National Academies Report - Continuation of the Well-Being Module

The Subjective Well-Being Module of the American Time Use
Survey: Assessment for Its Continuation

Panel on Measuring Subjective Well-Being in a Policy-Relevant Framework

Committee on National Statistics
Division of Behavioral and Social Sciences and Education

THE NATIONAL ACADEMIES PRESS

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NOTICE: The project that is the subject of this report was approved by the Governing Board of the National
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were chosen for their special competences and with regard for appropriate balance.
This study was supported by Task Order No. N01-OD-42139 between the U.S. National Institutes of Health and the
National Academy of Sciences, and award ID# 10000592 between the U.K. Economic and Social Research Council
and the National Academy of Sciences. Support for the Committee on National Statistics is provided by a
consortium of federal agencies through a grant from the National Science Foundation (award number SES1024012). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the
author(s) and do not necessarily reflect the views of the organizations or agencies that provided support for the
project.
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Suggested citation: National Research Council (2012). The Subjective Well-Being Module of the American Time Use
Survey: Assessment for Its Continuation. Panel on Measuring Subjective Well-Being in a Policy-Relevant
Framework. Committee on National Statistics, Division of Behavioral and Social Sciences and Education.
Washington, DC: The National Academies Press.

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iv

Panel on Measuring Subjective Well-Being in a
Policy-Relevant Framework
ARTHUR A. STONE (Chair), Department of Psychiatry and Behavioral Sciences, Stony Brook
University
NORMAN M. BRADBURN, Department of Psychology, University of Chicago
LAURA L. CARSTENSEN, Department of Psychology, Stanford University
EDWARD F. DIENER, Department of Psychology, University of Illinois at Urbana-Champaign
PAUL H. DOLAN, Department of Social Policy, London School of Economics and Political
Science
CAROL L. GRAHAM, The Brookings Institution, Washington, DC
V. JOSEPH HOTZ, Department of Economics, Duke University
DANIEL KAHNEMAN, Woodrow Wilson School, Princeton University
ARIE KAPTEYN, The RAND Corporation, Santa Monica, CA
AMANDA SACKER, Institute for Social and Economic Research, University of Essex, United
Kingdom
NORBERT SCHWARZ, Department of Psychology, University of Michigan
JUSTIN WOLFERS, Business Economics and Public Policy Department, The Wharton School,
University of Pennsylvania
CHRISTOPHER MACKIE, Study Director
ANTHONY S. MANN, Program Associate

v

Committee on National Statistics
2012-2013
LAWRENCE D. BROWN (Chair), Department of Statistics, The Wharton School, University of
Pennsylvania
JOHN M. ABOWD, School of Industrial and Labor Relations, Cornell University
DAVID CARD, Department of Economics, University of California, Berkeley
ALICIA CARRIQUIRY, Department of Statistics, Iowa State University
CONSTANTINE GATSONIS, Center for Statistical Sciences, Brown University
JAMES S. HOUSE, Survey Research Center, Institute for Social Research, University of
Michigan
MICHAEL HOUT, Survey Research Center, University of California, Berkeley
SALLIE ANN KELLER, University of Waterloo, Ontario, Canada
LISA LYNCH, The Heller School for Social Policy and Management, Brandeis University
SALLIE C. MORTON, Department of Biostatistics, Graduate School of Public Health,
University of Pittsburgh
RUTH D. PETERSON, Criminal Justice Research Center, The Ohio State University
EDWARD H. SHORTLIFFE, Columbia University and Arizona State University
HAL STERN, Donald Bren School of Information and Computer Sciences, University of
California, Irvine
JOHN H. THOMPSON, National Opinion Research Center, University of Chicago
ROGER TOURANGEAU, Westat, Rockville, MD
CONSTANCE F. CITRO, Director

vi

Acknowledgments
This report has been reviewed in draft form by individuals chosen for their diverse
perspectives and technical expertise, in accordance with procedures approved by the Report
Review Committee of the National Research Council. The purpose of this independent review is
to provide candid and critical comments that assist the institution in making its reports as sound
as possible, and to ensure that the reports meet institutional standards for objectivity, evidence,
and responsiveness to the study charge. The review comments and draft manuscript remain
confidential to protect the integrity of the deliberative process.
The panel thanks the following individuals for their review of the interim report: Daniel
S. Hamermesh, Department of Economics, The University of Texas; Richard E. Lucas,
Department of Psychology, Michigan State University; Robert D. Putnam, Kennedy School of
Government, Harvard University; Dylan Smith, Associate Professor, Center for Medical
Humanities, Compassionate Care and Bioethics, Stony Brook University Medical Center; Frank
Stafford, Department of Economics, University of Michigan; and Roger Tourangeau, Westat,
Inc., Rockville, MD.
Although the reviewers listed above have provided many constructive comments and
suggestions, they were not asked to endorse the conclusions or recommendations, nor did they
see the final draft of the report before its release. The review of the report was overseen by
Edward Perrin (retired), Department of Health Services, University of Washington. Appointed
by the National Research Council, he was responsible for making certain that the independent
examination of this report was carried out in accordance with institutional procedures and that all
review comments were carefully considered. Responsibility for the final content of the report
rests entirely with the authoring panel and the National Research Council.
The panel would also like to thank Rachel Krantz-Kent, an economist in the Division of
Labor Force Statistics at the U.S. Bureau of Labor Statistics, who attended the panel’s first
meeting and presented a very informative overview of the American Time Use Survey.

vii

viii

Contents

Summary

1

1. Background and Overview
1.1. Structure and Content of ATUS and the SWB Module
1.2. Objectives of the SWB Module
1.3. Uses of Data on Subjective Well-Being

2
3
6
7

2. Ongoing and Potential Research Applications
2.1. Time Use, Emotional Well-Being, and Unemployment
2.2. Assessing Validity of Short Versions of the Day Reconstruction Method
2.3. Episode-Based Pain Studies
2.4. End-of-Life Care
2.5. Transportation

9
9
9
10
11
11

3. Assessment
3.1. Value of the SWB Module Data to Date
3.2. Cost of Discontinuing the Module
3.3. Value of a Third Wave

13
13
14
15

References

18

Appendix: Biographical Sketches of Panel Members

19

ix

x

Summary
The American Time Use Survey (ATUS), conducted by the Bureau of Labor Statistics,
included a Subjective Well-Being (SWB) module in 2010 and 2012; the module, funded by the
National Institute on Aging (NIA), is being considered for inclusion in the ATUS for 2013. The
National Research Council was asked to evaluate measures of self-reported well-being and offer
guidance about their adoption in official government surveys. The charge for the study included
an interim report to consider the usefulness of the ATUS SWB module and specifically the value
of continuing it for at least one more wave. Among the key points raised in this report are the
following:








Value The ATUS SWB module is the only federal government data source of its
kind—linking self-reported information on individuals’ well-being to their activities
and time use. Important research has already been conducted using the data (for
example, on the effects of unemployment and job search on people’s self-reported
well-being), and work conducted with other, similar data sets has indicated the
potential of the module to contribute to knowledge that could inform policies in such
areas as health care and transportation. While the NRC Panel has not yet concluded
its assessment of the policy usefulness of including one or more kinds of self-reported
well-being measures on a regular basis in government surveys, it sees a value to
continuing the ATUS SWB module in 2013. Not only will another year of data
support research, but it will also provide additional information to help refine any
SWB measurements that may be added to ongoing official statistics.
Methodological Benefits A third wave of data collection will enlarge samples by
pooling data across years, which will enable more detailed study and comparison than
has been possible to date of population subgroups, such as people in a given region
and specific demographic groups (e.g., young people, the elderly). Because two new
questions—one on overall life satisfaction and one on whether respondents’ reported
emotional experiences yesterday were “typical”—were introduced to the module only
in 2012, at least one additional wave of the survey is needed to assess changes in
responses to those questions over time.
Cost and Effects on the ATUS As a supplement to an existing survey, the marginal
cost of the module, which adds about 5 minutes to the ATUS, is small. While further
study of the module’s effects on response and bias in the main ATUS should be
undertaken, it appears likely that these effects are modest because the module comes
at the end of the survey after people have already been asked to report their activities
for the preceding day.
New Opportunities A third wave of the survey could also be used for experiments to
improve the survey structure, should the module become permanent. The ATUS
SWB module could be the basis for a standardized set of questions that could be
added to other surveys which, together, might provide useful information about the
causes and consequences of self-reported well-being in the general population.

1

1. Background and Overview1
Research on subjective or self-reported well-being (SWB) has been ongoing for several
decades, with the past few years seeing an increased interest by some countries in using SWB
measures to evaluate government policies and provide a broader assessment of the health of a
society than is provided by such standard economic measures as Gross Domestic Product (see,
for example, Stiglitz, Sen, and Fitoussi, 2009). The National Institute on Aging and the United
Kingdom Economic and Social Research Council asked a panel of the National Research
Council’s Committee on National Statistics to review the current state of research knowledge
and evaluate methods for measuring self-reported well-being and to offer guidance about
adopting SWB measures in official population surveys (see Box 1-1 for the full charge to the
panel). NIA also asked the panel to prepare an interim report on the usefulness of the Subjective
Well-Being module of the American Time Use Survey (ATUS), with a view as to the utility of
continuing the module in 2013.
The SWB module is the only national data source in the United States that links selfreported well-being information to individuals’ activities and time-use patterns. It provides
researchers with unique insights that are only revealed by melding ratings of affect with time use
information. The SWB module, overseen by the Bureau of Labor Statistics (BLS) and sponsored
by the National Institute on Aging (NIA), was developed with guidance from several noted
academics—Angus Deaton, Daniel Kahneman, Alan Krueger, David Schkade, and Arthur Stone
among them—working in the field.
Though the SWB module has only been in existence since 2010, it is not too early to
begin assessing its potential value to researchers and policy makers. The purpose of this report is
to inform planning discussions about the module’s future—it discusses the costs and benefits of a
third wave of data collection, whether the survey module should be modified, and whether
experiments should be done to improve the module should it become permanent.
This brief report is intended to fulfill only one narrow aspect of the panel’s broader task
as described in Box 1-1. It provides (1) an overview of the ATUS and the SWB module; (2) a
brief discussion of research applications to date; and (3) preliminary assessment of the value of
SWB module data. The panel’s final report will address issues of whether research has advanced
to the point that SWB measures—and which kinds of measures—should be regularly included in
major surveys of official statistical agencies to help inform government economic and social
policies.

1

This section draws heavily from a presentation to the panel by Rachel Kranz-Kent of BLS, and
from the Federal Register, Volume 76, Number 134 (July 13, 2011):
http://webapps.dol.gov/federalregister/HtmlDisplay.aspx?DocId=25169&AgencyId=6&Docume
ntType=3 (accessed on August 24, 2012).
2

BOX 1-1
Panel Charge
An ad hoc panel will review the current state of research and evaluate methods for the
measurement of subjective well-being (SWB) in population surveys. On the basis of this evaluation, the
panel will offer guidance about adopting SWB measures in official government surveys to inform social
and economic policies. The study will be carried out in two phases. The first phase, which is the subject
of this statement of task, is to consider whether research has advanced to a point that warrants the federal
government collecting data that allow aspects of the population’s SWB to be tracked and associated with
changing conditions. The study will focus on experienced well-being (e.g., reports of momentary positive
and rewarding, or negative and distressing, states) and time-based approaches (some of the most
promising of which are oriented toward monitoring misery and pain as opposed to “happiness”), though
their connection with life-evaluative measures will also be considered. Although primarily focused on
SWB measures for inclusion in U.S. government surveys, the panel will also consider inclusion of SWB
measures in surveys in the United Kingdom and European Union, in order to facilitate cross-national
comparisons in addition to comparisons over time and for population groups within the United States.
The panel will prepare a short interim report on the usefulness of the American Time Use Survey SWB
module, and a final report identifying potential indicators and offering recommendations for their
measurement. A later, separate second phase will seek to develop a framework modeled on the National
Income and Product Accounts to integrate time-based inputs and outputs, and SWB measures, into
selected satellite, or experimental, subaccounts.

1.1. Structure and Content of ATUS and the SWB Module
The ATUS is the first federally administered, continuous survey on time use in the
United States (and in the world). It is designed to obtain estimates of the time spent by
respondents in childcare, at work, traveling, sleeping, volunteering, engaged in leisure pursuits,
and a wide range of other activities. Time-use data augment income and wage data for
individuals and families that analysts can use to create a more complete picture of quality of life
in a society. Along with income and product data, information about time-use patterns is
essential for research that evaluates the contribution of nonmarket work to national economies.
The data also enable comparisons between nations that have different mixes of market and
nonmarket production modes. To illustrate, the households of two countries may enjoy similar
home services and amenities—quality of meals, level of home cleaning and maintenance, elder
and child care, etc.—but one may perform more of these tasks themselves (home production)
while the other may more typically hire the tasks out in the market. The latter economy will
register higher per capita gross domestic product even though the standard of living may be
comparable in the two countries. Relatedly, countries may vary in the amount of time that
individuals must work to achieve a given material standard of living, resulting in different
amounts of leisure. This difference would also not show up directly in market (only) measures of
economic activity, yet it is likely that it affects well-being.
The ATUS provides nationally representative estimates of how people spend their time. It
has been conducted continuously since 2003. The survey sample is a repeated cross-section of
individuals who are drawn from U.S. households completing their eighth and final month of
interviews for the Current Population Survey (CPS). One individual from each household is

3

selected to take part in one computer-assisted telephone interview. Respondents are interviewed
for the ATUS between two and five months after they rotate out of the CPS.
Interviewers ask respondents to report all of their activities for one specified 24-hour day,
the day prior to the interview. Respondents also report who was with them during activities,
where they were, how long each activity lasted, and if they were paid. For the ATUS (following
the core time diary questions but prior to the SWB module) some of the CPS information—for
example about who is living in the household and labor force status—is confirmed and updated.2
Measurement of socioeconomic well-being based on the ATUS is enhanced by its connection to
the CPS which is rich in socio-demographic variables—namely, characteristics of the individual
and the household including labor force status, income, state of residence, educational
attainment, race and ethnicity, nativity, detailed marital status (divorced, never married, etc.),
and disability status.3
The SWB module adds to the substantive content of the ATUS by revealing not only
what people are doing with their time, but also how they experience their time—specifically how
happy, tired, sad, stressed, and in pain they felt while engaged in specific activities on the day
prior to the interview.4 This information has numerous practical applications for sociologists,
economists, educators, government policy makers, businesspersons, health researchers, and
others. The module follows directly after the core ATUS; it was administered on an ongoing
basis during 2010 and is being done again during 2012. The module surveys individuals aged 15
and over from a nationally representative sample of approximately 2,190 households each
month.
Respondents are asked questions about three activities selected with equal probability
from those reported in the ATUS time diary (the well-being module questions are asked
immediately after the core ATUS) (see Box 1-2). A few activities—sleeping, grooming, and
private activities—are never included in the SWB module. The time diary refers to the core part
of the ATUS, in which respondents report the activities they did from 4 a.m. on the day before
the interview to 4 a.m. on the day of the interview. The precodes listed in Box 1-2 are for
activities that are straightforward to code, but they are in no way representative of the full
activity lexicon used by ATUS coders. The vast majority of ATUS activities are typed into the
collection instrument (verbatim) and then coded in a separate processing step.5 The module also
collects data on whether respondents were interacting with anyone while doing the selected
activities and how meaningful the activities were to them.

2

Technical details of the sample design and the survey methodology can be found in the
American Time Use Survey User’s Guide: Understanding ATUS 2003-2011 Available at:
http://www.bls.gov/tus/atususersguide.pdf (accessed on September 3, 2012).
3
Information about who is living in the household and about labor force status is updated in the
ATUS, which is important since the CPS data are a little dated by the time the ATUS interview
takes place.
4
The module questionnaire can be found at http://www.bls.gov/tus/wbmquestionnaire.pdf
[August 2012].
5
There are more than 400 possible activity codes; a full list can be found at
http://www.bls.gov/tus/lexiconnoex2011.pdf (accessed on June 27, 2012).
4

BOX 1-2
ATUS Question Identifying an Activity
So let’s begin. Yesterday, Monday, at 4:00 a.m., what were you doing?



Use the slash key (/) for recording separate/simultaneous activities.
Do not use precodes for secondary activities.

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.

Sleeping
Grooming (self)
Watching TV
Working at main job
Working at other job
Preparing meals or snacks
Eating and drinking
Cleaning kitchen
Laundry
Grocery shopping
Attending religious service
Paying household bills
Caring for animals and pets
Don’t know/Can’t remember
Refusal/None of your business

Respondents are asked to rate, for each of the three randomly selected activities, six
feelings—pain, happy, tired, sad, stressed, and meaningful—on a scale from 0 to 6: 0 means the
feeling was not present, and 6 means the feeling was very strong.
BOX 1-3
ATUS SWB Text Asking Respondents to Rate Strength of
Feeling During Specific Activities
Between 12:00 p.m. and 1:00 p.m. yesterday, you said you were eating and drinking. The next set of
questions asks how you felt during that particular time.
Please use a scale from 0 to 6, where 0 means you did not otherwise experience this feeling at all and a 6
means the feeling was very strong. You may choose any number 0, 1, 2, 3, 4, 5, or 6 to reflect how
strongly you experienced this feeling during this time.

The following health related questions (paraphrased here) are also asked after the three
random activity episodes are chosen:



Did you take pain medication yesterday?
When you woke up yesterday, how well rested did you feel?
5




Do you have hypertension?
Would you say your health in general is excellent, very good, good, fair, or poor?

This information creates opportunities to analyze interactions between health states and reported
assessments of emotional states. This is important because daily experience is linked to health
status and other outcomes via channels such as worry and stress on the one hand, and pleasure
and enjoyment on the other.
1.2. Objectives of the SWB Module
The ATUS SWB module was initially designed to collect information primarily on
experienced (“hedonic”) well-being—that is, about people’s emotions associated with a recent
time period and the activities that occurred during that period. The hedonic dimension of wellbeing is directly related to the environment or context in which people live—the quality of their
jobs, their immediate state of health, the nature of their commute to work, and the nature of their
social networks—and is reflected in positive and negative affective states. These kinds of
hedonic measures contrast with self-reported assessments of overall life satisfaction or
happiness. Such “evaluative” well-being measures are more likely to reflect people’s attitudes
about their lives as a whole.
The first, 2010, module included only hedonic measures. The second wave (conducted in
2012) includes two additional questions, one on overall life satisfaction and one on whether or
not recent emotional experience was typical. The life satisfaction responses are collected using
the Cantril ladder scale.6 As noted on the BLS supporting statement for the project (p. 2), asking
the Cantril ladder question enables researchers “to build a link between time use and day
reconstruction methods of measuring well-being on the one hand, and standard life evaluation
questions on the other . . . a direction of research that has not been possible to date.” The life
evaluation question enhances the value both of the ATUS supplement and other surveys that use
a Cantril ladder question.
Measurement of both experienced well-being (i.e., reports of momentary positive and
rewarding or negative and distressing states) and evaluative well-being (i.e., cognitive
judgments of overall life satisfaction or dissatisfaction) extends the policy value of the SWB
module data. The value added comes from what can be learned from differences between what
the two measures show. For example Kahneman and Deaton (2010, p. 1) find that “emotional
well being and life evaluation have different correlates in the circumstances of people’s lives”
and particularly striking “differences in the relationship of these aspects of well being to
income.”
Distinguishing between different dimensions of well-being also allows investigation of
psychological changes associated with aging (e.g., reduced mobility) that might affect both
these dimensions of well-being. Another area where the two dimensions provide
complementary information is job satisfaction. Getting promoted or obtaining a new job that
6

The Cantril Self-Anchoring Scale asks respondents to imagine a ladder with steps numbered
from 0 at the bottom to 10 at the top, in which the top of the ladder represents the best possible
life for them and the bottom of the ladder represents the worst possible life. They are asked
which step of the ladder they personally feel they stand on at this time (for a present assessment).
For a good description and discussion of the Cantril Scale, see Diener et al. (2009).
6

entails long hours might raise a worker’s evaluative well-being, but the associated stress might
reduce experienced well-being, at least in the short term. Similar comparisons could be made
across professions. Respondents’ reported differences between experience and evaluative
measures might also help explain why some people attach high meaning to work, career, and
related time commitments while others focus more on simple day-to-day contentment and how
or if these correlations vary across age, income, and other demographic or cohort factors. For
education research, measures of multiple dimensions of subjective well-being may help provide
an understanding of why students make (or do not make) the investments in schooling choices
that they do (or do not) make.
The second new question for 2012 asks whether the respondents’ emotional experience
yesterday (the day before the interview) was typical for that day of the week:
Thinking about yesterday as a whole, how would you say your feelings, both good and bad,
compared to a typical Monday? Were they better than a typical Monday, the same as a typical
Monday, or worse than a typical Monday (respondents answer “better,” “the same,” or “worse”).

This question may provide insights about day of week effects and day to day variation in
reported well-being scores.
1.3. Uses of Data on Subjective Well-Being
Data from the SWB module supports the BLS mission of providing relevant information
on economic and social issues. The data provide a richer description of work experience;
specifically, these data describe how individuals feel (tired, stressed, in pain) during work
episodes compared to non-work episodes, and how often workers interact on the job. Data from
the module can also be used to measure whether the amount of physical pain that workers
experience varies by occupation and disability status. The fact the SWB module can be linked to
demographic characteristics of respondents—labor force status, occupation, earnings, household
composition, school enrollment status, and other characteristics captured on the core ATUS and
CPS—opens up a wide array of possible studies on the correlates of self-reported well-being.7
Collection of data on subjective well-being also supports the mission of the module's
sponsor, the National Institute on Aging (NIA), to improve the health and well-being of older
Americans. Examples of questions that can be answered include:




Do older workers experience more pain than younger workers on and off the job?
Is the age-pain gradient related to differences in activities or differences in the
amount of pain experienced during a given set of activities?
Do those in poor health spend time in different activities relative to those in good
health?

To date, much of the research on nonmarket components of health and well-being has
been informed by global assessments of positive or negative affect averaged over time that are
divorced from measures of time use or context. Nor has that research typically addressed age
differences or age-related changes in these associations. In this vein, data from the SWB
7

In addition, because the ATUS is conducted through the year, it is possible to study seasonal
effects on well-being—a topic of interest in a number of research areas.
7

module might inform policies on redesigning cities to support healthy aging, the allocation of
funds to programs that affect older populations, and changes to the health care system to
support better maintenance of good health. Researchers have already begun to explore which
aspects of experienced and evaluative well-being, time use, and context promote or impede
healthy aging. Further work can be done to examine the unique correlative and predictive
associations of evaluated and experienced well-being with health and with differences related to
life stage, retirement status, and individual characteristics.

8

2. Ongoing and Potential Research Applications
Compelling evidence indicates that higher levels of subjective or self-reported well-being
are associated with a range of desirable outcomes, from better health and greater longevity to
stable social relationships and even to economic productivity. Daily stress, for example, has been
shown to correlate quite strongly with illness, and higher levels of hedonic well-being (positive
feelings) with lower incidence of cardiovascular disease (Boeham and Kubzansky, 2012;
Huppert, 2009). Based on the current evidence, generated from research using a variety of
methods, one could even reasonably conclude that SWB is likely a causal factor for some health
outcomes. This in itself is a compelling reason to gather data on and analyze the subjective-wellbeing of the population.
Though data from the 2010 ATUS SWB module have only been publicly available since
November 2011 (2012 data will not be available until next year), research using those data is
already emerging. This section identifies some of that work to provide a sense of the range of
applications.
2.1. Time Use, Emotional Well-Being, and Unemployment
In an analysis of the differences in time use and emotional well-being between employed
and unemployed people—for specific activities identified using the ATUS sample—Kreuger and
Mueller (2012) show that the unemployed get less enjoyment out of leisure and report higher
levels of sadness during specific activities relative to employed (the sadness decreases abruptly
at the time of employment).8 This study leans more heavily on data from the Survey of
Unemployed Workers in New Jersey since its longitudinal structure, in contrast to the repeated
cross-sectional measurement in ATUS, allows consideration of fixed effects—that is, to look at
within group variation—but is indicative of the importance of being able to link data on
subjective well-being to specific events.
2.2. Assessing Validity of Short Versions of the Day Reconstruction Method (DRM)
Vicki Freedman, Richard Gonzalez, Lindsay Ryan, Norbert Schwarz, Jacqui Smith, and
Robert Stawski, are comparing DRM—which involves asking respondents to reconstruct and
describe episodes of the previous day and the feelings they experienced during each—with
shorter survey approaches that retain a subset of DRM features.9 This work is comparing
findings from the Health and Retirement Study (HRS) with DRM data collected in the Panel
Study of Income Dynamics (PSID), the ATUS SWB module, and the American Life Panel.10
8

More generally, the ATUS SWB module has the potential to add richness to research on trends
in leisure and leisure inequality (see, e.g., Aguiar and Hurst, 2007) and on the link between
leisure and well-being (see, Meyer and Sullivan, 2009, which examines changes in the
distribution of well-being as a function of not just consumption of goods and services, but also
consumption of time, by incorporating information based on self-reported measures.
9
A brief description of this research in progress can be found at
http://micda.psc.isr.umich.edu/project/detail/35382 (accessed July 17, 2012).
10
One appealing argument for collecting time-use and hedonic data through an approach like that
of the day reconstruction method is that it can then be used to compute other measures of
9

The minimum features necessary for a short, reliable, and valid survey index of experienced
well-being are unknown, though the target length of a survey measure being tested in their study
is 3–5 minutes.
This kind of evaluation is central to determining how broadly subjective measures can
potentially be integrated into policy analyses and national statistics. Adding a standardized –
employment, etc.) is necessary for understanding covariates of (and developing statistics on)
population well-being. However, such an integrated strategy will only be feasible if the modules
are minimally burdensome and retain validity across contexts and if the short-version
questionnaires are sufficiently robust in the information they produce.
2.3. Episode-Based Pain Studies
Two additional sets of analyses that use ATUS or ATUS-like data are worth noting
because they provide an indication of potential uses of data from the SWB module. In a recent
study, Krueger and Stone (2008) measured pain during specific random periods of time, which
allowed them to study how reported (recalled) levels of pain affected activities of daily living in
particular segments of the sample population. This approach is novel relative to the global
assessment methodologies typically used in population studies. The authors used data from the
Princeton Affect and Time Survey (PATS), which employs a similar data collection
methodology and the same general procedures as ATUS: “yesterday” is reconstructed through
computer-assisted telephone interviews, and then three episodes from those identified are
randomly drawn and information is collected about affect and pain.
Similar studies could be done even more robustly using ATUS, as PATS allowed only
3,982 respondents, while there were more than 12,000 in the 2010 ATUS sample. In addition, the
PATS sample was likely less representative than the ATUS sample. Even with these limitations
in PATS (relative to ATUS), the finding from this study were clear and robust: one was that
those with lower income or less education reported higher average pain than did those with
higher income or more education, and another was that average pain ratings reached a plateau
between the ages of about 45 years and 75 years. The results of this study suggest even greater
potential for the value of ATUS for pain studies—an area where there is an increasing demand
for research.
Stone and Deaton have recently begun work, using the 2010 SWB module data, to
examine the hypothesis that people with different employment status (working/nonworking) and
occupations (using standard labor categories) experience different levels of pain throughout the
day—and not just on the job.11 Possible explanations for variation in reported pain levels include
the differing physical demands of different occupations; these pain-occupation relationships may
vary by age or gender. The researchers first examined pain, rated on a scale from 0 (did not feel
any pain) to 6 (severe pain), for a broad employment status variable. They found those who were
employed had less pain than those who were unemployed and were looking for work or who
experienced well-being such as the U-index, which measures the proportion of time individuals
spend in an “unpleasant,” “undesirable,” or “unhappy” state (see Krueger and Stone, 2008). A
focus on the U-index would be justified if policy makers want to pay attention to the incidence of
negative feelings and their health and other consequences.
11
This work is being done by Arthur Stone (Stony Brook University) and Angus Deaton
(Princeton University).
10

were retired or disabled. People in management, business, and financial occupations had lower
pain levels than almost all of the other occupational categories (controlling for age and sex).
People in occupations that are judged as having higher levels of manual labor also reported more
daily pain. Pain was also higher on average during times respondents reported being at work in
comparison with other activities. Other aspects of hedonic well-being—e.g., specific emotions,
such as stress or enjoyment—may ultimately be examined in much the same way.
Similarly, it is possible to test if pain was higher at work or during periods not at work,
and whether or not this distinction interacted with type of occupation: Do those with physically
demanding jobs experience more pain on the job than when not working? Is this pattern less
pronounced for less physically demanding occupations? These analyses have begun to reveal the
capability of the detailed, daily data of the ATUS to address both between- and within-subjects
questions, and highlight the richness of the data.
2.4. End-of-Life Care
Various well-being measures have been used for some time to supplement measures of
objective health in clinical and epidemiological research, particularly by those interested in
broadening the concept of health beyond the absence of illness to include the presence of
positive health, functioning, and other quality-of-life dimensions.
Policies oriented toward improving care for the chronically ill or for end-of-life care,
for example, could benefit from better data on the impact that various treatments have on
patients and on their families and careers. Data on subjective well-being could be useful in this
area, especially for monitoring those who are providing care, such as family members. The data
could identify where targeted studies are needed, such as when quality is at least as important as
quantity of life. The distinction between hedonic well-being and other dimensions of well-being
addressed in the 2012 SWB module may be especially important for the end of life, when the
balance between predominantly purposeful and pleasurable activities might change.
In addition, the well-being of eldercare providers is of interest to policy makers because
the elderly population is growing, along with a reliance on informal care providers to assist them.
Researchers may be able to take advantage of a change that was made to the ATUS in 2011,
when questions that identify eldercare providers and eldercare activities were added.
2.5. Transportation
Transportation has been identified as a potentially key determinant in the quality of
people’s lives. For example, when the transportation infrastructure is of poor quality or
overcrowded, congestion and unreliable travel times inhibit the ability of individuals to engage in
enjoyable or productive activities. Therefore, modeling the relationship between travel behavior
and activities with measures of well-being represents a potential policy application of time use
and well-being data (Diener, 2006; Steg and Gifford, 2005). Archer et al. (2012, p. 1) describe
how transportation forecasting models may be used to help inform policy and investment
decisions; they use the 2010 ATUS and SWB module data to develop a multivariate model
designed to “capture the influence of activity-travel characteristics on subjective well-being
while accounting for unobserved individual traits and attitudes that predispose people when it
comes to their emotional feelings.” They find that “activity duration, activity start time, and child
accompaniment significantly impact feelings of well-being for different activities” (including

11

travel). The authors add that “by integrating the well-being model presented in this paper with
activity-based microsimulation models of travel demand, measures of well-being for different
demographic segments may be estimated and the impacts of alternative policy and investment
decisions on quality of life can be better assessed.”

12

3. Assessment
3.1. Value of the SWB Module Data to Date
It is still early to gauge the research and policy value of data emerging from the ATUS
SWB module. Even so, the kinds of research described above provide a preliminary indication of
the insights that can be drawn from the ability to combine time-use information (as it links to
specific activities) and self-assessments of well-being during those periods, which have
relevance to policies ranging from commuting and home production to eldercare and maintaining
good health. Without established and consistent historical data that combine time use and
emotional experience, researchers would be limited to analyzing trends in evaluated time use that
are difficult to tie to specific determinants.
Several characteristics of the SWB module data contribute to its value:





Its status as the only national data source on subjective well-being that is linked to
activities and time use.
Its Day Reconstruction Method (DRM)-like capability, unavailable with most
other data sources on subjective well-being.
Its large enough sample sizes (especially if pooled over multiple survey years) to
accommodate analyses of important subgroups of the population.
Its ability to facilitate research to begin solving difficult measurement and
conceptual issues that have historically plagued work on subjective well-being.

The fact that the ATUS SWB module is the only federal government data source of its
kind gives it a potentially very high value. In particular, its approximation of the DRM is
unique.12 As described above, linking of emotional states to daily experience may be the most
directly relevant dimension of subjective well-being to policy. It is important to know how
people feel when they are working, commuting, taking care of the old and the young, etc. In
addition, identifying the context in which such activities take place, and asking respondents to
rate well-being in that context (in the case of the ATUS, of the previous day) has the advantage
of eliciting specific memories and, in turn, reducing bias associated with respondent recall.
More generally, there has been enough progress in research on the measurement of
subjective well-being to pinpoint specific policy domains and questions for which such data are
useful. For example, cross-sectional data have proven important for research assessing the
12

The day reconstruction method is itself an approximation of more time-consuming experience
sampling and ecological momentary assessment methods; however, the day reconstruction
method captures information about episodes while the ecological momentary assessment method
typically captures information about moments (Christodoulou, Schneider, and Stone, 2012).
Simplified versions of the experience sampling and ecological momentary assessment
methods—which, in some, sense represent the gold standard since they involve repeated
assessment in real time of people’s current hedonic well-being—are necessitated by burden,
time, and intrusiveness constraints in surveys. Though research is under way on the issue, it is
still an open question how well, and under what conditions, the day reconstruction method
approximation is adequate and useful.  
13

relative impact on people of income and unemployment13 and marriage and marital dissolution
(Deaton, 2011, p. 50) and, more generally, on the effect of policies where large nonmarket
components are involved (e.g., standard of living during end-of-life medical treatment). Data on
subjective well-being have the potential to augment information in any situation in which market
data are unavailable or not relevant and policy makers require criteria for choosing one course of
action among two or more alternatives. In these cases, a range of evidence—revealed preference,
stated preference, and subjective well-being measures—can usefully be drawn upon. And wellbeing measures that are tied to specific activities add a great deal of subtlety to these analysis; for
example, while perhaps unemployed persons are able to engage more in activities they like to do
(spend time with friends or relatives, rest, watch television, etc.), perhaps they enjoy each of
those activities less relative to the employed.
It will be a task for this Panel’s final report to provide an assessment of the extent to
which subjective measures—including both global, evaluative measures and the more
experiential measures that are the focus of this module—can or should be used to guide policy.
Collecting data within the context of the ATUS has the potential to help researchers and policy
makers evaluate whether these measures can be used in this way.
3.2. Cost of Discontinuing the Module
The cost of discontinuing the module could be large since—if the value of such data
became more apparent at some point in the future—restarting the survey would likely entail
repeating start-up tasks and drawing again on political capital to make it happen. More
importantly, the data continuity that is now being established (with the 2010 and 2012 waves and
the proposed 2013 wave) would be lost, affecting the ability of researchers to draw inferences
from trends in reported time use and well-being.
On the budget side, the marginal financial cost of adding the developed module to ATUS
is relatively modest—about $178,000.14 That said, it would be useful to perform a full accounting
to assess the quality of survey results and any effects that the addition of the SWB module may
have on the quality of the overall CPS and ATUS. At least in terms of respondent burden and
response rates, these concerns would seem to be modest for the former and unfounded for the
latter. Indeed, by design, the ATUS is asked of those who have rotated out of the CPS, and
modules are asked after the core ATUS is completed. This design element prevents modules
from impacting response to the core ATUS and CPS.15 Because the SWB questions are the last

13

One could reasonably conclude that addressing the recent high rate of unemployment was
made even more urgent by findings from research on subjective well-being showing that, in
terms of individuals’ utility, more was involved than simply an income effect. As Krueger and
Mueller (2012) note, unemployment takes an emotional toll on people even while they are
engaged in leisure activities. This calls into question an earlier conclusion by economists that
people’s decreases in well-being because of unemployment may be partially compensated by
increases in leisure.
14
The monetary cost of the 2012 module was higher ($273,000) as it included cognitive testing,
data editing, interviewer training, and call monitoring activities by BLS.
15
If ATUS interviewers indicated that the survey will take 5 minutes longer, addition of the
module could affect people’s willingness to participate (unit response rates). ATUS response
14

thing the respondent hears, the impact on the core ATUS is expected to be minimal. Similarly,
the SWB module cannot, by design, bias the core diary responses. On the respondent burden
question, for the 2012 SWB module, average time spent was approximately 5 minutes, which
adds up to an estimated 1,100 hours for the 12,800 respondents (Federal Register).
3.3. Value of a Third Wave
A third wave of data collection will add significant information beyond what has been
collected so far. Most obviously, another year for the survey means an increased capacity for
researchers to enlarge samples by pooling data across years. For some purposes—for example, to
look at well-being effects associated with changes in employment during recessions (only a
small percentage of the population is unemployed) or to investigate differences across population
subgroups—the number of observations needed to make valid statistical inferences well exceeds
the annual sample size. This is especially true for comparing self-reported well-being score
across smaller population subgroups. Almost all of the research to date using ATUS—which
covers a wide range of topics, from household production, to work and leisure patterns, to
childcare issues—has pooled data across years to increase the robustness of the statistical
estimates.16 The need to enlarge samples (pool data) will be true for research applications that
rely on the SWB module of the ATUS as well.
Crucially, the 2012 module (the second wave) is only the first version of the survey that
asks the overall life satisfaction (evaluative) well-being questions. In order to begin looking at
sensitivity of measures and changes over time in these questions, at least one additional round of
the survey—and ideally several more—are needed. A 2013 module would effectively double the
sample size of respondents who have answered the evaluative well-being questions.
Fielding another round of the SWB module will also add to the accumulating evidence
needed to determine the value of incorporating it into the ATUS (and possibly elsewhere) on
something more than an experimental basis. More generally, continuing the module will
encourage discussion of how measures of subjective well-being can play a useful role in
assessing the effects of public policies. On the research side, a third wave of data may shed light
on unanswered questions about survey issues, data quality, and reliability (e.g., nonresponse bias,
question ordering, context effects). Other technical issues that could be studied include mode of
administration effects (is reported well-being lower in face-to-face interviews than for telephone
or internet modes?); activation/valence (are positive and negative affect two ends of the same
bipolar dimension or are they separable unipolar dimensions? scaling (do populations from
difference cultures or age groups systematically respond differently? and memory bias (e.g., are
negative events reported more or less frequently than positive events?).
A third wave of the survey could also be used to explore opportunities for
experimentation designed to move toward an optimal survey structure, should the module
become a permanent biannual ATUS supplement. Although it is unlikely that major changes
could be made for a 2013 module, in the longer term it is certainly worth considering whether

rates have ranged from 52.5 to 57.8 percent. The response rates for 2010 (the first year of the
SWB Module) was 56.9 percent.
16
A bibliography of research that has used ATUS data can be found at
http://ideas.repec.org/k/atusbib.html (accessed August 7, 2012).
15

modifications could be made to increase its value. Examples of possible modifications to
consider include











Split sample surveys—one half the respondents could receive one question while the
other half gets another; this would be useful for testing such things as sensitivity to
different scales and question wording.17
Finding the optimal number of activities to ask about. It is not obvious that three
activities is the optimal number of activities to include on the module. It may be
useful to ask about hedonic well-being associated with more activities in order to
increase the reliability of daily estimates. Importantly, sampling more episodes
increases the power to examine activity-specific effects, which may be particularly
valuable for addressing policy questions. Doubling or even tripling the number of
episodes may be cost-effective, although that benefit would have to weighed against
considerations of participant burden and the potential impact on response rates.
Selecting the “right” positive and negative emotion adjectives for module questions.
Research supports the separation of positive and negative states but, more generally,
should the module be focused more on suffering or happiness. The module could
experiment with different adjectives and how interpretation varies across populations.
Expanding coverage to pain and other sensations. There are no good conceptual
criteria for differentiating between sensations and “pure” emotional states or for how
the two link together. Intuitively, sensations are principally physiological states, in
contrast to such feelings as anxiety, stress, and joy, which are principally subjective
states.
Additional or replacement questions for consideration. A possible example is adding
a question or two about sleep, such as: “How many hours of sleep do you usually get
during the week?” or “How many hours of sleep do you usually get on weekends?”
The objective of such questions would be to find out if respondents’ reports about
behaviors/emotions—feeling happy, tired, stressed, sad, pain—are influenced by
(chronic) sleep deprivation or other sleep patterns.18 A methodological question is
how well do people recall the previous night’s sleep?
Selecting among competing evaluative measures. Is the current Cantril approach,
which is perhaps the most remote from affect measures, optimal? Alternative versions
of the evaluative measure are common in the literature.

It would also be interesting to make modifications to the SWB module so that day-of-week
effects could be tested for different domains—health, education, transportation, etc.
17

In its well-being survey, the United Kingdom’s Office of National Statistics has used, or plans
to use, split trials to test for such things as sensitivity to different scales, question wording, and
order and placement of questions.
18
This idea was raised by Mathias Basner, of the University of Pennsylvania School of Medicine,
who noted that self-assessments of habitual sleep time overestimate physiological sleep time and
that estimates of habitual sleep time based on ATUS overestimate self-assessments of habitual
sleep times found in other population studies. Therefore, he suggested that it would be “very
elucidating” to compare self-assessments of sleep time for the two questions above against
estimates based on ATUS responses for the day before the interview day.
16

The merits of retaining some fraction of the sample for experimental work should be
strongly considered, presumably not for 2013 but for subsequent years. One such experiment
would be to determine sample sizes needed for subgroup analyses (e.g., day reconstruction
method questions, which rely some recall, are systematically answered differently by older and
younger populations; in an aging society, it is important to be cognizant of these effects).
The ATUS SWB questions could be the model for a standard set of questions that could
be added to other surveys. With effective data linking, this could yield a rich set of findings
about the relation to SWB of a wide range of covariates. If such a strategy were adopted, the
experience of the ATUS SWB module will provide insights about how questions might perform
on health, economic, and other kinds of surveys; and for determining candidate surveys such as
the National Health Interview Survey and the National Health and Nutrition Examination
Survey, administered by the National Center for Health Statistics, and the Survey of Income and
Program Participation, administered by the U.S. Census Bureau for adding modules. As noted
above, there are potentially major advantages in having similar questions embedded across
multiple surveys, especially as linking of microdata (including administrative) records becomes
increasingly feasible.
In light of changing budgets and priorities and emerging alternative data sources (e.g.,
private label, digital, Web-based), the nation’s statistical agencies have already begun to
reexamine the content, modes, and structure of their surveys and data programs more intensively
than ever before. New scrutiny of what trends in society are important to measure (such as those
recommended by the Commission on the Measurement of Economic Performance and Social
Progress; Stiglitz, Sen, and Fitoussi, 2009) may give rise to new opportunities to refocus
statistical program coverage (and the surveys on which they are built) and to move into new
research areas surrounding SWB. Smaller-scale studies and data collections, such as the ATUS
SWB module, are needed to help judge the value and feasibility of embarking on production of
national-level SWB statistics, such as those under development in the United Kingdom.
Moreover, determination of the place of measures of subjective well-being in monitoring the
economy and society cannot be done without the data. The question of whether self-reported
measures of well-being should one day be reported alongside more standard economic statistics,
such as those for income and employment and for financial markets, is as yet unanswered.
A careful assessment of the data emerging from ATUS and the SWB module may help
avoid mistakes if self-reported well-being statistics are ever produced on a larger scale. To the
extent that evidence can be accumulated on the research and policy value of such data, a better
basis for making these data collection and statistical program decisions can be established. The
fact that the United States has a decentralized statistical system makes coordinating of the survey
content related to subject well-being a greater challenge than in countries with centralized
statistics systems. However, it also affords the option of targeting development in the areas that
are identified as the most relevant for policy and measurement—such as health, employment, or
education—for which the argument is strongest for adding this kind of content. In light of these
arguments, it is the view of the panel that the cost of the proposed 2013 SWB module is quite
modest given its potential to inform decisions about potentially much larger statistical system
investments.

17

References
Aguiar, M., and Hurst, E. (2007). Measuring trends in leisure: The allocation of time over five
decades. The Quarterly Journal of Economics, 122(3), 969–1006, 1008.
Archer, M., Paleti, R., Konduri, K., and Pendyala, R. (2012). “Modeling the connection Between
Activity-Travel Patterns and Subjective Well-Being. Submitted for Presentation and
Publication, 92nd Annual Meeting of the Transportation Research Board.
Boeham, J.K., and Kubzansky, L.D. (2012). The Heart’s Content: The Association between
Positive Psychological Well-Being and Cardiovascular Health.” Psychological Bulletin,
online April 17, 2012. Available: http://www.rwjf.org/pioneer/product.jsp?id=73919
(accessed September 7, 2012).
Christodoulou, C., Schneider, S., and Stone, A. (2012). Validation of a Brief Yesterday Measure
of Hedonic Well-Being and Daily Activities: Comparison with the Day Reconstruction
Method. Working Paper, June 4.
Deaton, A.S. (2011). The Financial Crisis and the Well-Being of Americans. NBER Working
Papers 17128. National Bureau of Economic Research, Inc.
Available: http://www.nber.org/papers/w17128 (accessed July 29, 2012).
Diener, E. (2006). Guidelines for national indicators of subjective well-being and ill-being.
Applied Research in Quality of Life, 1(2), 151–157.
Diener, E., Kahneman, D., Tov, W., and Arora, R. (2009). Income’s Differential Influence on
Judgments of Life Versus Affective Wellbeing. Assessing Wellbeing. Oxford, UK:
Springer.
Huppert, F.A. (2009). Psychological well-being: Evidence regarding its causes and
consequences. Applied Psychology: Health and Well‐Being, 1, 137–164.
Kahneman, D., and Deaton, A. (2010, August). High Income Improves Evaluation of Life but
not Emotional Well-Being. Proceedings of the National Academy of Science.
Krueger, A.B., and Mueller, A. (2012). Time use, emotional well-being and unemployment:
Evidence from longitudinal data. American Economic Review, 102(3), 594–599.
Krueger, A.B., and Stone, A.A. (2008). Assessment of pain: A community-based diary survey in
the USA. Lancet, 371(May 3), 1519–1525.
Meyer, B.D., and Sullivan, J.X. (2009). Economic Well-Being and Time Use. Working paper,
June 22.
Steg, L., and Gifford, R. (2005). Sustainable transportation and quality of life. Journal of
Transport Geography, 13(1), 59–69.
Stiglitz, J., Sen, A., and Fitoussi, J.P. (2009). Report by the Commission on the Measurement of
Economic Performance and Social Progress. Available: http://www.stiglitz-senfitoussi.fr/documents/rapport_anglais.pdf (accessed August 2, 2012).

18

APPENDIX
Biographical Sketches of Panel Members
ARTHUR A. STONE (Chair) is distinguished professor of psychiatry and psychology, vice
chair of the Department of Psychiatry and Behavioral Sciences, and director of the Applied
Behavioral Medicine Research Institute, all at Stony Brook University. He is also a senior
scientist at Gallup. He specializes in the field of behavioral medicine, focusing on stress, coping,
physical illness, and self-report processes. He also works with Gallup researchers to explore how
employee engagement relates to worker’s physical health and well-being. He has been an
executive council member for the American Psychosomatic Society, a research committee
member for the American Psychological Association, and a past president and executive council
member of the Academy of Behavioral Medicine Research. He holds membership to the
American Psychological Society, the Society for Behavioral Medicine, and Academy of
Behavioral Medicine Research, among others. He has a B.A. degree from Hamilton College and
a Ph.D. degree in clinical psychology from Stony Brook University.
NORMAN M. BRADBURN is the Tiffany and Margaret Blake distinguished service professor
emeritus, at the University of Chicago, where he also serves on the faculties of the Department of
Psychology, the Irving B. Harris Graduate School of Public Policy Studies, the Booth School of
Business, and the college. He is also a senior fellow at the university’s National Opinion
Research Center and serves on the board of directors of the Chapin Hall Center for Children. He
previously served as assistant director for social, behavioral, and economic sciences at the
National Science Foundation. His research focuses on psychological well-being and the
assessment of quality of life using large-scale sample surveys. He is a past president of the
American Association of Public Opinion Research. He has an M.A. degree in clinical
psychology and a Ph.D. degree in social psychology, both from Harvard University.
LAURA L. CARSTENSEN is professor of psychology, the Fairleigh S. Dickinson Jr. professor
in public policy, and the founding director of the Stanford Center on Longevity, all at Stanford
University. Much of her work has focused on socioemotional selectivity theory—a life-span
theory of motivation. Her most current empirical research focuses on ways in which motivational
changes influence cognitive processing. She is a fellow of the Association for Psychological
Science, the American Psychological Association, and the Gerontological Society of America,
and she serves on the board of science advisors to the Max Planck Institute for Human
Development in Berlin, Germany. She is the recipient of the Richard Kalish award for innovative
research and the distinguished career award from the Gerontological Society of America,
Stanford University’s dean’s award for distinguished teaching, and a MERIT (Method to Extent
Research in Time) Award from the National Institute on Aging. She has a B.S. degree in
psychology from the University of Rochester, an M.A. degree in developmental psychology, and
a Ph.D. degree in clinical psychology, both from West Virginia University.
EDWARD F. DIENER is the Joseph R. Smiley distinguished professor of psychology in the
Department of Psychology at the University of Illinois at Urbana-Champaign and a senior
scientist at the Gallup Organization. His research focuses on the measurement of well-being,
temperament and personality influences on well-being, theories of well-being, income and well-

19

being, and cultural influences on well-being. He has served as president of the International
Society of Quality of Life Studies, the Society of Personality and Social Psychology, and the
International Positive Psychology Association. Among his many awards are an honorary
doctorate from the University of Berlin and a distinguished scientist award from the International
Society of Quality of Life Studies. He won the distinguished researcher award from the
International Society of Quality of Life Studies, the first Gallup academic leadership award, and
the Jack Block award for personality psychology. He has a B.A. degree in psychology from the
California State University of Fresno and a Ph.D. degree in psychology from the University of
Washington.
PAUL H. DOLAN is a professor of behavioral science in the Department of Social Policy at the
London School of Economics and Political Science. He is also chief academic adviser on
economic appraisal for the Government Economic Service in the United Kingdom. Previously,
he held academic posts at the universities of York, Newcastle, Sheffield, and Imperial, and he
has been a visiting scholar at Princeton University. His research interests focus primarily on
developing measures of subjective well-being that can be used in policy, particularly in the
valuation of nonmarket goods and in extending the ways in which the lessons from behavioral
economics can be used to understand and change individual behavior. He is a recipient of the
Philip Leverhulme Prize in economics—awarded by the Philip Leverhulme Trust in the United
Kingdom—for his contribution to health economics. He has served on many expert panels for
various government departments in the United Kingdom. He has M.Sc. and D.Phil. degrees in
economics from York University
CAROL L. GRAHAM is College Park professor in the School of Public Policy at the
University of Maryland and senior fellow in economic studies and Charles Robinson chair in
foreign policy studies at the Brookings Institution. Previously, she was codirector of the Center
on Social and Economic Dynamics at the Brookings Institution and research fellow at the
Institute for the Study of Labor. She has served as special advisor to the vice president of the
Inter-American Development Bank, as a visiting fellow in the office of the chief economist of
the World Bank, and as a consultant to the International Monetary Fund and the Harvard Institute
for International Development. Her research focuses on public health, poverty, inequality,
economics of happiness, and measures of subjective well-being. She has an A.B. degree from
Princeton University, an M.A. degree in international economics from the Johns Hopkins School
of Advanced International Studies, and a Ph.D. degree in political economy from Oxford
University.
V. JOSEPH HOTZ is the arts and sciences professor of economics in the Department of
Economics at Duke University, research affiliate at the Institute for Research on Poverty at the
University of Wisconsin-Madison, research fellow at the Institute for the Study of Labor, and
research associate at the National Bureau of Economic Research. He also serves as a research
affiliate at the National Poverty Center, the Gerald R. Ford School of Public Policy, and the
University of Michigan. Previously, he served as visiting scholar at the Cowles Foundation, Yale
University, and at the Russell Sage Foundation and as a professor and chair of the Department of
Economics at the University of California, Los Angeles. His areas of specialization include labor
economics, population economics, and applied econometrics. He has a B.A. degree from the

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University of Notre Dame, and M.S. and Ph.D. degrees in economics from the University of
Wisconsin-Madison.
DANIEL KAHNEMAN is professor of psychology and public affairs, emeritus, and senior
scholar at the Woodrow Wilson School at Princeton University. He is also the Eugene Higgins
professor of psychology, emeritus, at Princeton University and a fellow at the Center for
Rationality at The Hebrew University. Previously, he held positions as professor of psychology
at the University of California, Berkeley, associate fellow at the Canadian Institute for Advanced
Research, and visiting scholar at the Russell Sage Foundation. He is a member of the National
Academy of Sciences, the American Academy of Arts and Sciences, the American Philosophical
Society, and the Econometrical Society, and he is a fellow of the American Psychological
Association. He is a recipient of the 2002 Nobel Prize in economics, as well as the distinguished
scientific contribution award of the American Psychological Association, the Warren Medal of
the Society of Experimental Psychologists, and the Hilgard Award for career contributions to
general psychology from the American Psychological Association. He has a B.A. degree in
psychology and mathematics from The Hebrew University, and a Ph.D. degree in psychology
from the University of California, Berkeley.
ARIE KAPTEYN is a senior economist at RAND and director of its labor and population
division. He also serves as associate director of the Financial Literacy Center, a joint center of
RAND, Dartmouth College, and the Wharton School at the University of Pennsylvania. Before
joining RAND, he held several positions Tilburg University in The Netherlands, including dean
of the Faculty of Economics and Business Administration, founder and director of CentER, a
research institute and graduate school. He has held visiting positions at Princeton University, the
California Institute of Technology, Australian National University, the University of Canterbury
(New Zealand), the University of Bristol, and the University of Southern California. His research
expertise covers microeconomics, public finance, and econometrics. He is a fellow of the
Econometric Society, a member of the Netherlands Royal Academy of Arts and Sciences, and
past president of the European Society for Population Economics. He has a B.A. and an M.A. in
agricultural economics from State Agricultural University Wageningen, an M.A. in econometrics
from Erasmus University Rotterdam, and a Ph.D. degree in economics from Leyden University,
all in The Netherlands.
AMANDA SACKER is research professor in quantitative social science at the Institute for
Social and Economic Research at the University of Essex, England. Prior to this she was
principal research fellow at the University College London. She also holds numerous positions,
including honorary appointment at the Dalla Lana School of Public Health, University of
Toronto; member of the executive committee of the Society for Longitudinal and Life Course
Studies; member of Health Strategy Group, University of Essex; and honorary chair in the
Department of Epidemiology and Public Health, University College London. Her research
interests focus on life course epidemiology and inequalities in physical and mental health, with
particular interest in the use of mixture models that combine categorical and continuous latent
variable modeling techniques in longitudinal studies. She has a B.Sc. degree in psychology and a
Ph.D. degree in psychology and statistics.

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NORBERT SCHWARZ is the Charles Horton Cooley collegiate professor of psychology and
professor of business at the Stephen M. Ross School of Business, both at the University of
Michigan. He also serves as research professor at the Institute for Social Research at the
University of Michigan. Previously, he taught psychology at the University of Heidelberg and
served as scientific director of ZUMA, an interdisciplinary social science research center in
Mannheim. His research interests focus on human judgment and cognition, including the
interplay of feeling and thinking, the socially situated and embodied nature of cognition, and the
implications of basic cognitive and communicative processes for public opinion, consumer
behavior, and social science research. He is an elected member of the American Academy of
Arts and Sciences and the German National Academy of Science Leopoldina. He is a recipient of
the Heinz Maier-Leibnitz Prize of the German Department of Science and Education, and the
Wilhelm Wundt Medal of the German Psychological Association. He has a Ph.D. degree in
sociology and psychology from the University of Mannheim and a Ph.D. “Habilitation” degree
in psychology from the University of Heidelberg, both in Germany.
JUSTIN WOLFERS is visiting associate professor in the Department of Economics at
Princeton University, associate professor of business and public policy at the Wharton School at
the University of Pennsylvania, research associate at the National Bureau for Economic
Research, and a visiting scholar at the Federal Reserve Bank of San Francisco. He holds
numerous other positions, including nonresident senior fellow at the Brookings Institution and
senior scientist at the Gallup Organization. His research interests include law and economics,
labor economics, social policy, political economy, macroeconomics, and behavioral economics.
He is the recipient of numerous awards, including the Wharton M.B.A. core teaching award and
the excellence award in global economic research from the Kiel Institute, Germany. He serves as
a board member of the Prediction Markets Industry Association and on the board of advisors of
Crowdcast, and he is also a regular commentator on public radio’s “Marketplace.” He has a B.A.
degree in economics from the University of Sydney, and A.M. and Ph.D. degrees in economics
from Harvard University.

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