do people seek to maximize happiness? evidence from new
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Do People Seek to Maximize Happiness?
Evidence from New Surveys*
Daniel J. Benjamin
Cornell University, Institute for Social Research, and NBER
Ori Heffetz
Cornell University
Miles S. Kimball
University of Michigan, Institute for Social Research, and NBER
Alex Rees-Jones
Cornell University
*** PRELIMINARY AND INCOMPLETE DRAFT: PLEASE DO NOT CIRCULATE ***
Abstract
We present survey respondents with hypothetical, pairwise decision problems. We ask them which option they
would choose, and we also ask which option they believe would give them the highest subjective well-being
(SWB). We find that individuals‘ expected choices are strongly predicted by which option they expect will lead
to greater SWB, but we also find that their choice ranking of the options deviates systematically from their
SWB ranking. In some of our studies, we also ask respondents which option they believe would lead to higher
levels of other aspects of their lives besides SWB, such as health, control over life, and sense of purpose. We
find that people‘s rankings of the options based on these other aspects explain their choices, even controlling for
the SWB ranking. However, we also find that SWB ranking explains variation in choice better than all of these
non-SWB factors combined. On the one hand, our results support the view that individuals do not seek to
maximize happiness alone (at least the way happiness is typically measured); they are willing to make trade-offs
between happiness and other aspects of their lives. On the other hand, since people‘s prediction of what will
give them the greatest SWB is the best single predictor of choice, our results also suggest that the burgeoning
use of happiness data as an empirically-measurable proxy for welfare may nonetheless be justified in many
cases.
JEL Classification: D03, D60
Keywords: subjective well-being, SWB, happiness, life satisfaction, utility, choice
* We are extremely grateful to Dr. Robert Rees-Jones and his office staff for generously allowing us to survey their patients and to
Cornell‘s Survey Research Institute for allowing us to put questions in the 2009 Cornell National Social Survey. We thank Gregory
Besharov, John Ham, Benjamin Ho, Erzo Luttmer, Michael McBride, Ted O‘Donoghue, Matthew Rabin, Antonio Rangel, and Robert
J. Willis for especially valuable early comments and suggestions. We are grateful to participants at the CSIP Workshop on Happiness
and the Economy and at Cornell‘s Behavioral/Experimental Lab Meetings, Junior Faculty Lunch, and Behavioral Economics
Workshop for helpful comments. We thank Kristen Cooper, Isabel Fay, John Farragut, Geoffrey Fisher, Sean Garborg, Jesse Gould,
June Kim, Nathan McMahon, Greg Muenzen, John Schemitsch, Elizabeth Traux, Charles Whittaker, and Brehnen Wong for their
research assistance. We thank the National Institute on Aging (grant P01-AG026571/01) for financial support.
E-mail: db468@cornell.edu, oh33@cornell.edu, mkimball@umich.edu, arr34@cornell.edu.
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Economic researchers are increasingly using measures of subjective well-being (SWB), such as self-
reported happiness or life satisfaction, as an empirical proxy for utility. The hope is that these measures can
enable economists to conduct welfare evaluations that are difficult to conduct with standard revealed preference
methods, such as measuring the negative externality from neighbors‘ higher earnings (Luttmer, 2005), the
average American‘s tradeoff between inflation and unemployment (DiTella, MacCulloch, and Oswald, 2003),
and the effect of health status on the marginal utility of consumption (Finkelstein, Luttmer, and Notowidigdo,
2008). In order for this strategy to assess welfare as conceived in the standard economic approach, a necessary
assumption is that an agent‘s preferences over alternatives and her measured SWB under each alternative lead
to identical rankings of the alternatives. This paper provides evidence for evaluating that assumption. In other
words, the goal of this paper is to evaluate whether individuals seek to maximize SWB.
In pursuing this goal, this paper empirically addresses a recent theoretical literature on the economics of
happiness. There are two alternative views regarding the relationship of SWB to utility that give different
guidance regarding the use of SWB data in empirical work.1 The first view, reflected at least implicitly in a
large number of papers (e.g., Gruber and Mullainathan, 2002; Oreopoulos, 2007), is that SWB data represent
idealized revealed-preference utility in the sense of what individuals would choose if they were well-informed
about the consequences of their choices for SWB. The second view, explicitly laid out in Kimball and Willis
(2006) and Becker and Rayo (2008), is that even well-informed agents will be willing to trade off SWB with
other things they care about, making SWB and utility distinct.2 The present paper attempts to empirically assess
these two views by measuring the concordance between happiness and preferences.
In the economics literature, some of the interest in using measures of happiness has been to identify
possible mistakes that people make due to mispredicting happiness. In that literature, it is assumed that people
are trying to maximize SWB, and divergence between choice and ex post SWB are used to infer that people
mispredict how their choices will affect their SWB. Unlike that literature, we wish to test the assumption that
people are trying to maximize SWB. Doing so requires comparing an individual’s choice behavior with her
beliefs about how each possible choice would affect measured SWB.3 Our strategy is to confront individuals
with empirically-relevant choice situations to see whether they choose the alternative that they predict will
1 Some researchers adopt yet a third view, the philosophical position that happiness per se is the appropriate maximand for a
policymaker (e.g., Bentham, 1789; Layard, 2005; Bronsteen, Buccafusco, and Masur, forthcoming). See Bernheim (2009a, 2009b) for
a categorization and discussion of alternative philosophical approaches to welfare economics. Throughout this paper, we adopt the
standard economic perspective (common to the two views mentioned in the text) that the appropriate maximand for welfare analysis is
an individual’s preferences. However, the value of our results does not hinge on this perspective. Even if one believes that observable
preferences are not the appropriate maximand (such as in cases where someone feels social or moral pressure not to reveal their true
preferences; see Koszegi and Rabin, 2008), understanding the link between choice and subjective well-being is still valuable to a
policy maker. 2 For elaborations of this view, see also Kimball, Levy, Ohtake and Tsutsui (2006), Kimball, Ohtake and Tsutsui (forthcoming), and
Kimball, Nunn and Silverman (2010) 3 In the terminology of Kahneman, Wakker, and Sarin (1997), this would amount to comparing ―decision utility‖ with ―predicted
utility.‖
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maximize their own SWB.4 However, because of the difficulty of empirically measuring ex ante beliefs
regarding the SWB consequences of important real-world decisions, our strategy is to confront survey
participants with hypothetical decisions, and to ask the participants for which alternative they expect greater
SWB, as well as which alternative they would choose. At the empirical level, the question we are posing is:
How good is predicted SWB as a proxy for predicted preferences?
In this paper, we analyze choice and predicted happiness data for a variety of hypothetical decision
scenarios from 538 undergraduates, a convenience sample of 1,495 adults, and a representative sample of 1,000
Americans. Each scenario has two alternatives. For example, one scenario describes a choice between a job
that pays more but allows less sleep versus a job with lower pay and more sleep. We assess participants‘
predicted happiness (their beliefs about how happy they would be) using questions based on each of the three
measures of subjective well-being that are most commonly used in the empirical literature: life satisfaction,
happiness with life as a whole, and felt happiness. We have two main results.
First, we find that participants‘ predictions about happiness are a powerful predictor of their choices.
The strength of the relationship between predicted happiness and predicted choices depends on the measure of
happiness. For example, in one study, we find that life satisfaction predicts about 65% of the variation in
preferences, while happiness with life as a whole and felt happiness predict about 59% and 55% of the variation
in preferences, respectively. Of course, when the same participants are asked to assess predicted SWB and to
make choices, the relationship between the two may be inflated by participants‘ desires to appear consistent.
However, when we ask one group of participants to make choices and another group to assess predicted SWB,
we find the opposite; the difference in mean ratings is smaller and less statistically distinguishable in all
scenarios. This suggests the possibility that when we ask participants to rate the options only in terms of
predicted happiness, they report their overall assessment of the options, even if this assessment actually
incorporates aspects of the options other than happiness. To address this possibility, in another study we
measured happiness as part of a list of things one might care about: own happiness, happiness of one‘s family,
health, romance, social life, sense of control over life, spirituality, fun, social status, boredom, physical comfort
and sense of purpose. In that study, the difference in predicted happiness between the two alternatives explains
43% of the variation in intensity of preference for one alternative over the other when happiness is measured in
isolation, and explains 31%-39% of the variation when happiness is measured as part of the list, depending on
the phrasing of the happiness question. In comparison, the characteristic we measured that was second best at
predicting choice—predicted control over one‘s life—explains 8%-20% of the variation in choices in these
4 A large experimental psychology literature finds that people often make choices that ex post do not end up maximizing measured
happiness (Gilbert, 2006). Therefore, preferences do not always line up with ex post measured happiness. Relatedly, there is evidence
that predictions of happiness do not always accord with ex post measured happiness (Gilbert, 2006). However, as far as we are aware,
the question we focus on—whether predicted happiness lines up with preferences—has received relatively little attention. Becker and
Rayo (2008) propose empirical tests of whether things other than happiness matter for preferences, but their tests rely on ex post
measured happiness and hence assume that people correctly predict the happiness consequences of their choices.
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surveys (when happiness is measured as part of the list). The third best characteristic was predicted sense of
purpose, explaining 11%-12% of the variation. Regardless of the survey manipulation, happiness out-performs
the other characteristics as a proxy for decision utility. Our main takeaway message from this variety of ways of
measuring happiness and other attributes is that measured happiness is a strong predictor of preferences and
may be the single best empirical proxy.
Our second main result is that predicted happiness is not the only characteristic of the choice alternatives
that enters preferences. The fact that the other characteristics we measured have predictive power for choice
does not by itself imply that they enter preferences; it is possible that they matter only because they are
correlated with predicted happiness. However, in regressions of choice on predicted happiness and a single
other characteristic, the coefficient on the non-happiness characteristic typically remains statistically significant
despite controlling for predicted happiness. That finding may suggest that non-happiness characteristics enter
preferences independently of measured happiness. Consistent with the idea that individuals make trade-offs
between happiness and other objectives they care about, we also find that predicted happiness correlates more
strongly with choices for participants who indicate a higher ―willingness to pay‖ for happiness in a different
survey question.
Our results suggest that common SWB measures in general, and measured life satisfaction in particular,
may be good empirical proxies for preferences. However, since predicted SWB is not the only characteristic of
a choice alternative that enters preferences, caution is warranted in drawing welfare conclusions exclusively
from measured SWB.
To take a step toward providing practical guidance to empirical researchers about in which kinds of
situations measured SWB may be a better or worse proxy for preferences, we analyze whether our results differ
across scenarios. We find suggestive evidence hinting that SWB may be a bigger factor in everyday, minor
decisions (such as eating an apple versus orange, or doing menial work versus paying a fee) than in more major
decisions (such as taking a job that is interesting versus preparatory for career, or going to a college that is
selective versus one that promises a good social life). Our results are qualitatively similar across the subject
populations we study, and we do not find observable demographic characteristics of respondents (e.g. age, race,
income, gender) that predict how much they weight SWB in their choices.
We subjected our results to a variety of robustness checks, two of which we mention here. First, we
were worried about possible self-control problems: participants may forecast a different choice than the one that
they expect would maximize SWB because they anticipate a failure of self-control. To explore this possibility,
in addition to asking participants which alternative they would choose, we also asked them which alternative
they would want themselves to make. We find our results are largely similar when we restrict the sample to
observations where respondents said they were choosing what they would want themselves to choose. Second,
we cross-checked our main results by eliciting participants‘ willingness to pay for various services, each of
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which could improve one aspect of life, such as happiness, health, and so on. We find evidence that respondents
with higher willingness to pay for happiness have fewer instances where they choose an option which they do
not predict would maximize happiness.
Our work is related to a literature in philosophy that poses thought experiments in extreme hypothetical
scenarios in order to demonstrate that people‘s preferences encompass more than their own happiness.5 Unlike
that literature, by focusing on realistic choice situations, we seek to address to what extent measured happiness
is a good proxy for preferences in empirically-relevant choice situations. As far as we are aware, the most
closely related evidence to ours is one of the experiments described in Tversky and Griffin (1991). In a
between-subjects experiment with 66 undergraduates, participants were presented with a hypothetical choice
between a job with a higher salary where co-workers earn even more versus a job with a lower salary where co-
workers earn even less. Like we find in a similar choice problem, Tversky and Griffin found that participants
were more likely to choose the higher absolute salary but said they would be happier with the higher relative
salary. Tversky and Griffin interpret the result as supporting their theory that payoff levels are weighted more
heavily in choice, while contrasts between payoffs and a reference point are weighted more heavily in
subjective well-being judgments.
We wish to emphasize that our focus in this paper is the relationship between preferences and measures
of happiness typically used in empirical research. We do not address the question of whether a sufficiently
broad conception of happiness that is not captured by typical measures might be indistinguishable from
preferences. That being said, our methodology could be applied to test whether some measure of a broader
notion of happiness predicts choice better than the subjective well-being measures typically used in current
empirical research.
The paper is organized as follows. Section I presents a theoretical framework to clarify what can be
inferred about preferences from our surveys. Section II discusses the survey design and subject populations. In
Section III, we analyze the main results, and Section IV presents additional results and robustness analyses.
Section V concludes. Details of the experimental materials are described in the Appendix, and details of all
pilots are in the Web Appendix.
I. Theoretical Framework
Preferences may depend on happiness as well as on other factors. We assume that an individual‘s
preferences can be represented by an increasing, concave, and differentiable utility function,
(1) U(H(X, G), X, Q),
5 For example, in one famous thought experiment, a reader is asked to introspect whether he or she would choose to be hooked up to
an ―experience machine‖ that would guarantee complete happiness for the rest of life, without awareness that the experience is not real
(Nozick, 1974, pp.42-45). If a reader would refuse the machine, he or she must not care exclusively about happiness.
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where H is happiness, X is a vector of observed factors that both affect happiness and enter preferences directly,
G is a vector of unobserved factors that affect preferences only through happiness, and Q is a vector of
unobserved factors that enter preferences but do not affect happiness.
Do people make tradeoffs between happiness and other factors? To examine whether people make such
tradeoffs, we test whether there exist components of X (e.g. health, or sense of purpose) that enter preferences
directly and not just through their effects on happiness. Therefore, one central hypothesis we seek to test is
whether UX > 0. To do so, we present survey participants with a sequence of scenarios. Each scenario involves
making a choice between two options. Option 1 can be described by the levels of inputs it provides, (X1, G1,
Q1), and Option 2 can be described by (X2, G2, Q2). For a given scenario, define ∆X ≡ X1 – X2, ∆G ≡ G1 – G2,
∆Q ≡ Q1 – Q2, and ∆H ≡ H(X1, G1) – H(X2, G2). Assuming ∆H, ∆X, and ∆Q are small, the individual will
choose Option 1 if and only if
(2) ∆U ≈ UH ∆H + UX ∆X + UQ ∆Q ≥ 0
(as a tie-breaker, we assume an individual chooses Option 1 if she is indifferent). Assuming the unobserved
term UQ ∆Q is distributed normally with mean µQ and variance ζQ2, we can rewrite (2) as
(3) Pr{choose Option 1} = Φ(α + βH ∆H + βX ∆X),
where α = µQ / ζQ, βH = UH / ζQ and βX = UX / ζQ are normalized marginal utilities, and Φ is the cdf for a
standard normal random variable. For each scenario, we ask participants to predict how happy they would be
under Option 1 relative to Option 2, as well as how Option 1 ranks relative to Option 2 on improving a number
of other factors, such as health, physical comfort and sense of purpose. These survey questions give us measures
of ∆H and ∆X. To test whether UX > 0, we estimate probit regression (3) and test the null hypothesis that βX = 0.
Our key identifying assumption is E(∆Q | ∆H, ∆X) = 0. This assumption would be violated if we neglected to
measure factors important for choice that are correlated with the factors we did measure.
We also ask participants their intensity of preference for Option 1 relative to Option 2, and we use this to
run a linear regression. Assuming our intensity-of-preference measure is monotonically related to the utility
difference between the two options, P = Π(∆U(H(X, G), X, Q)) with Π′ > 0 and (0) = 0, then P ≈ Π(0) + Π′∆U
= Π′UH ∆H + Π′UX ∆X + Π′UQ ∆Q. We can estimate
(4) P = γ + δH ∆H + δX ∆X + ε,
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where γ = µQ, δH = UH Π′ and δX = UX Π′ are normalized marginal utilities, and ε ~ N(0, ζQ2 (Π′)
2). Under the
same identifying assumptions, we can test the null hypothesis that δX = 0. While the OLS regression (4) allows
us to use more information provided by the participants (namely, the intensity of their preferences) than (3)
does, it also requires the additional approximation that Π(∆U) is locally linear across the entire range of
decision scenarios.
How good is happiness as an empirical proxy for utility? Another central question we seek to answer is
how well measured happiness proxies for utility. To do so, we examine the fraction of scenarios where the
participant chose Option 1 when ∆H > 0, and Option 2 when ∆H < 0. We also run a univariate linear regression
of P on ∆H, which we interpret simply as a forecasting regression. The R2 from this regression captures how
well measured happiness predicts (intensity of) choice.
We return to this model in Section IV, where we extend it to provide theoretical basis for robustness
checks aimed at validating our main findings (the latter are reported in Section III). Before doing so, in the next
section we use the framework developed above to describe our survey design.
II. Survey Design and Subject Populations
We conducted eight different surveys among 3,033 respondents in total. In this section we describe the
design of four of the surveys, which ran among 2,280 participants and comprise the sample for our main
analyses. Table 1 provides complete details of these four surveys, which we call Studies 1-4: participant
population, sample size, scenarios used (see II.A below), type of questions asked (see II.B below),
randomizations of question order, etc. The other four surveys are described in the Web Appendix, which gives a
complete account of what we found and why we changed the questions from one survey to the next. We use
those other four surveys mainly for robustness checks.
II.A. Scenarios
At the core of our surveys is a sequence of hypothetical scenarios. In each scenario, respondents face a
choice between two options. The choice highlights a tradeoff. For example, one of the scenarios is:
Say you have to decide between two new jobs. The jobs are exactly the same in almost every way, but
have different work hours and pay different amounts.
Option 1: A job paying $80,000 per year. The hours for this job are reasonable, and you would be able to
get about 7.5 hours of sleep on the average work night.
Option 2: A job paying $140,000 per year. However, this job requires you to go to work at unusual hours,
and you would only be able to sleep around 6 hours on the average work night.
The full set of scenarios is given in the Appendix. In choosing the scenarios and the tradeoffs, we were guided
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by three considerations.
First, we wanted to collect evidence that would be as relevant and as close in subject matter as possible
to the happiness literature. For this purpose, we picked scenarios that highlight tradeoffs that the literature
suggests might be important determinants of SWB. Hence, in some of our scenarios respondents face choices
between jobs and housing options that are more attractive financially versus ones that bear a financial cost but
allow for more sleep (Kelly, 2004), a shorter commute (Stutzer and Frey, 2008), being around friends
(Kahneman, Krueger, Schkade, Schwarz, and Stone, 2004), or making more money relative to others (Luttmer,
2005; see Heffetz and Frank, 2008, for a survey).
Second, since some of us were initially unsure we would find any divergences between predicted SWB
and choice, in our earlier surveys we focused on choice situations where individual SWB may not be the only
consideration. Hence, in some scenarios respondents choose between a career path that promises an ―easier‖ life
with less individual sacrifices versus one that promises posthumous impact and fame, or between a more
convenient and ―fun‖ option versus an option that might be considered ―the right thing to do.‖
Third, once we found divergences between predicted SWB and choice, in our later surveys we wanted to
assess the magnitude of the relationship between predicted SWB and choice in the most empirically-relevant
scenarios. In order to pick scenarios that highlight choices and tradeoffs that are relevant and important in the
lives of our respondent populations, we asked a sample of 102 University of Chicago students to list the three
most important decisions they made in the last day, the last month, the last 2 years, and in their whole lives.
Consistently major themes in their responses were studying, sleeping, choosing classes, and choosing which
college to attend. We used their answers to construct scenarios that seem representative of ordinary decisions
for respondent college-age population, and we subsequently confirmed these themes in an independent sample
of 156 Cornell students.6 Hence, in the resulting scenarios, individuals have to choose between socializing and
fun versus sleep and schoolwork; traveling home for Thanksgiving versus saving the airfare money; attending a
more fun and social college versus a highly selective one; and following one‘s passion versus pursuing a more
practical career path. We added to these scenarios additional ones that focus on economic decisions that are
prevalent in almost everyone‘s life, like choosing one food item versus another, or trading time for money.
The three considerations above resulted in a pool of 13 scenarios, and respondents in different surveys
are asked different subsets of this pool. Additionally, some surveys come in different versions where the
scenarios appear in different order. Finally, some scenarios are asked in different variations (e.g. different
wording, different quantities of money/sleep/commute time, etc.) and some scenarios are tailored to different
respondent populations (e.g. while we asked students about school, we instead asked older respondents about
6 The most frequent responses to these questions involved basic decisions to accept a job or internship (4% of responses), decisions of
which college to attend (13% of responses), and decisions on maintaining contact with family (3% of responses). These responses
motivated the addition of scenarios 7-10 on these subjects (see the Appendix for full phrasing). In addition, 3% of responses involved
sleep tradeoffs and 5% of responses involved decisions about maintaining friendships, validating our previously formulated scenarios
on these topics (scenarios 1 and 2). Full details are in the Web Appendix.
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work).
II.B. Main Questions
Once subjects are presented with a scenario and its two associated choice options, they answer a few
questions. Our surveys include four types of questions, which we refer to in the text and in Table 1 as questions
about (a) choice; (b) meta-choice; (c) SWB; and (d) other aspects of life.
(a) Choice question
In all surveys and about each scenario, respondents are asked: ―If you were limited to these two options,
which do you think you would choose?‖ They answer by checking one of six checkboxes, horizontally
presented as a scale that moves from one option to the other: definitely choose Option 1; probably choose
Option 1; possibly choose Option 1; possibly choose Option 2; probably choose Option 2; and definitely choose
Option 2. We convert this six-point scale to an intensity-of-choice variable, ranging from 1 to 6, which can be
collapsed into a binary choice variable (―choose Option 1‖ versus ―choose Option 2‖).7 We treat answers to this
question as measures of preference.
(b) Meta-choice question
In all of the surveys except the CNSS, the choice question above is followed by the question: ―If you
were limited to these two options, which would you want yourself to choose?‖ Again, there are six possible
answers (definitely want Option 1; probably want Option 1; possibly want Option 1; possibly want Option 2;
probably want Option 2; and definitely want Option 2), which we convert to either an intensity variable (ranging
from 1 to 6) or a binary variable (―want to choose Option 1‖ versus ―want to choose Option 2‖). We defer a
detailed discussion of the purpose of this question to Section IV.B below, where we argue that one could view it
as a measure of meta-preference.
(c) SWB question
We asked three different versions of the SWB question, modeled after what we view as the three
―families‖ of SWB questions that are most common in the literature:8
(i) life satisfaction: ―Between these two options, which do you think would make you more satisfied
with life, all things considered?‖;
7 Responses to the Cornell National Social Survey (CNSS) were given only in this binary format instead of the six-point scale. This
format was chosen because the CNSS was conducted by telephone instead of on paper. The binary response scale was both briefer for
interviewers to convey and easier for respondents to understand. 8 See the Web Appendix for a list of SWB measures and the studies in the literature that use them, classified by the family of questions
they use.
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(ii) happiness with life as a whole: ―Between these two options, taking all things together, which do you
think would give you a happier life as a whole?‖; and
(iii) felt happiness: ―Between these two options, during a typical week, which do you think would make
you feel happier?‖
In some survey versions, described below, we ask respondents about 12 different aspects of life, of which (one‘s
own) happiness is only one. There, we slightly modified the wording of questions (ii) and (iii) to be comparable
with the other aspects we asked about. These modified versions of (ii) and (iii) are:
(iv) own happiness with life as a whole: ―Between these two options, taking all things together, which
option do you think would make your life as a whole better in terms of … [your own
happiness]‖; and
(v) immediately-felt own happiness: ―Between these two options, in the few minutes immediately after
making the choice, which option do you think would make you feel better in terms of …
[your own happiness].‖
From the perspective of psychological theory, ―felt happiness‖ may be the cleanest measure of SWB
because it is more directly accessible than complex cognitive judgments of overall well-being (e.g., Schwarz
and Clore, 1983). However, from the perspective of economic theory, a ―felt happiness‖ measure may be
deficient because it encourages participants to focus on how they will feel in the near-term aftermath of the
decision; decisions that increase felt happiness in the short term but reduce it in the long term might be
predicted to increase felt happiness even if they decrease overall well-being. The ―life satisfaction‖ and
―happiness with life as a whole‖ measures might be expected to correlate better with choice because they
require participants to integrate SWB over one‘s lifetime.
(d) Question about other aspects of life
As mentioned, in some survey versions, instead of asking only about predicted happiness, we ask
respondents to rate the two options in terms of these 12 aspects of life: your own happiness, your family‘s
happiness, your health, your romantic life, your social life, your control over your life, your life‘s level of
spirituality, your life‘s level of fun, your social status, your life‘s non-boringness, your physical comfort, and
your sense of purpose. While the list is limited by the length of the survey, we chose these aspects of life in an
attempt to capture what else, besides one‘s own happiness, might matter for preferences. In choosing these
items, we were guided by economists‘ and philosophers‘ enumeration of ―capabilities‖ (Sen, 1985; Nussbaum,
2000), non-hedonic components of SWB proposed by psychologists (White and Dolan, 2009), and our own
introspections. In some survey versions, we separate ―own happiness‖ from the other 11 aspects, and ask
respondents first just about own happiness in each scenario, and then, re-presenting each scenario, we ask about
the other aspects. We then call the happiness sub-question an ―isolated‖ measure of SWB. In other versions, the
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12 aspects appear together, either in the order specified above—in which case we call the happiness sub-
question ―first in a series‖ measure of SWB—or in reverse order, starting with ―your sense of purpose‖—in
which case the happiness sub-question is a ―last in a series‖ measure of SWB.
II.C. Additional Questions
Our main survey questions outlined in II.B (a)-(d) above can be summarized as follows. All subjects are
asked the choice question (a). For some subjects, this question is followed by the meta-choice question (b). All
subjects are asked a SWB question (c), but different variations of this question are asked to different subjects.9
The SWB question is asked either in isolation—where subjects are only asked to predict one SWB measure
under the alternative scenarios—or in series—where subjects are asked about twelve aspects of life, as
explained in (d).10
In order to validate the evidence from these scenario-based main questions, we added to our surveys
three types of additional questions. First, subjects were asked to assess the current levels of SWB (e.g.
happiness) and other aspects of their life. Second, they were asked questions aimed to elicit self-reported
willingness-to-pay (WTP) in time (rather than in money) for improving different aspects of their life. As
explained below, combining these two sets of questions allows us to construct a measure of the importance
individuals assign to the different aspects in their utility function. We can then examine how this measure
correlates with our scenario-based measures.
As a third type of validation questions, our survey participants were explicitly asked for interpretations
of their own behavior. For example, they were asked to explain their choices when these could not be explained
as maximizing predicted SWB.
We postpone a full discussion of these three types of additional questions to Section IV, where we
describe them in detail, explain the rationale behind them, provide the theory they are based on by further
developing the theory from Section I and, most importantly, use the evidence they provide to assess our main
findings. Before doing so, in the next section we first discuss our main findings.
9 The only exception is study 2 which employs a between-subject design, in which subjects are asked either the choice question or a
SWB question. 10
As mentioned above, Table 1 details this and other information by our four main surveys. In addition to reporting each survey’s
population type, size, and cell sizes, the table indicates which of the thirteen scenarios are used, which of the subjective well-being
questions (i)-(v) above is used, and whether it is used in isolation or in series. It also indicates the format of the choice question (6-
point scale versus binary question), whether a meta-choice question is included, and whether all subjects are asked both the choice and
the subjective well-being questions (a within-subject design) or half the subjects are asked the choice question and the other half the
subjective well-being question (a between-subject design). Finally, the table details the ordering of scenarios in different versions, as
well as the ordering or the SWB question versus the choice and meta-choice questions.
12
III. Main Results
Table 2 reports the distribution of responses to our surveys‘ main choice and SWB questions.11
For each
scenario in each study, we count how many subjects favored Option 1 over Option 2 in the choice question, and
how many subjects favored Option 1 over Option 2 in the SWB question. The joint frequencies are reported in
four cells (for the between-subjects scenarios in Study 2, the (separate) distributions are reported in two cells).
For each study and scenario, the table reports in an additional cell the p-value from a two-sided nonparametric
equality-of-proportions test, comparing the proportion of subjects who favored Option 1 in the choice question
with the proportion who favored Option 1 in the SWB question. In studies and scenarios that employ a within-
subject design, the test is the paired Liddell exact test, while in the between-study scenarios in Study 2, the test
is the unpaired Fisher exact test.12
Examining Table 2‘s leftmost column (titled ―Income vs. Sleep‖) in Study 1 illustrates some of our main
findings. First, the top two cells reveal that 87 percent of respondents predicted that they would choose the same
option that they predicted would be better in terms of SWB. This evidence can be interpreted as suggesting
that—at least in the current context—the SWB question is a good predictor of the choice question. Second, the
bottom two cells show that a non-trivial 13 percent of our respondents predicted a choice that does not align
with their predicted SWB under the alternative options.
A quick look through the rest of the columns in Study 1 verifies that Scenario 1 is rather typical in this
respect: in the rest of the scenarios, 10 to 19 percent of respondents‘ predicted choice does not align with their
predicted SWB difference. This evidence is consistent with the idea that some respondents are willing to trade
off SWB for other aspects of their lives. Of course, this evidence is also consistent with alternative explanations
(e.g. measurement error). These are addressed below.
In the rest of this section we analyze in detail the response patterns above, and show that they cannot be
entirely accounted for by explanations that focus on measurement error, special features of the questionnaires,
the survey design, or the sample. Specifically, we rule out classical measurement error as an explanation of our
findings, and provide evidence that respondents reacted systematically differently to the choice vs. the SWB
questions and that they carefully read the details in the scenarios. We further examine the replicability of our
results in a between-subject design and, separately, in a nationally-representative sample. We also examine
different versions of the SWB question, and show that the exact SWB measure used makes a difference and that
some measures might predict choice better than others. Finally, we explore how subjects‘ choices are related to
their rankings of the options in terms of non-SWB aspects of life. We find that (a) the other aspects statistically
11
Non-response was generally low. In the CNSS, less than 5% of respondents answered ―Do not know‖ or refused to answer on any
given question. In the Cornell sample, non-response was rare; virtually all questions had a non-response rate less than 2%. Only one
Cornell response was excluded due to obvious confusion with instructions. Due to the less-structured sampling method used in our
doctor‘s office sample, some questions from those studies had non-response rates as high as 20%. However, the majority of this non-
response is driven by respondents being called in for their appointments, alleviating concerns of selection bias. 12
In study 4, where respondents could indicate SWB indifference, responses indicating indifference were dropped from the tests.
13
significantly help predict choice beyond SWB, and (b) subjects‘ rankings of the options in terms of SWB far
outperforms their ranking in terms of other aspects for predicting choice.
III.A. Evidence on systematically different responses to the choice vs. the SWB questions
Our first test is a nonparametric equality-of-proportions test. It tests the null hypothesis that respondents
answered the SWB question the same way they answered the choice question. That is, we test the null
hypothesis that the ―treatment effect‖ of asking the choice question instead of the SWB question is equal to
zero. Looking at the Liddell test row for Study 1 in Table 2 reveals that the null is strongly rejected in five out
of the six scenarios. Indeed, that the proportion of subjects who preferred Option 1 choice-wise is, in most
scenarios, different from the proportion of subjects who preferred Option 1 SWB-wise is easily seen by
comparing study 1‘s ―Higher SWB: Option 1/ Chosen: Option 2‖ cells with the ―Higher SWB: Option 2/
Chosen: Option 1‖ cells. This finding suggests that the mean preference ranking across the two options differs
from the mean SWB ranking, but it is also consistent with the possibility that the measurement error (i.e., the
noise component of responses) has different variance for the choice question than for the SWB questions.
III.B. Evidence on systematic reactions to scenario content
Having established that our respondents respond differently to the choice vs. the SWB questions, we
now proceed to show that these different responses react in systematic (and expected) ways to the explicit
figures—e.g. dollar amounts—in the scenario questions.
To check whether participants responded sensibly to the questions, in Study 2—where subject
population was the same as that of Study 1—we asked four different versions of Scenario 4 (career legacy
versus income). In all versions, the annual income for the alternative of being an artist who will have a lasting
legacy was held constant at $40,000. The only difference across versions was the annual income for the
alternative of being a commercial artist, which equaled $42,000, $60,000, $80,000, or $100,000.
We found that reply patterns responded to annual income levels in the expected direction. For example,
at $42,000, 20% of respondents predicted they would choose to be a commercial artist and 19% predicted it
would make them more satisfied with life, compared to 71% and 63% at $100,000, respectively (p < 0.001).
Figure 1 shows that participants are monotonically more likely to predict choosing being the commercial artist,
as well as to predict that the alternative of being a commercial artist would lead to greater SWB, when annual
income is higher, consistent with non-random responding to the decision scenarios. Furthermore, importantly,
23 percent of the respondents predicted a choice that would not lead to greater SWB in at least one of the four
annual income levels above (including 3 percent who behaved this way in all four income levels). This finding
would be implied by a model in which different individuals are inconsistent between choice and SWB at
different income levels: as income for the commercial artist option increases, more subjects switch toward the
14
commercial artist option, but it takes a higher income for most subjects to rank that option as giving higher
SWB than it does to induce them to choose it. However, we cannot rule out the possibility that different
individuals exhibit choice-SWB inconsistencies at different income levels simply because measurement error
(in choice ranking, SWB ranking, or both) is drawn independently for each of the four income-level questions.
III.C. Evidence from within- vs. between-subject design
As mentioned above, the rest of the scenarios in Study 2 were conducted in a between-subject design:
while half the respondents only answered choice questions regarding the scenarios, the other half answered only
SWB questions (specifically, life satisfaction questions; we return to this point below).13
This was done to
verify that our findings in Study 1 are not entirely due to the within-subject design. Our main worry was that
subjects who are presented with both a choice and a SWB question might attempt—consciously or not—to
respond in what they perceive as a consistent way.14
Looking at the results for Study 2 in Table 2 reveals that our worry was not justified. In the three
scenarios that were kept identical between Study 1 and Study 2, we cannot reject cross-study equality of
proportions of subjects who preferred Option 2 in either the SWB or the choice question in five out of six cases
(see the third and fourth rows in study 2). And although our confidence intervals are too wide to allow us to
reject small differences, in two out of the three scenarios results in Study 2 look almost identical to those in
Study 1. Scenario 1 is an example: while in Study 1, 30 and 41 percent of respondents ranked Option 2 higher
on SWB and choice respectively, in Study 2 the corresponding percentages are 34 and 44. Equality of
proportions between the choice and SWB question is rejected in both studies (by the Liddell and Fisher tests,
respectively), although the p-value is larger (and the sample size smaller) in Study 2. Another example is
Scenario 3, where we cannot reject equality of proportions (choice vs. SWB) in either Study 1 or Study 2, and
where Option 2 is favored in the SWB and choice questions by 48 and 46 percent of respondents in Study 1 and
by 51 and 48 percent in Study 2.
On the other hand, SWB proportions in Scenario 13 change dramatically, from 36 percent in Study 1 to
54 percent in Study 2 (while choice proportions change from 48 to 53 percent, a difference that is not
statistically significant). This means that while equality of proportions between the choice and SWB questions
in scenario 13 can easily be rejected in Study 1, it cannot be rejected in Study 2. Overall, then, we interpret
Study 2 results as strongly rejecting the hypothesis that the within-subject design of Study 1 creates artificial
consistency in answering the two questions. Indeed our evidence may suggest that if anything, it is possible that
13
Notice that Scenario 4, which was presented in Study 2 four different times in a within-subject design, was presented only at the end
of the questionnaire (see the related footnote in Table 1 regarding the scenario order in that study). Thus, when responding to the other
scenarios’ questions, Study 2 subjects could not have imagined that they were answering only one of a pair of possible questions,
maintaining the integrity of the between-subject design. 14
As discussed in Section IV below, in Study 4 we explicitly asked respondents, at the end of the questionnaire, whether they thought
they had included such consistency considerations when answering the scenario questions. See discussion there.
15
replies in the between-subject design were more consistent. This interpretation may be consistent with Study 2‘s
results for additional two scenarios (2 and 12), which differed from their versions in Study 1 and hence cannot
be compared across studies, but failed to produce statistically significant proportion differences between the
questions.
III.D. Evidence from a national sample
In Study 3, we used Cornell‘s National Social Survey (CNSS) to present Scenario 1 to a nationally
representative sample. Looking at Study 3‘s results in Table 2 suggests that although the proportions in the four
cells differ from those in the two studies above—which is not surprising, given the different populations—our
main results are not driven by peculiar features of the respondent population in Studies 1 and 2. As in Study 1
above, a large share of subjects gave the same answer to the choice and the SWB questions (in fact, the share of
these subjects grew from 87 percent in Study 1 to 92 percent in Study 3). And, also as above, respondents who
answered the questions differently have done so in the same systematic way in which they have in Study 1:
almost all of them predicted that while Option 1 would be better for SWB, they would choose Option 2. This is
shown statistically by a strong result in the Liddell test.
III.E. Evidence regarding specific SWB question framing/language
As discussed above, participants‘ predictions of their SWB are in general highly predictive of their
choice. Averaging across all of our surveys, participants, scenarios, and SWB questions, 86 percent of the time
participants predict making the choice they predict will generate greater SWB, which is statistically
distinguishable from 50 percent (one-sample difference-of-proportions test statistic=59.53, p = 0.000)
Table 3 provides regression evidence on the relationship between predicted SWB and choice within each
survey. The left panel of the table displays probit regressions with scenario fixed effects, where the dependent
variable is a dummy for choice of one of the alternatives, and the independent variable is a dummy for
predicting higher SWB in that alternative. The right panel displays OLS regressions where the dependent
variable is the participant‘s intensity of choice (on a 6-point scale, demeaned at the scenario level) in favor of
one of the alternatives, and the independent variable is the participant‘s rating of the likelihood of being happier
in that alternative (also on a 6-point scale). The regressions are conducted separately for each of the five
versions of the SWB question (see Section II.B. (c) for details) and, in study 4, separate regressions are run on
isolated vs. in series SWB measures. Finally, since Study 3 included only Scenario 1 (―Income vs. Sleep‖), for
ease of comparison the table reports results both for all scenarios together, and for Scenario 1 separately.
Looking at the results for Study 1, Table 3 reveals that the isolated SWB measures (i), (ii), and (iii)
differ from each other in how predictive they are of the choice question. For example, looking at the
(demeaned) OLS regressions, where R2‘s are accounted for by only the SWB measure, the regressions for study
16
1 suggest that the (isolated) life satisfaction measure (i) is statistically significantly a better predictor of choice
(with R2 = 0.65, s.e. = 0.03 for all scenarios, and R
2 = 0.74, s.e. = 0.06 for scenario 1) than the (isolated) felt
happiness measure (iii) (R2 = 0.55, s.e. = 0.03 for all scenarios, and R
2 = 0.60, s.e. = 0.06 for scenario 1).
15 At
the same time, evidence for the happiness with life as a whole measure (ii) varies a little more. Compared with
the felt happiness measure, it is a better predictor in the OLS regressions than it is in the probit regressions (but
remember that the probit regressions for all scenarios include scenario fixed effects). Across studies, however,
when looking at only scenario 1, happiness with life as a whole is a better predictor in study 3 than in study 1,
although the difference is not statistically significant.
III.F. Evidence regarding isolated vs. in-series SWB measures
Having found that the language of the SWB measure could matter, we explore, in study 4, whether the
position and context of the SWB measure could matter as well. Looking at study 4‘s OLS regression results in
Table 3 reveals that in that study, the two SWB measures we use—own happiness with life as a whole (iv), and
immediately felt own happiness (v)—explain the same amount of choice variation (R2 = 0.43) when asked in
isolation, but possibly explain different amounts (which are slightly below the above R2) when asked in series.
To further explore this, we aggregate across the two own happiness measures (iv) and (v) and run OLS
regressions (not reported) separately for an isolated happiness measure (when happiness is the only
characteristic of the alternatives rated by participants), a first-in-series measure (when happiness is the first in a
list of characteristics rated by participants), and a last-in-series measure (when happiness is listed last). For
comparing R2‘s, we calculate bootstrapped standard errors, based on 100 resamplings of the data.
We find that own happiness is most predictive of choice when asked in isolation (R2 = 0. 43, s.e. = 0.02).
When asked first-in-series, R2
is 0.34 (s.e. = 0. 04), compared to 0.37 (s.e. = 0.04) as last-in-series. Although
statistically, these differences in R2 are not significant, our finding that an isolated own happiness measure
seems to predict choice no worse than an in-series measure may suggest that respondents‘ interpretation of
SWB measures might be context-dependent. For example, it is possible that when the SWB question is
presented in isolation, respondents interpret it as aiming at a more inclusive notion of SWB, whereas when it is
presented as part of a series of questions regarding other aspects of life, it is interpreted as a ―stripped down‖
notion of SWB, or as ―SWB controlled for other aspects of life.‖
III.G. Evidence that aspects of life other than SWB help predict choice
Finally, Tables 4-8 analyze our findings from Study 4. In that study we elicited from respondents the
relative rankings of the two options of the scenarios in terms of a list of twelve aspects of life, only one of
15
The standard errors we report above for R2‘s are not reported in the table. They are calculated using bootstrapping.
17
which was a SWB measure (―own happiness‖). The aspects-of-life measures came in two versions—a ―life as a
whole measure‖ and an ―immediately felt‖ version, so that the ―own happiness‖ measure came in two versions,
corresponding with SWB measures (iv) and (v) in II.B(c) above. Since these SWB measures specifically
measure own happiness, in this subsection we interchangeably refer to them as measures of SWB or as
measures of happiness.
Table 4 and Table 5, present evidence on the additional predictive power of aspects other than
happiness, and the comparative ability of each aspect to explain variation in choice. The dependent variable is
intensity of choice (the choice rating on a 6-point scale), and the dependent variables are predicted happiness
and other aspects of life (on a 7-point scale). The top panel estimates regressions of choice on happiness and
each other aspect individually. The bottom panel estimates regressions of choice on each aspect by itself. Data
are demeaned at the scenario level to control for scenario-specific fixed effects.
The two tables are identical in structure, and while Table 4 shows data for the isolated SWB measures
(see II.B(d)), Table 5 shows them for in-series measures of SWB. Since typical SWB survey-based data are
collected using isolated SWB measures, the results in Table 4 answer the question: ―By how much can we
improve the typical isolated SWB measures as predictors of choice?‖ At the same time, the results in Table 5
answer questions such as ―If happiness were measured in series along with measures of other aspects of life,
would it become a better or a worse predictor of choice compared with its isolated version?‖ and ―Will it
outperform in-series measures of other aspects of life?‖
Looking at the leftmost column of Tables 4 and 5 (in either panel, which for this column is identical)
reveals that own happiness is a strong and statistically significant predictor of choice. These univariate
regressions‘ R2 can directly be interpreted as a measure of the predictive power of the regressor. In the leftmost
column, it is 0.43 in Table 4, and 0.36 in Table 5, which suggests that an isolated version of own happiness is a
better predictor of choice than its in-series equivalent. The percentage of the variation in choice explained by
SWB is significantly lower in study 4 compared to studies 1 and 3. This difference might be due to one of
several factors, such as the different response scale used in study 4 (allowing indifference), the different sample
populations, or the different scenarios asked across different studies. These latter two possibilities are addressed
below, in sections IV.D and IV.E.
Each one of the rest of the columns of panel (a) (in either table) adds one in-series measure of an aspect
of life other than own happiness to the regression. The results reveal two interesting patterns. First, non-SWB
aspects of the options seem to matter. Most of the aspects—family happiness, health, control over life, life‘s
level of spirituality, social status, physical comfort, and sense of purpose—are statistically significantly
predictive of choice, controlling for either isolated own happiness (Table 4) or in-series own happiness (Table
5). Second, even when an additional aspect is highly statistically significant, R2s do not improve much. Looking
18
at the ―Incremental R2‖ column in panel (a) in either table shows that R
2‘s improve by no more than 0.02 (for
sense of purpose in Table 4, and for control over life in Table 5).
Looking at the rest of the columns at the bottom panel, all of the aspects are highly predictive of choice.
However, in terms of predictive power, own happiness is unambiguously superior to the rest on the list. In
Table 5, where all twelve aspects are measured in-series and hence are most directly comparable, control over
life is the aspect of life that has the highest R2 after own happiness, at 0.14, followed by sense of purpose (R
2 =
0.12), life‘s level of fun (R2 = 0.11), and life‘s non-boringness (R
2 = 0.11). In other words, we find that as a
single predictor of choice, among in-series measures, own happiness outperforms the rest by a wide margin.
While Tables 4-5 analyze how each of the non-happiness aspects compares with happiness individually,
Tables 6-8 break down the results by scenario, and analyze the relative explanatory power of happiness and the
non-happiness aspects when all are controlled for simultaneously. The three tables are again identical in
structure, and while Table 6 pools all the data, Tables 7 and 8 separate the results for isolated happiness
measure and for in-series happiness measure, respectively.
As is evident from, e.g., Table 8 (which can be compared with Table 5), the coefficient on happiness is
somewhat smaller when all of the aspects are controlled for. Nonetheless, it remains by far the most predictive
of choice, as assessed either by magnitude of coefficient or by the incremental R2 from adding all of the non-
happiness aspects to the happiness-only regression. Since subjects‘ rankings of the options in terms of one
aspect are correlated with their rankings in terms of other aspects, the aspect coefficients are quite different in
the fully-controlled regressions (Tables 7-8) compared with the one-aspect-at-a-time regressions (Tables 4-5).
This however does not change our main findings above. Far example, examining the results pooled across
scenarios in column 1 (Table 8), ―control over your life‖ has the second-largest coefficient after happiness. The
next most highly predictive aspects, not statistically different from control over life, are social status and sense
of purpose. These aspects‘ coefficients are well below the coefficient on own happiness.
IV. Robustness and Additional Results
In this section, we discuss supplementary analyses that are helpful for interpreting and elaborating on
our main findings. We outline the theory underlying these additional tests, describe the survey instruments used
to carry them, and report our findings.
IV.A. Participants’ interpretations of their own behavior
We would like to infer from discrepancies between participants‘ choices and their predicted SWB that
factors other than SWB enter preferences. However, a concern in any survey is that participants may interpret
the questions they are answering differently than we the researchers interpret them, potentially invalidating our
inferences. To obtain evidence about participants‘ own interpretation of their behavior, we asked them what
19
they were thinking when their choice differed from the option they predicted would maximize SWB.
Specifically, after participants finished all the decision scenarios, but before the WTP questions, we asked:
At times in the earlier scenarios, you might have chosen an alternative which you did not think would
make you happier.
If you made such a choice, do you believe it was a mistake? That is, if you could go back and change your
answer now, would you want to? (Please circle one).
YES / NO / I never made this kind of choice.
If you made such a choice, do you think you would regret it? That is, if your chosen alternative actually
occurred, do you think you would later wish you had instead chosen the other option? (Please circle one).
YES / NO / I never made this kind of choice.
If you made such a choice, please explain your reasoning: [blank space]
Most subjects think of their choice-SWB discrepancies as intentional. In response to the question whether such
a discrepancy ―was a mistake,‖ 7% said yes, 73% said no, 19% said they never made such a choice, and 1% did
not respond. In response to the question whether they ―would regret‖ such a discrepancy, 23% said yes, 57%
said no, 19% said they never made such a choice, and 1% did not respond. Moreover, our conclusions from
Section III remain essentially the same if we exclude participants who believed they were making a mistake or
believed they would regret their choices. The first column of Table 9 repeats our preferred specification from
Table 6, while the second and third column compares the results when we exclude ―mistake‖ subject and
―regret‖ subjects, respectively.
IV.B. Self-control problems
A related concern is that these discrepancies may reflect a self-control problem (e.g., as in Laibson,
1997; O‘Donoghue and Rabin, 1999), rather than a preference for non-SWB aspects of the options. In most of
our decision scenarios, it is not obvious how a self-control problem would be implicated, but there are a few
where it is. For example, in one decision scenario, one of the alternatives was staying out later with friends,
while the other was going to bed earlier to feel better and be more productive the next day. A participant who
correctly anticipates having a self-control problem might say she would choose to stay out late, even though her
welfare would be maximized by going to bed earlier.
In order to assess the potential importance of self-control problems, in some versions of the survey, in
addition to asking participants what they would choose, we asked them what they would want themselves to
choose (their meta-preference). We reasoned that if a participant preferred Option 1 but would choose Option 2
due to a self-control problem, the participant would indicate a greater likelihood of choosing Option 1 but a
20
meta-preference for Option 2. Aggregating across all surveys where we asked about meta-preferences, we
found discrepancies between choice and meta-preference in 25% of cases.
However, while self-control problems may explain some of subjects‘ choices, our main results from
Section III appear to be robust when we exclude these observations. The fourth column of Table 9 shows that
when we regress choice on SWB and all the non-SWB aspects, the results for the sample restricted to subjects
whose preference and meta-preference always coincide are qualitatively the same as the results for the full
sample.
IV.C. Artificial consistency between choice and SWB responses
One explanation for why SWB predicts choice so well is that subjects think they ought to give the same
answer to the two questions. In Section III.C, we discussed evidence from a between-subjects study (Study 2),
which suggests that this kind of ―artificial consistency‖ cannot fully account for our results. In Study 4, we also
collected additional data relevant for evaluating the importance of ―artificial consistency‖ in our studies. After
participants finished all the decision scenarios, we asked them:
Throughout this survey, we asked you to choose between two alternatives, and we also asked you to rate
the options in terms of how they would affect various aspects of your life. When you made these ratings,
were you trying to make your ratings consistent with what you chose? (Please circle one.)
(A) I rated the aspects independently from what I chose.
(B) When I rated the aspects, I tried to be unaffected by what I chose, but I was probably affected to some
extent.
(C) When I rated the aspects, I tried to be consistent with what I chose.
(D) Other (Please specify) ________________________
In this question, 30% of our respondents answered (A), 49% answered (B), 20% answered (C), and 1% did not
respond. These responses suggest that an attempt to be consistent may have consciously or (perhaps more
commonly) unconsciously played a role in subjects’ responses. Nonetheless, the fifth column of Table 9 shows
that our results from Table 6 remain qualitatively very similar when we exclude from the analysis all subjects
who did not respond either (A) or (B). This evidence reinforces the impression from Section III.C that artificial
consistency does not drive our main conclusions.
IV.D. Heterogeneity across decision scenarios
In a regression of choice on SWB and non-SWB aspects, the coefficients may vary depending on which
scenarios we presented to participants. Here we examine three dimensions of heterogeneity.
Local vs non-local tradeoffs. As outlined in Section I, the interpretation of the regression coefficients as
capturing marginal utilities assumes a linear approximation to the utility function. This approximation will be
21
more valid to the extent that the options in the decision problem involve small changes from the subject‘s status
quo. In Table 6, we label three of the scenarios as containing ―non-local tradeoffs.‖ These are all cases where
one of the options asked the subject to imagine having a particular level of income, which could well be quite
far from the subject‘s current income. In Table 10, we compare the regression coefficients for the scenarios with
non-local tradeoffs with the regression coefficients for the full sample. The first column of Table 10 shows the
regression of choice on SWB and the non-SWB aspects (repeating our preferred specification from Table 6),
while the second column restricts the sample to scenarios with non-local tradeoffs. Despite the theoretical
possibility of different results, the coefficient estimates are in fact for the most part quite similar.
Representative scenarios. When we started this project, some of us were unsure whether we would find
any discrepancies between predicted happiness and choice. In order to maximize the chance we could detect a
discrepancy, we chose our initial scenarios with a happiness tradeoff in mind. For example, the artist scenario
was designed to generate a tradeoff between happiness and a posthumous legacy that may contribute little to
happiness during life.
Once we found clear discrepancies between predicted happiness and choice, we became interested in
estimating the role of predicted happiness in the most empirically-relevant settings. To address this question,
we would ideally ask participants about a representative sample of decisions they face. We generated some
decision scenarios that we believed were representative, such as a basic tradeoff between time and money
(scenario 6). As discussed in Section II.A, we also generated decision scenarios that are common in our
participants‘ lives by asking students at the University of Chicago and at Cornell University to tell us about
some of the most important decisions they face.
The third column of Table 10 restricts the sample to scenarios chosen to be more representative of
important decisions for college students. Despite our concern that we initially chose scenarios that highlighted a
tradeoff with SWB, the regression coefficient on SWB is actually smaller in the representative scenarios than in
the full sample (the first column).
Everyday, minor decisions. From a theoretical perspective, our findings indicate that while SWB (as it is
typically measured in applied work) is a major component of preferences, it is not a direct measure of utility.
However, from a practical perspective, an important question is whether the marginal utility of happiness varies
across scenarios. If so, then empirical work that uses measured SWB as a proxy for utility is more valid in
scenarios where the marginal utility of happiness is high. On the other hand, in decision situations where the
marginal utility of happiness is low, welfare conclusions based on analyses of measured SWB may be
misleading. A hypothesis we can test with our data is whether SWB plays a more important role in everyday,
minor decisions, where other factors such as sense of purpose are simply not relevant. In Table 6, we categorize
six of our decision scenarios as being ―everyday/minor decisions.‖ The fourth column of Table 10 repeats the
regression from Table 6, but with the sample restricted to observations from decision scenarios featuring
22
everyday/minor decisions. We do indeed find that SWB has a higher coefficient here than in the full sample.
Nonetheless, we continue to find other aspects as statistically significant predictors of choice—including sense
of purpose and control over life, both of which one might have expected to be irrelevant in this kind of decision.
IV.E. Preference heterogeneity across individuals
Our main results can be interpreted as an examination of average or representative preferences; here we
examine the degree of heterogeneity seen across individual preferences. We regressed 6-point choice on 6-point
SWB, subject-level characteristics, and interactions between SWB and subject-level characteristics (results not
shown). These subject-level characteristics were age, gender, log income, race dummies, and—only in Study 4,
where we administered a personality questionnaire—scores on the ―Big 5‖ personality traits. None of the
demographic variables had statistically significant coefficients. Of the personality traits, only neuroticism had a
significant coefficient; a higher rating of neuroticism is associated with lower predictive power of SWB ratings.
V. Conclusion
In a variety of decision scenarios and with a variety of subject populations, we test whether individuals
think they would choose—or would like to choose—the same option they predict will maximize SWB. Our
findings suggest that choice and SWB are conceptually distinct, and that SWB appears to be only one among
several arguments in the utility function. However, SWB appears to be a uniquely important argument in the
utility function that predicts choice far better than other factors that enter preferences.
23
References
Becker, Gary S., and Luis Rayo. 2008. ―Comment on ‗Economic growth and subjective well-being: Reassessing
the Easterlin Paradox‘ by Betsey Stevenson and Justin Wolfers.‖ Brookings Papers on Economic
Activity, Spring, 88-95.
Bentham, Jeremy. 1789. An Introduction to the Principle of Morals and Legislations.Bernheim, Douglas B.
2009a. ―Behavioral Welfare Economics.‖ Journal of the European Economics Association, 7(2-3), 267-
319.
Bernheim, Douglas B. 2009b. ―Behavioral Welfare Economics.‖ Presentation at the First Annual Behavioral
Economics Conference, University of California--Berkeley, May 22.
Blanchflower, David G. and Andrew J. Oswald. 2004. ―Money, Sex and Happiness: An Empirical Study.‖
Scandinavian Journal of Economics, 106(3), 393-415.
Bronsteen, John, Christopher J. Buccafusco, and Jonathan S. Masur. Forthcoming. ―Well-being analysis.‖
Georgetown Law Journal.
DiTella, Rafael, Robert J. MacCulloch, and Andrew Oswald. 2003. ―The Macroeconomics of Happiness.‖
Review of Economics and Statistics, 85(4), 809-827.
Ellison, Christopher G. 1991. Religious involvement and subjective well-being. Journal of Health and Social
Behavior,32, 80–99.
Finkelstein, Amy, Erzo F.P. Luttmer, and Matthew J. Notowigdo. 2008. ―What good is wealth without health?
The effect of health on the marginal utility of consumption.‖ NBER W.P. 14089, June.
Gilbert, Daniel T. (2006). Stumbling on happiness. New York: Knopf.
Gruber, Jonathan, and Sendhil Mullainathan. 2002. ―Do cigarette taxes make smokers happier?‖ NBER W.P.
8872.
Heffetz, Ori, and Robert H. Frank. Forthcoming. ―Preferences for Status: Evidence and Economic
Implications.‖ Handbook of Social Economics, Jess Benhabib, Alberto Bisin, Matthew Jackson, eds.,
Elsevier.
John, Oliver P., and Sanjay Srivastava. 1999. The Big Five trait taxonomy: History, measurement, and
theoretical perspectives. In Lawrence A. Pervin and Oliver P. John (Eds.), Handbook of personality:
Theory and research (2nd ed., pp. 102-138). New York: Guilford.
Laibson, David I. 1997. ―Golden eggs and hyperbolic discounting.‖ Quarterly Journal of Economics, 112(2),
443-477.
Layard, Richard (2005). Happiness. Penguin Press.
Luttmer, Erzo F.P. 2005. ―Neighbors as Negatives: Relative Earnings and Well-Being.‖ Quarterly Journal of
Economics, 120(3), 963-1002.
24
Kahneman, Daniel, Alan B. Krueger, David A. Schkade, Nobert Schwarz, and Arthur A. Stone. 2004. ―A
survey method for characterizing daily life experience: The Day Reconstruction Method.‖ Science, 306,
1776-1780.
Kahneman, Daniel, Peter P. Wakker, and Rakesh Sarin. 1997. ―Back to Bentham? Explorations of experienced
utility.‖ Quarterly Journal of Economics, 112(2), 375-405.
Kelly, William. 2004. ―Sleep-Length and Life Satisfaction in a College Student Sample.‖ College Student
Journal, 38(3).
Kimball, Miles S., Helen Levy, Fumio Ohtake, and Yoshiro Tsutsui, 2006. ―Unhappiness After Hurricane
Katrina.‖ University of Michigan mimeo.
Kimball, Miles S., and Robert J. Willis. 2006. ―Happiness and Utility.‖ University of Michigan mimeo.
Koszegi, Botond, and Matthew Rabin. 2008. ―Choices, Situations, and Happiness.‖ Journal of Public
Economics, 92, 1821-1832.
Nozick, Robert. 1974. Anarchy, State, and Utopia. New York: Basic Books.
Nussbaum, Martha. 2000. Women and Human Development: The Capabilities Approach. Cambridge:
Cambridge University Press.
O‘Donoghue, Ted, and Matthew Rabin. 1999. ―Doing it now or later.‖ American Economic Review, 89(1), 103-
124.
Oreopoulos, Philip. 2007. ―Do dropouts drop out too soon? Wealth, health and happiness from compulsory
schooling.‖ Journal of Public Economics, 91, 2213–2229.
Schwarz, Norbert, and Gerald L. Clore. 1983. ―Mood, misattribution, and judgments of well-being: Informative
and directive functions of affective states.‖ Journal of Personality and Social Psychology, 45(3), 513-
523.
Sen, Amartya. 1985. Commodities and Capabilities. Oxford: Oxford University Press.
Stevenson, Betsey and Justin Wolfers. 2008. ―Economic Growth and Subjective Well-Being: Reassessing the
Easterlin Paradox.‖ NBER Working Paper No. 14282.
Stutzer, Alois and Bruno S. Frey, 2008. ―Stress that Doesn't Pay: The Commuting Paradox,‖ Scandinavian
Journal of Economics, 110(2), 339-366.
Stutzer, Alois. 2007. ―Limited Self-Control, Obesity and the Loss of Happiness,‖ IZA Discussion Papers 2925,
Institute for the Study of Labor (IZA).
Tversky, Amos, and Dale Griffin. 1991. ―Endowments and Contrast in Judgments of Well-Being.‖ .‖ In
Richard J. Zeckhauser, Strategy and Choice. Reprinted in Choices, Values, and Frames.
White, Mathew P., and Paul Dolan. 2009. ―Accounting for the richness of daily activities.‖ Psychological
Science, 20(8), 1000-1008.
25
Figure 1.
26
Table 1: Study-specific Information Study 1 Study 2 Study 3 Study 4
Sample Population Doctor‘s Office Doctor‘s Office CNSS Cornell Students
N 497 569 1000 214
Scenarios Presented
(See Appendix for text of
scenarios)
1, 3, 4, 11, 12, 13 1, 2, 3, 4, 12, 13 1 1-10
Choice and SWB variables†
Sample size of each cell in parenthesis
Life Satisfaction (i)
Isolated (164) (569)
Happiness with Life as a
Whole (ii)
Isolated (162) (1000)
Felt Happiness (iii)
Isolated (171)
Own Happiness with Life
as a Whole (iv)
Isolated (52)
In Series (52)
Immediately Felt Own
Happiness (v)
Isolated (55)
In Series (55)
Choice Question Format 6-point 6-point Binary 6-point
Meta-Choice Question? Yes Yes No Yes
Choice vs SWB: Within- or
Between- Subject Design
Within Between Within Within
Randomizations
Scenario order 4-1-11-12-13-3 1-2-12-13-3-4 1 1, 2, …, 9, 10
3-13-12-11-1-4 3-13-12-2-1-4‡
Choice Question order Choice-Meta-SWB Two opposite
orderings of
aspects of life SWB-Choice-Meta
SWB asked in isolation or
in series
SWB asked in
series
SWB asked in
isolation Notes: The different variations between studies 1-4 are presented above. In the first panel of the table, the number of responses in each cell is indicated in parenthesis.
Refer to section II.B for the text of the choice question and meta-choice question. ―Choice vs SWB: Within- or Between- Subject Design‖ indicates whether each subject was asked to predict both choice and SWB for a given scenario (the within condition), or only one of the two (the between condition). The sequence of numbers
under question order corresponds to the numbering of the scenarios found in the appendix. † The exact phrasing of SWB questions i-v above is explained in section II.B.
‡ Question 4 is asked last in both question orders because it is the only question in study 2 where we ask both choice and predicted SWB. In order to have a clean
between-subject design, we did not want subjects to know we were interested in both choice and SWB until after subjects were done with the rest of the questions.
27
Table 2: Summary of Scenario Responses Scenario Number (For Phrasing, see Appendix)
1 2 3 4 5 6 7 8 9 10 11 12 13 Tradeoff implicated in
decision problem: Income
vs
Sleep
Concert vs
Dinner
Abs. Inc. vs
Rel. Inc.
Income vs
Legacy
Apple vs
Orange
Income vs
Labor
Socialize vs
Sleep
Income vs
Dinner
Education vs
Social life
Interest vs
Career
Concert vs
Duty
Rent vs
Commute
Friends vs
Income
STUDY 1 Version 1 Version 1
Higher SWB: Option 1 Chosen: Option 1
58% 48% 24% 16% 52% 50%
Higher SWB: Option 2 Chosen: Option 2
29% 42% 60% 65% 32% 34%
Higher SWB: Option 1 Chosen: Option 2
12% 4% 14% 7% 5% 14%
Higher SWB: Option 2 Chosen: Option 1
1% 6% 2% 12% 11% 2%
p-value of Liddell Exact Test
0.000 n = 425
0.35 n = 420
0.000 n = 422
0.024 n = 422
0.002 n = 425
0.000 n = 422
STUDY 2 Version 2 Version 2
Higher SWB: Option 2 34% 86% 51% 54% 54%
Chosen: Option 2 44% 84% 48% 55% 53%
p-value of difference in proportion choosing option
1 between studies 1 and 2
0.430 0.640 --- --- 0.212
p-value of difference in
proportion with higher
SWB under option 1
between studies 1 and 2
0.398 0.342 --- --- 0.000
Higher SWB: Option 1 Chosen: Option 1
41%
Higher SWB: Option 2 Chosen: Option 2
44%
Higher SWB: Option 1 Chosen: Option 2
11%
Higher SWB: Option 2 Chosen: Option 1
4%
p-value of Fisher Test 0.020 n = 525
0.716 n = 524
0.433 n = 525
0.930 n = 526
0.793 n = 525
p-value of Liddell Exact
Test
0.000
n = 2852
28
Scenario Number (For Phrasing, see Appendix)
1 2 3 4 5 6 7 8 9 10 11 12 13
Tradeoff implicated in
decision problem:
Income
vs
Sleep
Concert
vs
Dinner
Abs. Inc.
vs
Rel. Inc.
Income
vs
Legacy
Apple
vs
Orange
Income
vs
Labor
Socialize
vs
Sleep
Income
vs
Dinner
Education
vs
Social life
Interest
vs
Career
Concert
vs
Duty
Rent
vs
Commute
Friends
vs
Income
STUDY 3
Higher SWB: Option 1 Chosen: Option 1
74%
Higher SWB: Option 2 Chosen: Option 2
18%
Higher SWB: Option 1 Chosen: Option 2
7%
Higher SWB: Option 2 Chosen: Option 1
1%
p-value of Fisher Test 0.001 n = 1968
p-value of McNemar Test 0.000 n = 980
STUDY 4 Version 2
Higher SWB: Option 1 Chosen: Option 1
28% 23% 35% 40% 30% 39% 51% 62% 51% 28%
Higher SWB: Option 2 Chosen: Option 2
44% 44% 37% 28% 32% 26% 14% 15% 22% 31%
Higher SWB: Option 1
Chosen: Option 2
17% 10% 2% 12% 1% 2% 6% 14% 3% 29%
Higher SWB: Option 2 Chosen: Option 1
1% 3% 12% 8% 1% 10% 16% 4% 19% 3%
Percent Indicating Indifference for SWB
10% 20% 14% 12% 36% 23% 13% 5% 5% 9%
p-value of Fisher Test 0.000 n =402
0.167 n =380
0.033 n =393
0.362 n =397
0.912 n =345
0.025 n =368
0.013 n =395
0.003 n =409
0.001 n =411
0.000 n =401
p-value of McNemar Test 0.000 n =192
0.008 n =170
0.000 n =183
0.280 n =187
1.000 n =135
0.001 n =160
0.003 n =185
0.001 n =200
0.000 n =201
0.000 n =191
Notes: Response distribution by study and scenario. For the complete text of each scenario, see the appendix. If a questions phrasing changed meaningfully between surveys, the
version of the question is indicated in the first row of the study block. For between-subject comparisons, we report the Fisher Test p-value testing the null-hypothesis that mean
response to choice question = mean response to SWB question (an unpaired equality-of-proportions test). For within-subject, we report the analogous Liddell Exact Test p-value (a
paired equality-of-proportions test). In cases where respondents could indicate SWB indifference, responses indicating indifference were dropped from these tests. In study 2, we
report the p-value of fisher exact tests of the difference of proportions between SWB (or choice) responses in study 2 and SWB (or choice) responses in study 1 to provide a check
of cross-sample consistency and comparability. The values for the Income vs Legacy scenario in study 2 are pooled across the four payoff amounts faced.
29
Table 3: Regressions of Choice on SWB
Probit Estimates OLS Estimates
Study 1 Scenario 1: Income vs Sleep
Study 1 Study 4 Scenario 1: Income Vs Sleep
Study 1 Study 3 Study 1 Study 4
Life Satisfaction (i)
Isolated 0.78
(0.02)
R2 =0.53
n=424
0.88
(0.04)
R2 =0.73
n=141
0.81
(0.02)
R2 =0.65
n=424
0.88
(0.04)
R2 =0.74
n=141
Happiness with Life as a Whole (ii)
Isolated 0.70
(0.03)
R2 =0.39
n=411
0.70
(0.03)
R2 = 0.40
n=137
0.86
(0.02)
R2 =0.51
n=980
0.79
(0.02)
R2 =0.59
n=411
0.91
(0.05)
R2 =0.69
n=137
Felt Happiness (iii)
Isolated 0.70
(0.03)
R2 = 0.40
n=433
0.76
(0.05)
R2 = 0.42
n=147
0.73
(0.02)
R2 = 0.55
n=433
0.81
(0.05)
R2 = 0.60
n=147
Own Happiness with Life as a Whole (iv)
Isolated 0.59
(0.03)
R2 =0.43
n=519
0.46
(0.09)
R2 =0.32
n=52
In Series 0.52
(0.03)
R2 = 0.31
n=510
0.48
(0.09)
R2 = 0.36
n=51
Immediately Felt Own Happiness (v)
Isolated 0.58
(0.03)
R2 =0.43
n=538
0.58
(0.08)
R2 =0.50
n=54
In Series 0.59
(0.03)
R2 =0.39
n=549
0.70
(0.07)
R2 =0.68
n=55
Notes: Standard errors in parentheses. The left panel reports probit regression results, predicting the option chosen from the option with higher predicted SWB. The right panel
reports OLS regression results, predicting the intensity of preference on the intensity of predicted SWB. For both types of regressions, we report the estimates from all scenarios in
the first columns, and restricted to the income vs sleep question (question 1 in the Appendix), which was the only scenario asked in all of our surveys. * p < 0.05,
** p < 0.01,
*** p < 0.001
30
Table 4: OLS Regressions of Choice on Happiness and Each Aspect (Study 4, happiness measured in isolation) Dependent Variable: 6-point Choice Own happiness 0.576*** 0.550*** 0.558*** 0.560*** 0.567*** 0.546*** 0.562*** 0.557*** 0.559*** 0.579*** 0.565*** 0.536***
(0.020) (0.021) (0.021) (0.021) (0.021) (0.022) (0.020) (0.021) (0.021) (0.022) (0.021) (0.021)
Family happiness 0.131***
(0.028)
Health 0.103**
(0.033)
Life's level of romance 0.123***
(0.034)
Social life 0.0403
(0.028)
Control over your life 0.116***
(0.029)
Life's level of
spirituality
0.153***
(0.034)
Life's level of fun 0.0957***
(0.028)
Social status 0.103***
(0.027)
Life's non-boringness -0.0122
(0.027)
Physical comfort 0.0663*
(0.027)
Sense of purpose 0.156***
(0.025)
Observations 1057 1057 1057 1056 1053 1055 1053 1056 1056 1052 1056 1057
R2 0.43 0.44 0.43 0.44 0.43 0.44 0.44 0.44 0.44 0.43 0.44 0.45
Incremental R2 0.01 0.00 0.01 0.000 0.01 0.01 0.01 0.01 0.000 0.01 0.02
Notes: Standard errors in parentheses. OLS regression coefficients are reported. The dependent variable is 6-point intensity of choice described in section II.B.a, with a higher value indicating
a higher probability of choosing option 2. The dependent variables are the 7-point responses described in section II.B.b,c, with a higher number indicating a higher predicted value of that aspect under
option 2. All variables are demeaned at a scenario-level, equivalent to including scenario-specific fixed effects. Incremental R2 is calculated as (R
2 of choice on happiness and another
aspect)-( R2 of choice on happiness), capturing the additional predictive power added by the additional aspect.
* p < 0.05,
** p < 0.01,
*** p < 0.001
OLS regression of Choice on Each Aspect (Study 4, happiness measured in isolation) Dependent Variable: 6-point Choice Regressor Own
happiness
Family
happiness
Health Life‘s
level of
romance
Social life Control
over your
life
Life‘s
level of
spirituality
Life‘s
level of
fun
Social
status
Life‘s
non-
boringness
Physical
comfort
Sense of
purpose
Regression coefficient 0.576*** 0.319*** 0.342*** 0.333*** 0.222*** 0.367*** 0.333*** 0.291*** 0.233*** 0.220*** 0.249*** 0.340***
(0.020) (0.042) (0.054) (0.060) (0.044) (0.047) (0.061) (0.046) (0.040) (0.046) (0.036) (0.039)
Observations 1057 1062 1065 1066 1059 1064 1063 1063 1060 1060 1057 1063
R2 0.43 0.05 0.04 0.03 0.02 0.05 0.03 0.04 0.03 0.02 0.04 0.07
Notes: Standard errors in parentheses. Each column reports the OLS regression coefficients of 6-point intensity of choice on the 7-point rating of one aspect. All variables are
demeaned at a scenario-level, equivalent to including scenario-specific fixed effects. * p < 0.05,
** p < 0.01,
*** p < 0.001
31
Table 5: OLS Regressions of Choice on Happiness and Each Aspect (Study 4, happiness measured in series)
Dependent Variable: 6-point Choice Own happiness 0.555*** 0.527*** 0.525*** 0.540*** 0.542*** 0.495*** 0.538*** 0.536*** 0.531*** 0.527*** 0.519*** 0.508***
(0.023) (0.025) (0.025) (0.025) (0.025) (0.025) (0.024) (0.027) (0.024) (0.026) (0.025) (0.025)
Family happiness 0.097**
(0.033)
Health 0.124**
(0.038)
Life's level of romance 0.071
(0.044)
Social life 0.058
(0.037)
Control over your life 0.174***
(0.032)
Life's level of
spirituality
0.089*
(0.044)
Life's level of fun 0.051
(0.036)
Social status 0.132***
(0.032)
Life's non-boringness 0.082*
(0.035)
Physical comfort 0.129***
(0.032)
Sense of purpose 0.144***
(0.031)
Observations 1049 1048 1049 1046 1047 1048 1047 1049 1048 1049 1048 1048
R2 0.36 0.36 0.36 0.36 0.36 0.38 0.36 0.36 0.37 0.36 0.37 0.37
Incremental R2 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.01 0.00 0.01 0.01
Notes: Standard errors in parentheses. OLS regression coefficients are reported. The dependent variable is 6-point intensity of choice described in section II.B.a, with a higher value indicating
a higher probability of choosing option 2. The dependent variables are the 7-point responses described in section II.B.b,c, with a higher number indicating a higher predicted value of that aspect under
option 2. All variables are demeaned at a scenario-level, equivalent to including scenario-specific fixed effects. Incremental R2 is calculated as (R
2 of choice on happiness and another
aspect)-( R2 of choice on happiness), capturing the additional predictive power added by the additional aspect.
* p < 0.05,
** p < 0.01,
*** p < 0.001
OLS regression of Choice on Each Aspect (Study 4, happiness measured in series) Dependent Variable: 6-point Choice Regressor Own
happiness
Family
happiness
Health Life‘s
level of
romance
Social life Control
over your
life
Life‘s
level of
spirituality
Life‘s
level of
fun
Social
status
Life‘s
non-
boringness
Physical
comfort
Sense of
purpose
Regression coefficient 0.555*** 0.365*** 0.424*** 0.421*** 0.362*** 0.441*** 0.398*** 0.413*** 0.315*** 0.411*** 0.381*** 0.401***
(0.023) (0.037) (0.043) (0.049) (0.041) (0.033) (0.051) (0.037) (0.038) (0.037) (0.036) (0.034)
Observations 1049 1049 1050 1047 1048 1049 1048 1050 1049 1050 1049 1049
R2 0.36 0.09 0.09 0.07 0.07 0.14 0.06 0.11 0.06 0.11 0.10 0.12
Notes: Standard errors in parentheses. Each column reports the OLS regression coefficients of 6-point intensity of choice on the 7-point rating of one aspect. All variables are
demeaned at a scenario-level, equivalent to including scenario-specific fixed effects. * p < 0.05,
** p < 0.01,
*** p < 0.001
32
Table 6: Choice on Happiness and All Aspects, with Incremental R2 (Study 4, all data pooled)
Decision problem: Pooled 1 2 3 4 5 6 7 8 9 10
Tradeoff implicated in decision
problem:
Income
vs
Sleep
Concert
vs
Dinner
Abs. Inc.
vs
Rel. Inc.
Income
vs
Legacy
Apple
vs
Orange
Income
vs
Labor
Socialize
vs
Sleep
Income
vs
Dinner
Education
vs
Social life
Interest
vs
Career
Non-local tradeoff?
Everyday/minor decision?
Dependent Variable: 6-point Choice
Own happiness 0.487*** 0.463*** 0.543*** 0.538*** 0.469*** 0.779*** 0.587*** 0.322*** 0.510*** 0.361*** 0.343***
(0.018) (0.054) (0.054) (0.058) (0.058) (0.060) (0.066) (0.058) (0.057) (0.056) (0.059)
Family happiness 0.076** 0.024 -0.079 0.190* 0.068 0.314 0.139 -0.109 -0.005 0.176** 0.220**
(0.023) (0.053) (0.119) (0.081) (0.067) (0.216) (0.095) (0.091) (0.077) (0.064) (0.070)
Health 0.018 -0.088 -0.031 -0.033 0.148 -0.083 0.071 0.241* 0.091 -0.069 -0.215*
(0.030) (0.080) (0.122) (0.129) (0.100) (0.103) (0.120) (0.093) (0.103) (0.081) (0.102)
Life's level of romance 0.008 0.091 -0.042 0.232 -0.076 -1.532*** -0.159 -0.084 -0.028 -0.022 0.280*
(0.034) (0.094) (0.101) (0.136) (0.111) (0.379) (0.136) (0.102) (0.113) (0.090) (0.110)
Social life -0.045 -0.025 0.018 -0.105 -0.011 0.256 0.071 0.010 -0.017 -0.112 -0.137
(0.027) (0.086) (0.072) (0.093) (0.093) (0.304) (0.112) (0.105) (0.096) (0.088) (0.084)
Control over your life 0.079** -0.000 0.075 -0.093 0.142* 0.107 0.124 0.110 0.019 -0.026 0.171**
(0.024) (0.068) (0.095) (0.095) (0.064) (0.143) (0.090) (0.083) (0.092) (0.068) (0.060)
Life's level of spirituality -0.009 -0.090 0.017 -0.116 0.064 -0.177 -0.071 0.046 -0.060 0.104 -0.119
(0.032) (0.077) (0.085) (0.140) (0.089) (0.313) (0.128) (0.125) (0.114) (0.087) (0.100)
Life's level of fun 0.046 0.028 0.124 0.026 0.036 -0.210 0.046 -0.182 0.082 0.138 0.202*
(0.028) (0.066) (0.080) (0.102) (0.078) (0.198) (0.107) (0.142) (0.103) (0.098) (0.093)
Social status 0.077*** 0.070 -0.026 0.091 0.031 0.611 0.059 0.191 -0.022 0.092 0.181*
(0.023) (0.059) (0.076) (0.067) (0.063) (0.364) (0.096) (0.101) (0.105) (0.055) (0.073)
Life's non-boringness -0.076** -0.020 -0.141 0.269* -0.029 0.062 -0.086 0.178 0.003 -0.074 -0.236*
(0.027) (0.061) (0.092) (0.125) (0.075) (0.177) (0.093) (0.105) (0.100) (0.091) (0.094)
Physical comfort 0.034 0.175** 0.013 -0.071 0.037 0.180 -0.038 0.019 -0.127 0.096 -0.111
(0.022) (0.062) (0.094) (0.085) (0.063) (0.107) (0.077) (0.081) (0.072) (0.070) (0.074)
Sense of purpose 0.101*** 0.118 0.161 0.091 0.039 0.453** -0.083 0.148 0.178* 0.136* 0.125*
(0.021) (0.062) (0.082) (0.072) (0.063) (0.139) (0.084) (0.077) (0.085) (0.065) (0.051)
Constant 0.345* 1.282*** 1.300** -0.931 0.008 0.475 0.335 -1.150* 1.273** -0.131 1.187*
(0.141) (0.344) (0.495) (0.495) (0.443) (0.631) (0.597) (0.575) (0.406) (0.421) (0.517)
Observations 2089 210 209 208 209 210 208 208 207 211 209
R2 0.50 0.52 0.49 0.56 0.46 0.66 0.43 0.34 0.42 0.44 0.38
Incremental R2 0.03 0.06 0.04 0.08 0.05 0.07 0.02 0.11 0.03 0.10 0.17
Notes: Standard errors in parentheses. OLS regression coefficients reported. The dependent variable is 6-point intensity of choice described in section II.B.a, with a higher value
indicating a higher probability of choosing option 2. The dependent variables are the 7-point responses described in section II.B.b,c, with a higher number indicating a higher
predicted value of that aspect under option 2. The first column reports the regression pooling all scenarios and including scenario fixed effects. The remaining columns report the
regression restricted to a specific scenario. For the full wording of the scenario, see the Appendix. * p < 0.05,
** p < 0.01,
*** p < 0.00
33
Table 7: Choice on Happiness and All Aspects, with Incremental R2 (Study 4, happiness measured in isolation)
Decision problem: Pooled 1 2 3 4 5 6 7 8 9 10
Tradeoff implicated in decision
problem:
Income
vs
Sleep
Concert
vs
Dinner
Abs. Inc.
vs
Rel. Inc.
Income
vs
Legacy
Apple
vs
Orange
Income
vs
Labor
Socialize
vs
Sleep
Income
vs
Dinner
Education
vs
Social life
Interest
vs
Career
Non-local tradeoff?
Everyday/minor decision?
Dependent Variable: 6-point Choice
Own happiness 0.534*** 0.459*** 0.636*** 0.558*** 0.482*** 0.802*** 0.702*** 0.305*** 0.596*** 0.400*** 0.430***
(0.022) (0.072) (0.065) (0.070) (0.071) (0.072) (0.075) (0.077) (0.077) (0.075) (0.079)
Family happiness 0.122*** -0.042 0.075 0.293** 0.167 0.307 0.087 0.174 0.051 0.341*** 0.237*
(0.031) (0.082) (0.153) (0.105) (0.086) (0.288) (0.107) (0.149) (0.105) (0.095) (0.108)
Health -0.018 -0.094 -0.266 -0.024 0.094 -0.092 0.135 0.176 -0.026 -0.050 -0.271
(0.039) (0.116) (0.159) (0.170) (0.119) (0.143) (0.135) (0.144) (0.146) (0.117) (0.145)
Life's level of romance 0.032 0.181 -0.124 0.207 -0.106 -0.864 -0.048 -0.092 0.032 -0.072 0.291*
(0.042) (0.129) (0.129) (0.165) (0.138) (0.787) (0.164) (0.147) (0.146) (0.124) (0.134)
Social life -0.053 -0.140 -0.032 -0.225 -0.087 0.146 -0.057 0.066 0.000 -0.120 -0.146
(0.033) (0.121) (0.097) (0.145) (0.113) (0.335) (0.111) (0.133) (0.135) (0.117) (0.108)
Control over your life 0.018 0.001 0.140 -0.061 0.101 0.045 0.074 0.067 -0.121 -0.196* 0.167
(0.033) (0.099) (0.120) (0.123) (0.087) (0.216) (0.103) (0.125) (0.131) (0.097) (0.086)
Life's level of spirituality 0.019 -0.001 0.011 -0.087 0.002 0.579 -0.003 0.126 0.026 0.128 -0.089
(0.042) (0.106) (0.107) (0.187) (0.119) (0.515) (0.155) (0.192) (0.161) (0.128) (0.146)
Life's level of fun 0.128*** -0.010 0.250* 0.143 0.108 -0.737* 0.067 -0.089 0.221 0.381** 0.186
(0.036) (0.101) (0.108) (0.134) (0.096) (0.348) (0.120) (0.187) (0.140) (0.124) (0.131)
Social status 0.048 0.075 0.056 -0.026 0.080 -0.113 0.032 0.120 -0.118 0.180* 0.084
(0.030) (0.088) (0.099) (0.090) (0.086) (0.648) (0.112) (0.164) (0.144) (0.077) (0.098)
Life's non-boringness -0.149*** -0.078 -0.317* 0.217 -0.184 -0.084 -0.149 0.012 -0.082 -0.185 -0.269*
(0.034) (0.089) (0.127) (0.156) (0.095) (0.276) (0.106) (0.142) (0.149) (0.132) (0.126)
Physical comfort -0.002 0.248** -0.133 -0.083 -0.007 0.112 0.020 -0.071 -0.133 0.096 -0.133
(0.029) (0.093) (0.122) (0.105) (0.079) (0.171) (0.079) (0.100) (0.102) (0.105) (0.091)
Sense of purpose 0.124*** 0.089 0.323** 0.160 0.111 0.673*** -0.055 0.179 0.177 0.013 0.076
(0.028) (0.101) (0.107) (0.089) (0.078) (0.155) (0.093) (0.104) (0.130) (0.089) (0.070)
Constant 0.337 1.714*** 1.038 -1.004 0.720 0.312 -0.125 -1.689* 1.649** -0.604 1.582*
(0.182) (0.481) (0.659) (0.642) (0.592) (0.941) (0.624) (0.848) (0.510) (0.627) (0.707)
Observations 1040 105 104 103 104 105 102 103 104 106 104
R2 0.55 0.51 0.63 0.66 0.51 0.72 0.63 0.38 0.52 0.54 0.45
Incremental R2 0.04 0.11 0.11 0.12 0.08 0.13 0.00 0.10 0.04 0.20 0.15
Notes: Standard errors in parentheses. OLS regression coefficients reported. The dependent variable is 6-point intensity of choice described in section II.B.a, with a higher value
indicating a higher probability of choosing option 2. The dependent variables are the 7-point responses described in section II.B.b,c, with a higher number indicating a higher
predicted value of that aspect under option 2. The first column reports the regression pooling all scenarios and including scenario fixed effects. The remaining columns report the
regression restricted to a specific scenario. For the full wording of the scenario, see the Appendix. * p < 0.05,
** p < 0.01,
*** p < 0.00
34
Table 8: Choice on Happiness and All Aspects, with Incremental R2 (Study 4, happiness measured in series)
Decision problem: Pooled 1 2 3 4 5 6 7 8 9 10
Tradeoff implicated in decision
problem:
Income
vs
Sleep
Concert
vs
Dinner
Abs. Inc.
vs
Rel. Inc.
Income
vs
Legacy
Apple
vs
Orange
Income
vs
Labor
Socialize
vs
Sleep
Income
vs
Dinner
Education
vs
Social life
Interest
vs
Career
Non-local tradeoff?
Everyday/minor decision?
Dependent Variable: 6-point Choice
Own happiness 0.455*** 0.478*** 0.397*** 0.534*** 0.450*** 0.720*** 0.488*** 0.402*** 0.388*** 0.419*** 0.208
(0.030) (0.083) (0.093) (0.112) (0.110) (0.106) (0.116) (0.102) (0.098) (0.096) (0.108)
Family happiness 0.033 0.113 -0.090 0.041 -0.046 0.022 0.204 -0.299* -0.087 0.017 0.207*
(0.035) (0.072) (0.194) (0.139) (0.108) (0.397) (0.164) (0.131) (0.135) (0.088) (0.101)
Health 0.056 -0.040 0.117 0.060 0.340 0.124 -0.207 0.339* 0.208 -0.087 -0.153
(0.046) (0.113) (0.208) (0.210) (0.199) (0.168) (0.244) (0.135) (0.160) (0.116) (0.158)
Life's level of romance -0.065 0.021 -0.064 0.297 -0.100 -1.883* -0.516* -0.094 0.037 -0.003 0.234
(0.056) (0.142) (0.175) (0.237) (0.193) (0.857) (0.252) (0.156) (0.212) (0.154) (0.231)
Social life -0.015 0.070 -0.044 -0.074 0.045 0.828 0.546 -0.051 -0.096 -0.035 -0.105
(0.046) (0.122) (0.110) (0.143) (0.160) (0.764) (0.287) (0.197) (0.170) (0.160) (0.167)
Control over your life 0.118*** 0.013 -0.095 -0.165 0.180 0.263 0.194 0.147 0.155 0.121 0.174
(0.036) (0.092) (0.150) (0.154) (0.096) (0.205) (0.153) (0.124) (0.136) (0.101) (0.093)
Life's level of spirituality -0.047 -0.081 0.001 -0.195 0.120 -0.978 -0.062 -0.075 -0.117 -0.034 -0.106
(0.050) (0.118) (0.135) (0.228) (0.138) (0.775) (0.235) (0.201) (0.177) (0.133) (0.160)
Life's level of fun -0.045 -0.147 0.138 -0.089 -0.045 0.229 -0.068 -0.382 0.069 -0.089 0.174
(0.047) (0.105) (0.126) (0.195) (0.144) (0.321) (0.223) (0.246) (0.177) (0.170) (0.154)
Social status 0.108** 0.104 -0.092 0.290** -0.011 0.939 0.086 0.329* 0.161 0.017 0.263*
(0.035) (0.082) (0.124) (0.107) (0.102) (0.513) (0.174) (0.154) (0.165) (0.081) (0.127)
Life's non-boringness 0.041 0.153 0.060 0.219 0.175 -0.234 -0.116 0.328 0.053 0.037 -0.133
(0.044) (0.090) (0.137) (0.222) (0.128) (0.313) (0.182) (0.186) (0.150) (0.136) (0.153)
Physical comfort 0.075* 0.072 0.176 0.059 0.069 0.308* -0.009 0.053 -0.147 0.069 -0.071
(0.036) (0.086) (0.146) (0.151) (0.116) (0.147) (0.167) (0.155) (0.114) (0.098) (0.139)
Sense of purpose 0.084* 0.088 0.110 0.021 -0.047 0.467 -0.016 0.138 0.150 0.198* 0.178*
(0.033) (0.081) (0.131) (0.122) (0.111) (0.714) (0.153) (0.121) (0.119) (0.098) (0.083)
Constant 0.301 0.608 1.796* -1.109 -0.993 0.315 0.724 -0.879 0.256 0.416 0.598
(0.223) (0.487) (0.734) (0.860) (0.685) (0.892) (1.239) (0.867) (0.790) (0.620) (0.885)
Observations 1049 105 105 105 105 105 106 105 103 105 105
R2 0.48 0.60 0.43 0.52 0.49 0.65 0.34 0.39 0.38 0.44 0.37
Incremental R2 0.03 0.07 0.03 0.08 0.11 0.06 0.08 0.17 0.06 0.08 0.19
Notes: Standard errors in parentheses. OLS regression coefficients reported. The dependent variable is 6-point intensity of choice described in section II.B.a, with a higher value
indicating a higher probability of choosing option 2. The dependent variables are the 7-point responses described in section II.B.b,c, with a higher number indicating a higher
predicted value of that aspect under option 2. The first column reports the regression pooling all scenarios and including scenario fixed effects. The remaining columns report the
regression restricted to a specific scenario. For the full wording of the scenario, see the Appendix. * p < 0.05,
** p < 0.01,
*** p < 0.00
35
Table 9: Robustness checks Full Sample Excluding people who
believed they would regret
the cases when they did not
maximize happiness
Excluding people who believed they made a
mistake when they did not
maximize happiness
Excluding people whose answer for choice was not
the answer they wanted
themselves to choose
Only people who responded A or B in question 14
Own happiness 0.487*** 0.497*** 0.503*** 0.577*** 0.462***
(0.018) (0.020) (0.018) (0.019) (0.021)
Family happiness 0.076** 0.080** 0.071** 0.021 0.082**
(0.023) (0.027) (0.024) (0.026) (0.026)
Health 0.018 0.015 0.027 0.026 0.032
(0.030) (0.034) (0.031) (0.034) (0.033)
Life's level of romance 0.008 0.022 0.030 0.042 -0.007
(0.034) (0.037) (0.035) (0.038) (0.038)
Social life -0.045 -0.040 -0.040 -0.114*** -0.045
(0.027) (0.031) (0.028) (0.032) (0.030)
Control over your life 0.079** 0.088** 0.064* 0.077** 0.082**
(0.024) (0.028) (0.025) (0.028) (0.028)
Life's level of spirituality -0.009 0.012 0.001 -0.065 -0.002
(0.032) (0.037) (0.033) (0.037) (0.036)
Life's level of fun 0.046 0.049 0.046 0.080* 0.065*
(0.028) (0.032) (0.029) (0.032) (0.031)
Social status 0.077*** 0.053* 0.071** 0.085** 0.092***
(0.023) (0.026) (0.023) (0.026) (0.025)
Life's non-boringness -0.076** -0.091** -0.095*** -0.096** -0.073*
(0.027) (0.030) (0.028) (0.030) (0.030)
Physical comfort 0.034 0.012 0.032 0.045 0.036
(0.022) (0.026) (0.023) (0.026) (0.025)
Sense of purpose 0.101*** 0.108*** 0.098*** 0.145*** 0.117***
(0.021) (0.025) (0.022) (0.025) (0.024)
Constant 0.345* 0.326* 0.292* 0.178 0.183
(0.141) (0.156) (0.146) (0.141) (0.169)
Observations 2089 1617 1932 1429 1658
R2 0.50 0.51 0.50 0.63 0.48
F-statistic and p-value of test of different slope coefficients
from the full-sample results
1.00
p=0.444
1.99
p=0.022
13.35
p=0.000
1.18
p=0.295
Notes: Standard errors in parentheses. OLS regression coefficients reported. The dependent variable is 6-point intensity of choice described in section II.B.a, with a higher value indicating a higher
probability of choosing option 2. The dependent variables are the 7-point responses described in section II.B.b,c, with a higher number indicating a higher predicted value of that aspect under option
2. The first column reports the regression pooling all scenarios and including scenario fixed effects. The remaining columns report robustness checks, excluding portions of the sample population.. For
full details, see section IV. * p < 0.05, ** p < 0.01, *** p < 0.00
Table 10: Results Grouped by Scenario Type Full Sample Non-local Tradeoffs Representative Scenarios Minor Decisions
Own happiness 0.487*** 0.490*** 0.379*** 0.550***
(0.018) (0.033) (0.028) (0.026)
Family happiness 0.076** 0.060 0.109** -0.020
(0.023) (0.036) (0.035) (0.042)
Health 0.018 0.029 0.019 0.087*
(0.030) (0.055) (0.044) (0.044)
Life's level of romance 0.008 0.092 0.011 -0.089
(0.034) (0.061) (0.049) (0.052)
Social life -0.045 -0.077 -0.048 0.010
(0.027) (0.049) (0.043) (0.042)
Control over your life 0.079** 0.042 0.100** 0.074
(0.024) (0.041) (0.036) (0.041)
Life's level of spirituality -0.009 -0.028 -0.015 -0.028
(0.032) (0.054) (0.050) (0.052)
Life's level of fun 0.046 0.034 0.072 0.040
(0.028) (0.045) (0.048) (0.046)
Social status 0.077*** 0.081* 0.091* 0.036 (0.023) (0.034) (0.036) (0.044)
Life's non-boringness -0.076** -0.004 -0.097* -0.059 (0.027) (0.043) (0.045) (0.042)
Physical comfort 0.034 0.077* -0.017 0.019 (0.022) (0.038) (0.034) (0.035)
Sense of purpose 0.101*** 0.073* 0.125*** 0.127*** (0.021) (0.037) (0.032) (0.038)
Constant 0.345* 0.253 0.495* 0.350
(0.141) (0.224) (0.219) (0.225)
Observations 2089 627 835 1042
R2 0.50 0.52 0.46 0.49
F-statistic and p-valueof test of different slope coefficients from the
full-sample results
0.89 p=0.556
2.61 p=0.002
3.67 p=0.000
Notes: Standard errors in parentheses. OLS regression coefficients reported. The dependent variable is 6-point intensity of choice
described in section II.B.a, with a higher value indicating a higher probability of choosing option 2. The dependent variables are the
7-point responses described in section II.B.b,c, with a higher number indicating a higher predicted value of that aspect under option 2.
The first column reports the regression pooling all scenarios and including scenario fixed effects. The second column restricts the
sample to non-local decision scenarios. The third column restricts the sample to minor decision scenarios. The fourth column restricts
the sample to scenarios generated from surveys of important decisions, which generated scenarios we believe to be representative of
common decisions. * p < 0.05,
** p < 0.01,
*** p < 0.00
37
Appendix: Scenarios Presented in Surveys
1) Say you have to decide between two new jobs. The jobs are exactly the same in almost every way, but
have different work hours and pay different amounts.
Option 1: A job paying $80,000 per year. The hours for this job are reasonable, and you would be able to get
about 7.5 hours of sleep on the average work night.
Option 2: A job paying $140,000 per year. However, this job requires you to go to work at unusual hours, and
you would only be able to sleep around 6 hours on the average work night.
2) Suppose you promised a close friend that you would attend his or her 50st [―21st‖ in student samples]
birthday dinner. However, at the last minute you find out that you have won front row seats to see your favorite
musician, and the concert is at the same time as the dinner. This is the musician’s last night in town. You face
two options:
Option 1: Skip your friend’s birthday dinner to attend the concert.
Option 2: Attend your friend’s birthday dinner and miss the concert.
3) Suppose you are considering a new job, and have offers from two companies. Even though all aspects of
the two jobs are identical, employees’ salaries are different across the two companies due to arbitrary timing of
when salary benchmarks happened to be set. Everyone in each company knows the other employees’ salaries.
You must choose one of the two companies, which means you must decide between the following two options:
Option 1: Your yearly income is $105,000, while on average others at your level earn $120,000.
Option 2: Your yearly income is $100,000, while on average others at your level earn $85,000.
38
4) (Phrasing in study 1): Suppose you are a skilled artist, and you have to decide between two career
paths for your life.
Option 1: You devote yourself to your own style of painting. This would require a number of sacrifices, such as
having less time for friends and family, and making less money. For example, you expect that selling your
paintings will give you an income of $40,000 a year. If you choose this path, you don’t expect that your work
will be appreciated in your lifetime, but posthumously you will make an impact on the history of art, achieve
fame, and be remembered in your work.
Option 2: You become a graphic designer at an advertising company. This would give you more money and
more time with friends and family than Option 1. The company is offering you a salary of $60,000 a year,
which will afford you a much more comfortable lifestyle, but you will have no impact and leave no legacy to be
remembered.
(Phrasing in studies 2 and 4) : Suppose you are a skilled artist, and you have to decide between two
career paths for your life. There are two styles of painting that you consider to be your own style, and you enjoy
both equally. Style 1 happens to be much less popular than Style 2 today, but you know it will be an important
style in the future.
Option 1: You devote yourself to Style 1. You expect that selling your paintings will give you an income of
$40,000 a year. If you choose this path, you don’t expect that your work will be appreciated in your lifetime, but
posthumously you will make an impact on the history of art, achieve fame, and be remembered in your work.
Option 2: You devote yourself to Style 2. You expect that selling your paintings will give you an income of
$60,000 a year, but you will have no memorable impact. [In Study 2, each subject saw this question three times,
with different salaries in option 2. This number took a value of either $42,000, $60,000, $80,000, or $100,000.]
5) Suppose you are checking out a new supermarket that just opened near where you live. As you walk by
the fresh fruit display, you are offered your choice of a free snack:
Option 1: A freshly sliced apple.
39
Option 2: A freshly sliced orange.
6) Suppose that due to budget cuts, the school implements a ―student activities fee‖ of $15 dollars a week
to help pay for maintenance of facilities used for extracurricular student activities. However, the school allows
you to not pay the fee if instead you put in 2 hours of service a week shelving books at the library. You face two
options:
Option 1: Spend 2 hours a week shelving books.
Option 2: Pay $15 a week.
7) Say you are hanging out with a group of friends at your friend’s room. You are having a really good
time, but it is getting to be late at night. You have to decide between two options.
Option 1: Stay up another hour. It is likely you will feel tired all day tomorrow, but this particular evening you
are having an especially fun time.
Option 2: Excuse yourself from the group, and go to bed. You will be disappointed to miss the fun, but you
know you will feel better the next day and be more productive at paying attention in class and doing your
homework.
8) Imagine that for the first time in three years, your parents (or if your parents are gone, your closest
relatives who are older than you) have arranged for a special family gathering that will happen the day after
Thanksgiving, with everyone also invited to Thanksgiving dinner. You face two options. Would you choose to
go to the family gathering the day after Thanksgiving (and maybe to Thanksgiving dinner) if getting there
required a $500 roundtrip plane ticket for plane flights that were 5 hours each way?
Option 1: Go to the thanksgiving gathering, which requires a $500 round trip plane ticket.
Option 2: Miss the thanksgiving gathering, but save the money.
40
9) Suppose you have decided to leave Cornell, and are transferring to a new school. You have been
accepted to two schools, and are deciding where to go. The first school is extremely selective and high quality,
but is in a small town out in the country with a less active social scene. The second school is in a major city
with a great social scene, but is slightly less renowned. Which would you choose?
Option 1: Highly selective school, isolated socially and geographically.
Option 2: Less selective school, socially active and in a major city.
10) Suppose you are considering two summer internships. One is extremely interesting and involves work
you are passionate about, but does not advance your career. The other will likely be boring, but will help you
get a job in the future. Which would you choose?
Option 1: Interesting internship which does not advance career.
Option 2: Boring internship which will help you get a job.
11) Say you are driving by yourself to see your favorite musician in concert on their last day in town. You
are five minutes away, and the concert starts in ten minutes. On the drive, you witness a truck hit a parked car,
causing roughly $500 in damages, and then drive away without leaving their information. You notice the truck’s
license plate, and you are the only witness. You face two options:
Option 1: Keep driving and get to the concert on time.
Option 2: Call the police, in which case you will have to wait around the parked car to give a testimony. This
would take about half an hour. You would have trouble finding a seat and might miss the whole concert.
41
12) (Phrasing in study 1): Say you are moving to a new town. You are trying to decide between two similar
apartments which you could rent. The two apartments are identical in almost everything – including floor plan,
amenities, neighborhood character, schools, safety, etc. However, they have different rents and are located at
different distances from your work.
Option 1: An apartment which requires a 45-minute drive to work. The rent is about 20% of your monthly
income.
Option 2: A similar apartment, with only a 10-minute drive. The rent is about 40% of your monthly income.
(Phrasing in study 2): Say you are moving to a new town. The new town is known for its terrible traffic
jams, and driving there is widely considered to be unpleasant. You are trying to decide between two similar
apartments which you could rent. The two apartments are identical in almost everything – including floor plan,
amenities, neighborhood character, schools, safety, etc. However, they have different rents and are located at
different distances from your work.
Option 1: An apartment which requires a 45-minute drive each way to work. The commute has heavy traffic
almost the whole way. The rent is about 20% of your monthly income.
Option 2: A similar apartment which requires a 10-minute drive each way to work. The commute has heavy
traffic almost the whole way. The rent is about 40% of your monthly income.
13) Say you have been reassigned at your job, and will be moved to a new location. There are two offices
where you could request to work. One office is in a city where many of your friends happen to live, and pays
20% less than your current salary. The other office is in a city where you don’t know anyone, and pays 10%
more than your current salary. Your job will be exactly the same at either office. You must decide between the
following two options:
Option 1: Make 20% less than your current salary and move to the city with your friends.
Option 2: Make 10% more than your current salary and move to a city where you do not know anyone.
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