Center for Translational Research on Aging Projects

Funded Projects

The Center for Health and Wellbeing is home to a National Institute of Aging Roybal Center, called the Center for Translational Research on Aging. The center has eight active pilot projects, two of which were carried over from a prior grant. 

Age and the Correlates of Wellbeing: Moving from Correlation to Policy

PI: Angus Deaton, Princeton University
The goal of this project is to determine whether, under what circumstances, and exactly how the large literature on the correlates of wellbeing should be used in setting policy. “Happiness” regressions find, for example, that people who are older, better educated, better-off, more religious, or who live in Wyoming or Hawaii, have higher life-evaluation and/or better emotional lives. How might such regressions be useful for policy? One possibility is that they could be used to inform people about what makes lives better: for example, people may be happier in middle age if they realize that being elderly is on average associated with high life evaluation and emotional wellbeing. Another possibility is to use the regressions to calculate “shadow prices” for circumstances, by converting each circumstance’s effect on wellbeing into its equivalent money value using the effect of income as a base. These shadow prices measure the monetary value of outcomes and can be used in benefit-cost analysis to compare with the costs of interventions. Of particular interest here is the extent to which shadow prices change with the age of the individual. For example, it seems that additional income has its largest effect on life evaluation in middle age, and that the effect declines after age 50. Knowing this would be useful for information purposes—telling people about what is likely to matter for them, which they often do not know—as well as in the design of policy, such as social security policy. In spite of the superficial attractiveness of such ideas, there remain unresolved the fundamental questions of just when the numbers that come out of these happiness regressions can be legitimately used in this way, whether they mean what they seem to mean.

An In-depth Examination of DRM-EMA Concordance

PIs: Joseph E. Schwartz, Ph.D., Stony Brook University and Arthur A. Stone, Ph.D., University of Southern California
The Day Reconstruction Method (DRM) is a data collection method developed as a means to obtaining data similar to that from momentary data acquisition methods such as Ecological Momentary Assessment (EMA). There are now scores of studies that have used the DRM in the behavioral, social, and economic sciences. Despite the widespread use of the method, the concordance between DRM and EMA data, which is central to the validation of the DRM, is largely unknown.

To date, evidence that the data obtained by DRM are comparable to those from EMA is spotty at best. The few studies that have attempted to address the issue have yielded a mixed and inconclusive picture, largely due to methodological limitations. A large number of individual points where both EMA and DRM data are available is necessary for adequate statistical analysis of concordance, but so far studies have had only a small number of points. For example, the analyses by Krueger of three days of EMA and DRM data yielded only 105 usable points (in part due to a programming error). Given the numerous activities and emotions that are compared, a minimum of many hundreds of points are needed for precise and reliable concordance estimates, especially polled estimates of within-person concordance.

This study seeks to provide a large number of DRM—EMA comparisons, approximately 2,500 – by piggybacking a DRM administration on a study that collects about 25 momentary reports over a single 24-hour monitoring period. Contemporaneous heart rate, blood pressure, and actigraphy data will also be available. This will be a very efficient way of providing validation data for the DRM; to conduct a new study with similar design features would be a large multiple of the proposed budget.

The Masked Hypertension study conducted at Stony Brook and Columbia Universities, led by Dr. Joseph Schwartz, is a large-scale, observational study intended to increase knowledge about the phenomenon known as Masked Hypertension (MHT). The project is now completing a 7-year follow-up of the original cohort and there are approximately 150 participants left to run.

As part of the ambulatory assessment, the MHT Study includes an EMA assessment every 30 minutes with a dedicated smartphone programmed to administer EMA questions and to accept screen-inputted responses. Every assessment has 27 questions, which can be grouped into five conceptual areas: Environmental setting/context; Activities; Affect; Physical activity; and, Social Interactions. For this study we will focus on question content that has been the object of many DRM studies, affective states and activity content, with the aim of testing the hypothesis that reports of this content by DRM will be confirmed by momentary reports (EMA) occurring within DRM episodes. We will also compare whether the within-person associations of DRM-assessed activities/affect with physiological measures (blood pressure, heart rate, and physical movement) correspond to those of EMA-assessed activities/affect with physiological measures.

The MHT Project participants will also have an Internet-based, self-administered version of the DRM to complete following their day of ambulatory BP monitoring with EMA recording.

In summary, this project hopes fill a major gap in the psychometric profile of the DRM, one that sorely needs filling; in fact, a recent NIA-supported meeting devoted to the DRM highlighted the need for this kind of study. The proposed design is especially attractive because of its efficient use on an ongoing NIH-funded study.

Digging into the Age Gradients of Evaluative and Experiential Wellbeing

PIs: Arthur Stone and Joan Broderick, University of Southern California
Over the last 40 years there is been increasing interest in how subjective well-being (SWB) changes as people age. The rationale for the interest stems from broadening the definition of health from focusing solely on physical manifestations of disease to subjective realms of how satisfied they are with life and how they are feeling.

The accrued knowledge about the SWB age gradient is both compelling and paradoxical. Here we refer to some of our own work (Steptoe, Deaton, & Stone, 2015; Stone, Schwartz, Broderick, & Deaton, 2010). We gathered data on evaluative well-being (judgments people make of the quality of how their lives are going) and experiential well-being (how people feel in a day in and day out manner), which are perceptions of daily stress. The results were striking: younger people have higher levels of well-being, middle-aged people have the lowest levels, and older people return to high levels. The pattern for daily stress is different: younger and middle-aged people have high levels of daily stress, but in the early 50s there is a precipitous drop in daily stress through the early 70s. The paradox in both cases is that well-being is improving (higher life satisfaction and lower stress levels) as people age, yet a reasonable expectation prior to knowing these results was that the increasing disease burden with older age could have resulted in lower levels of SWB and higher levels of stress.

To date, the most prevalent explanation for the paradox is psychological in nature: as people age they improve management their social relationships and emotions, an improvement caused by an awareness of their decreasing years of life (Carstensen, Pasupathi, Mayr, & Nesselroade, 2000). To our knowledge, there are no studies that explain why this shift happens around age 50, that is, an explanation for the improvement in evaluative SWB and the decline in daily stress.

The goal of this study is to advance our understanding for the age-gradients in subjective well-being. Our first goal is premised on the idea that there are factors that may impact subjective SWB (and explain the gradient) that have simply been overlooked or are not known, and we propose an exploratory study for doing this. Our second goal proposes to collect new, more granular data, with respect to age, to allow an investigation of the psychological changes mentioned earlier and to determine if they actually shift around age 50. Furthermore, do they explain the age gradient in SWB. These two goals correspond with the two proposed studies: the first is a hypothesis-generating, qualitative study of individuals in order to explore people’s perceptions of their lives over the age gradient.  The second study is more traditional hypothesis-testing: we hypothesize that indicators of the cognitive characteristics mentioned earlier will show a covariance pattern with age that explains the age–SWB gradient.

Apart from our basic interest in understanding the age gradient of SWB, we believe that there are potential practical outcomes from this investigation. If we are able to identify new facets of experience and subsequently confirm that they are responsible for the well-being increase, then it might be possible for policy to be developed to improve peoples’ lives. If we are able to confirm that cognitive processes are responsible for the age gradient, then we could subsequently identify factors to facilitate those cognitive changes. Again, the ultimate hope would be that these someday lead to actionable manipulations that could improve peoples’ lives.

Does Access to New Pharmaceuticals Improve Wellbeing Among Children with ADHD?

PIs: Anna Chorniy and Janet Currie, Princeton University, and Lyudmyla Sonchak, SUNY Oswego
In 2012 there were five million U.S. children diagnosed with ADHD, and 68% of them were taking prescription drugs for their condition.  This number represents about 11% of 4 to 17 year old American children (Hinshaw and Scheffler, 2014).  Previous research has shown that children with ADHD are at risk for poor outcomes ranging from school failure, lower future earnings, and a higher prevalence of accidents and other mental health problems such as depression in adulthood (Currie and Stabile, 2014; 2006).  Thus, the development of new and more effective treatments for ADHD could potentially have a large impact on the wellbeing of those affected both in childhood and as they grow into adulthood.

The market for ADHD drugs has seen a great deal of innovation: Between 1999 and 2012, 15 drugs were approved by the Food and Drug administration for ADHD.  These innovations have led to rising costs of the prescription drugs consumed by children with ADHD, which has become an issue especially in the publicly funded Medicaid program that covers low income children and people with disabilities.

While the newer drugs are certainly more expensive than their older counterparts, it is not yet clear if they are more effective in improving patient health and/or whether they reduce spending on other types of medical care, by, for example preventing Emergency room visits.

Evaluating the welfare effects of drug innovation is also complicated by the fact that new medications may have helped to fuel the increases in the caseload.  It is possible, for example that some of the increase in ADHD diagnosis and treatment is driven by access to new drugs with fewer side effects, or that are more convenient to take.  In turn, changes in the caseload may make it difficult to assess the effects of new drug treatment, given that the marginal patients drawn into care may be either more or less sick than the original patient population.

To implement the study, we will rely on the Centers for Medicaid and Medicare Services database of administrative claims from the Medicaid Analytic Extract (MAX) for 1999 to 2013.  The MAX data provide basic patient demographic information in addition to pharmacy claims, outpatient, and inpatient claims data.

We will first ask whether and how pharmaceutical innovation has affected the number of Medicaid children receiving ADHD medications, and then we will ask whether it led to improved outcomes among children and teens diagnosed with the condition.  We will distinguish between medications composed of new molecules, and the so called “me-too” drugs, and we will seek to examine which attributes of ADHD drugs (e.g. extended release formulations, new non-stimulant molecules, abuse deterrent properties, or novel administration methods such as patches or liquids) appear to be most appealing to users. 

This analysis will inform a more structural model that will enable us to quantify the new welfare benefits (or costs) of new ADHD drug innovation.

Does Being Surveyed Affect Subsequent Reports of Subjective Well-being?

PIs: Johannes Haushofer and Jeremy Shapiro, Princeton University
The central challenge of measuring well-being in social science is that “true” well-being is unobserved and usually has to be estimated through self-reports. This fact creates a number of concerns for the interpretation of such reports of subjective well-being (SWB). Apart from the obvious question how survey measures of SWB relate to the latent target variable, a further important concern is that eliciting self-reports of SWB may itself affect SWB, or reports of SWB.

The literature has described a number of survey effects. “Question-behavior” effects refer to a change in behavior or SWB due to being asked prospective questions about one’s own future actions or outcomes. Thus, subsequent SWB might be affected by being asked about one’s own projections for it. In contrast, the well-known Hawthorne refers to a change in behavior as a result of being observed in an experiment; for instance, participants in a randomized controlled trial which aims to improve productivity using an intervention can affect productivity simply because participants know about the goal of the experiment and respond in terms of productivity (demand effect). We propose to study a third type of survey effect: simply being surveyed about SWB may affect subsequent self-reports of SWB. This effect differs from a “question-behavior” effect because respondents are asked about current, not future SWB; and it differs from a Hawthorne effect because respondents are not exposed to an intervention – they survey is the intervention.

We are studying this question in the context of an ongoing field study in rural Western Kenya. We have previously conducted a randomized field experiment on the Unconditional Cash Transfer program by the NGO GiveDirectly in this region. In this study, a treatment group received unconditional cash transfers, and their survey responses were compared to two control groups: a spillover group, i.e. control households in treatment villages, and pure control households, i.e. control households in control villages. Importantly, the treatment villages were surveyed both at baseline (wave 0) and endline (wave 1), while the pure control villages were surveyed only at endline (wave 1). This poses a threat to the integrity of the study, and more broadly raises the question whether this double surveying affected responses. 

Interactions between Economic Status, Psychological Well-being, and Age: Analysis of an Unconditional Cash Transfer Trial in Kenya 

PIs: Johannes Haushofer and Jeremy Shapiro, Princeton University
Within the aid industry and the research practitioners and organizations that support it there is a great deal of emphasis on “sustainability.” This term tends to mean one of two things within the industry: a) organizational sustainability or b) individual sustainability. Organizational sustainability refers to an organization being able to sustain its operations on a long-term basis without ongoing receipt of donor funds. Individual sustainability refers to a single intervention or development program being able to lift an individual or individual family out of poverty on a long-term basis. This focus on sustainability has fueled support for interventions such as microfinance or agricultural support services, which have the potential to allow poor recipients to earn returns and increase their future income. Critically, however, these interventions may have substantively different impacts for older recipients, and the research that has evaluated these interventions has not adequately considered potentially heterogeneous effects for the elderly.

The purpose of this project is to fill this gap in the literature.  Secondary analysis on a dataset was created during a randomized control trial impact evaluation of an unconditional cash transfer program. The initial study evaluated the impacts of cash transfers on economic outcomes (such as expenditure and income) as well as measures of psychological well-being. In this secondary analysis, heterogeneous effects of cash transfers on the elderly and widows in particular was explored. The purpose of this analysis will be to assess whether cash transfers “work” for the elderly in significantly different ways than they do for younger, and potentially more economically active, recipients. We will further link economic impacts to effects on subjective and objective (measured through cortisol levels, a stress hormone) indicators of psychological wellbeing.

Investigating the Relation between DRM and Experienced Yesterday Measures

PI: Arie Kapteyn, University of Southern California
The Day Reconstruction Method (DRM) elicits a full-day diary of episodes accompanied by an inventory of emotions/moods during each of the episodes. The DRM proves to be successful in reinstating feelings of the previous day (Kahneman et al., 2004) and has become a widely adopted method for eliciting experienced well-being. The DRM however takes considerable response time and hence is hard to incorporate in on-going major population surveys. Several simplifications have been proposed, where rather than asking about the whole previous day, one selects a small number of activities and asks for emotions during the performance of these activities.

A different approach to simplification is taken by the Hedonic Well-Being 12 measure (HWB12; Smith and Stone, 2011), which asks respondents for an overall day assessment on 12 dimensions (happy, enthusiastic, content, angry, frustrated, tired, sad, stressed, lonely, worried, bored, pain) on a 5-point scale. Similarly, the Gallup Experienced Well-Being Index includes 10 items (anger, depression, enjoyment, happiness, sadness, stress, worry, learning or doing something interesting, smiling or laughing a lot, being treated with respect) on a binary scale, in addition to whether the respondent would like to have more days like yesterday.

This project aims to investigate the relation between aggregate measures like the HWB12 and DRM-like approaches. A time diary study for one day (“yesterday”) among 2000 respondents in the Understanding America Study was conducted. Emotions were elicited for a number of periods. By also asking the HWB12 for “yesterday” we can study the relation between the HWB12 and the DRM. Proper randomizations will take care of possible anchoring effects.

Mobile Phone Sensing to Predict Depression: An Analysis of Experiential Well-Being in Kenya

PIs: Johannes Haushofer and Chaning Jang, Princeton University
Poor experiential wellbeing and common mental disorders, such as depression and anxiety, are particularly prevalent among low-income populations, yet often go undetected and untreated in developing countries (BMJ, 2001; Lorant et al., 2003; Kenya National Commission on Human Rights, 2011; Saeb et al., 2015). Currently, experiential wellbeing can be captured through the measurement of positive and negative experiences in the context of the Day Reconstruction Method (DRM), a structured diary methodology of recounting activities as episodes throughout the previous day. Although the DRM is designed to reduce recall biases, other methodologies, such as Ecological Momentary Assessment (EMA), have been proposed to measure current behaviors and experiences, thus capturing experiential wellbeing in real time. Still, EMA is limited by the subjectivity and inconsistency of self-reported mood and, as a result, is sensitive to survey mode effects, contextual effects, and other biases that pose a challenge in using it as a tool to measure objective experiential wellbeing (Ma et al., 2012). Furthermore, both EMA and DRM place a high burden on respondents, relying on lengthy interviews or a high degree of adherence in filling out diaries.  This makes the assessment of experiential wellbeing difficult to do in low-income, low resource settings where the study population is difficult to access and/or illiterate. 

In this project, we propose a novel approach to using mobile phone remote sensing to predict states of experiential wellbeing that is both objective and non-intrusive to the target respondent. Remote sensing uses passively collected data from a variety of sensors in an effort to quantify user behavior. Consumer-facing remote sensing has been the quantification of physical activity (such as steps, calories burned, and sleep), though researchers have been increasingly using the same sensors to predict other behaviors such as traffic patterns, environmental monitoring (Lane et al., 2010), land use (Toole et al., 2012) and poverty (Blumenstock et al., 2015).

Predicting experiential wellbeing via mobile phone remote sensing has clear potential advantages compared to EMA and DRM. For one, the objective quantification of behavior frees the respondent from surveyor effects. Second, the increasing ubiquity of mobile phones, even in low-income settings, allows for high resolution, long-term data that can be collected inexpensively.

The purpose of this proposed project is to validate remote mobile phone sensing with various measures of reconstructive wellbeing in a low-income setting. Today, mobile phones are ubiquitous and have a large complement of sensors (including GPS, accelerometers, ambient light, sound, and phone usage) that can be used to extract human behavior patterns and assess daily experiential wellbeing. This project aims to correlate this objectively measured activity with subjective wellbeing, with the ultimate goal of identifying behavioral antecedents to changes in subjective wellbeing such that one could provide a “just-in-time” intervention to mitigate the deleterious effects of adverse changes to life situations such as the onset of depression or other chronic disease.

Setting Limits and Their Relation to Wellbeing in End of Life Care

PIs: Jeremy Shapiro, Princeton University; Geoffrey Rees, University of Chicago
Since passage of the Patient Self-Determination Act (1991), it has become a standard of care to attempt to match the care patients receive at the end-of-life with their known preferences. Underlying these efforts is the conviction that self-determination enables people to live more comfortably in the present and die with greater dignity in the future.

Even as the costs of end-of-life care continue to increase, becoming particularly intense in the last six months of life, little research so far has focused specifically on public attitudes about this spending, and about how people relate policy on spending to evaluations of their own well-being. This gap in research is particularly notable when considered in light of the widely acknowledged discrepancy between spending and quality in medical care at the end of life, that greater spending does not equal greater well-being, and that they are often inversely correlated.

This research aims to fill this gap and thereby contribute to a growing national conversation about how to improve the quality of dying in the United States, by asking about how people assess spending on end-of-life care in relation to their evaluative well-being. 

This research will explore how people think about intensive care at the end of life as a public good that supports individual well-being and how assessments of thriving relate to determinations of appropriate spending. 

This research consequently promises clinical application by generating insight into the decision making process of patients and families, opening opportunities to improve shared-decision making through empirically grounded understanding of how patients and families value medical care in their present as well as their future evaluations of well-being. 

Small Area Variations in Mental Health Treatment: Towards New Measures of Prescribing Patterns and Patient Wellbeing

PIs: Janet Currie, Princeton University and Bentley MacLeod, Columbia University
Case and Deaton (2015) document increases in deaths due to self-harm in recent years, which can be viewed as one of the most alarming symptoms of a rising epidemic of mental health problems that is undermining the wellbeing of both patients and their families.  Suicide rates for Americans 35 to 64 rose from 13.7 per 100,000 in 1999 to 17.6 per 100,000 in 2010 (Sullivan et al, 2013).  Antidepressant use increased nearly 400 percent between 1990 and 2010 (Pratt et al. 2011) and antidepressants are the most frequently used type of medications taken by middle aged Americans 18 to 44.  Roughly a quarter of women 40 to 59 take antidepressants.  These figures suggest that it is extremely important to understand patterns in the prescription of anti-depressant drugs, and how these patterns bear on patient outcomes.

This project will combine data from several sources in order to address this question in an innovative way.  First, we have purchased data on all prescriptions for anti-depressants and anti-psychotics (which are increasingly prescribed for depression as well) for 2006 to 2014 from QuintilesIMS.  This company collects data from all retail pharmacies in the U.S.  The dataset contains information on the address of the pharmacy and also about each prescriber, the latter being provided by the American Medical Association. Using this data it will be possible to examine so-called small area variations in prescribing patterns.  The existence of unexplained variations in medical practice have been popularized by the famous Dartmouth Atlas which focuses on variations in the treatment of elderly Medicare covered individuals (Dartmouth-Atlas Project, 2013) but have not been examined previously for the entire population. 

In addition to prescription types and prescription levels, variation in the types of medications prescribed could have an independent positive or negative effect.  Since individuals can respond very differently to the same anti-depressant (leaving trial and error as the accepted way to find an efficacious medication) prescribers who use a wider variety of medications may have a higher chance of success. 

A second innovation of our project will be to propose and explore a novel measure of variation in prescribing practices: the Shannon (1948) entropy score which is defined as the sum over all k, where k are drugs, of [prob(k)*log(prob(k))]/log(nk), and nk is the number of available drugs.  This index has several nice properties: It can be aggregated from the individual, to the county, to the state and national level; More aggregate measures are concave in the constituent measures, e.g. a patient weighted average of the physician entropy measures is <= the county level measure for the physicians in the county; It is not affected by the addition or subtraction of options, which is important in the prescription drug market where there is product entry and exit.  Using this index we will be able to locate the geographical unit responsible for most of the variation in prescribing practices.  That is, we can ask whether most of the variability is associated with differences in individual physicians, counties, states, or regions.  This fact may give some insight into possible drivers of the variability.

Third, we will propose and construct a novel geographic measure of the prevalence of mental health problems using data from Emergency Room (ER) visits. Virtually all psychiatrists, psychologists, and other mental health professionals advise their patients to proceed to the nearest ER in the case of a crisis.  ER admissions for mental health diagnoses can be tabulated using the Healthcare Cost and Utilization Project (HCUP) data collected from state hospital administrators by the U.S. Agency for Health Research and Quality.  The HCUP data can be matched to the prescription drug data at the county level in order to examine the impact of prescribing patterns on ER utilization for mental health conditions.  

While ER admissions are of interest in their own right, we will also, for large counties, explore the correlations between our measures and suicides, including whether lagged ER admissions can be used to predict future suicides.

A final innovation is that we will be able to examine the cost associated with different prescribing patterns.  Drug Prices are available in the “Red Book” published by Truven Health Analytics / Micromedex Solutions. Using these data, we will be able to price each counties basket of anti-depressant drugs and relate the levels and variability in the portfolio chosen to the mean prescription price.

In summary, this pilot involves the construction and analysis of several new measures of small area variation in anti-depressant drug prescription and proposes a way to measure mental health outcomes (through ER visits) at a finer level of geographic disaggregation than is possible with vital statistics mortality data.  Given these new data, it will be possible to estimate models relating prescribing patterns to outcomes (including ER rates and costs.

Survey Methods For Measuring Affect

PI: Dylan Smith, Stony Brook University
There is a growing emphasis on capturing subjective appraisals of emotional experiences, as a supplement to traditional “objective” economic performance measures such as income, or epidemiological measures of health such as longevity. This pilot study tests the validity of several recall-based methods for capturing affect in daily life. Specifically, it examines and compares assessments taken on the same day as the observation day (the “end of day” approach), versus assessments taken the next day. The next day assessment will be either with or without “instantiation” of the previous day, as operationalized in the Day Reconstruction Method (DRM). DRM is a structured diary methodology designed to enhance accurate recall via instantiation of the previous day, which is done by dividing the day into discrete “episodes.” These three approaches differ substantially in terms of the level of burden placed on respondents, and in terms of cost of implementation. The “next day” approach, which relies on memories of the previous day, is the least burdensome as it involves a (potentially) brief survey that can be administered by phone, in written form, or by web survey at any time of day, and at the respondent’s convenience. The “end of day” approach is also brief, but as its name suggests must be completed at the end of the day, which represents a significant constraint. Finally, a full version of the DRM requires around 45 minutes to complete, and thus is not amenable to a brief phone survey (although abbreviated versions have been successfully scaled up for national surveys). To explore the validity of these three approaches, this study will compare the different recall-based methods to real-time assessments taken during the observation day (ecological momentary assessments [EMA]). It will also employ a mood manipulation on the observation day, to examine how well it is captured by differing recall methods, and how it may affect recollection of other parts of the day.

Toward New Evaluative Well-being Questions

PIs: Marc Fleurbaey, Princeton University; Dan Benjamin, Jakina Debnam, and Ori Heffetz, Cornell University

The overall goal of this project is to come up with recommendations regarding specific wordings for SWB questions to be asked on large scale surveys. Such recommendations would hopefully be useful to governments, national statistical offices, and others who conduct large scale social surveys.

The research focuses on evaluative questions (such as ladder-of-life or life satisfaction) and examines them from three perspectives: a) due to imprecise wording, is there heterogeneity in how people perceive the scope of the questions (such as the relevant time period, whether it is about the respondent’s strictly personal situation or includes others)? b) do different respondents use the scales (typically 0-10) in different ways due to self-anchoring to personal goals, reference groups, past experience, so that answers are not comparable? c) how do the answers relate to the respondents “ideal” (i.e., informed, pondered) preferences?

This project will therefore, with respect to  existing evaluative questions, contribute to a better knowledge of the problems with their formulation as well as a better understanding of self-anchoring, which includes the adaptation phenomenon, It will also contribute to constructing new, hopefully more effective, SWB questions, with the goal of reducing noise and getting responses that are more comparable across the population and across time, and are as faithful as possible to the respondents “ideal” preferences.

The project can be viewed as pursuing efforts at probing SWB questions made by Krueger and Schkade (2008), Steffel and Oppenheimer (2009) and Ralph et al. (2012). It also builds on previous work by members of the team (references below), that has shown that various SWB questions do not equally relate to the respondents’ preferences and choices, and has explored the ethical and empirical conditions for constructing interpersonally comparable well-being indexes.

Wellbeing, Self-reported Health, and Suicide

PI: Anne Case and Angus Deaton, Princeton University
The goal of this project is to document and better understand the relationship between measures of self-reported wellbeing (SWB), self-reported health status (SRHS), and suicide. The pilot will look at the changing patterns of suicide by age, both in the US and around the world. In recent years, the suicide rate for the elderly in the US has fallen below the suicide rate for those in middle-age, and this pilot will investigate a possible link between the reversal of the age pattern of suicide and a matching deterioration in SRHS in middle age—one not observed among the elderly. The pilot will examine whether changing levels of pain experienced by different age groups can explain both outcomes. The pilot will match vital statistics information on causes of death from the CDC and the WHO to data on SWB from Gallup’s World and US national polls, to data on SRHS and health conditions from the National Health Interview Survey and the National Health and Nutrition Examination Survey (NHANES), and to data on risk factors and SWB from the Behavioral Risk Factor Surveillance System (BRFSS).