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Happiness and the Law
By John Bronsteen, Christopher Buccafusco, Jonathan S. Masur The University of Chicago Press
Copyright © 2015 The University of Chicago
All rights reserved.
ISBN: 978-0-226-19566-7
CHAPTER 1
Measuring Happiness
What is it like to be injured on a job site and lose a limb? What is it like to be unemployed for a period of time, or to be imprisoned? What is it like to live with poor air quality, or to be prevented from engaging in free expression? Being able to answer these questions accurately is essential to the proper functioning of a legal system. If the law fails to do so, it will struggle to provide adequate compensation for injuries, to punish people for their crimes, and to protect people from harm. Moreover, if the tools a legal system uses to provide answers to these questions are unreliable and inconsistent, similar cases may not be treated similarly. Yet despite the centrality of these questions to the law, there have been surprisingly few attempts to answer them in a rigorous and systematic way for use in legal analysis. This shortcoming can probably be blamed on limited data and problematic assumptions. It was simply too difficult to know, in a way that could be tested meaningfully by the best tools of social science, what losing a limb or being sent to prison is like.
That is no longer the case. The rapidly emerging field of hedonic psychology is now supplying valid and reliable data that can help lawmakers and legal scholars answer these (and many more) important questions. It is now possible to estimate fairly accurately how the experience of losing a limb or being imprisoned is going to make most people feel. How? Simply by asking people who are undergoing those experiences. Relying on people's self-reports of their subjective well-being (SWB), researchers in a number of fields have developed sophisticated and scientifically validated methods for measuring the effects of many circumstances on people's happiness. Importantly, their discoveries are often highly counter intuitive. For example, research has shown that human beings have an astonishing ability to hedonically adapt to changes in their life circumstances. Many seemingly momentous changes will exert surprisingly little long-term hedonic effect on our lives. Yet, also counterintuitively, some seemingly minor changes may have extended effects on our happiness.
This relates to the second important discovery from hedonic psychology. People are often not very good at predicting what will make them happy. Certainly people accurately predict that hitting a hole-in-one will feel better than being hit by a truck, but people often make systematic errors in their estimates of the magnitude and duration of changes in their lives. Often these "affective forecasting errors" occur because people neglect the effects of hedonic adaptation, causing them to overestimate how happy or unhappy many changes will make them feel.
In this chapter we introduce this research in hedonic psychology. We begin by discussing the techniques used to gather happiness data, and then we report on some of hedonic psychology's major findings, those that will be useful again and again throughout the book. Finally, we briefly address some of the most common questions and concerns about using happiness data to inform legal analysis.
The Data of Hedonic Psychology
How can we learn what makes people feel good or bad? The primary way is simply to ask them how they feel at various moments during their day and during their life. Happiness is thus principally studied via self-reports: psychologists learn how people feel by recording what they say about their feelings. Then psychologists try to replicate the results by repeating the studies, and they also compare people's self-reports to other indicia of happiness such as others' reports and neurological and other physiological indicators. These efforts have been highly successful in validating the self-reports, which is why the field of happiness research has received so much attention in recent years.
Social scientists have been attracted to the idea of measuring human welfare directly for a long time, but until recently they have had difficulty securing valid and reliable data. Over the last fifteen years or so, new social science techniques have emerged that enable researchers to study subjective well-being from a variety of different perspectives with a number of different tools. These techniques allow the more or less direct measurement of people's happiness levels, overcoming the problem that had initially driven economists to seek monetary proxies for welfare. Importantly, they enable the measurement of what Daniel Kahneman has termed "experienced utility" (how good people feel) in contrast to the "decision utility" that is typically studied in the tradition of law and economics. "Decision utility" measures only whether people get what they want, on the assumption that getting it will make them better off. But because that assumption has been shown to be flawed, Kahneman and others have turned toward measuring directly the quality of people's experience of life. This section will briefly discuss a few of the most promising techniques for collecting such experiential data and their relative strengths and weaknesses.
Experience sampling methods
The best way to figure out how an experience makes a person feel is to ask her about it while she is experiencing it. The "gold standard" of such measures is the experience sampling method (ESM), which uses handheld computers and smartphones to survey people about their experiences. Subjects are beeped randomly throughout the day and asked to record what they are doing and how they feel about it. The data that emerge from such studies provide a detailed picture of how people spend their time and how their experiences affect them. The data can also be combined with socio-economic and demographic data via regression analyses for even greater insight (e.g., do the unemployed spend more time in leisure activities than the employed, and do they enjoy them as much?).
Unlike some of the other measures of well-being discussed below, ESM studies do not require people to engage in difficult cognitive processes like remembering and aggregating experiences over large chunks of time. Those processes can cause errors in data collection that ESM seeks to avoid. ESM studies can, however, be expensive and difficult to run, so researchers have sought other methods that produce most of the advantages of ESM but at a lower price. One such technique is the day reconstruction method (DRM) pioneered by Daniel Kahneman and his colleagues. DRM uses daily diary entries about each day's experiences to reconstruct an account of subjects' emotional lives. DRM studies correlate strongly with ESM studies and can be run at lower cost. Similarly, the Princeton Affect and Time Survey (PATS) asks subjects to report and evaluate their experiences from the previous day. It can be distributed via telephone and incorporated into other survey devices, enabling it to reach a larger population.
Life satisfaction surveys
The oldest method of measuring SWB is the life satisfaction survey. These surveys ask individuals to respond to a question such as, "All things considered, how satisfied with your life are you these days?" Respondents answer on a scale that ranges from "not very happy" to "very happy." Life satisfaction surveys have been included in the U.S. General Social Survey since the 1970s; as a result, we now have substantial quantities of longitudinal data on thousands of individuals. The principal value in such surveys is the ability to correlate SWB data with a variety of other facts about people's lives. Using multivariate regression analyses that control for different circumstances, researchers are able to estimate the strength of the correlations between SWB and factors such as income, divorce, unemployment, disability, and the death of family members. For example, on average, the death of a parent will yield the loss of 0.25 life satisfaction points on a scale of 1 to 7 for a period of time, while the death of a spouse will typically yield the loss of 0.89 points.
Life satisfaction surveys are relatively inexpensive to administer and can be easily included in a variety of larger survey instruments. Accordingly, they are most valuable as sources of large-scale data about many subjects and of longitudinal data about changes in SWB over time. In longitudinal studies, subjects are tracked over long periods of time so that changes in their well-being can be followed. This is especially valuable in assessing the causal effects of life events (such as marriage, disability, or unemployment) on SWB, because the same individual can be surveyed both before and after the event, eliminating the need to make comparisons between people who might be different in a number of important but unmeasured ways. Life satisfaction surveys are less helpful, however, for assessing particularly granular changes in circumstances. More importantly, they rely on global judgments about how people's lives are going, rather than those individuals' moment-by-moment hedonic experiences. Because hedonic experiences are often poorly remembered and aggregated, such judgments can be biased because of a person's momentary mood or the order in which questions are posed, among other errors.
The quality of the data
The ability to generate data is not the same as the ability to actually measure the thing sought to be measured. Nor is it the ability to measure it well. Data are only useful if they are reliable and valid. Although he donic psychology is a relatively young science, it is already producing data that are trustworthy.
Reliability is an indication of the consistency of a measurement instrument. For example, a scale that reported very similar numbers every time the same weight was placed on it would be judged highly reliable. In the context of well-being measures, reliability can be assessed by examining correlations between tests and retests of the same question at separate times, as well as correlations between different questions that ask about similar concepts. Meta-analyses of different well-being tools have found high levels of reliability for both life satisfaction and experience sampling methods. This is especially true of more advanced multiitem measures.
The fact that a measure reliably provides consistent data does not mean that it is measuring what you want it to measure. The ability to actually measure the thing sought to be measured is called validity. Although a full review of the validity of well-being measures is unnecessary here, it is worth noting a number of findings that support the conclusion that a person's well-being can be validly measured by the tools discussed above.
One way of thinking about the validity of happiness measures is to ask how well they are associated with other indicators of well-being. If happiness data are measuring a valid concept, then they should be correlated both with other subjective well-being data and with other, objective well-being indicators. The happiness data score well on both of these fronts. First, despite the rather different techniques used to collect data, the various measures of well-being tend to correlate with one another. Overall life satisfaction is correlated both with the amount of positive and negative affect (emotion) that a person feels and with her satisfaction with the domains of her life (e.g., family, work, friends). As most theories of well-being would predict, the happier a person feels on a moment-by-moment basis, the happier she judges her life to be. In addition, if a person is not happy with areas of his life that we might believe are important to him, then he is not likely to be as happy overall as someone who is satisfied with those areas.
Not only are subjective reports of well-being correlated with one another, but they are also correlated with external, objective measures of well-being. People who report themselves to be happy are rated as happy by third-party informant reports, they smile more, and their neurological activity is consistent with feelings of pleasure. People who rate themselves low on happiness scales are also much more likely to commit suicide than moderately or very happy people. Finally, positive affect is correlated with extraversion and inversely correlated with neuroticism, as most personality theories would predict, but subjective well-being is also clearly different from those traits.
Another way of thinking about the validity of well-being data involves analyzing their responsiveness to events in people's lives. If these data showed no difference between the ways people feel on the day of their marriage and the day of their spouse's death, we would have strong reason to doubt that they are telling us anything about human happiness. The subjective well-being data upon which we rely score well here, too. Well-being measures tend to be fairly stable over time for a given individual and exhibit high test-retest reliability. This is consistent both with our intuitive sense that people tend to have happy or unhappy dispositions that do not change significantly and with psychological theories of stable personalities. But despite their overall stability, subjective well-being data are also sensitive to changes in life circumstances: people who experience apparently positive or negative events do indeed report higher or lower levels of well-being—at least for a time, before they adapt. As we would predict, the death of a spouse represents a significant blow to someone's self-reported happiness.
In addition to accurately measuring valence (good vs. bad), we would expect valid happiness measures to be sensitive to the degree to which an event is good or bad. And again, despite some counterintuitive findings, well-being scales can detect the relative magnitude of life events. For example, people who are more seriously injured predictably report lower happiness ratings than do people who are less seriously injured. And, as mentioned above, the death of a spouse tends to have a stronger negative effect on happiness than the death of a parent. These findings suggest that people are capable of consistently reporting how experiences make them feel, and that their emotional responses generally exhibit credible and predictable patterns following specific events.
Depending on the situation, legal scholars might draw on each of the data sources mentioned in the preceding pages to address different kinds of issues. In some cases, longitudinal studies of overall well-being may provide the best data available for tracking people after events with potentially long-term effects. These studies have been used, for example, by researchers to understand the hedonic impact of no-fault divorce laws on women in different states. In other circumstances, the availability of ESM studies will enable more fine-grained analyses of laws' effects on people's lives.
Hedonic Psychology's Key Findings
Using the techniques discussed above, researchers have been able to study the kinds of things that make people happy, the intensity and duration of people's affective responses, and their ability to predict what will make them happy. Although some of their findings are relatively unsurprising, many are highly counterintuitive.
Hedonic adaptation
Without question, the most surprising findings from hedonic psychology have to do with humans' ability to adapt rapidly to changes in their lives. Both positive changes, like increases in salary, and negative changes, like physical disabilities, often tend not to substantially alter how happy we are in the long term. Accordingly, it has been said that we are on a "happiness treadmill" or that we all have "happiness set points" to which we return after each new experience. In some situations, this will be the case, but in other cases, events can exert long-lasting effects on our SWB. It is not always easy, however, to guess when adaptation will occur and when it won't. In this section, we review some of the areas where adaptation and its limits have been most thoroughly studied.
Some of the earliest uses of hedonic data looked at the effects of income on well-being. For years, the chief economic indicators of national and individual well-being were monetary. Both countries and people were thought to be better off the more money they had to spend. For nations, well-being could be scored according to gross domestic product, and for individuals, wealth and income were believed to be the best proxies for welfare. Of course, economists understood that money was subject to diminishing marginal utility (i.e., each additional dollar is worth less than the one before it), but generally they believed that the more money someone had the better off she was.
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Excerpted from Happiness and the Law by John Bronsteen, Christopher Buccafusco, Jonathan S. Masur. Copyright © 2015 The University of Chicago. Excerpted by permission of The University of Chicago Press.
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