information in a

bundle. The results of this version are reported in Section 2.4.

The Self/Charity-Sum version is like the Self/Charity version, except for what participants

must learn about each bundle. In particular, in Self/Charity-Sum, participants must view all of the

amounts in the bundle before making a choice, just like in the Self/Charity version, but they are

also shown the sum of the amounts in the bundle on the decision screen (i.e., the computer sums the

given to the Make-A-Wish Foundation national chapter and an amount of money for themselves that varies from0 cents to 150 cents in five-cent increments (i.e., the price list gives 5 × (r − 1) cents to the participant on the rth

row). If a participant switches from choosing the first payment option on the rth to the second payment option onthe (r + 1)th row, then that participant is indifferent between 150 cents for the national chapter and X cents forthemselves, where 5 × (r − 1) ≤ X ≤ 5 × r. As noted in the main text, a participant’s X value is set at the lowerbound of a participant’s possible X range (i.e., we set X = 5(r − 1) cents), unless this would set X = 0 cents, inwhich case we set X = 5 cents. Setting X to the lower bound ensures that, if anything, participants should preferbundles over their outside option more when the outside option is X cents for themselves than when it is 150 centsfor the national chapter of the Make-A-Wish Foundation.

20As will be shown throughout the paper, our results are robust to a restricted sample that excludes the 12% ofparticipants whose lower bound implies X = 0 and for whom we assign X = 5 cents.

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amounts for them and displays this sum). This version allows us to examine participants’ decisions

when the already simple decision environment is simplified further. The results of this version are

reported in Section 2.4.

2.2 Documenting the behavioral bias

In the Self/Charity version, we find clear evidence that participants make systematic computa-

tional errors, demonstrating a behavioral bias. In particular, participants are less likely to choose

a bundle when a zero is added to it, even though the donation made by the bundle (i.e., the sum

of the amounts in the bundle) has not changed.

Figure 2 shows our results graphically, collapsing across all our main bundles. The shading

of the bars indicates the number of non-zero amounts in the bundle, which determines the sum

of the bundle and whether the sum is above or below 150 cents.21 It is clear that participants’

willingness to choose a bundle is not solely driven by the number of non-zero amounts. For each

of the four-amount bundles (i.e., the 4/4-bundles, the 3/4-bundles, and the 2/4-bundles), there are

corresponding five-amount bundles that involve the same number of non-zero donation amounts

(i.e., the 4/5-bundles, the 3/5-bundles, and the 2/5-bundles). The fact that these five-amount

bundles contain an additional zero is payoff irrelevant, but adding a zero causes a substantial drop

in participants’ willingness to choose a bundle.

Figure 2: In the Self/Charity version of Study 1, fraction choosing a main bundle

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5/5 4/4 4/5 3/4 3/5 2/4 2/5Description of bundles

Data include all participants’ decisions in all main bundles in the Self/Charity version of Study 1.

Table 2 presents the results from the main bundles in a regression framework that includes

additional controls and carefully isolates the impact of adding a zero and the impact of adding a

21In the 5/5-bundles, 5 of the donation amounts are non-zero, so the sum is 255 to 295 cents. In the 4/4- and4/5-bundles, 4 of the donation amounts are non-zero, so the sum is 204 to 236 cents. In the 3/4- and 3/5-bundles, 3of the donation amounts are non-zero, so the sum is 153 to 177 cents. Finally in the 2/4- and 2/5-bundles, 2 of thedonation amounts are non-zero, so the sum is 102 to 118 cents.

10

non-zero amount to a baseline n/4-bundle. In particular, we report results from the following linear

probability model:

P(choose bundle) = β1(+0) + β2(

+1) +4∑

n=2

59∑d=51

kn × ld + ε (1)

where (+0) is an indicator for an n/5-bundle that is constructed by adding a fifth amount that is

equal to zero to a baseline n/4-bundle, (+1) is an indicator for an (n+1)/5-bundle that is constructed

by adding a fifth amount that is non-zero to a baseline n/4-bundle (averaging the effect over the

possible d values), kn are dummies for the number of non-zero amounts within the underlying

baseline n/4-bundle (see Table A.1), and ld are dummies for the value of the non-zero amounts in

the bundle, which range from 51 to 59 cents.

The coefficient estimate on (+0) in Column 1 of Table 2 shows that adding a zero significantly

decreases participants’ willingness to choose a bundle by 6 percentage points. This effect is large.

It is 10% of the likelihood of choosing a baseline bundle, which is 0.58. It is more than half the

magnitude of the 10 percentage point increase observed from adding a non-zero amount to a bundle

(see the coefficient estimate on (+1)), which on average increases the total amount donated in a

main bundle by 33%. In addition, the 6 percentage point average effect reflects a large fraction of

participants responding to the addition of the zero in this biased way. In particular, 50% of our

participants engage in behavior consistent with this bias by at least once choosing an n/4-bundle

but not the n/5-bundle constructed by adding a zero to it.

What can we say about why participants respond to the addition of the zero? First, participants

do not solely interpret five-amount bundles more negatively than four-amount bundles, since adding

a non-zero amount to a bundle increases participants’ willingness to choose it. More is not less.22

Our effect is instead driven by participants responding to the addition of a zero to a bundle. Adding

a zero makes a bundle less attractive, even though it does not change the sum of donations to charity.

Second, our results are not solely about the presence of a zero in a bundle.23 Column 2 of Table

2 examines the impact of adding a zero to a baseline bundle absent any zeros (i.e., to 4/4-bundles)

while Column 3 of Table 2 examines the impact of adding a zero to a baseline bundle with one or

two zeros (i.e., to 2/4-bundles or 3/4-bundles). The negative effect of adding a zero persists in both

cases: adding a zero decreases participants’ willingness to choose a bundle by 4 percentage points

when a zero is not already present and by 7 percentage points when a zero is already present.

Our findings are also robust to different restrictions on the set of participants we consider.

22This is not surprising. The donation from choosing a bundle in our experiment is known with certainty, and soour setting differs from prior literature that has documented a “more is less” phenomenon in environments in whichunderlying uncertainty about the value of a bundle allows agents to update about the bundle’s overall quality whena good is added (Hsee, 1998; List, 2002; Leszczyc, Pracejus and Shen, 2008).

23This result helps us to differentiate from effects related to the presence of a zero, such as those observed inMagen, Dweck and Gross (2008) and Read, Olivola and Hardisty (2016), which show that decision-makers choosingbetween money now and money later can be made more patient by reminding them that taking money now meansreceiving $0 later.

11

Table 2: In the Self/Charity version of Study 1, regression of choosing a main bundle

Sample: full choice varies X is lower boundmain if 4/4 if 2/4 or 3/4 main main

bundles baseline baseline bundles bundles(1) (2) (3) (4) (5)

(+0) -0.06∗∗∗ -0.04∗∗∗ -0.07∗∗∗ -0.08∗∗∗ -0.07∗∗∗

(0.01) (0.02) (0.01) (0.01) (0.01)(+1) 0.11∗∗∗ 0.03∗∗ 0.15∗∗∗ 0.14∗∗∗ 0.12∗∗∗

(0.01) (0.02) (0.02) (0.02) (0.01)

N 7128 2376 4752 5616 6336kn ∗ ld FEs yes yes yes yes yes

∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are clustered at the participant-level and shown inparentheses. The results are from a linear probability model of the likelihood to choose a main bundle in theSelf/Charity version of Study 1, where (+0) is an indicator for an n/5-bundle that is constructed by adding afifth amount that is equal to zero to a baseline n/4-bundle, (+1) is an indicator for an (n+1)/5-bundle that isconstructed by adding a fifth amount that is non-zero to a baseline n/4-bundle, kn ∗ ld FEs include all possibleinteractions of dummies for the number of non-zero amounts within the underlying baseline n/4-bundle (seeTable A.1) and dummies for the value of the non-zero amount d in the bundle to fully control for the sum ofthe amounts in the baseline bundle. Columns 1-3 analyze all participants’ decisions: in all main bundles inColumn 1, involving the baseline 4/4-bundles in Column 2, and involving the baseline 2/4- and 3/4-bundlesin Column 3. Column 4 analyzes all main bundles but among a restricted sample of participants who choosethe bundle at least once and choose their outside option at least once across all 48 decisions. Column 5analyzes all main bundles but among a restricted sample of participants with outside option X set to thelower bound of their indifference range (and thus excludes participants with a zero lower bound).

Column 4 and Column 5 of Table 2 examine whether our effect persists with more restricted samples

of participants. The restricted sample in Column 4 only includes participants who choose the bundle

at least once and choose their outside option at least once.24 Not surprisingly, the impact of adding

a zero is even larger (i.e., it is 8 percentage points) for this restricted sample. The restricted sample

in Column 5 shows that our results are robust to excluding participants for whom we assigned an

outside option of 5 cents because the lower bound of their indifference range was 0 cents.

2.3 Showing it is a motivated cognitive limitation

In the previous subsection, we document a clear behavioral bias. When a zero is added to a

bundle, participants are less likely to choose that bundle, even though the additional zero does not

change the donation made by the bundle. Put differently, participants act as though (n× d) + 0 <

(n × d). A natural inclination for behaviorally minded researchers is to attempt to identify an

unmotivated cognitive limitation that might explain this effect. For example, one might hypothesize

that participants systematically miscalculate the amount in the bundle when a zero is added because

they think in terms of the average amount (which is mechanically lower when there are more zeros)

or because they overweight the last amount in the bundle (which is zero when a zero is added).25

24Across all 48 decisions, 10% of participants never choose the outside option, and 11% of participants alwayschoose the outside option.

25As evidence against this latter hypothesis, we do not observe any differences in behavior due to the location ofzeros within a bundle in the Self/Charity version.

12

A key feature of these explanations is that something about the additional zero makes participants

unable to do the calculation of the sum correctly.

We explore an alternative explanation for this behavioral bias. We posit that participants are

motivated to estimate the sum incorrectly due to self-serving motives. To examine this explanation,

we introduce two additional versions of Study 1 that eliminate self-serving motives to see if agents

still display the same inability to add a zero.

As described above, participants in the Self/Charity version made binary decisions between a

bundle of money for a charity and an outside option of money for themselves and so had a potentially

motivated reason to choose the outside option. In the Charity/Charity version, we eliminate the

self-serving motive by having participants chose between the bundle for charity an an outside option

of 150 cents for the same charity. Similarly, participants in the Self(150)/Self version chose between

the bundle for themselves an an outside option of 150 cents for themselves. In these two versions,

there is no self-serving motive to choose the outside option.

Panel A of Figure 3 reproduces Figure 2 for the Charity/Charity version. As expected, whether

there are 3 or more non-zero amounts in a bundle (and thus the sum of the bundle is more than 150

cents) is the key determinant in whether the bundle is selected. Adding a zero to a bundle does not

influence whether the bundle is selected. Participants’ unresponsiveness to the addition of a zero is

confirmed by the near-zero coefficient estimates on (+0) in Panel A of Table 3. Similarly, Panel B

of Figure 3 reproduces the figure for the Self(150)/Self version. The pattern looks almost identical

to Panel A and participants’ unresponsiveness to the addition of a zero is again confirmed by the

near-zero coefficient estimates on (+0) in Panel B of Table 3.

Figure 3: In the Charity/Charity and Self(150)/Self versions of Study 1, fraction choosing a mainbundle

Panel A: Charity/Charity version

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Panel B: Self(150)/Self version

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Data include all participants’ decisions in all main bundles: in the Charity/Charity version of Study 1 in Panel Aand in the Self(150)/Self version of Study 1 in Panel B.

13

Table 3: In the Charity/Charity and Self(150)/Self versions of Study 1, regression of choosinga main bundle



Panel A: Charity/Charity version(+0) 0.01 0.01 0.01 0.01 0.01

(0.01) (0.01) (0.01) (0.01) (0.01)(+1) 0.28∗∗∗ 0.02 0.42∗∗∗ 0.29∗∗∗ 0.29∗∗∗

(0.01) (0.01) (0.02) (0.01) (0.01)


Panel B: Self(150)/Self version(+0) -0.00 0.01 -0.01 -0.00 -0.01

(0.01) (0.01) (0.01) (0.01) (0.01)(+1) 0.29∗∗∗ 0.01 0.42∗∗∗ 0.29∗∗∗ 0.28∗∗∗

(0.01) (0.01) (0.01) (0.01) (0.01)


∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are clustered at the participant-level and shown inparentheses. The results are from a linear probability model of the likelihood to choose a main bundle inthe Charity/Charity version of Study 1 in Panel A and in the Self(150)/Self version of Study 1 in PanelB, where (+0) is an indicator for an n/5-bundle that is constructed by adding a fifth amount that is equalto zero to a baseline n/4-bundle, (+1) is an indicator for an (n+1)/5-bundle that is constructed by addinga fifth amount that is non-zero to a baseline n/4-bundle, kn ∗ ld FEs include all possible interactions ofdummies for the number of non-zero amounts within the underlying baseline n/4-bundle (see Table A.1)and dummies for the value of the non-zero amount d in the bundle to fully control for the sum of the amountsin the baseline bundle. Columns 1-3 analyze all participants’ decisions: in all main bundles in Column 1,involving the baseline 4/4-bundles in Column 2, and involving the baseline 2/4- and 3/4-bundles in Column3. Column 4 analyzes all main bundles but among a restricted sample of participants who choose the bundleat least once and choose their outside option at least once across all 48 decisions. Column 5 analyzes allmain bundles but among a restricted sample of participants with outside option X set to the lower boundof their indifference range (and thus excludes participants with a zero lower bound).

That participants do not respond to the addition of a zero in the absence of self-serving motives

means that participants are capable of correctly adding a zero. This implies that participants in

the Self/Charity version were not unable to correctly add a zero to a bundle but rather they were

motivated to add it incorrectly.

To statistically confirm that the effect of adding a zero is different when self-serving motives are

present and absent, we compare results from the Self/Charity and Charity/Charity versions. In both

versions, participants face the same bundles going to the Make-A-Wish Foundation national chapter,

and the only difference is the outside option to choosing a bundle, which is X for participants in

Self/Charity and 150 cents for the national chapter in Charity/Charity.26 Since we estimate each

26This comparison between the Self/Charity version and the Charity/Charity version keeps all other features

14

participant’s X value to make them indifferent between X for themselves and 150 cents for national

chapter, the comparison between these versions isolates the impact of removing self-serving motives

without changing stakes.

Appendix Table B.1 directly compares the results from these two versions. The coefficients on

(+0) and (+1) show the effects in Self/Charity version (which are mechanically the same as in Table

2). The coefficient on Charity/Charity and the associated interactions show how these effects differ

in the Charity/Charity version. In particular, the coefficient on Charity/Charity*(+0) shows that

the effect of adding a zero is fully eliminated when self-serving motives are removed.27 That the

systematic inability to add a zero to a bundle is eliminated when self-serving motives are removed

reveals that we have documented a motivated cognitive limitation.

2.4 Attempting to debias the motivated cognitive limitation

We have documented evidence of a motivated cognitive limitation. In this subsection, we explore

whether common de-biasing strategies mitigate the motivated cognitive limitation, drawing from a

vast related literature. We specifically consider experience, inattention, and complexity.

The role of experience

We identify our motivated cognitive limitation as it arises within an individual, so we can ask

whether it is mitigated as a participant gains experience over the 48 decisions in our study. Put

differently, we can ask whether the negative response to adding a zero lessens or disappears with

experience. We answer this question in two ways. First, we exploit that participants either make

all 24 decisions involving four-amount bundles and then make all 24 decisions involving five-amount

bundles or vice versa. Second, we exploit that the order of bundles randomly varies within the set

of 24 four-amount bundles and within the set of 24 five-amount bundles.

Appendix Table B.2 examines whether our results differ as participants gain experience. For

simplicity, only the results related to adding a zero are shown. Columns 1 and 2 split participants

based on whether they faced the four-amount bundles first (and so the zeros were added in the

second half of the study, Column 1) or the five-amount bundles first (so the zeros were added in the

first half of the study, Column 2). Columns 3 and 4 show the results from decisions involving main

bundles that occur “early” in each set (from the first half of each set, decisions 1-12 and 25-36,

of the experimental design constant, and so our results also rule out any potential explanations for the response toadding a zero that are related to the experimental design itself, including experimenter demand effects (see De Quidt,Haushofer and Roth (2017)).

27The difference across versions is also readily apparent at the individual level. The fraction of participants whoengage in our observed biased behavior — at least once choosing an n/4-bundle but not the n/5-bundle constructedfrom it — is only 26% in the Charity/Charity version (statistically significantly less than the 50% observed in theSelf/Charity version, p < 0.01). In addition, participants in the Charity/Charity version are just as likely to dothe opposite: 31% of participants in the Charity/Charity version at least once reject an n/4-bundle but choose then/5-bundle constructed from it (this is similar to the 26% of participants who do this at least once in the Self/Charityversion). Finally, we find evidence that participants in the Self/Charity version are more likely to be inconsistentthan participants in the Charity/Charity version. We call a participant inconsistent if, for one or more pairs of ann/4-bundle and its corresponding n/5-bundle, the participant chooses the outside option in one decision and thebundle in the other. 42% of participants are inconsistent in the Charity/Charity version, while 58% are inconsistentin the Self/Charity version (p < 0.01).

15

Column 3) or “late” in each set (from the second half of each set, decisions 13-24 and 37-48, Column

4). Rather than mitigating our motivated cognitive limitation, experience, if anything, makes the

behavioral bias larger (i.e., the estimated magnitude is larger in Column 4 than in Column 3).

The role of inattention

Even though participants must reveal all of the amounts in the Self/Charity version, they may

fail to carefully attend to the amounts in the bundle, which might drive the motivated cognitive

limitation. To assess whether inattention is driving our motivated cognitive limitation, we ran

the Self/Charity-Choice version in which participants have the option to avoid information about

a bundle. While participants must still view the first amount in a bundle (as it is revealed by

default), they can choose whether to click to reveal each of the remaining amounts in the bundle

before making their choice. If our motivated cognitive limitation is driven by individuals who choose

not pay attention to the information in a bundle, then it will not persist among decisions in which

participants self-select into acquiring all of the information about a bundle before making their

choice. Decisions in which all information is revealed we call “attentive” decisions.

Column 1 of Appendix Table B.3 presents results from all decisions involving the main bun-

dles in the Self/Charity version and the 44% of decisions involving the main bundles in the

Self/Charity-Choice version that we classify as attentive because the participant chooses to re-

veal all the information about the bundle. Our motivated cognitive limitation is present even when

restricting to attentive decisions. The coefficient on (+0) applies to the attentive decisions in the

Self/Charity-Choice version and shows that adding a zero significantly decreases participants’ will-

ingness to choose a bundle by 11 percentage points. The statistically significant positive coefficient

on Self/Charity*(+0) shows that the negative effect of adding a zero is larger among attentive de-

cisions in the Self/Charity-Choice version than across all the decisions in the Self/Charity version.

That is, restricting to the attentive decisions makes the behavioral bias worse. In addition, the

statistically significant negative coefficient on Self/Charity shows that the baseline four-amount

bundles are more likely to be chosen in attentive decisions in the Self/Charity-Choice version than

in the Self/Charity version. Consequently, that our motivated cognitive limitation is prevalent in

attentive decisions directly implies that it persists in decisions where participants are particularly

inclined to choose the bundle.

The role of complexity

While our environment is exceedingly simple — it only requires participants to add a few two-

digit numbers in a manner that can also be achieved with basic multiplication — one could theoret-

ically imagine making it even simpler. In particular, an extreme intervention to debias participants

would be to do the requisite math for them by directly showing them the sum of the amounts in the

bundle. Such an intervention reveals that a bundle generates the same donation to charity whether

or not the zero is added to the bundle.

In the Self/Charity-Sum version, we provide this information on the sum. In particular, in

addition to being required to reveal each amount in a bundle, participants are directly informed of

16

the sum of the amounts in the bundle when making the choice, as shown in Panel B of Figure 4

(Panel A of Figure 4 shows how the corresponding decision appears in the Self/Charity version).

Figure 4: Example question faced by participants in the Self/Charity version versus theSelf/Charity-Sum versions

(a) Self/Charity version (b) Self/Charity-Sum version

The only difference between the two study versions is a sentence stating the sum of the amounts in the bundle oneach decision screen. In these examples, X is 100 cents.

Column 2 of Appendix Table B.3 presents results from decisions involving the main bundles in

the Self/Charity-Sum and Self/Charity versions. The coefficient on (+0) applies to the decisions in

the Self/Charity-Sum version and its statistically significant negative coefficient demonstrates that

our motivated cognitive limitation persists when we further simplify the decision environment by

presenting the sum of donations made by the bundle. Even telling participants how much money is

donated if the bundle is chosen does not completely eliminate the bias. However, the statistically

significant negative coefficient on Self/Charity*(+0) reveals that participants are less biased in the

Self/Charity-Sum version than in the Self/Charity version. Presenting the sum does somewhat

mitigate the motivated cognitive limitation.

That the motivated cognitive limitation persists in the Self/Charity-Sum version, that it is not

eliminated by experience, and that it persists among decisions in which participants choose to be

attentive, all suggest that motivated cognitive limitations may be particularly difficult to overcome.

Next, we explore the robustness of our result and test whether motivated cognitive limitations can

cause another behavioral bias by presenting the design and results of Study 2.

3 Study 2: SalienceIn Study 2, participants respond to the salience of information that is known to them. Choices

suggest that participants dislike when a charity that is known not to receive a donation is made

salient by being included on — rather than excluded from — a list of potential recipient charities.

We show that this effect is primarily driven by a motivated cognitive limitation. When self-serving

motives are present, over 50% of the response to salience is due to a motivated cognitive limitation.

When we remove the self-serving motive to respond to salience, participants are significantly less

likely to engage in the biased behavior. Study 2 both highlights the robustness of motivated

cognitive limitations and demonstrates that a motivated cognitive limitation can be active alongside

17

an unmotivated cognitive limitation that also leads to biased behavior.

In Study 2, we again show that the motivated cognitive limitation survives attempts to debias

participants by giving them experience and that the motivated cognitive limitation is present even

when participants are attentive. Unlike Study 1, however, in Study 2 we find that the motivated

cognitive limitation is not mitigated by further simplifying the decision environment.


A total of 1596 individuals participated in one of eight versions of Study 2.28 As in Study 1,

each participant received $4 for completing the 25-minute study. In addition, one randomly selected

decision for each participant was implemented for bonus payment and resulted in an additional

payment for the participant or a donation to charity.

Participants in Study 2 face the same 48 binary decisions as participants in Study 1, except for

one difference. In Study 2, each amount in a bundle is given to a different Make-A-Wish Foundation

state chapter (rather than the sum of the amounts going to the national chapter).29 Which state

chapters receive which amounts in a bundle is displayed on the decision screen for participants (see

Figure 5 for an example). Participants are informed that any state chapter not included in a bundle

receives no donation, and understanding questions ensure comprehension of this structure.

Figure 5: Example of how a bundle initially appears in Study 2

Clicking on each header reveals the number of cents donated to that state chapter.

The eight versions of Study 2 — Self/Charity, Charity/Charity, Self/Charity-Choice, Charity/Charity-

Choice, Self/Charity-Sum, Charity/Charity-Sum, Self(150)/Charity, and Charity(ARC)/Charity —

28From October 10-13, 2016, we recruited and randomized 1200 participants into one of six study versions in a 2×3design: {Self/, Charity/ } × {Charity, Charity-Choice, Charity-Sum}, and 1196 participants completed the study. OnMarch 13, 2017, we recruited and randomized 400 participants into one of two study versions: Self(150)/Charity andCharity(ARC)/Charity, and all 400 participants completed the study. Overall, 50% of participants are female, themedian age is 33 years old, and the median educational attainment is an Associate’s Degree. There are not significantdifferences across the Self/ version and the Charity/ version for any of {Charity, Charity-Choice, Charity-Sum} orbetween Self(150)/Charity and Charity(ARC)/Charity, demonstrating successful randomization.

29Due to constraints (related to which chapters were IRB approved and to how some states shared chapters),we randomly drew states from a list of 28 states that we matched with corresponding Make-A-Wish Foundationchapters. This list of states was: Alaska, California, Colorado, Connecticut, Florida, Georgia, Illinois, Indiana, Iowa,Kentucky, Louisiana, Maine, Michigan, Missouri, Nebraska, Nevada, New Hampshire, New York, North Carolina,Ohio, Oklahoma, South Carolina, Tennessee, Texas, Utah, Virginia, Washington, and Wisconsin.

18

vary along two dimensions: (1) the recipient and level of the outside option and (2) what informa-

tion about the bundle participants must learn before making each choice.30 The differences across

the eight versions of Study 2 are best visualized in Table 4, and for the versions that do not mirror

those in Study 1, are discussed in more detail as they become relevant to the analyses that follow.

Table 4: Study 2 Versions

Outside Option to... ...Charity ...Self

InformationOptional

Charity/Charity-Choice

(n = 215)

Self /Charity-Choice

(n = 190)

RequiredCharity(ARC)/

Charity(n = 200)

Charity/Charity

(n = 191)

Self /Charity

(n = 203)

Self(150)/Charity

(n = 200)Requiredand SumShown

Charity/Charity-Sum

(n = 202)

Self /Charity-Sum

(n = 195)Bundle to... ...Charity

3.2 Another motivated cognitive limitation

Figure 6 shows the main results from the Self/Charity and Charity/Charity versions of Study 2.

Panel A shows that making salient a charity that does not receive a donation (i.e., including it in

the bundle rather than excluding it from the bundle) decreases participants’ willingness to choose

the bundle in the Self/Charity version, even though the charity is known to receive no donation

regardless of whether or not it is included in the bundle. Panel B shows that the effect is attenuated,

but still present, when self-serving motives are removed in the Charity/Charity version.31

30Unlike Study 1, the recipients of the bundles in Study 2 (i.e., included state chapters) vary across decisions.31That this effect persists absent self-serving motives may relate to how salience influences narrow framing; see,

e.g., Barberis, Huang and Thaler (2006), Rabin and Weizsacker (2009), and Imas (2016). For example, participantsmay only consider how a decision affects state chapters in a bundle — which can be influenced by salience — ratherthan all state chapters. Indeed, Exley and Kessler (2017) suggests narrow framing may establish the domain overwhich inequity preferences are applied.

19

Figure 6: In the Self/Charity version and the Charity/Charity version of Study 2, fraction choosinga main bundle

Panel A: Self/Charity

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Panel B: Charity/Charity

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Data include all participants’ decisions in all main bundles: in the Self/Charity version of Study 2 in Panel A andthe Charity/Charity version of Study 2 in Panel B.

Table 5 presents results from regressions that include data from the Self/Charity version in

Panel A and from the Charity/Charity version in Panel B. The coefficient on (+0) in Column 1 of

Panel A shows that making salient a charity that does not receive a donation significantly decreases

participants’ willingness to choose a bundle by 9 percentage points in the Self/Charity version. This

effect is large. It is 21% of the likelihood of choosing a baseline bundle, which is 0.42. It is the

same size as the increase observed from adding a state chapter that receives a donation (see the

coefficient estimate on (+1)), which increases a bundle’s total donations by 33% on average.

The coefficient on (+0) in Column 1 of Panel B shows that making salient a charity that does

not receive a donation also statistically significantly decreases participants’ willingness to choose

a bundle in the Charity/Charity version. Comparing the magnitudes of the impact of adding

a zero across these two versions, however, reveals the role of the motivated cognitive limitation.

The magnitude of the coefficient on (+0) is reduced by 56% (from 0.09 to 0.04) when self-serving

motives are removed. As shown in Column 1 of Appendix Table B.4, this difference is statistically

significant.32 These results persist when restricting to the type of bundles or restricting the sample

of participants as shown in Columns 2-5 of Table 5 (and of Appendix Table B.4).

32In Appendix Table B.4, the positive coefficient on Charity/Charity also shows a level-effect of choosing thebundle that is itself reflective of self-serving motives. Even though we calibrated the outside option to be weaklyless desirable in the Self/Charity version (by choosing the lower bound of a participant’s X range), participantsare on average more likely to choose the outside option in the Self/Charity than in the Charity/Charity version ofStudy 2. This result may reflect the development of uniform excuses to keep money for oneself, such as participantsoverweighting their dislike of state chapters or overweighting a dislike of the inequity in bundles given that only asubset of state chapters receive a donation.

20

Table 5: In the Self/Charity and Charity/Charity versions of Study 2, regression of choosing amain bundle



Panel A: Self/Charity version(+0) -0.09∗∗∗ -0.10∗∗∗ -0.08∗∗∗ -0.13∗∗∗ -0.10∗∗∗

(0.01) (0.02) (0.01) (0.02) (0.01)(+1) 0.07∗∗∗ 0.01 0.10∗∗∗ 0.10∗∗∗ 0.08∗∗∗

(0.01) (0.01) (0.02) (0.02) (0.01)


Panel B: Charity/Charity version(+0) -0.04∗∗∗ -0.03∗ -0.05∗∗∗ -0.05∗∗∗ -0.04∗∗∗

(0.01) (0.02) (0.01) (0.01) (0.01)(+1) 0.17∗∗∗ 0.00 0.26∗∗∗ 0.19∗∗∗ 0.18∗∗∗

(0.02) (0.02) (0.02) (0.02) (0.02)


∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are clustered at the participant-level and shown inparentheses. The results are from a linear probability model of the likelihood to choose a main bundle in theSelf/Charity version of Study 2 in Panel A and in the Charity/Charity version of Study 2 in Panel B, where(+0) is an indicator for an n/5-bundle that is constructed by adding a fifth amount that is equal to zero to abaseline n/4-bundle, (+1) is an indicator for an (n+1)/5-bundle that is constructed by adding a fifth amountthat is non-zero to a baseline n/4-bundle, kn ∗ ld FEs include all possible interactions of dummies for thenumber of non-zero amounts within the underlying baseline n/4-bundle (see Table A.1) and dummies forthe value of the non-zero amount d in the bundle to fully control for the sum of the amounts in the baselinebundle. Columns 1-3 analyze all participants’ decisions: in all main bundles in Column 1, involving thebaseline 4/4-bundles in Column 2, and involving the baseline 2/4- and 3/4-bundles in Column 3. Column 4analyzes all main bundles but among a restricted sample of participants who choose the bundle at least onceand choose their outside option at least once across all 48 decisions. Column 5 analyzes all main bundles butamong a restricted sample of participants with outside option X set to the lower bound of their indifferencerange (and thus excludes participants with a zero lower bound).

3.3 Attempting to debias the motivated cognitive limitation

We have again documented evidence of a motivated cognitive limitation. In this subsection,

we follow the approach we took for Study 1 and explore the role of experience, inattention, and

complexity in potentially mitigating this motivated cognitive limitation.

The role of experience

Following the analysis we performed for Study 1, we investigate whether experience mitigates

the motivated cognitive limitation identified in Study 2. Appendix Table B.5 shows regressions

exploring the role of experience. Panel A presents the results from the Self/Charity version of

Study 2. Looking across Columns 1 - 4, we see that experience does not mitigate our motivated

21

cognitive limitation.33 Like the motivated cognitive limitation that caused computational errors in

Study 1, the motivated cognitive limitation that causes a response to salience in Study 2 is not

mitigated by experience.

The role of inattention

Following our analysis from Study 1, Column 1 of Panel A of Appendix Table B.6 presents

results from the 30% of decisions involving main bundles in the Self/Charity-Choice version in which

participants fully revealed all the information about the bundle — which we again call “attentive”

decisions — and all decisions involving main bundles in the Self/Charity version. The coefficient on

(+0) shows that restricting to the attentive decisions in the Self/Charity-Choice version generates

a large, 16 percentage point effect of salience. The statistically significant positive coefficient on

Self/Charity*(+0) shows that the effect of salience is indeed larger among these attentive decisions

than among decisions in the Self/Charity version.34 Thus, like the motivated cognitive limitation

in Study 1, the motivated cognitive limitation in Study 2 is not mitigated by attention.

The role of complexity

In Study 1, further simplifying the (already simple) decision environment by summing the do-

nations made to charity in the bundle mitigated the motivated cognitive limitation. We explore

whether the same intervention mitigates the motivated cognitive limitation in Study 2.35 In the

Self/Charity-Sum version of Study 2, participants are directly informed of the total amount donated

to state chapters in the bundle. Column 2 of Panel A of Appendix Table B.6 presents results from

the Self/Charity-Sum and Self/Charity versions. The coefficient on (+0) applies to the decisions in

the Self/Charity-Sum version and shows that our result persists even when the sum of donations

is provided on the decision screen. The near-zero coefficient on Self/Charity*(+0) suggests that

the salience effect we observe is just as big in the Self/Charity-Sum version as in the Self/Charity

version, demonstrating that the motivated cognitive limitation is not mitigated by simplifying the

decision environment in Study 2.36

33Panel B of Appendix Table B.5 presents the results from the Charity/Charity version of Study 2. The extentof the bias caused by the motivated cognitive limitation is best captured by the difference between the coefficienton (+0) in the Self/Charity version and the coefficient on (+0) in the Charity/Charity version. This difference isconsistently 4 to 6 percentage points, and thus the motivated part of the bias is not mitigated. For a discussion ofwhy the bias persists in the Charity/Charity version see footnote 31.

34Column 1 of Panel B of Appendix Table B.6 shows the equivalent results for the Charity/Charity-Choice version,although it is harder to compare coefficients across these study versions because of selection into a decision beingattentive. 50% of decisions are attentive in the Charity/Charity-Choice version, which is statistically significantlygreater than the 30% of decisions that are attentive in the Self/Charity-Choice version, a difference we highlight ingreater depth in Section 5.2. For a discussion of why the bias persists in the Charity/Charity-Choice version seefootnote 31.

35To the extent that participants care about the distribution of donations made to various state chapters, the sumof donations is not a sufficient statistic about the bundle, so this intervention may not simplify the decision as muchas it did in Study 1.

36Column 2 of Panel B of Appendix Table B.6 replicates the results for the Charity/Charity-Sum and Char-ity/Charity versions of Study 2. The extent of the bias caused by the motivated cognitive limitation in the Sumversions is best captured by the difference between the coefficient on (+0) in Panel A and the coefficient on (+0) inPanel B. This difference is 5 percentage points, the same difference as we saw in the main versions of Study 2 without

22

3.4 Robustness of our motivated cognitive limitations

In this subsection, we present results from the final two versions of Study 2 to show robustness

of our results and to further confirm that observed differences between the Self/ and Charity/

versions of each study are due to self-serving motives.

We first present results from the Self(150)/Charity version and show that our results are not

driven by the calibration procedure. We then present results from the Charity(ARC)/Charity

version and show that our results are not driven by the recipient of the bundle and the recipient

of the outside option being more different in the Self/Charity versions than in the Charity/Charity

versions.

The role of the calibration procedure

The calibration procedure described in Section 2.1 ensures that each participant in Study 1 and

Study 2 values their outside options roughly equivalently regardless of whether they are randomized

into a Self/ or Charity/ version of the study.

While the calibration has a number of important advantages, one might harbor concerns that a

feature of the calibration might cause differential behavior across the Self/ and Charity/ versions.

For example, for many participants, the calibration sets the nominal level of the outside option far

from the sum of the donations in the bundle, which might make the amounts harder to compare.37

Note that for a feature of the calibration to drive the differences across the versions it would have

to explain why adding a zero would become harder (in Study 1), and why the salience of a charity

that does not receive a donation would become more relevant (in Study 2), in the presence of the

calibrated outside option.38 That is, for a feature of the calibration to drive the differences across

our versions it could not simply be that the calibration makes decisions “harder” in general in the

Self/Charity versions, but rather that this difficulty interacts with adding a zero or changing the

salience of a state chapter. However, rather than speculate about the existence of such features of

the calibration or debate how unlikely they might be, we ran the Self(150)/Charity version of Study

2 to help assuage any potential concerns. In the Self(150)/Charity version, we still ask participants

the calibration question (to keep procedures identical to the other treatments), but all participants

make all decisions with 150 cents for themselves as the outside option.

In this Self(150)/Charity version, the rate of choosing a baseline n/4-bundle is only 0.24. This is

substantially and statistically significantly lower than the 0.42 rate of choosing a baseline n/4-bundle

in the Self/Charity version of Study 2. This difference suggests the need for the calibration in order

to avoid censoring concerns from participants being too far from indifferent between the outside

option and the bundles. Indeed, while only 25% of participants in the Self/Charity version of Study

2 choose their outside option in all 48 decisions, this rate doubles to 51% in the Self(150)/Charity

the sum shown. For a discussion of why the bias persists in the Charity/Charity-Sum version see footnote 31.37We are grateful to George Loewenstein for raising this potential critique to us and inspiring the final two versions

of the study, which are presented in this subsection.38Note also that we observe our results among participants with various X values, including participants with X

that are close to or exactly 150 cents.

23

version. In spite of the lower rate of selecting bundles mechanically shrinking the effect in percentage

point terms, Appendix Table B.7 shows that the response to making salient a charity that does not

receive a donation is robust to this change in the outside option. Column 1 shows that participants

are 5 percentage points less likely to choose a bundle when we add to it a charity that does not

receive a donation. Given the lower rate of choosing the bundles in this version, the 5 percentage

point reduction is the same percent effect (21%) as the percent effect in the Self/Charity version

of Study 2 (21%). Columns 2 - 5 confirm the robustness of this result. Notably, in Column 4,

when we focus on the restricted sample of participants who choose the bundle in at least one of

the 48 decisions and choose the outside option in at least one decision, we see a similarly sized

magnitude of the framing effect (10 percentage points here as compared to 9 percentage points in

the Self/Charity version of Study 2). Thus, these results indicate that self-serving motives — rather

than something about the calibration — are driving the larger effects we observe the Self/ versions

than in the Charity/ versions.

The role of difficulty in making decisions involving different recipients

In Study 1, in the Charity/Charity version (and also in the Self(150)/Self version), money goes

to the same recipient regardless of whether the outside option or bundle is selected. In Study 2, the

recipients of the outside option and of the bundle are similar in the Charity/Charity versions (i.e.,

the Make-A-Wish Foundation national chapter for the outside option and Make-A-Wish Foundation

state chapters for the bundle). In contrast, the recipient of the outside option and the recipient of

the bundle must be different in the Self/Charity versions of each study so that a self-other trade-

off can be established. That the recipient of the outside option and the bundle are more similar

in the Charity/Charity versions than in the Self/Charity versions may contribute to our results

if comparisons between less similar recipients are somehow harder to make. Although, again, to

explain our pattern of results, this difficulty would have to interact with a zero being added in

Study 1 or with salience being manipulated in Study 2.

As before, rather than speculate about whether the similarity of the recipients might contribute

to differential responses across our versions, we ran the Charity(ARC)/Charity version of Study

2. In this version, the bundle continues to go to Make-A-Wish Foundation state chapters, but the

outside option is now 150 cents for the American Red Cross, a charity that differs from the Make-

A-Wish Foundation in both its mission and the types of people that it serves. If similarity between

the recipients is relevant in generating the effects we see, then this difference should exacerbate the

effect of making salient a charity that does not receive a donation (relative to the Charity/Charity

version of Study 2 in which the recipients are more similar).

As shown in Appendix Table B.7, the magnitude of the behavioral bias does not increase in

the Charity(ARC)/Charity and instead becomes statistically indistinguishable from 0. In fact, the

estimated coefficient estimated on (+0) in Charity(ARC)/Charity is significantly smaller than that

observed in the Charity/Charity version. This evidence directly counters the hypothesis that the

difference between the recipient of the outside option and the recipient of the bundle is a key driver

24

of the size of the bias.39

4 Study 3: Uninformative SignalThe first two studies provided evidence for the existence of motivated cognitive limitations that

induce computational errors (in Study 1) and lead to large responses to salience (in Study 2). The

structure of those studies allowed us to observe participants making repeated decisions and allowed

us to run a variety of additional versions to test debiasing strategies and evaluate the robustness

of our results. However, both of the two previous studies were in the domain of prosocial behavior.

In the third study, we show that motivated cognitive limitations extend to a completely different

setting in which individuals are motivated to hold high beliefs about their ability.


Study 3 included 600 participants randomized into one of four study versions arising from a

2 × 2 design of {Self, Other} × {Two-Robots, Three-Robots}.40 All versions consisted of two

rounds, and each participant received $3 for completing the 20-minute study. In addition, one

randomly selected round for each participant was implemented for bonus payment and determined

the additional payment, if any, for the participant.

In the first round, participants in all versions have 10 minutes to answer a series of questions from

practice tests of the Armed Services Vocational Aptitude Battery (ASVAB). They are presented

with a total of 20 questions, four of which are selected from each of the following five categories:

General Science, Arithmetic Reasoning, Math Knowledge, Mechanical Comprehension, and As-

sembling Objects. Participants are informed that: “In addition to being used by the military to

determine which jobs armed service members are qualified for, performance on the ASVAB is often

used as a measure of cognitive ability by academic researchers.” Participants are told that if the

first round is randomly selected to count for payment, they will receive 10 cents for each question

they answer correctly.

In the second round, participants in all versions learn that they will be ranked against other

study participants based on the number of questions they answered correctly in Round 1 (with ties

being broken by giving better rankings to participants who answered the questions more quickly in

Round 1). Participants also learn that this ranking determines whether they place in the bottom

group (those ranked in the lowest third), the middle group (those ranked in the middle third), or the

top group (those ranked in the top third). Participants are then informed that they will be asked

39Note that any variant of this difference-in-recipient argument that claims the differential effects across our Self/and Charity/ versions arise due to particular difficulties associated with making trade-offs between self and otherswill be isomorphic to our argument that self-serving motives (arising from a desire to keep money for oneself) are atplay. We are happy to call any response particular to a self-other trade-off a result of self-serving motives.

40From January 22-24, 2018, we recruited and randomized 600 participants into one of these four versions. Ourrandomization was weighted such that approximately twice as many participants would be randomized into one ofthe Three-Robots versions, since the third robot is equally likely to provide a “good signal” or a “bad signal.” All600 participants completed the study. Overall, 51% of participants are female, the median age is 33 years old, andthe median educational attainment is an Associate’s Degree. There are no significant differences on these observablecharacteristics across any of the pairs of study versions.

25

to predict whether a specific study participant will place in the bottom group, middle group, or top

group. Participants are told that if the second round is randomly selected to count for payment,

they will receive 50 cents if their prediction is correct.

Participants randomly assigned to the Self versions are asked to make this prediction about

themselves. Participants randomly assigned to the Other versions are asked to make this prediction

about a randomly selected other subject in the study. To help with their prediction, all participants

are provided with messages from two “honest” robots. The “honest” robots each pick a different,

randomly selected question out of the 20 questions asked in Round 1 and report whether the

participant (in the Self versions) or the randomly selected other subject (in the Other versions)

answered it correctly or incorrectly. This is all the information participants randomly assigned to the

Two-Robot versions receive prior to making their predictions. Participants assigned to the Three-

Robot versions also receive a message from a third “random” robot. This third robot randomly

selects a third, different question but is equally likely to say that the answer is correct or incorrect.

Participants must answer an understanding question to ensure that they comprehend that the third

robot is equally likely to tell a lie or the truth. Thus, participants in both the Two-Robot version

and the Three-Robot versions are provided with exactly the same number of informative signals. All

that differs between these two versions is that participants in the Three-Robot versions are provided

with a third, entirely uninformative signal (about themselves in the Self version and about the

randomly selected other subject in the Other version). Obviously, in either case the participant

should ignore the message from the third robot. However, participants in the Self versions may be

motivated to use the message from the third robot to inflate beliefs about how well they scored in

Round 1, while this motive will be absent for participants in the Other versions.

4.2 A third motivated cognitive limitation

Table 6 presents the results from ordinary least squares regressions of the performance prediction,

which equals 1, 2, or 3 if the performance is predicted to fall into the bottom group, the middle

group, or the top group, respectively. All regressions include fixed effects for the messages sent by

the first two robots and fixed effects for the number of questions a participant correctly answered in

Round 1. All regressions include an indicator for whether the third (entirely uninformative) robot

provided a message that the third randomly selected answer was correct (see the coefficients on

uninformative good signal).41

In Column 1, the coefficient on uninformative good signal shows that participants make signif-

icantly more favorable predictions about their own performance when the third robot tells them

that their third randomly selected answer was correct — even though participants know this third

41Columns 4 - 6 include an indicator for whether the third randomly selected answer was incorrect (see thecoefficients on uninformative bad signal). Columns 3 and 6 include an indicator for whether participants are askedto make a prediction about another subject (see the coefficients on other prediction) as well as interactions of thisindicator with uninformative good signal (in Columns 3 and 6) and uninformative bad signal (in Column 6) to capturehow participants update differently to an uninformative signal when asked to make predictions about others insteadof themselves.

26

Table 6: Regression of predicted performance

Excluding uninformative bad Including all decisionssignals when prediction is about... when prediction is about...

...self ...other ...self orother

...self ...other ...self orother

(1) (2) (3) (4) (5) (6)

uninformative good signal 0.24∗∗∗ -0.03 0.24∗∗∗ 0.22∗∗∗ -0.01 0.22∗∗∗

(0.08) (0.07) (0.08) (0.08) (0.07) (0.08)

other prediction 0.12 0.12(0.10) (0.08)

other prediction* -0.27∗∗ -0.23∗∗

uninformative good signal (0.11) (0.11)

uninformative bad signal 0.04 -0.12 0.04(0.09) (0.08) (0.09)

other prediction* -0.16uninformative bad signal (0.12)

N 197 212 409 300 300 600Robot 1 and 2 FEs yes yes yes yes yes yesPerformance FEs yes yes yes yes yes yes

∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are robust and shown in parentheses. The results arefrom an ordinary least squares regression of the prediction, which equals 1 if participants predict the bottomthird, 2 if participants predict the middle third, and 3 if participants predict the top third. uninformativegood signal is an indicator for the third robot’s message being good, uninformative bad signal is an indicatorfor the third robot’s message being bad, other prediction is an indicator for participants making predictionsabout another randomly selected subject rather than themselves. Robot 1 and 2 FEs include fixed effects forall combination of messages sent by robot 1 and 2 as well as interactions of those fixed effects with the otherprediction indicator. Performance FEs include fixed effects for each potential performance level as well asinteractions of those fixed effects with the other prediction indicator. Columns 1 and 4 include participantsasked to make predictions about their own performance, Columns 2 and 5 include participants asked tomake predictions about another randomly selected subject’s performance, and Columns 3 and 6 include allparticipants.

robot is equally likely to tell a lie or the truth. The estimated average increase of 0.24 corresponds

to a 12% increase in the predicted performance (i.e., the average predicted performance in the Self-

Two-Robot version is 1.99, almost exactly a prediction of being in the middle group). By contrast,

the coefficient on uninformative good signal in Column 2 shows that participants making a predic-

tion about another subject do not update off of the uninformative “good” message sent from the

third robot. The coefficient on other prediction*uninformative good signal in Column 3 confirms

that this differential response when participants make predictions about themselves versus others

is statistically significant.

Consequently, our results document another motivated cognitive limitation. Participants update

based on an entirely irrelevant signal, but only when it allows them to formulate better beliefs about

27

their own performance. Columns 4 - 6 show that our results are robust to also including in the

analysis the participants who are provided with an uninformative bad signal. Results show that

participants do not respond significantly to an uninformative “bad” signal about themselves or

others.42

5 Discussion SectionAcross our three studies, participants are provided with relevant information (positive donations

to charity in Study 1 and 2 and informative signals about performance in Study 3) and irrelevant

information (zero-cent donations to charity in Study 1 and 2 and irrelevant signals about perfor-

mance in Study 3). Rather than only responding to the relevant information, participants display

behavioral biases by systematically responding to the irrelevant information. In addition, the ex-

tent to which participants display such biases depends on whether there are self-serving motives to

do so. When self-serving motives are present, participants engage in biased behavior that results

in keeping money for themselves (in Study 1 and Study 2) and reporting that they are of higher

ability (in Study 3). Absent self-serving motives, participants’ decisions are not at all reflective of

a behavioral bias (in Study 1 and Study 3) or substantially less reflective of a behavioral bias (in

Study 2). Thus, in all three settings we find evidence of a motivated cognitive limitation causing

or exacerbating a behavioral bias. Table 7 summarizes our findings across all versions of our three

studies.

In this section, we interpret our findings and present new results to make two additional con-

tributions. In Section 5.1, we highlight the likely role of signaling in generating the behavior we

observe and provide results that may be of particular interest to those aiming to model motivated

cognitive limitations. In Section 5.2, we show how our findings relate to the literature on information

avoidance and provide new results for that literature.

42The extent to which participants make accurate predictions is not significantly different across our study versions.In the Self versions, participants make accurate predictions 41% of the time if they only view messages from tworobots, 42% of the time if they view a “good” message from the third robot, and 51% of the time if they view a“bad” message from the third robot. In the Other versions, participants make accurate predictions 42% of the timeif they only view messages from two robots, 35% of the time if they view a “good” message from the third robot,and 44% of the time if they view a “bad” message from the third robot.

28

Table 7: The impact of irrelevant information in each study version

Impact of irrelevant informationN Self-serving

motives?BaselineAverage

Change inAverage

PercentChange

Study 1Self/Charity 198 Yes 0.58 -0.06∗∗∗ -10%Self/Charity-Choice 195 Yes 0.52 -0.04∗∗∗ -8%Self/Charity-Sum 206 Yes 0.54 -0.03∗∗∗ -6%Charity/Charity 199 No 0.62 0.01 2%Self(150)/Self 202 No 0.63 -0.00 0%

Study 2Self/Charity 203 Yes 0.42 -0.09∗∗∗ -21%Self/Charity-Choice 190 Yes 0.38 -0.06∗∗∗ -16%Self/Charity-Sum 195 Yes 0.42 -0.08∗∗∗ -19%Self(150)/Charity 200 Yes 0.24 -0.05∗∗∗ -21%Charity/Charity 191 No 0.58 -0.04∗∗∗ -7%Charity/Charity-Choice 215 No 0.50 -0.01 -2%Charity/Charity-Sum 202 No 0.56 -0.03∗∗∗ -5%Charity(ARC)/Charity 200 No 0.57 -0.01 -2%

Study 3Self 197 Yes 1.99 0.24∗∗ 12%Other 212 No 2.02 -0.03 -1%

p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. For Study 1 and Study 2, the baseline average is the fraction ofbaseline n/4-bundles chosen, and the change in average is the estimated coefficient on (+0) from theregression specification detailed for Column 1 of Table 2, run separately for each study version. ForStudy 3, the baseline average is the average predicted performance in the Two-Robot version, and thechange in average is the estimated coefficient on uninformative good signal in Column 1 (for the Selfversion) and in Column 2 (for the Other version) of Table 6.

5.1 The role of signaling and results on non-monotonic behavior

Participants’ use of irrelevant information to avoid choosing the bundle in the first two studies

and to rate themselves as more able in the third study suggests that participants want to keep

money for themselves and want to rate themselves as high ability. If participants are motivated to

behave in this way, why should participants display the behavioral bias in response to the irrelevant

information? Why not just keep the money for themselves in the first two studies, regardless of

the information about the bundle? Why not just report the favorable beliefs in the third study,

regardless of the information provided?

One potential answer relates to signaling — for a related review, see Benabou and Tirole (2016).43

Participants may desire to keep money for themselves without signaling to themselves that they are

selfish.44 Participants may desire to rank their own ability highly without signaling to themselves

43See also Benabou and Tirole (2002), Koszegi (2006), Benabou and Tirole (2006), Grossman (2015), Grossmanand van der Weele (2017), and Foerster and van der Weele (2018). Related to the idea that individuals need some“justification” to engage in self-serving behavior, see a discussion of the vast literature in psychology in Kunda (1990).See also a discussion of a desire to manage self-image in Cialdini and Trost (1998).

44Participants may additionally, or alternatively, be engaged in a form of social signaling to the experimenter. The

29

that they are inflating predictions about their own ability. Thus, participants may exploit irrelevant

information that, if not processed as irrelevant, could justify choosing money for themselves or

holding more optimistic beliefs about their own ability.45 For example, when participants choose

money for themselves over a bundle with an additional zero, they may view themselves as less

selfish by thinking of the additional zero as indicative of the bundle generating less in charitable

donations. Similarly, when provided with an irrelevant “good” signal about their performance,

participants may form more optimistic beliefs about their own ability by thinking the good signal

is somehow relevant.

The way in which participants use irrelevant information in a self-serving manner is arguably

more subtle in our studies than in the prior literature. Self-serving behavior in our studies requires a

distorted response to information that is not avoided, that is not conflicting or ambiguous, and that

participants are (largely) capably of processing accurately. Such a distorted self-serving response

to the irrelevant information in our settings may come at a cost of failing to process relevant

information correctly, suggesting a trade-off in how agents respond to irrelevant information and

respond to relevant information.

Additional evidence from our first study suggests that participants do face such a trade-off.46

To assess this trade-off, we construct a measure of whether participants fail to process relevant

information by looking at whether their choices display a particular form of non-monotonic behav-

ior with regard to the sum of donations made by a bundle (focusing on a set of 16 decisions with

bundles that have similar sums).47 We additionally construct a measure of whether participants

process irrelevant information by looking at whether they are less likely to choose a bundle when

a zero is added to it (focusing on a distinct set of 16 bundles from among our main bundles).48

In the Self/Charity version of Study 1, 37% of participants are non-monotonic and 38% of par-

ticipants respond to irrelevant information by our measures. The correlation between these two is

discussion in this section could apply to both types of signaling.45That participants are not fully aware of the irrelevance of the irrelevant information is likely important for such

a signaling channel to arise. That being said, such a signaling channel can still arise if participants are, to somedegree, choosing to be “ignorant” by not carefully processing the information (e.g., see Grossman and van der Weele(2017)). Moreover, self-serving but unconscious motives can also influence how we make decisions (e.g., see Simlerand Hanson (2018)).

46This evidence includes choices of some non-main bundles from Study 1 that were not analyzed in the previoussections. We use decisions from four bundles that are denoted as 4L/4-bundles because all four amounts are non-zero, but each amount is smaller than the amounts in the main bundles. The non-zero amounts in these bundles arerandomly selected to be dL cents, where dL ∈ {30, 31, 32, 33, 34, 35, 36, 37, 38} (for more details about these bundles,see Appendix Table A.2). These bundles were constructed so that the sum of each bundle was close to, but lowerthan, the sum of each 3/4-bundle and each 3/5-bundle (i.e., 3× d > 4× dL for all d and dL).

47In particular, we ask whether each participant chooses one or more 4L/4-bundles and fails to choose all of the3/4-bundles and 3/5-bundles. While we could construct other measures of non-monotonic behavior, even among thisset of 16 bundles, this measure seems particularly natural since it utilizes bundles designed to be close in sum to ourmain bundles but with significantly lower donation amounts.

48In particular, we look at whether participants respond negatively to a zero by at least once choosing a baseline2/4-bundle or 4/4-bundle but not the corresponding 2/5-bundle or 4/5-bundle constructed from it. Note that weexclude the 3/4-bundles and 3/5-bundles from this measure since we use those bundles to construct the measure ofnon-monotonicity, and we want to avoid introducing a mechanical relationship between the measures.

30

statistically significant (ρ = 0.37, p < 0.01), suggesting that responding to irrelevant information

(i.e., responding to the zero) is associated with failing to respond perfectly to relevant information

(i.e., failing to be monotonic with respect to donation amounts). In addition, along with being less

likely to display biased behavior in the Charity/Charity and Self(150)/Self versions, participants

are also much less likely to be classified as non-monotonic in the Charity/Charity version (only 25%

of participants, p < 0.01 when compared to Self/Charity) and in the Self(150)/Self version (only

18% of participants, p < 0.01 when compared to Self/Charity).

Our main results suggest that agents may be engaged in self-signaling in their use of motivated

cognitive limitations, and our additional analysis indicates that agents experience a trade-off in

taking advantage of irrelevant information and carefully processing relevant information. These

findings may serve useful in modeling behavioral biases driven by motivated cognitive limitations,

which we view as a fruitful avenue for future work.

5.2 Results on information avoidance

Our results in the prior subsection suggest that individuals maintain a positive self-image by

failing to fully process the information presented to them in our studies. This insight is related

to mechanisms at play in the literature on information avoidance, which shows that individuals

often avoid information in order to maintain “moral wiggle room” about the extent to which a

decision is selfish (Dana, Weber and Kuang, 2007).49 However, the motivated cognitive limitations

we document are not driven by individuals who would simply avoid information if given an oppor-

tunity. As noted in Sections 2.4 and 3.3, the motivated cognitive limitations documented in our

studies arise when individuals cannot avoid information (in the Self/Charity versions of Study 1

and Study 2), and even when individuals can avoid information but choose to fully reveal it (in

the Self/Charity-Choice versions of Study 1 and Study 2). Despite our main results not being

driven by motivated information avoidance, four of our versions in Study 2 — the Self/Charity,

Charity/Charity, Self/Charity-Choice, and Charity/Charity-Choice versions — generate rich data

that allow us to speak directly to the related literature on information avoidance.

Before describing our new contributions, we show that we can replicate a common finding in the

information avoidance literature: participants who avoid information make more selfish decisions.

First, we note that looking across all 48 bundles in the Self/Charity-Choice version, participants

avoid revealing all information on a bundle in 70% of decisions. Consistent with the previous

literature, when we look at settings where information is likely to encourage giving (i.e., decisions

where the sum of donations in the bundle is greater than 150 cents) participants who can avoid

information are significantly less likely to choose the bundle than participants who are forced to

fully reveal information in the Self/Charity version (these bundles are chosen 49% of the time in

49For more work related to motivated information avoidance, see Larson and Capra (2009); Nyborg (2011); Conradsand Irlenbusch (2013); Bartling, Engl and Weber (2014); Feiler (2014); Grossman (2014); van der Weele et al.(2014); Grossman and van der Weele (2017); Bartos et al. (2016); Exley and Petrie (2018); Golman, Hagmann andLoewenstein (2017).

31

the Self/Charity-Choice version vs. 41% in the Self/Charity version, p < 0.05 with standard errors

clustered at the participant level). This finding is similar to “moral wiggle room” studies, where

the ability to avoid information leads to less generous behavior (Dana, Weber and Kuang, 2007).

In addition to replicating this common finding, our results allow us to make two additional

contributions to the literature on information avoidance. First, unlike most of the prior literature,

our experiments additionally include decisions in which information is likely to discourage giving

(i.e., decisions where the sum of donations in the bundle is less than 150 cents).50 In these settings,

and perhaps not surprisingly given the nature of the information, we no longer find that the ability

to avoid information results in reduced giving. Participants who can avoid the information in

the Self/Charity-Choice version are, if anything, more likely to choose bundles than participants

who are forced to fully reveal information in the Self/Charity version (these bundles are chosen

23% of the time in Self/Charity-Choice version vs. 28% in the Self/Charity version, p = 0.16

with standard errors clustered at the participant level). This finding suggests that in settings where

there is uncertainty about whether revealing information is going to encourage or discourage giving,

information avoidance may backfire as a strategy to behave selfishly.51

Second, our results provide the first test, to our knowledge, of whether individuals avoid in-

formation more when they have a self-serving motive than when they do not. This test is worth

performing because there may be other, unmotivated reasons to avoid information in decision en-

vironments, including the implicit costs of collecting and processing information. We observe sig-

nificant unmotivated information avoidance: participants avoid fully revealing information about

the bundles in 50% of decisions in the Charity/Charity-Choice version when self-serving motives

are not relevant. However, we also observe evidence of motivated information avoidance. The rate

at which participants avoid fully revealing information about the bundles statistically significantly

increases to 70% in the Self/Charity-Choice version when self-serving motives are relevant.52 Thus,

our estimates suggest that over 71% (i.e., 0.50/0.70) of the information avoidance we observe in

50In Dana, Weber and Kuang (2007), revealing information either eliminates the possibility to engage in costlyprosocial behavior (i.e., when subjects find themselves in an “aligned” state where the option that is most beneficialto them is also most beneficial to another subject) or encourages costly prosocial behavior (i.e., when subjects findthemselves in an “unaligned” state and thus learn that sacrificing some of their own payoff would be very beneficialto another subject). In our study, while revealing information may also encourage costly prosocial behavior (e.g.,if participants learn that sacrificing the outside option that benefits themselves would be very beneficial to charity,resulting in a large donation of more than 150 cents), it may also discourage costly prosocial behavior (e.g., ifparticipants learn that sacrificing the outside option would be only somewhat beneficial to charity, resulting in asmall donation of less than 150 cents).

51Because we have only slightly more bundles with sums greater than 150 cents than bundles with sums less than150 cents, the overall rate at which participants choose bundles is approximately the same in the Self/Charity-Choiceversion (when they choose 35% of all bundles) as in the Self/Charity version (when they choose 38% of all bundles),a difference that is not statistically significant.

52When clustering standard errors at the participant level, the likelihood that participants choose a bundle (ora main bundle) is 20 percentage points lower in the Self/Charity-Choice version than in Charity/Charity-Choiceversion in Study 2 (p < 0.01). As discussed in Section 2.1, the individual-level calibration allows us to compareinformation avoidance across decision environments that differ in whether self-serving motives are present but inwhich participants face similar stakes.

32

the Self/Charity-Choice version is unmotivated in nature and only 29% (i.e., 0.20/0.70) is due to

self-serving motives. Given the high rates of unmotivated information avoidance, future work on

motivated information avoidance may seek to net out possible unmotivated information avoidance

by considering settings where self-serving motives are and are not relevant.

6 ConclusionBehavioral biases observed in practice have historically been attributed to unmotivated cog-

nitive limitations, and a robust empirical and theoretical literature in behavioral economics has

been devoted to exploring and explaining these biases. In this paper, we document that behav-

ioral biases can also be attributed to motivated cognitive limitations, which look like limitations of

cognitive ability but are motivated in nature. We show that these motivated cognitive limitations

can cause behavioral biases on their own or in conjunction with unmotivated cognitive limitations.

We additionally show that traditional interventions aimed at debiasing agents — making environ-

ments simpler, giving agents experience, making agents attentive — may not succeed in mitigating

motivated cognitive limitations.

Our experiments investigate motivated cognitive limitations arising from a desire to make selfish

decisions and from a desire to hold more favorable beliefs about one’s own ability. In addition, many

other self-serving motives arise regularly in daily life. Agents may be motivated to avoid costly

investments that would benefit their future selves — they may desire to avoid saving, to avoid

exercising, to avoid investing in their education, and to indulge in temptation goods such as junk

food.53 Agents may also desire to view news, events, and other information in a manner that aligns

with their own worldview or allows them to hold more favorable beliefs about themselves or their

in-group. Biases arising in these domains may be driven (at least in part) by motivated cognitive

limitations. Our results suggest significant value in exploring an observed bias to determine whether

a motivated cognitive limitation is contributing to it or causing it. How to debias agents and whether

doing so will be good for them may depend on whether the bias is driven by a motivated cognitive

limitation, an unmotivated cognitive limitation, or both.

53Handel and Schwartzstein (2018) document biases — arising in health care, financial decision making, and inother domains — that often persist in the presence of relevant available information. Our results suggest anotherreason why these biases may persist: they may be driven by agents with motivated cognitive limitations.

33

ReferencesAndreoni, James, and B. Douglas Bernheim. 2009. “Social Image and the 50–50 Norm: A

Theoretical and Experimental Analysis of Audience Effects.” Econometrica, 77(5): 1607–1636.

Andreoni, James, Justin M. Rao, and Hannah Trachtman. 2016. “Avoiding the ask: A

field experiment on altruism, empathy, and charitable giving.” Journal of Political Economy.

Ariely, Dan, George Loewenstein, and Drazen Prelec. 2003. “?Coherent arbitrariness?: Sta-

ble demand curves without stable preferences.” The Quarterly Journal of Economics, 118(1): 73–

106.

Babcock, Linda, George Loewenstein, Samuel Issacharoff, and Colin Camerer. 1995.

“Biased Judgments of Fairness in Bargaining.” The American Economic Review, 85(5): 1337–

1343.

Barberis, Nicholas, Ming Huang, and Richard H Thaler. 2006. “Individual preferences,

monetary gambles, and stock market participation: A case for narrow framing.” The American

economic review, 96(4): 1069–1090.

Bartling, Bjorn, and Urs Fischbacher. 2012. “Shifting the Blame: On Delegation and Respon-

sibility.” Review of Economic Studies, 79(1): 67–87.

Bartling, Bjorn, Florian Engl, and Roberto A. Weber. 2014. “Does willful ignorance deflect

punishment? – An experimental study.” European Economic Review, 70(0): 512 – 524.

Bartos, Vojtech, Michal Bauer, Julie Chytilova, and Filip Matejka. 2016. “Attention Dis-

crimination: Theory and Field Experiments with Monitoring Information Acquisition.” American

Economic Review, 106(6): 1437–1475.

Bazerman, Max H, George F Loewenstein, and Sally Blount White. 1992. “Reversals of

preference in allocation decisions: Judging an alternative versus choosing among alternatives.”

Administrative Science Quarterly, 37(2): 220–240.

Benabou, Roland, and Jean Tirole. 2002. “Self-confidence and personal motivation.” The

Quarterly Journal of Economics, 117(3): 871–915.

Benabou, Roland, and Jean Tirole. 2006. “Incentives and Prosocial Behavior.” American


Benabou, Roland, and Jean Tirole. 2016. “Mindful Economics: The Production, Consumption,

and Value of Beliefs.” Journal of Economic Perspectives, 30(3): 141–164.

Bohnet, Iris, Alexandra van Geen, and Max Bazerman. 2016. “When Performance Trumps

Gender Bias: Joint Versus Separate Evaluation.” Management Science, 62(5): 1225–1234.

34

Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer. 2012. “Salience theory of choice

under risk.” The Quarterly Journal of Economics, 127(3): 1243–1285.

Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer. 2013. “Salience and consumer

choice.” Journal of Political Economy, 121(5): 803–843.

Broberg, Tomas, Tore Ellingsen, and Magnus Johannesson. 2007. “Is generosity involun-

tary?” Economics Letters, 94(1): 32–37.

Brocas, Isabelle, Juan D Carrillo, Stephanie W Wang, and Colin F Camerer. 2014.

“Imperfect choice or imperfect attention? Understanding strategic thinking in private information

games.” Review of Economic Studies, 81(3): 944–970.

Bushong, Benjamin, Matthew Rabin, and Josh Schwartzstein. 2017. “A Model of Relative

Thinking.” Working Paper.

Busse, Meghan R, Devin G Pope, Jaren C Pope, and Jorge Silva-Risso. 2015. “The psy-

chological effect of weather on car purchases.” The Quarterly Journal of Economics, 130(1): 371–

414.

Caplin, Andrew, Mark Dean, and Daniel Martin. 2011. “Search and satisficing.” American


Caplin, Andrew, Mark Dean, and John Leahy. 2016. “Rational Inattention, Optimal consid-

eration sets and stochastic choice.” Working paper.

Chetty, Raj. 2015. “Behavioral economics and public policy: A pragmatic perspective.” American


Chetty, Raj, Adam Looney, and Kory Kroft. 2009. “Salience and taxation: Theory and

evidence.” The American Economic Review, 99(4): 1145–1177.

Cialdini, Robert. 1984. Influence, the Psychology of Persuasion. New York:Harper Collins.

Cialdini, Robert B, and Melanie R Trost. 1998. “Social influence: Social norms, conformity

and compliance.” In Handbook of Social Psychoogy. Vol. 2. 4th ed., , ed. G Lindzey DT Gilbert,

ST Fiske, 151–192. Boston:McGraw-Hill.

Coffman, Lucas C. 2011. “Intermediation Reduces Punishment (and Reward).” American Eco-

nomic Journal: Microeconomics, 3(4): 1–30.

Conlisk, John. 1996. “Why bounded rationality?” Journal of economic literature, 34(2): 669–700.

Conrads, Julian, and Bernd Irlenbusch. 2013. “Strategic ignorance in ultimatum bargaining.”

Journal of Economic Behavior and Organization, 92(C): 104–115.

35

Cunningham, Tom. 2013. “Comparisons and Choice.” Working Paper.

Dana, Jason, Roberto A. Weber, and Jason Xi Kuang. 2007. “Exploiting moral wiggle room:

experiments demonstrating an illusory preference for fairness.” Economic Theory, 33: 67–80.

Danilov, Anastasia, and Silvia Saccardo. 2016. “Disguised Discrimination.” Working Paper.

Dean, Mark, Ozgur Kıbrıs, and Yusufcan Masatlioglu. 2017. “Limited attention and status

quo bias.” Journal of Economic Theory, 169: 93–127.

DellaVigna, Stefano. 2009. “Psychology and economics: Evidence from the field.” Journal of

Economic literature, 47(2): 315–72.

DellaVigna, Stefano, John List, and Ulrike Malmendier. 2012. “Testing for Altruism and

Social Pressure in Charitable Giving.” Quarterly Journal of Economics, 127(1): 1–56.

De Quidt, Jonathan, Johannes Haushofer, and Christopher Roth. 2017. “Measuring and

Bounding Experimenter Demand.” National Bureau of Economic Research.

Di Tella, Rafael, Ricardo Perez-Truglia, Andres Babino, and Mariano Sigman. 2015.

“Conveniently Upset: Avoiding Altruism by Distorting Beliefs about Others’ Altruism.” Ameri-

can Economic Review, 105(11): 3416–42.

Eil, David, and Justin M. Rao. 2011. “The Good News-Bad News Effect: Asymmetric Pro-

cessing of Objective Information about Yourself.” American Economic Journal: Microeconomics,

3(2): 114–138.

Engel, Christoph. 2011. “Dictator games: a meta study.” Experimental Economics, 14(4): 583–

610.

Enke, Benjamin. 2017. “What You See Is All There Is.” Working Paper.

Enke, Benjamin, and Florian Zimmermann. Forthcoming. “Correlation Neglect in Belief

Formation.” Review of Economic Studies.

Exley, Christine L. 2015. “Excusing Selfishness in Charitable Giving: The Role of Risk.” Review

of Economic Studies, 83(2): 587–628.

Exley, Christine L. 2018. “Using Charity Performance Metrics as an Excuse Not To Give.”

Working Paper.

Exley, Christine L., and Judd B. Kessler. 2017. “Inequity aversion and narrow bracketing:

Why money cannot buy time.” Working paper.

36

Exley, Christine L., and Ragan Petrie. 2018. “The Impact of a Surprise Donation Ask.”

Journal of Public Economics, 158(152-167).

Falk, Armin, and Florian Zimmermann. 2016. “Consistency as a Signal of Skills.” Management

Science, 63(7): 2197–2210.

Falk, Armin, and Florian Zimmermann. Forthcoming. “Information Processing and Commit-

ment.” The Economic Journal.

Falk, Armin, and Nora Szech. 2013. “Organizations, Diffused Pivotality and Immoral Out-

comes.” IZA Discussion Paper 7442.

Feiler, Lauren. 2014. “Testing Models of Information Avoidance with Binary Choice Dictator

Games.” Journal of Economic Psychology.

Finkelstein, Amy. 2009. “E-ztax: Tax salience and tax rates.” The Quarterly Journal of Eco-

nomics, 124(3): 969–1010.

Foerster, Manuel, and Joel J van der Weele. 2018. “Denial and Alarmism in Collective Action

Problems.” Working Paper.

Gabaix, Xavier. 2014. “A sparsity-based model of bounded rationality.” The Quarterly Journal

of Economics, 129(4): 1661–1710.

Gabaix, Xavier. 2017. “Behavioral Inattention.” NBER Working Paper No. 24096.

Gino, Francesca, and Dan Ariely. 2012. “The dark side of creativity: original thinkers can be

more dishonest.” Journal of personality and social psychology, 102(3): 445.

Gino, Francesca, Michael I. Norton, and Roberto A. Weber. 2016. “Motivated Bayesians:

Feeling Moral While Acting Egoistically.” Journal of Economic Perspectives, 30(3): 189–212.

Gino, Francesca, Shahar Ayal, and Dan Ariely. 2013. “Self-serving altruism? The lure of

unethical actions that benefit others.” Journal of economic behavior & organization, 93(285-292).

Gneezy, Uri, Silvia Saccardo, and Roel van Veldhuizen. Forthcoming. “Bribery: Behavioral

Drivers of Distorted Decisions.” Journal of the European Economic Association.

Gneezy, Uri, Silvia Saccardo, Marta Serra-Garcia, and Roel van Veldhuizen. 2017.

“Bribing the Self.” Working paper.

Golman, Russell, David Hagmann, and George Loewenstein. 2017. “Information Avoid-

ance.” Journal of Economic Literature, 55(1): 1–40.

37

Grossman, Zachary. 2014. “Strategic ignorance and the robustness of social preferences.” Man-

agement Science, 60(11): 2659–2665.

Grossman, Zachary. 2015. “Self-signaling and social-signaling in giving.” Journal of Economic

Behavior & Organization, 117(0): 26–39.

Grossman, Zachary, and Joel J van der Weele. 2017. “Self-image and willful ignorance in

social decisions.” Journal of the European Economic Association, 15(1).

Haggag, Kareem, and Devin G. Pope. Forthcoming. “Attribution Bias in Consumer Choice.”

Review of Economic Studies.

Haisley, Emily C., and Roberto A. Weber. 2010. “Self-serving interpretations of ambiguity

in other-regarding behavior.” Games and Economic Behavior, 68: 614–625.

Hamman, John R., George Loewenstein, and Roberto A. Weber. 2010. “Self-Interest

through Delegation: An Additional Rationale for the Principal-Agent Relationship.” American


Handel, Benjamin, and Joshua Schwartzstein. 2018. “Frictions or Mental Gaps: What’s Be-

hind the Information We (Don’t) Use and When Do We Care?” Journal of Economic Perspectives,

32(1): 155–178.

Hanna, Rema, Sendhil Mullainathan, and Joshua Schwartzstein. 2014. “Learning through

noticing: Theory and evidence from a field experiment.” The Quarterly Journal of Economics,

129(3): 1311–1353.

Hsee, Christopher K. 1996. “Elastic justification: How unjustifiable factors influence judgments.”

Organizational Behavior and Human Decision Processes, , (1).

Hsee, Christopher K. 1998. “Less is better: When low-value options are valued more highly than

high-value options.” Journal of Behavioral Decision Making, 11(107-121).

Imas, Alex. 2016. “The realization effect: Risk-taking after realized versus paper losses.” The

American Economic Review, 106(8): 2086–2109.

Jacobsen, Karin J, Kari H Eika, Leif Helland, Jo Thori Lind, and Karine Nyborg.

2011. “Are nurses more altruistic than real estate brokers?” Journal of Economic Psychology,

32(5): 818–831.

Kahneman, Daniel. 2011. Thinking, fast and slow. Macmillan.

Kamdar, Amee, Steven D. Levitt, John A. List, Brian Mullaney, and Chad Syverson.

2015. “Once and Done: Leveraging Behavioral Economics to Increase Charitable Contributions.”

38

Konow, James. 2000. “Fair Shares: Accountability and Cognitive Dissonance in Allocation Deci-

sions.” The American Economic Review, 90(4): 1072–1092.

Koszegi, Botond. 2006. “Ego utility, overconfidence, and task choice.” Journal of the European

Economic Association, 4(4): 673–707.

Koszegi, Botond, and Adam Szeidl. 2013. “A model of focusing in economic choice.” Quarterly

Journal of Economics, 128(1): 53–104.

Kunda, Ziva. 1990. “The Case for Motivated Reasoning.” Psychological Bulletin, 108(3): 480–498.

Larson, Tara, and Monica C. Capra. 2009. “Exploiting moral wiggle room: Illusory preference

for fairness? A comment.” Judgment and Decision Making, 4(6): 467–474.

Lazear, Edward P., Ulrike Malmendier, and Roberto A. Weber. 2012. “Sorting in experi-

ments with application to social preferences.” American Economic Journal: Applied Economics,

4(1): 136–163.

Leszczyc, Peter TL Popkowski, John W Pracejus, and Yingtao Shen. 2008. “Why more

can be less: An inference-based explanation for hyper-subadditivity in bundle valuation.” Orga-

nizational Behavior and Human Decision Processes, 105(2): 233–246.

Linardi, Sera, and Margaret A. McConnell. 2011. “No excuses for good behavior: Volunteer-

ing and the social environment.” Journal of Public Economics, 95: 445–454.

Lin, Stephanie C, Julian J Zlatev, and Dale T Miller. 2016. “Moral traps: When self-serving

attributions backfire in prosocial behavior.” Journal of Experimental Social Psychology.

Lin, Stephanie C., Rebecca L. Schaumberg, and Taly Reich. 2016. “Sidestepping the rock

and the hard place: The private avoidance of prosocial requests.” Journal of Experimental Social

Psychology, 35–40.

List, John A. 2002. “Preference reversals of a different kind: The ”More is less” Phenomenon.”

American Economic Review, 92(5): 1636–1643.

List, John A. 2003. “Does Market Experience Eliminate Market Anomalies?” Quarterly Journal

of Economics, 118(1): 41–71.

Loewenstein, George, Ted O’Donoghue, and Matthew Rabin. 2003. “Projection bias in

predicting future utility.” The Quarterly Journal of Economics, 118(4): 1209–1248.

Madrian, Brigitte C. 2014. “Applying insights from behavioral economics to policy design.”

Annual Review of Economics, 6(1): 663–688.

39

Magen, Eran, Carol S Dweck, and James J Gross. 2008. “The hidden-zero effect repre-

senting a single choice as an extended sequence reduces impulsive choice.” Psychological Science,

‘9(7): 648–649.

Mobius, Markus M., Muriel Niederle, Paul Niehaus, and Tanya S. Rosenblat. 2014.

“Managing Self-Confidence: Theory and Experimental Evidence.” Working Paper.

Nyborg, Karine. 2011. “I don’t want to hear about it: Rational ignorance among duty-oriented

consumers.” Journal of Economic Behavior & Organization, 79(3): 263–274.

Oberholzer-Gee, Felix, and Reiner Eichenberger. 2008. “Fairness in Extended Dictator Game

Experiments.” The B.E. Journal of Economic Analysis & Policy, 8(1).

Pittarello, Andrea, Margarita Leib, Tom Gordon-Hecker, and Shaul Shalvi. 2015. “Jus-

tifications shape ethical blind spots.” Psychological Science.

Rabin, Matthew. 1998. “Psychology and economics.” Journal of Economic Literature, 36(1): 11–

46.

Rabin, Matthew, and Georg Weizsacker. 2009. “Narrow bracketing and dominated choices.”

The American Economic Review, 1508–1543.

Read, Daniel, Christopher Y Olivola, and David J Hardisty. 2016. “The value of nothing:

Asymmetric attention to opportunity costs drives intertemporal decision making.” Management

Science.

Schwardman, Peter, and Joel van der Weele. 2017. “Deception and Self-Deception.” Working

Paper.

Schwartzstein, Joshua. 2014. “Selective attention and learning.” Journal of the European Eco-

nomic Association, 12(6): 1423–1452.

Shalvi, Shaul, Jason Dana, Michel JJ Handgraaf, and Carsten KW De Dreu. 2011. “Jus-

tified ethicality: Observing desired counterfactuals modifies ethical perceptions and behavior.”

Organizational Behavior and Human Decision Processes, 115(2): 181–190.

Shalvi, Shaul, Ori Eldar, and Yoella Bereby-Meyer. 2012. “Honesty requires time (and lack

of justifications).” Psychological science, 10(1264-1270).

Simler, Kevin, and Robin Hanson. 2018. The Elephant in the Brain: Hidden Motives in

Everyday Life Kindle Edition. New York:Oxford University Press.

Simon, Herbert A. 1955. “A behavioral model of rational choice.” The quarterly journal of

economics, 69(1): 99–118.

40

Simonsohn, Uri, and George Loewenstein. 2006. “Mistake# 37: The effect of previously

encountered prices on current housing demand.” The Economic Journal, 116(508): 175–199.

Sims, Christopher A. 2003. “Implications of rational inattention.” Journal of Monetary Eco-

nomics, 50(3): 665–690.

Snyder, Melvin L, Robert E Kleck, Angelo Strenta, and Steven J Mentzer. 1979. “Avoid-

ance of the handicapped: an attributional ambiguity analysis.” Journal of personality and social

psychology, 37(12): 2297–2306.

Taubinsky, Dmitry, and Alex Rees-Jones. Forthcoming. “Attention variation and welfare:

theory and evidence from a tax salience experiment.” Review of Economic Studies.

Thaler, Richard. 1985. “Mental accounting and consumer choice.” Marketing science, 4(3): 199–

214.

Trachtman, Hannah, Andrew Steinkruger, Mackenzie Wood, Adam Wooster, James

Andreoni, James J. Murphy, and Justin M. Rao. 2015. “Fair weather avoidance: unpacking

the costs and benefits of “Avoiding the Ask”.” Journal of the Economic Science Association, 1–7.

Tversky, Amos. 1972. “Elimination by aspects: A theory of choice.” Psychological review, 79(4).

Tversky, Amos, and Daniel Kahneman. 1973. “Availability: A heuristic for judging frequency

and probability.” Cognitive psychology, 5(2): 207–232.

Tversky, Amos, and Daniel Kahneman. 1981. “The framing of decisions and the psychology

of choice.” Science, 211(4481): 453–458.

Tversky, Amos, and Daniel Kahneman. 1986. “Rational choice and the framing of decisions.”

Journal of business, S251–S278.

van der Weele, Joel J., Julija Kulisa, Michael Kosfeld, and Guido Friebel. 2014. “Resist-

ing Moral Wiggle Room: How Robust Is Reciprocal Behavior?” American Economic Journal:

Microeconomics, 6(3): 256–264.

Zimmermann, Florian. 2018. “The Dynamics of Motivated Beliefs.” Working Paper.

41

Appendixes

A Additional Information on Experimental Design

Table A.1: The 36 main bundles

i = 4 i = 3 i = 2

n/4-bundles1st amount d d d d 0 d d d 0 d d 02nd amount d d d d d 0 d d 0 0 d d3rd amount d d d d d d 0 d d 0 0 d4th amount d d d d d d d 0 d d 0 0

Total amount 4d 4d 4d 4d 3d 3d 3d 3d 2d 2d 2d 2d

n/5-bundles1st-4th amount ———————— same as in n/4-bundles ————————5th amount 0 0 0 0 0 0 0 0 0 0 0 0


(n+1)/5-bundles1st-4th amount ———————— same as in n/4-bundles ————————5th amount d d d d d d d d d d d d


Each column within the top, middle, or bottom panel indicates the amounts associated witheach bundle. In the n/5-bundles and (n+1)/5-bundles, the payoff structure for the first fouramounts is the same as in the corresponding n/4-bundle. 0 indicates a zero-amount, and dindicates a non-zero of d that is randomly selected on the participant-bundle level such thatd ∈ {51, 52, 53, 54, 55, 56, 57, 58, 59}.

Table A.2: The 12 non-main bundles

i = 4L i = 3L i = 1

n/4-bundles1st amount dL dL dL dL 0 dL dL dL d 0 0 02nd amount dL dL dL dL dL 0 dL dL 0 d 0 03rd amount dL dL dL dL dL dL 0 dL 0 0 d 04th amount dL dL dL dL dL dL dL 0 0 0 0 d

Total amount 4dL 4dL 4dL 4dL 3dL 3dL 3dL 3dL d d d d

Each column indicates the amounts associated with each bundle. 0 indicates a zero-amount, dL

indicates a non-zero of dL that is randomly selected on the participant-bundle level such that dL ∈{30, 31, 32, 33, 34, 35, 36, 37, 38} and d indicates a non-zero of d that is randomly selected on theparticipant-bundle level such that d ∈ {51, 52, 53, 54, 55, 56, 57, 58, 59}.

42

B Additional Results

Figure B.1: Distribution of X values

(a) Study 1

05

1015

2025

Perc

ent

0 50 100 150Outside option X

(b) Study 2

05

1015

2025

Perc

ent

0 50 100 150Outside option X

Data include all participants’ decisions in the calibration procedure across all versions of Study 1 in Panel A andacross all versions of Study 2 in Panel B. X is set to the lower bound of participants’ implied indifference range fromthe calibration procedure except for when there is a zero lower bound and so X is set to 5 cents. There is a zerolower bound for 12% of the 1000 participants in Study 1 and for 13% of the 1596 participants in Study 2.

43

Table B.1: In the Self/Charity and the Charity/Charity version of Study 1, regression of choosinga main bundle



(+0) -0.06∗∗∗ -0.04∗∗∗ -0.07∗∗∗ -0.08∗∗∗ -0.07∗∗∗

(0.01) (0.02) (0.01) (0.01) (0.01)(+1) 0.11∗∗∗ 0.03∗∗ 0.15∗∗∗ 0.14∗∗∗ 0.12∗∗∗

(0.01) (0.02) (0.02) (0.02) (0.01)Charity/Charity*(+0) 0.07∗∗∗ 0.06∗∗∗ 0.08∗∗∗ 0.09∗∗∗ 0.08∗∗∗

(0.02) (0.02) (0.02) (0.02) (0.02)Charity/Charity*(+1) 0.17∗∗∗ -0.02 0.27∗∗∗ 0.15∗∗∗ 0.16∗∗∗

(0.02) (0.02) (0.02) (0.02) (0.02)Charity/Charity 0.03 0.15∗∗∗ -0.03 0.00 0.01

(0.03) (0.03) (0.03) (0.02) (0.03)


∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are clustered at the participant-level and shown inparentheses. The results are from a linear probability model of the likelihood to choose a main bundle in theSelf/Charity version or in the Charity/Charity version of of Study 1, where (+0) is an indicator for an n/5-bundlethat is constructed by adding a fifth amount that is equal to zero to a baseline n/4-bundle, (+1) is an indicatorfor an (n+1)/5-bundle that is constructed by adding a fifth amount that is non-zero to a baseline n/4-bundle,Charity is an indicator for the Charity/Charity version, kn ∗ ld FEs include all possible interactions of dummiesfor the number of non-zero amounts within the underlying baseline n/4-bundle (see Table A.1) and dummiesfor the value of the non-zero amount d in the bundle to fully control for the sum of the amounts in the baselinebundle. Columns 1-3 analyze all participants’ decisions: in all main bundles in Column 1, involving the baseline4/4-bundles in Column 2, and involving the baseline 2/4- and 3/4-bundles in Column 3. Column 4 analyzesall main bundles but among a restricted sample of participants who choose the bundle at least once and choosetheir outside option at least once across all 48 decisions. Column 5 analyzes all main bundles but among arestricted sample of participants with outside option X set to the lower bound of their indifference range (andthus excludes participants with a zero lower bound).

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Table B.2: Considering the role of experience in the Self/Charity version of Study1, regression of choosing a main bundle

5-bundles first 4-bundles first early bundles late bundles(1) (2) (3) (4)

(+0) -0.06∗∗∗ -0.06∗∗∗ -0.04∗∗ -0.08∗∗∗

(0.02) (0.02) (0.02) (0.02)

N 3744 3384 3568 3560(+1) controls yes yes yes yeskn ∗ ld FEs yes yes yes yes

∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are clustered at the participant-leveland shown in parentheses. The results are from a linear probability model of the likelihoodto choose a main bundle in the Self/Charity version of Study 1, where (+0) is an indicator foran n/5-bundle that is constructed by adding a fifth amount that is equal to zero to a baselinen/4-bundle, (+1) controls involve an indicator for an (n+1)/5-bundle that is constructedby adding a fifth amount that is non-zero to a baseline n/4-bundle, kn ∗ ld FEs include allpossible interactions of dummies for the number of non-zero amounts within the underlyingbaseline n/4-bundle (see Table A.1) and dummies for the value of the non-zero amount d inthe bundle to fully control for the sum of the amounts in the baseline bundle. Columns 1-2analyze decisions in all main bundles by participants who first view the set of five-amountbundles than the set of four-amount bundles in Column 1 and instead by participants whofirst view the set of four-amount bundles than the set of five-amount in Column 2. Columns3-4 analyze all participants’ decisions in main bundles that occur “early” within each set ofbundles (i.e., decisions 1-12 and 25-36) in Column 3 and that instead occur “late” within theset of bundles (i.e., decisions 13-24 and 37-48) in Column 4.

45

Table B.3: Considering the role of inattention and simplifying the decision environment in Study1, regression of choosing a main bundle

Self/Charity andattentive decisions from Self/Charity-SumSelf/Charity-Choice

(1) (2)

(+0) -0.11∗∗∗ -0.03∗∗∗

(0.02) (0.01)Self/Charity*(+0) 0.05∗∗ -0.03∗

(0.02) (0.02)Self/Charity -0.12∗∗∗ 0.04

(0.03) (0.03)

N 10209 14544(+1) controls yes yeskn ∗ ld FEs yes yes

∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are clustered at the participant-level and shown inparentheses. The results are from a linear probability model of the likelihood to choose a main bundle in theSelf/Charity or Self/Charity-Choice versions of Study 1 in Column 1 and in the Self/Charity or Self/Charity-Sum versions of Study 1 in Column 2, where (+0) is an indicator for an n/5-bundle that is constructed byadding a fifth amount that is equal to zero to a baseline n/4-bundle, Self/Charity is an indicator for beingin the Self/Charity version, (+1) controls involve an indicator for an (n+1)/5-bundle that is constructed byadding a fifth amount that is non-zero to a baseline n/4-bundle as well as an interaction of that indicator withthe Self/Charity indicator, kn ∗ ld FEs include all possible interactions of dummies for the number of non-zeroamounts within the underlying baseline n/4-bundle (see Table A.1) and dummies for the value of the non-zeroamount d in the bundle to fully control for the sum of the amounts in the baseline bundle. Column 1 analyzesall participants’ decisions in all main bundles in the Self/Charity version of Study 1 and all participants’decisions that are “attentive” (as indicated by them fully revealing information in that decision) in all mainbundles in the Self/Charity-Choice version of Study 1. Column 2 analyzes all participants’ decisions in allmain bundles in the Self/Charity version of Study 1 and all participants’ decisions in all main bundles in theSelf/Charity-Sum version of Study 1.

46

Table B.4: In the Self/Charity and the Charity/Charity version of Study 2, regression of choosinga main bundle



(+0) -0.09∗∗∗ -0.10∗∗∗ -0.08∗∗∗ -0.13∗∗∗ -0.10∗∗∗

(0.01) (0.02) (0.01) (0.02) (0.01)(+1) 0.07∗∗∗ 0.01 0.10∗∗∗ 0.10∗∗∗ 0.08∗∗∗

(0.01) (0.01) (0.02) (0.02) (0.01)Charity/Charity*(+0) 0.05∗∗∗ 0.07∗∗ 0.04∗ 0.08∗∗∗ 0.06∗∗∗

(0.02) (0.03) (0.02) (0.02) (0.02)Charity/Charity*(+1) 0.10∗∗∗ -0.01 0.16∗∗∗ 0.09∗∗∗ 0.10∗∗∗

(0.02) (0.02) (0.03) (0.02) (0.02)Charity/Charity 0.16∗∗∗ 0.21∗∗∗ 0.13∗∗∗ 0.06∗∗ 0.13∗∗∗

(0.03) (0.04) (0.03) (0.03) (0.03)


∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are clustered at the participant-level and shown inparentheses. The results are from a linear probability model of the likelihood to choose a main bundle in theSelf/Charity version or in the Charity/Charity version, of Study 1, where (+0) is an indicator for an n/5-bundlethat is constructed by adding a fifth amount that is equal to zero to a baseline n/4-bundle, (+1) is an indicatorfor an (n+1)/5-bundle that is constructed by adding a fifth amount that is non-zero to a baseline n/4-bundle,Charity/Charity is an indicator for the Charity/Charity version, kn ∗ ld FEs include all possible interactions ofdummies for the number of non-zero amounts within the underlying baseline n/4-bundle (see Table A.1) anddummies for the value of the non-zero amount d in the bundle to fully control for the sum of the amounts inthe baseline bundle. Columns 1-3 analyze all participants’ decisions: in all main bundles in Column 1, involvingthe baseline 4/4-bundles in Column 2, and involving the baseline 2/4- and 3/4-bundles in Column 3. Column 4analyzes all main bundles but among a restricted sample of participants who choose the bundle at least once andchoose their outside option at least once across all 48 decisions. Column 5 analyzes all main bundles but amonga restricted sample of participants with outside option X set to the lower bound of their indifference range (andthus excludes participants with a zero lower bound).

47

Table B.5: Considering the role of inexperience in the Self/Charity and Char-ity/Charity versions of Study 2, regression of choosing a main bundle

5-bundles first 4-bundles first early bundles late bundles(1) (2) (3) (4)

Panel A: Self/Charity(+0) -0.09∗∗∗ -0.09∗∗∗ -0.07∗∗∗ -0.11∗∗∗

(0.02) (0.02) (0.02) (0.02)


Panel B: Charity/Charity(+0) -0.03 -0.05∗∗∗ -0.02 -0.06∗∗∗

(0.02) (0.02) (0.02) (0.02)


∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are clustered at the participant-leveland shown in parentheses. The results are from a linear probability model of the likelihoodto choose a main bundle in in the Self/Charity version of Study 2 in Panel A and in theCharity/Charity version of Study 2 in Panel B, where (+0) is an indicator for an n/5-bundlethat is constructed by adding a fifth amount that is equal to zero to a baseline n/4-bundle,(+1) controls involve an indicator for an (n+1)/5-bundle that is constructed by adding a fifthamount that is non-zero to a baseline n/4-bundle, kn ∗ ld FEs include all possible interactionsof dummies for the number of non-zero amounts within the underlying baseline n/4-bundle(see Table A.1) and dummies for the value of the non-zero amount d in the bundle to fullycontrol for the sum of the amounts in the baseline bundle. Columns 1-2 analyze decisionsin all main bundles by participants who first view the set of five-amount bundles than theset of four-amount bundles in Column 1 and instead by participants who first view the setof four-amount bundles than the set of five-amount in Column 2. Columns 3-4 analyze allparticipants’ decisions in main bundles that occur “early” within each set of bundles (i.e.,decisions 1-12 and 25-36) in Column 3 and that instead occur “late” within the set of bundles(i.e., decisions 13-24 and 37-48) in Column 4.

48

Table B.6: Considering the role of inattention and simplifying the decision en-vironment in Study 2, regression of choosing a main bundle

Panel A: Self/Charity versionsSelf/Charity and

attentive decisions from Self/Charity-SumSelf/Charity-Choice

(1) (2)

(+0) -0.16∗∗∗ -0.08∗∗∗

(0.03) (0.01)Self/Charity*(+0) 0.07∗∗ -0.01

(0.03) (0.02)Self/Charity -0.20∗∗∗ -0.00

(0.04) (0.04)


Panel B: Charity/Charity versionsCharity/Charity and

attentive decisions from Charity/Charity-SumCharity/Charity-Choice

(1) (2)

(+0) -0.06∗∗∗ -0.03∗∗∗

(0.01) (0.01)Charity/Charity*(+0) 0.02 -0.01

(0.02) (0.02)Charity/Charity -0.04∗ 0.01

(0.02) (0.02)


∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are clustered at the participant-leveland shown in parentheses. The results are from a linear probability model of the likelihoodto choose a main bundle in the Self/Charity or Self/Charity-Choice versions of Study 2 inColumn 1 and in the Self/Charity or Self/Charity-Sum versions of Study 2 in Column 2,where (+0) is an indicator for an n/5-bundle that is constructed by adding a fifth amountthat is equal to zero to a baseline n/4-bundle, Self/Charity is an indicator for being inthe Self/Charity version, (+1) controls involve an indicator for an (n+1)/5-bundle thatis constructed by adding a fifth amount that is non-zero to a baseline n/4-bundle as wellas an interaction of that indicator with the Self/Charity indicator, kn ∗ ld FEs include allpossible interactions of dummies for the number of non-zero amounts within the underlyingbaseline n/4-bundle (see Table A.1) and dummies for the value of the non-zero amount din the bundle to fully control for the sum of the amounts in the baseline bundle. Column1 analyzes all participants’ decisions in all main bundles in the Self/Charity version ofStudy 2 and all participants’ decisions that are “attentive” (as indicated by them fullyrevealing information in that decision) in all main bundles in the Self/Charity-Choiceversion of Study 2. Column 2 analyzes all participants’ decisions in all main bundles inthe Self/Charity version of Study 2 and all participants’ decisions in all main bundles inthe Self/Charity-Sum version of Study 2.

49

Table B.7: In the Self(150)/Charity and Charity(ARC)/Charity versions of Study 2, regressionof choosing a main bundle



Panel A: Self(150)/Charity version(+0) -0.05∗∗∗ -0.07∗∗∗ -0.03∗∗∗ -0.10∗∗∗ -0.05∗∗∗

(0.01) (0.02) (0.01) (0.02) (0.01)(+1) 0.07∗∗∗ 0.01 0.10∗∗∗ 0.14∗∗∗ 0.07∗∗∗

(0.01) (0.01) (0.01) (0.02) (0.01)


Panel B: Charity(ARC)/Charity version(+0) -0.01 -0.00 -0.02 -0.02 -0.02∗

(0.01) (0.01) (0.01) (0.01) (0.01)(+1) 0.21∗∗∗ 0.03∗∗ 0.30∗∗∗ 0.23∗∗∗ 0.22∗∗∗

(0.01) (0.01) (0.02) (0.01) (0.01)


∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors are clustered at the participant-level and shown inparentheses. The results are from a linear probability model of the likelihood to choose a main bundle inthe Self(150)/Charity version of Study 2 in Panel A and in the Charity(ARC)/Charity version of Study 2in Panel B, where (+0) is an indicator for an n/5-bundle that is constructed by adding a fifth amount thatis equal to zero to a baseline n/4-bundle, (+1) is an indicator for an (n+1)/5-bundle that is constructed byadding a fifth amount that is non-zero to a baseline n/4-bundle, kn ∗ ld FEs include all possible interactionsof dummies for the number of non-zero amounts within the underlying baseline n/4-bundle (see Table A.1)and dummies for the value of the non-zero amount d in the bundle to fully control for the sum of the amountsin the baseline bundle. Columns 1-3 analyze all participants’ decisions: in all main bundles in Column 1,involving the baseline 4/4-bundles in Column 2, and involving the baseline 2/4- and 3/4-bundles in Column3. Column 4 analyzes all main bundles but among a restricted sample of participants who choose the bundleat least once and choose their outside option at least once across all 48 decisions. Column 5 analyzes allmain bundles but among a restricted sample of participants with outside option X set to the lower boundof their indifference range (and thus excludes participants with a zero lower bound).

50

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