Original citation:
Oswald, Andrew J. , Proto, Eugenio and Sgroi, Daniel. (2015) Happiness and productivity.
Journal of Labor Economics, 33 (4). pp. 789-822.
Permanent WRAP URL:
http://wrap.warwick.ac.uk/63228
Copyright and reuse:
The Warwick Research Archive Portal (WRAP) makes this work by researchers of the
University of Warwick available open access under the following conditions. Copyright ©
and all moral rights to the version of the paper presented here belong to the individual
author(s) and/or other copyright owners. To the extent reasonable and practicable thematerial made available in WRAP has been checked for eligibility before being madeavailable.
Copies of full items can be used for personal research or study, educational, or not-for-profit
purposes without prior permission or charge. Provided that the authors, title and full
bibliographic details are credited, a hyperlink and/or URL is given for the original metadata
page and the content is not changed in any way.
Publisher’s statement:© 2015 University of Chicago Press.
A note on versions:
The version presented here may differ from the published version or, version of record, if
you wish to cite this item you are advised to consult the publisher’s version. Please see the‘permanent WRAP URL’ above for details on accessing the published version and note thataccess may require a subscription.
For more information, please contact the WRAP Team at: wrap@warwick.ac.uk

warwick.ac.uk/lib-publications

Happiness and Productivity
Andrew J. Oswald, University of Warwick and IZA
Eugenio Proto, University of Warwick and IZA
Daniel Sgroi, University of Warwick
Some firms say they care about the well-being and “happiness” oftheir employees. But are such claims hype or scientific good sense?
We provide evidence, for a classic piece rate setting, that happinessmakes people more productive. In three different styles of experiment, randomly selected individuals are made happier. The treatedindividuals have approximately 12% greater productivity. A fourthexperiment studies major real-world shocks ðbereavement and family illnessÞ. Lower happiness is systematically associated with lower
productivity. These different forms of evidence, with complementary strengths and weaknesses, are consistent with the existenceof a causal link between human well-being and human performance.
At Google, we know that health, family and wellbeing are an
important aspect of Googlers’ lives. We have also noticed that
employees who are happy . . . demonstrate increased motivation. . . .
½We . . . work to ensure that Google is . . . an emotionally healthy
place to work. ðLara Harding, people programs manager, GoogleÞ
The first version of this paper was circulated in 2008. For subsequent advice, wewould like to record our deep gratitude to the late Alice Isen. For fine research assistance and valuable discussions, we are indebted to Malena Digiuni, Alex Dobson,
Stephen Lovelady, and Lucy Rippon. Insightful suggestions were provided by seminar audiences in Berlin, Birmingham, Bonn, Leicester, Glasgow, HM Treasury London, London School of Economics, Maastricht, Paris School of Economics, Warwick,
York, and Zurich. Special thanks also go to Johannes Abeler, Eve Caroli, Emanuele
Castano, Andrew Clark, Alain Cohn, Ernst Fehr, Justina Fischer, Bruno Frey, Dan
Gilbert, Amanda Goodall, Greg Jones, Graham Loomes, Rocco Macchiavello, Michel[ Journal of Labor Economics, 2015, vol. 33, no. 4]© 2015 by The University of Chicago. All rights reserved. 0734-306X/2015/3304-0001$10.00
Submitted March 23, 2013; Accepted March 3, 2014; Electronically published August 7, 2015

789

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

790

Oswald et al.
Supporting our people must begin at the most fundamental level—
their physical and mental health and well-being. It is only fromstrong foundations that they can handle . . . complex issues. ðMatthew Thomas, manager, employee relations, Ernst and YoungÞ
ðBoth quotes are from the undated report by the Business Action
on Health and UK Government’s Health Work Wellbeing Initiative,“Healthy People 5 Healthy Profits,” http://webarchive.nationalarchives.gov.uk/+/http:/www.dwp.gov.uk/docs/hwwb-healthy-people-healthy-profits.pdfÞ

I. Introduction
This study explores a question of interest to economists, behavioral
scientists, employers, and policy makers: Does “happiness” make humanbeings more productive? Consistent with claims such as those in the above
quote from the Google Corporation, we provide evidence that it does. We
show this in a piece rate setting with otherwise well-understood properties ðour work uses the timed mathematical additions task of Niederle
and VesterlundÞ.1 In a series of experiments, we experimentally assign happiness in the laboratory and also exploit data on major real-life ðunÞhappiness shocks.2 This combination makes it possible to consider the distinction between long-term well-being and short-term positive “affect.”3
The sample size in our study, which took place over a number of years, is
713 individuals. Mean productivity in our entire sample is just under 20 correct additions. The happiness treatments improve that productivity by approximately 2 additions, namely, by approximately 10%–12%.
The study’s key result is demonstrated in four ways. Each of these employs a different form of experiment ðexperiments 1– 4Þ. All the laboratory
subjects are young men and women who attend an elite English university
with required entry grades among the highest in the country.
Marechal, Sharun Mukand, Paul Oyer, Steve Pischke, Nick Powdthavee, Tommaso
Reggiani, Daniel Schunk, Claudia Senik, Tania Singer, and Luca Stanca. The firstauthor thanks the University of Zurich and Cornell University for their hospitality,and he is grateful to the ESRC for a research professorship. The ESRC ðthroughCAGEÞ and the Leverhulme Trust provided further research support. Contact thecorresponding author, Andrew J. Oswald, at andrew.oswald@warwick.ac.uk. Information concerning access to the data used in this article is available as supplementary material online.
1
See Niederle and Vesterlund ð2007Þ.
2
The relevance of this effect is witnessed by a business press literature suggesting that employee happiness is a common goal in firms, with the expectationthat happier people are more productive. The formal economics literature has
contributed relatively little to this discussion.
3
This is a distinction emphasized in Lyubomirsky, King, and Diener ð2005Þ.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

791

In experiment 1, a comedy movie clip is played to a group of subjects.
The subjects’ later measured productivity on a standardized task is foundto be substantially greater than in groups of control subjects who did not
see the clip. This result is a simple cross-sectional one. However, the finding has a causal interpretation because it rests on a randomized treatment.
In experiment 2, a comedy clip is again used. This time, however, repeatedlongitudinal measurements are taken. The greatest productivity boost is
shown to occur among the subjects who experience the greatest improvement in happiness. In experiment 3, a different treatment—at the suggestion of an editorial reader of this journal—is adopted. In an attempt tomirror somewhat more closely—admittedly still in a stylized way—thesort of policies that might potentially be provided by actual employers,
our treatment subjects are provided with chocolate, fruit, and drinks. As
before, a positive productivity effect is produced, and again the size of that
effect is substantial. In a fourth trial, experiment 4, subjects’ productivities
are measured at the very outset. At the end of the experiment, these subjects are quizzed, by questionnaire, about recent tragedies in their families’ lives ða kind of unhappy randomization by nature, rather than by us,
it might be arguedÞ. Those who report tragedies at the end of the laboratory trial are disproportionately ones who had significantly lower productivity at its start. Those individuals also report lower happiness.
One caveat should be made clear. Although our work suggests thathappier workers are more productive, we cannot say categorically that theemployers we observe in the real world should expend more resources onmaking their employees happier. In some of the experiments described
below, half of the time in the laboratory was spent in raising the subjects’happiness levels, and in one of the other experiments we spent approximately $2 per person on fruit and chocolate to raise productivity byalmost 20% for a short period of concentrated work. This study illustrates
the existence of a potentially important mechanism. However, it cannot
adjudicate, and is not designed to adjudicate, on the net benefits and costs
within existing business settings ðalthough it suggests that research in suchsettings would be of interestÞ.
To our knowledge, this study is the first to have the following set offeatures:4 We implement a monetary piece rate setup. We examine large
real-world shocks to happiness and not solely small laboratory ones.
Using a range of different experimental designs, we offer various types ofevidence. We also collect longitudinal data in a way that provides us with4
We are conscious that this is difficult to determine unambiguously, especiallyon a topic that crosses various social science disciplines, so we should say that the
judgment is made as best we can after literature searches and having had the paper
read by a number of economists, psychologists, and management researchers.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

792

Oswald et al.

an opportunity to scrutinize the changes in happiness within our subjects.
In a more strictly psychological tradition, research by the late Alice Isen ofCornell University has been important in this area. The closest previous
paper to our own is arguably Erez and Isen ð2002Þ. Those authors wish
to assess the impact of positive affect on motivation. In their experiment,
97 subjects—half of them mood-manipulated by the gift of a candy bag—are asked to solve nine anagrams ðthree of which are unsolvableÞ and are
rewarded with the chance of a lottery prize. Their framework might perhaps be seen as an informal kind of piece rate set-up. The subjects who receive the candy solve more anagrams. In later work, Isen and Reeve ð2005Þ
demonstrate that positive well-being induces subjects to change their allocation of time toward more interesting tasks and that, despite this, the subjects retain similar levels of performance in the less interesting tasks. Moregenerally, it is now known that positive well-being can influence the capacities of choice and innovative content.5 That research has concentrated on
unpaid experimental settings.6
The background to our project is that there is a large literature on productivity at the personal and plant level ðe.g., Caves 1974; Lazear 1981;
Ichniowski and Shaw 1999; Siebert and Zubanov 2010; Segal 2012Þ. Thereis a growing one on the measurement of human well-being ðe.g., Easterlin
2003; Van Praag and Ferrer-I-Carbonell 2004; Layard 2005; Ifcher andZarghamee 2011; and Benjamin et al. 2012Þ. Yet economists and management scientists still know relatively little about the causal linkages between these two variables. Empirically, our work connects to, and might
eventually offer elements of a microeconomic foundation for, the innovative recent study by Edmans ð2012Þ, who finds that levels of job satisfaction appear to be predictive of future stock market performance. Similarly, Bockerman and Ilmakunnas ð2012Þ show in longitudinal European
data that with instrumental variables estimation, an increase in the measure of job satisfaction by one within-plant standard deviation increases
value-added per hours worked in manufacturing by 6.6%. Conceptually,5
A body of related empirical research by psychologists has existed for someyears. We list a number of them in the paper’s references; these include Argyle
ð1989Þ, Ashby, Isen, and Turke ð1999Þ, and Isen ð2000Þ. See also Amabile et al.
ð2005Þ. The work of Wright and Staw ð1998Þ examines the connections between
worker well-being and supervisors’ ratings of workers. The authors find mixed
results. Our study also links to ideas in the broaden-and-build approach of Frederickson and Joiner ð2002Þ and to material examined in Lyubomirsky et al. ð2005Þ.
6
See also the non–piece rate work of Kavanagh ð1987Þ, Forgas ð1989Þ, Melton
ð1995Þ, Sinclair and Mark ð1995Þ, Steele and Aronson ð1995Þ, Sanna, Turley, andMark ð1996Þ, Baker, Frith, and Dolan ð1997Þ, Estrada, Isen, and Young ð1997Þ,Jundt and Hinsz ð2001Þ, Patterson, Warr, and West ð2004Þ, Tsai et al. ð2007Þ, andZelenski, Murphy, and Jenkins ð2008Þ.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

793

our work relates to Bewley ð1999Þ, which finds that firms cite likely lossof morale as the reason they do not cut wages, and to Dickinson ð1999Þ,
which provides evidence that an increase of a piece rate wage can decrease
hours but increase labor intensity, and also to Banerjee and Mullainathan
ð2008Þ, which considers a model where labor intensity depends on outside
worries and this generates a form of nonlinear dynamics between wealthand effort. Recent work by Segal ð2012Þ also distinguishes between two
underlying elements of motivation. Gneezy and Rustichini ð2000Þ showthat an increase in monetary compensation raises performance but thatoffering no monetary compensation can be better than offering some.7 Suchwritings reflect an increasing interest among economists in how to reconcile external incentives with intrinsic forces such as self-motivation.8
Our work may eventually offer an explanation for the longitudinal findings of Graham, Eggers, and Sukhtankar ð2004Þ that use Russian data andalso the results of De Neve and Oswald ð2012Þ that use young Americans’earnings from the Add Health data set. The latter show that even after
controlling for sibling fixed effects and other covariates, it is the “happier”
individuals—where happiness can be measured in different ways—who goon years later to have higher incomes.
We draw upon empirical ideas and methods used in sources such as
Kirchsteiger, Rigotti, and Rustichini ð2006Þ and Ifcher and Zarghameeð2011Þ. Our paper lends theoretical support to concepts emphasized by
Kimball and Willis ð2006Þ and Benjamin et al. ð2012Þ. A key idea is thathappiness may be an argument of the utility function.9 Like Oswald andWu ð2010Þ—who show, as a validation of life satisfaction data, that for
the US states, there is a match with the objective pattern implied by spatial compensating differentials theory—this study’s later results are consistent with the view that there is genuine informational content in wellbeing data.
The paper concentrates on regression equations. Appendix A also laysout graphical demonstrations of some of the study’s key results; this is because our points can be made with elementary t-tests and because we hope
they might interest behavioral scientists who do not work with the style ofregression equation favored by economists. Appendix B also contains a range

See also Benabou and Tirole ð2003Þ, which examines the relationship between
both types of motivation.
8
Diener et al. ð1999Þ reviews the links between choices and emotional states.
9
A considerable literature in economics has studied happiness and well-being as
a dependent variable, including Winkelmann and Winkelmann ð1998Þ, Di Tella,
MacCulloch, and Oswald ð2001Þ, Frey and Stutzer ð2002Þ, Blanchflower and
Oswald ð2004Þ, Senik ð2004Þ, Luttmer ð2005Þ, Clark et al. ð2008Þ, and Powdthavee
ð2010Þ. See Freeman ð1978Þ and Pugno and Depedri ð2009Þ on job satisfaction andwork performance. Other relevant work includes Compte and Postlewaite ð2004Þ.
7This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

794

Oswald et al.

of robustness checks. Appendix C presents a microeconomic model of distracted worrying and some supplementary cases to the model.
II. A Series of Experiments
Four kinds of experiment were done. Each produced evidence consistent with the idea that “happier” workers are intrinsically more productive. In total, more than 700 subjects took part in the trials.10
The experiments were deliberately varied in their design. Here we list
the main features upon which we draw. The experimental instructions, thelayout of a GMAT-style math test, and the questionnaires are explained ina supplement, available online. In different experiments, we chose different combinations of the following features:
FEATURE 1. An initial questionnaire when the person arrived in the laboratory. This asked: “How would you rate your happiness at the moment? Please use a 7-point scale where 1 is completely sad, 2 is very sad,
3 is sad, 4 is neither happy nor sad, 5 is fairly happy, 6 is very happy and7 is completely happy.”
FEATURE 2. A mood-induction procedure that changed the person’s
happiness. In two cases this was done by showing movie clips. This procedure was used in experiments 1 and 2. The treatment was a 10-minuteclip of sketches in which there are jokes told by a well-known comedian.11 As a control, we used either a calm “placebo” clip or no clip.12
We also checked one alternative. In that further case, experiment 3, thetreated subjects were instead provided with fruit, chocolate, and bottled
drinks.
FEATURE 3. A mid-experiment questionnaire. This asked the person’s
happiness immediately after the movie clip.
FEATURE 4. A task designed to measure productivity. We borrowed
ours from Niederle and Vesterlund ð2007Þ. The subjects were asked toanswer correctly as many different additions of five two-digit numbers
as possible in 10 minutes. This task is simple but is taxing under pressure.
We think of it as representing—admittedly in a stylized way—a whitecollar job: both intellectual ability and effort are rewarded. The labo10

All subjects were university students, as is common in the literature.
The questionnaire results indicate that the clip was generally found to be entertaining and had a direct impact on reported happiness levels. We also have direct evidence that the clip raised happiness through a comparison of questionnairehappiness reports directly before and after the clip.
12
See James Gross’s resources site ðhttp://www-psych.stanford.edu/˜psyphy/movs/computer_ graphic.movÞ for the clip we used as a placebo.
11This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

795

ratory subjects were allowed to use pen and paper but not a calculator
or anything similar. Each subject had a randomly designed sequenceof these arithmetical questions and was paid at a rate of £0.25 per correct
answer. Numerical additions were undertaken directly through a protected Excel spreadsheet, with a typical example as below:
31

56

14

44

87

Total 5?

Adding Five Two-Digit Numbers under Timed Pressure

FEATURE 5. A short GMAT-style math test. This had five questions
along similar lines to that of Gneezy and Rustichini ð2000Þ. Subjects
had 5 minutes to complete this and were paid at a rate of £0.50 per
correct answer. To help to disentangle effort from ability, we used thistest to measure underlying ability.13
FEATURE 6. A final questionnaire. This took two possible forms. It
was either ðaÞ a last happiness report of the exact same wording as in thefirst questionnaire and further demographic questions or ðbÞ the sameas a plus a number of questions designed to reveal any bad life eventðsÞ
ðhenceforth, BLEÞ that had taken place in the last 5 years for the subject.
Crucially, we requested information about these life events at the endof the experiment. This was to ensure that the questions would not,through a priming effect, influence reported happiness measures taken
earlier in the experiment. The final questionnaire included a measure ofprior exposure to mathematics and school exam performance, which
we could also use as controls to supplement the GMAT results fromfeature 5.
The precise elements in each experimental session differed depending
upon the specific aim. They can be grouped into four:• Experiment 1 on short-run happiness shocks, induced by a movie
clip, within the laboratory• Experiment 2, which was similar to experiment 1 but also asked
happiness questions throughout the lab experiment

13
We deliberately kept the number of GMAT MATH-style questions low. This
was to try to remove any effort component from the task so as to keep it a cleaner
measure of raw ability: five questions in 5 minutes is a relatively generous amountof time for an IQ-based test, and casual observation indicated that subjects did nothave any difficulty giving some answers to the GMAT MATH-style questions,
often well within the 5-minute deadline.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

796

Oswald et al.

• Experiment 3, using a different form of short-happiness shock ðfruit,chocolate, drinksÞ in the laboratory• Experiment 4 on severe happiness shocks from the real world
We randomly assigned subjects to different treatments. No subject took
part in more than a single experiment; individuals were told that the tasks
would be completed anonymously; they were asked to refrain from communication with each other; they were told not to use electronic devices
for assistance. Subjects were told in advance that there would be a showup fee ðof £5Þ and the likely range of bonus ðperformance-relatedÞ payments ðtypically up to a further £20 for the hour’s workÞ. Following the
economist’s tradition, a reason to pay subjects more for correct answers
was to emphasize that they would benefit from higher performance. We
wished to avoid the idea that they might be paying back effort—as in akind of “reciprocity” effect—to investigators. That concern is not relevantin experiment 4 because productivity was measured before the question
on bad life events.
A. Experiment 1: Mood Induction and Short-Run Happiness Shocks
In experiment 1, we used a short-run happiness shock, namely, a comedy clip, within the laboratory ðfeature 2Þ. The control group individuals
were not present in the same room with the treated subjects; they neveroverheard laughter or had any other interaction. The experiment was carried out with deliberate alternation of the early- and late-afternoon slots.
This was to avoid time-of-day effects.
Here we use features 2, 4, 5, and 6ðaÞ from the features list.14 The finalquestionnaire inquired into both the happiness level of subjects ðbefore
and after the clip for treatment 1Þ and their level of mathematical expertise.
In day 5 and day 6, we added extra questions ðas detailed in appendix BÞ tothe final questionnaire. These were a check designed to inquire into subjects’ motivations and their own perceptions of what was happening tothem. The core sessions took place over 4 days. We then added four moresessions in 2 additional days designed to check for the robustness of thecentral result to the introduction of an explicit payment and a placebo filmðshown to the otherwise untreated groupÞ.
Subjects received £0.25 per correct answer on the arithmetic task and£0.50 on each correct GMAT-style math answer, and this was rounded up
to avoid the need to give them large numbers of coins as payment. We
used two different forms of wording:

14

In this experiment, we choose not to measure the happiness level at the
beginning; this is to avoid the possibility that subjects treated with the comedy clip
could guess the nature of the experiment.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

797• For days 1–4 we did not specify exact details of payments, although
we communicated clearly to the subjects that the payment did depend heavily on performance.
• For days 5–6 the subjects were told the explicit rate of pay both forthe numerical additions ð£0.25 per correct answerÞ and the GMATstyle math questions ð£0.50 per correct answerÞ.
This achieved several things. First, in the latter case, we have a revealedpayment set-up, which is a proxy for many real-world piece rate contractsðdays 5–6Þ, and in the former, we mimic those situations in real life where
workers do not have a contract where they know the precise return fromeach productive action they take ðdays 1–4Þ. Second, this difference provides the opportunity to check that the wording of the payment method
does not have a significant effect—thereby making one set of days a robustness check on the other.
In experiment 1, 276 subjects participated. Here we present the resultsof the four basic sessions of this experiment. Our productivity variable in
the analysis is the number of correct additions in the allotted 10 minutes.
It has a mean of 17.40. The key independent variable is whether or not a
person observed the happiness movie clip.15
Our results point to the existence of a positive association between
human happiness and human productivity. The findings can be illustrated
in regressions or graphically. Here, in column 1 of table 1, the treatedgroup’s mean performance in experiment 1 is higher by 2.11 additions
than the performance of the control group. This productivity difference
is approximately 13%. It is significantly different from zero ð p 5 .02Þ. As
shown in the figures of appendix A, male and female groups have a fairly
similar increment in their productivity. One subgroup was noticeable inthe data. Encouragingly for our account, the performance of those 16 subjects in the treated group who did not report an increase in happiness is notstatistically different from the performance of the untreated group ð p 5 .67Þ.
The increase in performance thus seems to be linked to the rise in happiness rather than merely to the fact of watching a movie clip. However, wereturn to this issue later and examine it more systematically.
We perform a set of robustness tests, in columns 2 and 3 of table 1’s regression equations, to provide a check on both the inclusion of a placebo
15
Our movie clip is successful in increasing the happiness levels of subjects. The
subjects report an average rise of almost one point on the scale of 1 to 7. Moreover,comparing the ex post happiness of the treated subjects with that of the nontreatedsubjects, we observe that the average of the former is higher by 0.85 points. Using a
two-sided t-test, this difference is statistically significant ð p < .01Þ. Finally, it isuseful to notice that the reported level of happiness before the clip for the treatedgroup is not statistically significantly different ðthe difference is just 0.13Þ from thehappiness of the untreated group ð p 5 .20 on the differenceÞ.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

798

Oswald et al.
Table 1
Regression Equations for Productivity in Experiment 1
Variables
Treatment dummy

Additionsð1Þ

Additionsð2Þ

Attempts
ð3Þ

2.11**
ð.85Þ

1.41*
ð.83Þ
2.71**
ð1.24Þ
.012
ð1.66Þ
1.58*
ð.85Þ
7.82***
ð1.64Þ
1.33***
ð.31Þ
.63
ð1.37Þ
2.20
ð1.36Þ
1.12
ð1.32Þ
6.06***
ð1.56Þ
269.248

1.69**
ð.82Þ
2.85**
ð1.22Þ
.45
ð1.63Þ
1.35
ð.84Þ
7.78***
ð1.61Þ
1.37***
ð.31Þ
1.10
ð1.35Þ
3.00**
ð1.34Þ
2.34*
ð1.30Þ
8.66***
ð1.54Þ
269.258

Explicit payment
Placebo clip
Male
High school grades
GMAT
Session 2
Session 3
Session 4
Constant
No. of observations
R2

16.3***
ð.61Þ
276
.022

NOTE.—Productivity—here and in later tables—is the number of correct numerical additions done in a timed task. For completeness, the third column also reports
an equation for the number of attempted answers ðsome of these answers may havebeen wrongÞ. Standard errors are in parentheses.
* p < .10.
** p < .05.
*** p < .01.

clip and explicit payment, and we report also an “attempts” equation. A
range of covariates are added as additional independent variables. In column 2 of table 1, the estimated size of the effect is now approximately 1.4,
and the standard error has increased. Within this data set, there are two
extreme outliers, and if these are excluded, then the standard error on thistreatment coefficient is considerably smaller. Nevertheless, we prefer to report the full-sample results and to turn to additional experiments to probe
the strength of the current finding.
B. Experiment 2: Before-and-After HappinessMeasurements in the Laboratory
In experiment 1, it is not possible to observe in real time the happinesslevels of individuals before and after the comedy movie clip, although thesubjects are asked some retrospective questions. To deal with this, we designed experiment 2. A group of 52 males and 52 females participated. Differently from the other experiment, we ask happiness questions before

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

799

playing the movie clip and then longitudinally. Appendix table B4 describes the data.
We asked subjects about their happiness level on three occasions. The
initial measurement was at the very start of the experiment. The second
was immediately after the comedy or placebo film. The third time was at
the end of the experiment. Experiment 2 used explicit payment instructions and a placebo clip ðwithout a placebo clip there would have been no
gap between features 1 and 3 for the control subjectsÞ. The timeline wasthus features 1–5 and 6ðaÞ. Appendix B provides further details.
In experiment 2, the individuals exposed to the comedy clip made 22.96
correct additions; those in the control group, who watched only a calm
placebo film, scored 18.81. This difference of 4.15 additions in column 1 oftable 2 is significantly different from zero ð p-value < .01Þ. The effect isfound in both genders, although it is larger among men. The number ofattempts made—as in column 3 of table 2—is significantly higher among
the individuals treated with the comedy clip ð p-value 5 .018Þ. In contrast
to experiment 1, in this second experiment the precision is slightly higher

Table 2
Regression Equations for Productivity in Experiment 2
Variable
Treatment dummy

Additions OLS
ð1Þ

Additions OLS
ð2Þ

Attempts OLS
ð3Þ

4.15**
ð1.71Þ

5.01***
ð1.68Þ

4.47***
ð1.69Þ

Change in happinessHappiness before
Male
AgeHigh school grades
GMAT
Day dummy
Constant
No. of observations
R2

18.8***
104.054

4.08**
ð1.68Þ
.16
ð.26Þ
3.37
ð3.26Þ
2.25**
ð.88Þ
1.99
ð1.76Þ
.75
100
.213

5.64***
ð1.69Þ
.19
ð.26Þ
3.65
ð3.28Þ
2.09**
ð.89Þ
.79
ð1.77Þ
3.96
100
.219

Additions IV
ð4Þ

8.92**
ð3.70Þ
1.63
ð1.56Þ
2.12
ð2.15Þ
2.05
ð.33Þ
4.23
ð4.17Þ
2.89**
ð1.12Þ
1.09
ð2.25Þ
21.67
100

NOTE.—The change in happiness is that between the start of experiment 2 and the middle of that experiment ði.e., after the happiness treatment but before the additions productivity taskÞ. It is instrumented
here with a treatment 1–0 dummy variable. The treatment is exposure to the comedy clip. The control
individuals watch the placebo film. The first-stage equation for the instrumented equation in col. 4 can befound in table B5 of appendix B. Standard errors are in parentheses.
** p < .05.
*** p < .01.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

800

Oswald et al.

among the individuals treated with the comedy clip, namely, 0.88, than in
individuals treated with the placebo, 0.83. This difference, captured in column 3 of table 2, is statistically significant ð p-value 5 .03Þ. The structureof the formal regression equations in table 2 provides information about
the determinants of subjects’ productivity in this experiment.
However, is it really extra happiness that causes the enhanced productivity? The nature of experiment 2 makes it possible to check. Because people are randomly assigned to the treatment group, we know that the baseline levels of productivity of the treatment and control group are identical.
It is therefore possible to find out, for these laboratory subjects, whether
there is a link between their measured rise in happiness and the measured
implied effect on productivity. In appendix figure A5, we report a simple
plot for the full changes. A more formal test, using data on the midpoint
reading of happiness, is in table 2. Here we have to instrument the change
in happiness because that change is endogenous. Under the null hypothesis, the treatment dummy variable is itself an appropriate instrumental
variable.
Column 4 of table 2 shows that the change in happiness—here between
the start and middle of the experiment—is positive and statistically significant in an equation for the number of correct additions. The key
coefficient is 8.92, with a standard error of 3.70. This implies that a ðlargeÞ
one-point rise in happiness would be associated with almost nine extra correct answers in the productivity task. Table B5 in appendix B demonstrates
that, as might be expected, the comedy clip treatment does lead to greater
reported happiness in the subjects.
Finally, it should be explained that these two experiments’ conclusions
are unaffected by omitting the use of GMAT scores as a control variable.
They are also unaffected by the use or not of a calm placebo film.
C. Experiment 3: Mood Induction andOther Kinds of Short-Run Happiness Shocks
On the suggestion of an editorial reader, we ran a further trial, experiment 3. This variant used food and drink “shocks.” The underlying idea
is that such interventions are of a kind that would, in principle, be moreeasily implementable in a commercial organization ðmore easily, one mightsay, than getting a comedian to tell jokes in the factory at 8 a.m. every morningÞ. Therefore, we performed a variation on experiment 1. This was with anadditional 148 participants. Rather than using a comedy clip as the treatment
to induce happiness, we offered a selection of snacks and drinks to thetreatment group ðcomprising 74 subjects in four sessionsÞ. We provided none
for the control group ðwho were a different set of 74 individuals, also in four
sessionsÞ.
For these four treatment sessions, a table was first laid with a variety ofsnacks ðseveral large bowls full of miniature chocolate bars from the Cad-

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

801bury’s Heroes and Mars Celebrations range and various different types offruitÞ together with bottled spring water. The participants were then invited to take from the snacks and water and to sit for 10 minutes to eat/drink immediately after registration and just prior to the start of the main
experiment. These 10 minutes mirrored the same 10 minutes of time spent
watching the comedy clip in the main experiment 1. The instructions wereidentical to those in experiment 1 except for the addition of two lines.
First, individuals were invited to take from the table on entry ð“Please
help yourself to the snacks and water that have been provided which you
are free to consume before the experiment begins.”Þ. Second, just prior to
the experimental instructions, they were told: “Please now stop eating or
drinking until the end of the experiment where you will be free to continue partaking of any snacks you picked up as you entered.” For the four
control sessions, we invited participants to enter, but there was no availability of snacks or bottled water. They were still asked to sit for 10 minutes prior to the experiment beginning; this was to ensure that any effectwas not due to the additional minutes of experimental time for the treatedgroup.
Other than the treatment being different, the key features of experiment 1
were retained: the participants were first asked to carry out the numerical additions, then undertake the GMAT math-style test, and finally complete a questionnaire. There were two minor alterations. First, the questionnaire for the treated participants asked afterward whether the provided
snacks and water had an effect on their happiness ðinstead of asking the samequestion about the comedy clip as in the main experiment 1Þ. Second, thepayment rates were made explicit ðat 25p per correct addition and 50p percorrect GAMT math-style answerÞ as in the explicit payment variation on
experiment I.
As in the previous experiments, productivity was higher in the treatment group. The results are illustrated in table 3. For example, in column 1of table 3, the productivity difference is 3.07 extra correct additions, which
is a boost to the number of correct numerical additions of approximately
15%. The increase is even larger, in column 2, when additional independent variables are included. Column 3 of table 3 reveals that a strong effectcomes through also in the sheer number of attempts made by laboratory
participants ðby 4.22 with a standard error of 1.38Þ. We checked also thatthe chocolate-fruit treatment did raise participants’ reported happiness.
Relative to the price of fruit and chocolate, which came in our experiment to the equivalent of approximately $2 per person within the laboratory, the observed boost in productivity may or may not be large enoughto make it possible to think of the extra happiness as paying for itself. The
reason is a cautionary one. It is that, although the results in table 3 suggestthat this particular intervention increases people’s productivity by a sizable
15%–20%, it is not possible here to be sure how long such productivity

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

802Oswald et al.
Table 3
Regression Equations for Productivity in Experiment 3
Variable
Treatment dummy

Additionsð1Þ

Additionsð2Þ

Attempts
ð3Þ

3.07**
ð1.43Þ

3.78***
ð1.42Þ
2.95*
ð1.49Þ
.18
ð.12Þ
6.33**
ð3.12Þ
.73
ð.52Þ
Yes
8.27**
ð4.09Þ
145.122

4.22***
ð1.38Þ
3.49**
ð1.45Þ
.16
ð.11Þ
5.54*
ð3.03Þ
.78
ð.51Þ
Yes
12.0***
ð3.97Þ
145
.145

Male
Age
High school grades
GMAT
Day dummy
Constant
No. of observations
R2

No
19.6***
ð1.01Þ
148.031* p < .10.
** p < .05.
*** p < .01.

boosts would persist in a real-world setting. If this were to translate in a
lasting way into the busy offices of the real world—as Google’s spokesperson apparently believes—it could be expected to outweigh the additional costs. If the boost is a short-lasting one, however, it could not. Thisissue seems to demand attention in future research.
D. Experiment 4: Family Tragedies as Real-Life Happiness Shocks
The preceding sections have studied small happiness interventions. For
ethical reasons, it is not feasible in experiments to induce huge changes
in the happiness of people’s lives. Nevertheless, it is possible to exploit
data on the naturally occurring shocks of life. In experiment 4, we study
real-life unhappiness events assigned by nature rather than by us. These
shocks—for which we use the generic term bad life events ðBLEsÞ—arefamily tragedies such as recent bereavement.
The design here uses a short questionnaire asking about people’s happiness; then we initiate the productivity task; then there is the GMATstyle math test to check people’s background mathematical ability; thenwe finish with a questionnaire. Hence, we use features 1, 4, 5, and 6ðbÞfrom the features list. One aspect is particularly important. In this experiment, we asked subjects to report their level of happiness right at the startof the experimental session. This was to avoid “priming” problems. The
underlying logic is that we wanted to see if people’s initial happiness answers could be shown to be correlated with the individuals’ later answers
and behavior.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

803

We informed the subjects of the precise payment system prior to features 4 and 5 ðamounts £0.25 and £0.50 per correct answer, respectivelyÞ.
The final questionnaire included supplementary questions designed tofind out whether the subjects had experienced at least one of the following BLEs: close family bereavement, extended family bereavement, serious
life-threatening illness in the close family, and/or parental divorce. Although we did not know it when we designed our project, the idea of examining such events has also been followed in interesting work on CEOs
by Bennedsen, Perez-Gonzalez, and Wolfenzon ð2010Þ, who suggest thatcompany performance may be impeded by traumatic family events.
Again all the laboratory subjects were young men and women who attend an elite English university. Compared to any random slice of an adult
population, they are thus—usefully for our experiment—rather homogeneous individuals. Those among them who have experienced family tragedies are, to the outside observer, approximately indistinguishable fromthe others.
In the empirical work, we define a “bad life event” to be either bereavement or illness in the family.16 The data suggested that it was appropriate
to aggregate these happiness-shock events by using a single variable, BLE.
There were eight sessions across 2 days. Appendix table B12 summarizes
the means and standard deviations of the variables.
In experiment 4, we can think of nature as allocating extreme “unhappiness” shocks. The sample size here is 179; the mean of productivity inthe sample is 18.40, with a standard deviation of 6.71. Those subjects who
have recently been through a bad life event are noticeably less happy andless productive. Compared to the control group, they mark themselves
nearly half a point lower on the happiness scale, and they achieve approximately 2 fewer correct additions. They also make fewer attempts. These are
noticeable differences when compared to individuals in the no-BLE group.
The effects are statistically significant in the full samples; they are also
statistically significant in the majority of the subsamples. In column 1 of table 4, for example, the productivity difference is 2.31 additions, with a standard error of 1.12. On closer inspection ðnot reported hereÞ, it is not possible to reject the null hypothesis that the effects are of the same size formales and females. The regression equations in table 4 and table 5 illustrate what happens when a variety of covariates are included. They also
illustrate one notable result. Consistent with the idea of slow “hedonic
adaptation,” the family tragedies that happened longer ago seem to havesmaller consequences for people’s current happiness and productivity.
16
In the questionnaire, we also asked about parental divorce, but this turned out
to have a tiny ðoccasionally positiveÞ and statistically insignificant effect on theindividual, so the divorce of parents, at least in our data set, does not appear toqualify as a bad life event.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

804

Oswald et al.

Table 4
Regression Equations for Productivity in Experiment 4 (Where a Bad Life
Event [BLE] is Deﬁned as Family Illness or Bereavement)
Variable
BLE in the last 2 years

Additionsð1Þ
22.31**
ð1.12Þ

BLE in the last 2 yearsˆ

Additionsð2Þ

22.05**
ð1.04Þ

BLE in the last 5 years

Additionsð3Þ

2.73
ð1.04Þ

BLE less than 1 year ago
BLE 1 year ago
BLE 2 years ago
BLE 3 years ago
BLE 4 years ago
BLE 5 years ago
Male
AgeHigh school grades
GMAT
Session dummies
Constant
No. of observations
R2

2.77
ð1.15Þ
.30
ð.44Þ
3.75*
ð2.04Þ
1.22***
ð.38Þ
Yes
5.79
ð9.04Þ
142
.218

2.83
ð1.03Þ
2.14
ð.33Þ
3.20*
ð1.92Þ
.98**
ð.35Þ
Yes
15.22**
ð7.15Þ
164
.143

2.72
ð1.16Þ
.26
ð.44Þ
3.73*
ð2.07Þ
1.27***
ð.39Þ
Yes
6.22
ð9.18Þ
142.195

Additionsð4Þ

23.81*
ð2.25Þ
.50
ð1.35Þ
22.64
ð2.13Þ
2.52
ð2.25Þ
4.97**
ð1.98Þ
21.20
ð2.22Þ
2.77
ð1.15Þ
.18
ð.43Þ
3.87*
ð2.03Þ
1.09***
ð.38Þ
Yes
7.70
ð9.02Þ
142.266

NOTE.—“BLE in the last 2 yearsˆ” is a variable set equal to 1 when a bad life event happened in the last
2 years and set equal to 0 when no bad life event happened or the year is missing. Standard errors are inparentheses.
* p < .10.
** p < .05.
*** p < .01.

It is possible to think of some important potential objections to experiment 4. A natural one is that the happiness shock is assigned by nature
rather than us. This means that it is not necessarily randomly distributed
across the sample. For example, those families most prone to bad life
shocks such as bereavement could, in principle, also be ones where unhappiness is intrinsically more common and where productivity is intrinsicallyThis content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

805

Table 5Regression Equations for Happiness in Experiment 4 (Where a Bad Life Event[BLE] Is Deﬁned as Family Illness or Bereavement): Ordered Probit Estimation
Variable
BLE in the last 2 years

Happiness
ð1Þ
2.55***
ð.21Þ

BLE in the last 2 yearsˆ

Happiness
ð2Þ

2.54***
ð.20Þ

BLE in the last 5 years

Happiness
ð3Þ

2.47**
ð.19Þ

BLE less than 1 year ago
BLE 1 year ago
BLE 2 years ago
BLE 3 years ago
BLE 4 years ago
BLE 5 years ago
Male
AgeHigh school grades
GMAT
Session dummies
No. of observations

.20
ð.21Þ
2.12
ð.080Þ
2.043
ð.38Þ
2.099
ð.070Þ
Yes
142

.35*
ð.19Þ
2.088
ð.061Þ
.016
ð.36Þ
2.12*
ð.065Þ
Yes
164

.18
ð.21Þ
2.12
ð.080Þ
2.032
ð.38Þ
2.087
ð.070ÞYes
142

Happiness
ð4Þ

21.09**
ð.42Þ
2.41
ð.25Þ
21.14***
ð.40Þ
.19
ð.42Þ
2.42
ð.37Þ
2.57
ð.42Þ
.23
ð.22Þ
2.13
ð.081Þ
2.12
ð.38Þ
2.10
ð.071ÞYes
142

NOTE.—“BLE in the last 2 yearsˆ” is a variable set equal to 1 when a bad life event happened in the last
2 years and set equal to 0 when no bad life event happened or the year is missing. Standard errors are inparentheses.
* p < .10.
** p < .05.
*** p < .01.

lower. This criticism is perhaps likely to have less force among a group ofelite students than in a general cross section of the population, but it isnevertheless a potential weakness of experiment 4. Hence the associationin the data could be real in a statistical sense but illusory in a causal sense.
A second difficulty is that it is not possible in experiment 4 to be certainthat lower happiness causes the lower productivity. Both might be triggered by the existence of the BLE. A third difficulty is that, strictly speaking, experiment 4 demonstrates that unhappiness is associated with lower

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

806

Oswald et al.

productivity in the additions task. It does not show the reverse, namely,that a boost to happiness promotes a boost in productivity.
In tables 4 and 5, the regression equations for experiment 4 provide
information about, respectively, the statistical impact of a bad life eventin each year from year 0 to year 5 ðas declared at the end of the experimentÞ upon both individuals’ productivities and those individuals’ levelsof happiness ðas declared at the beginning of the experimentÞ. At 20.55, inthe top row of column 1 of table 5, the immediate estimate on well-being islarge and negative. In column 4 of table 5, the immediate-run loss of happiness is apparently even greater, at approximately one full point. Therefore, although our subjects may not be aware of it, their happiness answers
at the start of experiment 4 are correlated with whether later on they report that a BLE recently occurred in their family. The pattern in the happiness coefficients is itself broadly consistent with hedonic adaptation—thewell-being effect declines through time. Overall, the consequence of a badlife event is empirically strong if it happened less than a year ago, and it
becomes insignificantly different from zero after approximately 3 years.
Our results are consistent with a range of adaptation findings in the surveybased research literature on the economics of human well-being ðe.g., Clark
et al. 2008Þ.
We are especially interested in the effects of a bad life event upon human performance. The regressions in table 4 provide a range of estimatesof the impact of BLE on productivity. Having had a bad event in the previous year is associated with particularly low performance on the additions task. Across the columns, the size of the productivity effect is large;
it is typically more than two additions and thus greater than 10%. The extent of the deleterious effect of a BLE upon subjects’ productivity is a declining function of the elapsed time since the event. This finding may repay
scrutiny in future empirical research.
III. Conclusions
This study provides evidence of a link between human happiness andhuman productivity. To our knowledge, it is the first such evidence—
though we would like to acknowledge the important work of the late
Alice Isen—in a true piece rate setting. Our study is also the first to exploit information on tragic family life events as a “natural” experiment andto gather within-person information in a longitudinal way.
Four kinds of trial ðdenoted experiments 1–4Þ have been described. The
last of these is an attempt to estimate the repercussions of life events assigned by nature. The design, in that case, has the disadvantage that wecannot directly control the happiness shock, but it has the advantage thatit allows us to study large shocks—ones that no social science funding bodyThis content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

807

would allow us to impose on laboratory subjects—of a fundamental kindin real human beings’ lives. The other three experiments examine the consequences of truly randomly assigned happiness. These experiments havethe advantage that we can directly control the happiness shock but the disadvantage that shocks are inevitably small and of a special kind in the laboratory. It is conceivable in the last experiment that there is some unobservable feature of people that makes them both less productive and more
likely to report a bad life event. Yet such a mechanism cannot explain the results in the other three experiments. By design, the four experiments havecomplementary strengths and weaknesses.
We have not, within this research project, attempted to discriminate between different theoretical explanations for our key result. That will eventually require a different form of inquiry. Tsai, Chen, and Liu ð2007Þ andHermalin and Isen ð2008Þ discuss potential pathways, and the results ofKillingsworth and Gilbert ð2010Þ suggest the possibility that unhappiness
may lead to a lack of mental concentration.17 A related possible mechanism is sketched in the appendix: this proposes a model of “worrying” anddistraction. Such an approach is consistent with ideas in sources such as
Benjamin et al. ð2012Þ and Mani et al. ð2013Þ. One possibility is that background unhappiness acts to distract rationally-optimizing individuals awayfrom their work tasks.18
Various implications emerge. First, it appears that economists and othersocial scientists may need to pay more attention to emotional well-being asa causal force. Second, better bridges may be required between currently
disparate scholarly disciplines. Third, if happiness in a workplace carries
with it a return in productivity, the paper’s findings may have consequences for firms’ promotion policies,19 and they may be relevant for managers and human resources specialists. Finally, if well-being boosts people’s performance at work, this raises the possibility, at the microeconomic
level and perhaps even the macroeconomic level, of self-sustaining spirals
between human productivity and human well-being.

One approach, as in Hermalin and Isen ð2008Þ, is to allow a general dynamic
utility function where good mood is an argument in the utility function and thatmood can, in principle, affect the marginal rate of substitution between other elements in the utility function.
18
See the model in appendix C.
19
The paper’s arguments do not rest on standard kinds of neoclassical payeffort mechanisms discussed in sources such as Lazear ð1981Þ and Oswald ð1984Þ.
17This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Appendix A
Appendix Figures

FIG. A1.—Those exposed to the randomized happiness treatment in the laboratory have higher productivity in experiment 1. Here the happiness treatment is acomedy movie clip in the laboratory. 95% confidence intervals.

FIG. A2.—Those exposed to the randomized happiness treatment in the laboratory have higher productivity in experiment 2. Here the happiness treatment is acomedy movie clip in the laboratory. 95% confidence intervals.

808This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

FIG. A3.—Those exposed to the randomized happiness treatment in the laboratory have higher happiness in experiment 2. Here the happiness treatment is acomedy movie clip in the laboratory. 95% confidence intervals.

FIG. A4.—Those exposed to the randomized happiness treatment in the laboratory have higher productivity in experiment 3. Here the happiness treatment ischocolates 1 fruit 1 drinks in the laboratory. 95% confidence intervals.

809

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

FIG. A5.—Correlational evidence ðnoncausalÞ that the greatest rise in happinessduring experiment 2 is associated with the greatest productivity gain. Here those
not exposed to the happiness treatment have the same baseline productivity; hence,
the y-axis can be viewed as a change in productivity from the common baseline.

FIG. A6.—Individuals with a recent bad life event ðBLEÞ have lower productivityinexperiment 4. Here a BLE is bereavement or family illness in the last 2 years. 95%
confidence intervals.
810

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

FIG. A7.—Individuals with a recent bad life event ðBLEÞ report lower happiness inexperiment 4. Here a bad life event is bereavement or family illness in the last 2 years.
95% confidence intervals.

Appendix B
Data Detail and Robustness Checks
The purpose of this appendix is to give more details on the data and toprovide some robustness checks
Experiment 1
Table B1
Summary Statistics by Treatment
Day/Session

Subjects

Additions

Attempts

Payment

1 Treatment 0
1 Treatment 1
Treatment 1 2 treatment 0
ðHa: difference > 0Þ

24
24

15.38
18.21
1.18
ð.0476Þ

17.67
21.33
3.66
ð.0126Þ

Implicit
Implicit

2 Treatment 0
2 Treatment 1
Treatment 1 2 treatment 0
ðHa: difference > 0Þ

23
20

16.85
16.48
2.37
ð.5669Þ

20.92
19.39
.81
ð.6393Þ

Implicit
Implicit

3 Treatment 0
3 Treatment 1
Treatment 1 2 treatment 0
ðHa: difference > 0Þ

23
24

16.26
19.52
3.26
ð.0521Þ

19.73
23
3.26
ð.0513Þ

Implicit
Implicit

811

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Table B1 (Continued )Day/Session

Subjects

Additions

Attempts

Payment

4 Treatment 0
4 Treatment 1
Treatment 1 2 treatment 0
ðHa: difference > 0Þ

24
25

16.04
17.72
1.68
ð.3109Þ

20.36
21.45
1.09
ð.3018Þ

Implicit
Implicit

5 Treatment placebo
5 Treatment 1

25
25

14.84
19.8

17.8
23.8

Implicit
Explicit

6 Treatment 0
6 Treatment 1
Treatment 1 2 treatment 0
ðHa: difference > 0Þ

23
21

18.52
19
.90
ð.3426Þ

20.90
22.26
1.78
ð.2003Þ

Explicit
Explicit

Table B2
Data Description (Experiment 1): 94 Males and 88 Females
Variable

No. ofObservations

Mean

SD

Min

Max

94
94
93
94

17.91
3.48
.50
5.93

5.99
1.39
.27
.68

7
0
0
5

39
5
1
7

88
88
85

16.20
3.36
.48

7.16
1.37
.24

2
1
0

43
5
1

25
25
24
24

14.84
3.08
.47
3.67

6.43
1.63
.23
1.27

5
0
.06
1

34
5
.93
6

48
48
47
48

19.41
3.54
.48
5.81

8.88
1.30
.24
1.04

0
0
.06
2

42
5
1
7

21
21
20

18.52
3.38
.58

7.08
1.60
.25

7
0
.14

34
5
1

Treated individuals:
No. of correct additions
GMAT
High school grades
Enjoyment-of-clip
Nontreated individuals:
No. of correct additions:
GMAT
High school grades
Individuals treated with placebo clip:
No. of correct additions:
GMAT
High school grades
Enjoyment-of-clip
Treated individuals ðprecise-payment caseÞ:No. of correct additions
GMAT
High school grades
Enjoyment-of-clip
Nontreated individuals ðprecise-paymentÞ:No. of correct additions
GMAT
High school grades

NOTE.—The measure called “High school grades” asks students to consider all of their qualifications
and gives a percentage of those qualifications that are at the highest possible grade. It therefore measures
their past performance against the highest possible performance. More precisely, on the questionnaire we
asked two questions: “How many school level qualifications have you taken ðincluding GCSEs, A-levels
and equivalentÞ?” ðforming the denominatorÞ and “How many of these qualifications were at the best
grade possible? ðe.g., A* in GCSE, A is A-levelÞ” ðforming the numeratorÞ.

812

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Table B3
Checking the Robustness of the Results to an Explicit Payment
in Experiment 1
Variable
Treatment dummy
Treatment  explicit payment

Additionsð1Þ

Additionsð2Þ

Attempts
ð3Þ

2.11**
ð.85Þ

2.01**
ð.98Þ
21.12
ð2.09Þ
2.62
ð1.68Þ
15.9***
ð.66Þ
.036

2.21**
ð.98Þ
2.43
ð2.08Þ
1.82
ð1.67Þ
19.1***
ð.66Þ
.037

Explicit payment
Constant

16.3***
ð.61Þ
.022

R2

NOTE.—No. of observations 5 276. If all potential covariates from the table B2 areincluded, the coefficient on the treatment dummy in col. 2 drops to approximately 1.4
and is not significantly different from zero; the interaction term remains insignificant.
Standard errors are in parentheses.
** p < .05.
*** p < .01.

Experiment 2
Table B4
Data Description (Experiment 2)
Variable
Treated individuals:
No. of correct additions
Happy before
Happy after
GMAT
High school grades
Individuals treated with placebo clip:
No. of correct additions
Happy before
Happy after
GMAT
High school grades

No. ofobservations

Mean

SD

Min

Max

51
51
51
51
48

22.96
5.02
5.24
3.92
.59

10.39
.81
.84
1.07
.25

4
3
3
0
.07

75
7
7
5
1

53
53
53
53
52

18.81
4.85
4.57
3.94
.65

6.76
.86
.95
1.13
.28

7
3
3
0
0

35
7
7
5
1

813

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Table B5
The First-Stage Regression Used for the Fourth
Column of Table 2
Variable

Change in Happiness

Treatment dummy.57***
ð.14Þ
2.27***
ð.08Þ
.22
ð.14Þ
.02
ð.02Þ
2.11
ð.27Þ
2.07
ð.07Þ
.09
ð.15Þ
.78
ð.75Þ
100
.252

Happiness before
Male
AgeHigh school marks
GMAT
Day dummy
Constant
No. of observations
R2*** p < .01.

Table B6
Excluding the Top and Bottom Performers from Experiments 1, 2,
and 3 as a Form of Robustness Check
Variable
Treatment
Explicit payment

Additionsð1Þ

Additionsð2Þ

Additionsð3Þ

1.75**
ð.80Þ
2.72**
ð1.19Þ

4.23***
ð1.30Þ
NA

3.20**
ð1.24Þ
NA

.17
ð.20Þ

.19*
ð.100Þ

2.64**
ð1.31Þ
2.64
ð2.51Þ
2.10***
ð.68Þ
No
Yes
2.63
ð5.51Þ
98
.237

1.92
ð1.31Þ
7.65***
ð2.72Þ
.66
ð.46Þ
No
Yes
8.55**
ð3.56Þ
141
.151

Age
Placebo
MaleHigh school grades
GMAT
Session dummy
Day dummy
Constant
No. of observations
R2.10
ð1.59Þ
1.31
ð.82Þ
7.82***
ð1.57Þ
1.32***
ð.30Þ
Yes
No
6.15***
ð1.51Þ
267.265

NOTE.—This table excludes two outliers in the data set. In col. 1, age is excluded because it
was not recorded consistently. Standard errors are in parentheses.
* p < .10.
** p < .05.
*** p < .01.

814

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

FIG. B1.—The distribution of longitudinal changes in happiness in experiment 2

Experiment 3
Table B7
Data Description for Experiment 3
Variable
Treated individuals:
No. of correct additions
No. of attempts
GMATHigh school grades
Male
Nontreated individuals:
No. of correct additions
No. of attempts
GMATHigh school grades
Male

No. ofObservations

Mean

SD

Min

Max

74
74
74
73
74

22.70
26.35
3.78
.47
.59

8.75
8.50
1.52
.24
.49

8
9
0
0
0

49
56
5
1
1

74
74
74
72
74

19.62
22.91
3.84
.52
.46

8.59
8.36
1.49
.23
.50

0
5
0
0
0

49
50
5
1
1

815

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

816

Oswald et al.

Experiment 4
Table B8
Data Description in Experiment 4 (Where Bad Life Event Is Family Illness
or Bereavement)
Variable
No. of correct additions
Happiness
GMAT MATHHigh school grades
No bad life event
BLE less than 1 year ago
BLE 1 year ago
BLE 2 years ago
BLE 3 years ago
BLE 4 years ago
BLE 5 years ago
Male
Age

No. of Observations

Mean

SE

Min

Max

179
179
179
164
179
154
154
154
154
154
154
170
169

18.40
4.82
3.63
.57
.7
.06
.19
.06
.05
.08
.08
.5
19.49

6.71
.95
1.46
.25
.46
.23
.23
.23
.22
.26
.25
.5
1.48

1
2
0
0
0
0
0
0
0
0
0
0
18

47
7
5
1
1
1
1
1
1
1
1
1
30

Appendix C
A Microeconomic Model of Distracted Worrying
Consider the following model.20 It proposes a link between “happiness”
and productivity. Put informally, the model’s main result stems from aform of emotional internal resource allocation by the worker. In this framework, a positive happiness shock, h, allows the employee to devote more
attention and effort to solving problems at work ðessentially because theworker can switch attention from worryingÞ.
Let the worker be uncertain about his or her randomly distributed
ability, z. This has a density function fðzÞ. Denote p as the piece-rate levelof pay. Let e be the effort the employee devotes to solving tasks at work.
Let w be the effort the worker devotes to “worrying” about other things.
Define R as the worker’s psychological resources. Assume ðe 1 wÞ has tobe less than or equal to R. Let u be the utility from working. It depends on
income and effort. Let v be the utility from worrying ði.e., from being
distractedÞ. Worrying can be thought of as rational concern for issues in
the worker’s life that need his or her attention. In a paid-task setting, it
might be stress about the possibility of failure at the task. But, more
broadly, it can be any general form of distraction from the job at hand. For
20
Although we proposed this in the first 2008 draft of the current paper, theapproach has much in common with the independently developed, and much more
empirically supported, important ideas of Benjamin et al. ð2012Þ. Mani et al. ð2013Þ
contains related ideas.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

817

human beings, it might be plausible to think of a worker as alternating,during the day, between concentrating on the task and feeling anxious
about his or her life.
Assume there is an initial happiness shock, h. Define overall utility as
u 1 v. People therefore solve the problem: choose work-effort e to maximize ∫uð p; e; h; zÞf ðzÞdz 1 vðw; hÞ subject to R ≥ e 1 w.
The first-order condition for a maximum in this problem is
Eue 2 vw 5 0:

ðC1Þ

The comparative-static result of particular interest is the response of productivity, given by work effort e, to a rise in the initial happiness shock, h.
It is determined in a standard way. The sign of de*/dh takes the sign of thecross partial of the maximand, namely,
de* =dh takes the sign of Eueh 2 vwh :ðC2Þ

Without more restrictions, this sign could be positive or negative. The
happiness shock could increase or decrease productivity in the work task.
However, to get some insight into the likely outcome, consider simple
forms of the utility functions, and assume that workers know their own
productivity, so are not subject to the uncertainty, and that R is normalized to unity. Set z to unity for simplicity. Assume u and v are both concave
functions.
An Additively Separable Case
Assume additive separability. Then, assuming the worker gets the h happiness shock whether she subsequently works or worries, the worker solves
max uðpeÞ 1 vð1 2 eÞ 1 2h;

ðC3Þ

and hence at an interior maximum,u0 ð peÞp 2 v0ð1 2 eÞ 5 0;

ðC4Þ

so here the optimal work effort e* is independent of the happiness shock, h.
Another Concavity Case
A more plausible form of utility function has the happiness shock withina concave form. Here the worker solves
max uð pe 1 hÞ 1 vð1 2 e 1 hÞ;which is the assumption that h is a shift variable within the utility function
itself rather than an additive part of that function.
Now the first-order condition is
u0 ð pe 1 hÞp 2 v0ð1 2 e 1 hÞ 5 0:ðC5ÞThis content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

818

Oswald et al.

Here the optimal level of energy devoted to solving problems at work, e*,does depend on the level of the happiness shock, h.
00
00
The sign of de*/dh now takes the sign of u ðpe 1 hÞp 2 v ð1 2 e 1 hÞ:
Its first element is negative and its second is positive. By the first-ordercondition, we can replace the piece rate wage term p by the ratio of themarginal utilities from working and worrying. Hence, after substitution,
the sign of the comparative static response of work effort, e, with respect
to the size of the happiness shock, h, is greater than or equal to zero as
u00 ðÞv00 ðÞ2≥ 0:u0 ðÞ
v0 ðÞ

ðC6Þ

These terms can be viewed as slightly unconventional versions of thedegrees of absolute risk aversion from two sources—the utility from workand the utility from worrying. If the marginal utility of worry declines
quickly enough as energy is transferred from working to worrying, then apositive happiness shock will successfully raise the worker’s chosen productivity, e*.
This framework is a very simple one. But it has the attraction that it offers a formal way to think about the role of background stress in a workplace. Unhappiness in the background can be conceived of as an employee’s ðrationalÞ need to devote psychic attention away from the job task.
Happier workers need to do so less. In consequence, they achieve higher
productivity.

References
Amabile, Teresa M., Sigal G. Barsade, Jennifer S. Mueller, and Barry M.
Staw. 2005. Affect and creativity at work. Administrative Science Quarterly 50:367–403.
Argyle, Michael. 1989. Do happy workers work harder? The effect of job
satisfaction on job performance. In How harmful is happiness? Consequences of enjoying life or not, ed. Ruut Veenhoven. Rotterdam: Universitaire Pers.
Ashby, F. Gregory, Alice M. Isen, and And U. Turken. 1999. A neuropsychological theory of positive affect and its influence on cognition.
Psychological Review 106, no. 3:529–50.
Baker, S. C., Christopher D. Frith, and Raymond J. Dolan. 1997. Theinteraction between mood and cognitive function studied with PET.
Psychological Medicine 27, no. 3:565–78.
Banerjee, Abhijit, and Sendhil Mullainathan. 2008. Limited attention andincome distribution. American Economic Review 98, no. 2:489–93.
Benabou, Roland, and Jean Tirole. 2003. Intrinsic and extrinsic motivation. Review of Economic Studies 70, no. 3:489–520.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

819

Benjamin Daniel, Ori Heffetz, Miles S. Kimball, and Alex Rees-Jones.
2012. What do you think would make you happier? What do you
think you would choose? American Economic Review, 102, no. 5:2083–
2110.
Bennedsen, Morten, Francisco Perez-Gonzalez, and Daniel Wolfenzon.
2010. Do CEOs matter? Working paper, Graduate School of Business,
Stanford University.
Bewley, Truman. 1999. Why wages don’t fall in a recession. Cambridge,
MA: Harvard University Press.
Blanchflower, David G., and Andrew J. Oswald. 2004. Well-being overtime in Britain and the USA. Journal of Public Economics 88, nos. 7–
8:1359–86.
Bockerman, Petri, and Pekka Ilmakunnas. 2012. The job satisfactionproductivity nexus: A study using matched survey and register data. Industrial and Labor Relations Review 65, no. 2:244–62.
Caves, Richard E. 1974. Multinational firms, competition, and productivity in host-country markets. Economica 41, no. 162:176–93.
Clark, Andrew E., Edward Diener, Yannis Georgellis, and Richard E.
Lucas. 2008. Lags and leads in life satisfaction: A test of the baseline
hypothesis. Economic Journal 118, no. 529:F222–F243.
Compte, Olivier, and Andrew Postlewaite. 2004. Confidence-enhanced
performance. American Economic Review 94, no. 5:1536–57.
De Neve, Jan-Emmanuel, and Andrew J. Oswald. 2012. Estimating theinfluence of life satisfaction and positive affect on later income using
sibling fixed effects. Proceedings of the National Academy of Sciences of
the United States of America 109:19953–58.
Dickinson, David L. 1999. An experimental examination of labor supply
and work intensities. Journal of Labor Economics 17, no. 4:638–70.
Diener, Edward, Eunkook M. Suh, Richard E. Lucas, and Heidi L. Smith.
1999. Subjective well-being: Three decades of progress. Psychological
Bulletin 125, no. 2:276–302.
Di Tella, Rafael, Robert J. MacCulloch, and Andrew J. Oswald. 2001.
Preferences over inflation and unemployment: Evidence from surveysof happiness. American Economic Review 91, no. 1:335– 41.
Easterlin, Richard A. 2003. Explaining happiness. Proceedings of the National Academy of Sciences 100, no. 19:11176–83.
Edmans, Alex. 2012. The link between job satisfaction and firm value, with
implications for corporate social responsibility. Academy of Management Perspectives 26, no. 4:1–19.
Erez, Amir, and Alice M. Isen. 2002. The influence of positive affect on
the components of expectancy motivation. Journal of Applied Psychology
87, no. 6:1055–67.
Estrada, Carlos A., Alice M. Isen, and Mark J. Young. 1997. Positive affectfacilitates integration of information and decreases anchoring in reasoning

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

820

Oswald et al.

among physicians. Organizational Behavior and Human Decision Processes 72, no. 1:117–35.
Forgas, Joseph P. 1989. Mood effects on decision making strategies. Australian Journal of Psychology 41, no. 2:197–214.
Frederickson, Barbara L., and Thomas Joiner. 2002. Positive emotions
trigger upward spirals toward emotional well-being. Psychological Science 13, no. 2:172–75.
Freeman, Richard B. 1978. Job satisfaction as an economic variable. American Economic Review 68, no. 2:135–41.
Frey, Bruno S., and Alois Stutzer. 2002. Happiness and economics. Princeton, NJ: Princeton University Press.
Gneezy, Uri, and Aldo Rustichini. 2000. Pay enough or don’t pay at all.
Quarterly Journal of Economics 115, no. 3:791–810.
Graham, Carol, Andrew Eggers, and Sandeep Sukhtankar. 2004. Does
happiness pay? An exploration on panel data for Russia. Journal of
Economic Behavior and Organization 55, no. 3:319– 42.
Hermalin, Benjamin E., and Alice M. Isen. 2008. A model of the effect ofaffect on economic decision-making. Quantitative Marketing and Economics 6:17–40.
Ichniowski, Casey, and Kathryn Shaw. 1999. The effects of human resource management systems on economic performance: An international
comparison of US and Japanese plants. Management Science 45, no. 5:
704–21.
Ifcher, John, and Homa Zarghamee. 2011. Happiness and time preference:
The effect of positive affect in a random-assignment experiment. American Economic Review 101, no. 7:3109–29.
Isen, Alice M. 2000. Positive affect and decision making. In Handbook of
emotions, ed. Jeannette M. Haviland-Jones and Michael Lewis. 2nd ed.
New York: Guilford.
Isen, Alice M., and Johnmarshall Reeve. 2005. The influence of positive
affect on intrinsic and extrinisic motivation: Facilitating enjoyment ofplay, responsible work behavior, and self-control. Motivation and Emotion 29, no. 4:297–325.
Jundt, Dustin, and Verlin B. Hinsz. 2001. Are happier workers more productive workers? The impact of mood on self-set goals, self-efficacy, andtask performance. Paper presented at the annual meeting of the Midwestern Psychological Association, Chicago.
Kavanagh, David J. 1987. Mood, persistence, and success. Australian Journal of Psychology 39, no. 3:307–18.
Killingsworth, Matthew A., and Daniel T. Gilbert. 2010. A wandering mind
is an unhappy mind. Science 330 ðNovemberÞ: 932–32.
Kimball, Miles S., and Robert J. Willis. 2006. Happiness and utility. Working paper, University of Michigan.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

Happiness and Productivity

821

Kirchsteiger, Georg, Luca Rigotti, and Aldo Rustichini. 2006. Your morals
might be your moods. Journal of Economic Behavior and Organization
59, no. 2:155–72.
Layard, Richard. 2006. Happiness: Lessons from a new science. London:
Penguin.
Lazear, Edward. 1981. Agency, earnings profiles, productivity, and hours
restrictions. American Economic Review 71, no. 4:606–20.
Luttmer, Erzo F. P. 2005. Neighbors as negatives: Relative earnings andwell-being. Quarterly Journal of Economics 120, no. 3:963–1002.
Lyubomirsky, Sonja, Laura King, and Edward Diener. 2005. The benefitsof frequent positive affect: Does happiness lead to success? Psychological Bulletin 131, no. 6:803–55.
Mani, Anandi, Sendhil Mullainathan, Eldar Shafir, and Jiaying Y. Zhao.
2013. Poverty impedes cognitive function. Science 341 ðAugustÞ: 976–80.
Melton, R. Jeffrey. 1995. The role of positive affect in syllogism performance. Personality and Social Psychology Bulletin 21, no. 8:788–94.
Niederle, Muriel, and Lisa Vesterlund. 2007. Do women shy away fromcompetition? Do men compete too much? Quarterly Journal of Economics 122 ðAugustÞ: 1067–1101.
Oswald, Andrew J. 1984. Wage and employment structure in an economy
with internal labor markets. Quarterly Journal of Economics 99, no. 4:
693–716.
Oswald, Andrew J., and Steve Wu. 2010. Objective confirmation of subjective measures of human well-being: Evidence from the USA. Science
327, no. 5965:576–79.
Patterson, Michael, Peter Warr, and Michael West. 2004. Organizational
climate and company productivity: The role of employee affect and employee level. Journal of Occupational and Organizational Psychology 77,
no. 2:193–216.
Powdthavee, Nattavudh. 2010. The happiness equation: The surprising economics of our most valuable asset. London: Icon.
Pugno, Maurizio, and Sara Depedri. 2009. Job performance and job satisfaction: An integrated survey. Working paper, Università di Trento.
Sanna, Lawrence J., Kandi J. Turley, and Melvin M. Mark. 1996. Expectedevaluation, goals, and performance: Mood as input. Personality and Social
Psychology Bulletin 22, no. 4:323–25.
Segal, Carmit. 2012. Working when no one is watching: Motivation, testscores, and economic success. Management Science 58, no. 8:1438–57.
Senik, Claudia. 2004. When information dominates comparison: Learningfrom Russian subjective data. Journal of Public Economics 88, nos. 9–
10:2099–2123.
Siebert, Stanley W., and Nikolay Zubanov. 2010. Management economics
in a large retail company. Management Science 56, no. 8:1398–1414.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).

822

Oswald et al.

Sinclair, Robert C., and Melvin M. Mark. 1995. The effects of mood state
on judgmental accuracy: Processing strategy as a mechanism. Cognition
and Emotion 9:417–38.
Steele, Claude M., and Joshua Aronson. 1995. Stereotype threat and theintellectual test performance of African-Americans. Journal of Personality and Social Psychology 69, no. 5:797–811.
Tsai, Wei-Chi., Chien-Cheng Chen, and Hui-Lu Liu. 2007. Test of a model
linking employee positive moods and task performance. Journal of Applied Psychology 92, no. 6:1570–83.
Van Praag, Bernard, and Ada Ferrer-I-Carbonell. 2004. Happiness quantified: A satisfaction calculus approach. Oxford: Oxford University Press.
Winkelmann, Lilliana, and Rainer Winkelmann. 1998. Why are the unemployed so unhappy? Evidence from panel data. Economica 65, no. 257:
1–15.
Wright, Thomas A., and Barry A. Staw. 1998. Affect and favorable work
outcomes: Two longitudinal tests of the happy-productive worker thesis.
Journal of Organizational Behavior 20:1–23.
Zelenski, John M., Stephen A. Murphy, and David A. Jenkins. 2008. The
happy-productive worker thesis revisited. Journal of Happiness Studies
9 ðFebruaryÞ: 521–37.

This content downloaded from 217.112.157.113 on May 04, 2016 03:14:08 AM
All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c).