research papers on happiness

ORIGINAL RESEARCH article

The art of happiness: an explorative study of a contemplative program for subjective well-being.

• 1 Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
• 2 Department of Psychology, Sapienza University of Rome, Rome, Italy
• 3 Institute Lama Tzong Khapa, Pisa, Italy

In recent decades, psychological research on the effects of mindfulness-based interventions has greatly developed and demonstrated a range of beneficial outcomes in a variety of populations and contexts. Yet, the question of how to foster subjective well-being and happiness remains open. Here, we assessed the effectiveness of an integrated mental training program The Art of Happiness on psychological well-being in a general population. The mental training program was designed to help practitioners develop new ways to nurture their own happiness. This was achieved by seven modules aimed at cultivating positive cognition strategies and behaviors using both formal (i.e., lectures, meditations) and informal practices (i.e., open discussions). The program was conducted over a period of 9 months, also comprising two retreats, one in the middle and one at the end of the course. By using a set of established psychometric tools, we assessed the effects of such a mental training program on several psychological well-being dimensions, taking into account both the longitudinal effects of the course and the short-term effects arising from the intensive retreat experiences. The results showed that several psychological well-being measures gradually increased within participants from the beginning to the end of the course. This was especially true for life satisfaction, self-awareness, and emotional regulation, highlighting both short-term and longitudinal effects of the program. In conclusion, these findings suggest the potential of the mental training program, such as The Art of Happiness , for psychological well-being.

Introduction

People desire many valuable things in their life, but—more than anything else—they want happiness ( Diener, 2000 ). The sense of happiness has been conceptualized as people's experienced well-being in both thoughts and feelings ( Diener, 2000 ; Kahneman and Krueger, 2006 ). Indeed, research on well-being suggests that the resources valued by society, such as mental health ( Koivumaa-Honkanen et al., 2004 ) and a long life ( Danner et al., 2001 ), associate with high happiness levels. Since the earliest studies, subjective well-being has been defined as the way in which individuals experience the quality of their life in three different but interrelated mental aspects: infrequent negative affect, frequent positive affect, and cognitive evaluations of life satisfaction in various domains (physical health, relationships, and work) ( Diener, 1984 , 1994 , 2000 ; Argyle et al., 1999 ; Diener et al., 1999 ; Lyubomksky et al., 2005 ; Pressman and Cohen, 2005 ). A growing body of research has been carried out aimed at identifying the factors that affect happiness, operationalized as subjective well-being. In particular, the construct of happiness is mainly studied within the research fields of positive psychology or contemplative practices, which are grounded in ancient wisdom traditions. Positive psychology has been defined as the “the scientific study of human strengths and virtues” ( Sheldon and King, 2001 ), and it can be traced back to the reflections of Aristotle about different perspectives on well-being ( Ryan and Deci, 2001 ). On the other end, contemplative practices include a great variety of mental exercises, such as mindfulness, which has been conceived as a form of awareness that emerges from experiencing the present moment without judging those experiences ( Kabat-Zinn, 2003 ; Bishop et al., 2004 ). Most of these exercises stem from different Buddhist contemplative traditions such as Vipassana and Mahayana ( Kornfield, 2012 ). Notably, both perspective share the idea of overcoming suffering and achieving happiness ( Seligman, 2002 ). Particularly, Buddhism supports “the cultivation of happiness, genuine inner transformation, deliberately selecting and focusing on positive mental states” ( Lama and Cutler, 2008 ). In addition, mindfulness has been shown to be positively related to happiness ( Shultz and Ryan, 2015 ), contributing to eudemonic and hedonic well-being ( Howell et al., 2011 ).

In fact, although the definition of happiness has a long history and goes back to philosophical arguments and the search for practical wisdom, in modern times, happiness has been equated with hedonism. It relies on the achievement of immediate pleasure, on the absence of negative affect, and on a high degree of satisfaction with one's life ( Argyle et al., 1999 ). Nonetheless, scholars now argue that authentic subjective well-being goes beyond this limited view and support an interpretation of happiness as a eudemonic endeavor ( Ryff, 1989 ; Keyes, 2006 ; Seligman, 2011 ; Hone et al., 2014 ). Within this view, individuals seem to focus more on optimal psychological functioning, living a deeply satisfying life and actualizing their own potential, personal growth, and a sense of autonomy ( Deci and Ryan, 2008 ; Ryff, 2013 ; Vazquez and Hervas, 2013 ; Ivtzan et al., 2016 ). In psychology, such a view finds one of its primary supports in Maslow's (1981) theory of human motivation. Maslow argued that experience of a higher degree of satisfaction derives from a more wholesome life conduct. In Maslow's hierarchy of needs theory, once lower and more localized needs are satisfied, the unlimited gratification of needs at the highest level brings people to a full and deep experience of happiness ( Inglehart et al., 2008 ). Consequently, today, several scholars argue that high levels of subjective well-being depend on a multi-dimensional perspective, which encompasses both hedonic and eudemonic components ( Huta and Ryan, 2010 ; Ryff and Boylan, 2016 ). Under a wider perspective, the process of developing well-being reflects the notion that mental health and good functioning are more than a lack of illness ( Keyes, 2005 ). This approach is especially evident if we consider that even the definition of mental health has been re-defined by the World Health Organization (1948) , which conceives health not merely as the absence of illness, but as a whole state of biological, psychological, and social well-being.

To date, evidence exists suggesting that happiness is, in some extent, modulable and trainable. Thus, simple cognitive and behavioral strategies that individuals choose in their lives could enhance happiness ( Lyubomirsky et al., 2005 ; Sin and Lyubomirsky, 2009 ). In the history of psychology, a multitude of clinical treatments have been applied to minimize the symptoms of a variety of conditions that might hamper people from being happy, such as anger, anxiety, and depression (for instance, see Forman et al., 2007 ; Spinhoven et al., 2017 ). In parallel with this view, an alternative—and less developed—perspective found in psychology focuses on the scientific study of individual experiences and positive traits, not for clinical ends, but instead for personal well-being and flourishing (e.g., Fredrickson and Losada, 2005 ; Sin and Lyubomirsky, 2009 ). Yet, the question of exactly how to foster subjective well-being and happiness, given its complexity and importance, remains open to research. Answering this question is of course of pivotal importance, both individually and at the societal level. Positive Psychology Interventions encompass simple, self-administered cognitive behavioral strategies intended to reflect the beliefs and behaviors of individuals and, in response to that, to increase the happiness of the people practicing them ( Sin and Lyubomirsky, 2009 ; Hone et al., 2015 ). Specifically, a series of comprehensive psychological programs to boost happiness exist, such as Fordyce's program ( Fordyce, 1977 ), Well-Being Therapy ( Fava, 1999 ), and Quality of Life Therapy ( Frisch, 2006 ). Similarly, a variety of meditation-based programs aim to develop mindfulness and emotional regulatory skills ( Carmody and Baer, 2008 ; Fredrickson et al., 2008 ; Weytens et al., 2014 ), such as Mindfulness-Based Stress Reduction (MBSR; Kabat-Zinn, 1990 ) and Mindfulness-Based Cognitive Therapy (MBCT; Teasdale et al., 2000 ). Far from being a mere trend ( De Pisapia and Grecucci, 2017 ), those mindfulness-based interventions have been shown to lead to increased well-being ( Baer et al., 2006 ; Keng et al., 2011 ; Choi et al., 2012 ; Coo and Salanova, 2018 ; Lambert et al., 2019 ) in several domains, such as cognition, consciousness, self, and affective processing ( Raffone and Srinivasan, 2017 ). Typically, mindfulness programs consist of informal and formal practice that educate attention and develop one's capacity to respond to unpredicted and/or negative thoughts and experiences ( Segal and Teasdale, 2002 ). In this context, individuals are gradually introduced to meditation practices, focusing first on the body and their own breath, and later on thoughts and mental states. The effects of these programs encompass positive emotions and reappraisal ( Fredrickson et al., 2008 ; Grecucci et al., 2015 ; Calabrese and Raffone, 2017 ) and satisfaction in life ( Fredrickson et al., 2008 ; Kong et al., 2014 ) and are related to a reduction of emotional reactivity to negative affect, stress ( Arch and Craske, 2006 ; Jha et al., 2017 ), and aggressive behavior ( Fix and Fix, 2013 ). All these effects mediate the relationship between meditation frequency and happiness ( Campos et al., 2016 ). This allows positive psychology interventions to improve subjective well-being and happiness and also reduce depressive symptoms and negative affect along with other psychopathologies ( Seligman, 2002 ; Quoidbach et al., 2015 ). Engaging in mindfulness might enhance in participants the awareness of what is valuable to them ( Shultz and Ryan, 2015 ). This aspect has been related to the growth of self-efficacy and autonomous functioning and is attributable to an enhancement in eudemonic well-being ( Deci and Ryan, 1980 ). Moreover, being aware of the present moment provides a clearer vision of the existing experience, which in turn has been associated with increases in hedonic well-being ( Coo and Salanova, 2018 ). Following these approaches, recent research provides evidence that trainings that encompass both hedonic and eudemonic well-being are correlated with tangible improved health outcomes ( Sin and Lyubomirsky, 2009 ).

Although there is a consistent interest in scientific research on the general topic of happiness, such studies present several limitations. Firstly, most of the research has focused on clinical studies to assess the effectiveness of happiness-based interventions—in line with more traditional psychological research, which is primarily concerned with the study of mental disorders ( Garland et al., 2015 , 2017 ; Groves, 2016 ). Secondly, most of the existing interventions are narrowly focused on the observation of single dimensions (i.e., expressing gratitude or developing emotional regulation skills) ( Boehm et al., 2011 ; Weytens et al., 2014 ). Moreover, typically studies involve brief 1- to 2-week interventions ( Gander et al., 2016 ), in contrast with the view that eudemonia is related to deep and long-lasting aspects of one's personal lifestyle. Furthermore, while the effectiveness of mindfulness-based therapies is well-documented, research that investigates the effects of mindfulness retreats has been lacking, which are characterized by the involvement of more intense practice from days to even years [for meta-analysis and review, see Khoury et al. (2017) , McClintock et al. (2019) , Howarth et al. (2019) ].

In this article, we report the effects on subjective well-being of an integrated mental training program called The Art of Happiness , which was developed and taught by two of the authors (CM for the core course subject matter and NDP for the scientific presentations). The course lasted 9 months and included three different modules (see Methods and Supplementary Material for all details), namely, seven weekends (from Friday evening to Sunday afternoon) dedicated to a wide range of specific topics, two 5-day long retreats, and several free activities at home during the entire period. The course was designed to help practitioners develop new ways to nurture their own happiness, cultivating both self-awareness and their openness to others, thereby fostering their own emotional and social well-being. The basic idea was to let students discover how the union of ancient wisdom and spiritual practices with scientific discoveries from current neuropsychological research can be applied beneficially to their daily lives. This approach and mental training program was inspired by a book of the Fourteenth Dalai Lama Tenzin Gyatso and the psychiatrist Lama and Cutler (2008) . The program rests on the principle that happiness is inextricably linked to the development of inner equilibrium, a kinder and more open perspective of self, others, and the world, with a key role given to several types of meditation practices. Additionally, happiness is viewed as linked to a conceptual understanding of the human mind and brain, as well as their limitations and potentiality, in the light of the most recent scientific discoveries. To this end, several scientific topics and discoveries from neuropsychology were addressed in the program, with a particular focus on cognitive, affective, and social neuroscience. Topics were taught and discussed with language suitable for the general public, in line with several recent books (e.g., Hanson and Mendius, 2011 ; Dorjee, 2013 ; Goleman and Davidson, 2017 ). The aim of this study was to examine how several psychological measures, related to psychological well-being, changed among participants in parallel with course attendance and meditation practices. Given the abovementioned results of the positive effects on well-being ( Baer et al., 2006 ; Fredrickson et al., 2008 ; Keng et al., 2011 ; Choi et al., 2012 ; Kong et al., 2014 ; Coo and Salanova, 2018 ; Lambert et al., 2019 ), we predicted to find a significant increase in the dimensions of life satisfaction, control of anger, and mindfulness abilities. Conversely, we expected to observe a reduction of negative emotions and mental states ( Arch and Craske, 2006 ; Fix and Fix, 2013 ; Jha et al., 2017 )—i.e., stress, anxiety and anger. Moreover, our aim was to explore how those measures changed during the course of the mental training program, considering not only the general effects of the course (longitudinal effects) but also specific effects within each retreat (short-term effects). Our expectation for this study was therefore that the retreats would have had an effect on the psychological dimensions of well-being linked to the emotional states of our participants, while the whole course would have had a greater effect on the traits related to well-being. The conceptual distinction between states and traits was initially introduced in regard to anxiety by Cattell and Scheier (1961) , and then subsequently further elaborated by Spielberger et al. (1983) . When considering a mental construct (e.g., anxiety or anger), we refer to trait as a relatively stable feature, a general behavioral attitude, which reflects the way in which a person tends to perceive stimuli and environmental situations in the long term ( Spielberger et al., 1983 ; Spielberger, 2010 ). For example, subjects with high trait anxiety have indeed anxiety as a habitual way of responding to stimuli and situations. The state, on the other hand, can be defined as a temporary phase within the emotional continuum, which, for example, in anxiety is expressed through a subjective sensation of tension, apprehension, and nervousness, and is associated with activation of the autonomic nervous system in the short term ( Spielberger et al., 1983 ; Saviola et al., 2020 ). Here, in the adopted tests and analyses, we keep the two time scales separated, and we investigate the results with the aim of understanding the effects of the program on states and traits of different emotional and well-being measures. As a first effect of the course, we expect that the retreats affect mostly psychological states (as measured in the comparison of psychological variables between start and end of each retreat), whereas the full course is predicted to affect mainly psychological traits (as measured in the comparison of the psychological variables between start, middle, and end of the entire 9-month period).

Materials and Methods

Participants.

The participants in the mental training program and in the related research were recruited from the Institute Lama Tzong Khapa (Pomaia, Italy) in a 9-month longitudinal study (seven modules and two retreats) on the effects of a program called The Art of Happiness (see Supplementary Material for full details of the program). Twenty-nine participants followed the entire program (there were nine dropouts after the first module). Their mean age was 52.86 years (range = 39–66; SD = 7.61); 72% were female. Participants described themselves as Caucasian, reaching a medium-high scholarly level with 59% of the participants holding an academic degree and 41% holding a high school degree. The participants were not randomly selected, as they were volunteers in the program. Most of them had no serious prior experience of meditation, only basic experience consisting of personal readings or watching video courses on the web, which overall we considered of no impact to the study. The only exclusion criteria were absence of a history of psychiatric or neurological disease, and not being currently on psychoactive medications. The study was approved by the Ethics Committee of the Sapienza University of Rome, and all participants gave written informed consent. The participants did not receive any compensation for participation in the study.

The overall effectiveness of the 9-month training was examined using a within-subjects design, with perceived stress, mindfulness abilities, etc. (Time: pre–mid–end) as the dependent variable. The effectiveness of the retreats was examined using a 2 × 2 factor within-subjects design (condition: pre vs. post; retreat: 1 vs. 2), with the same dependent variables. The specific contemplative techniques that were applied in the program are described in the Supplementary Material , the procedure is described in the Procedure section, and the measurements are described in the Materials section.

Mental Training Program

The program was developed and offered at the Institute Lama Tzong Khapa (Pomaia, Italy). It was one of several courses that are part of the Institute's ongoing programs under the umbrella of “Secular Ethics and Universal Values.” These various programs provide participants with opportunities to discover how the interaction of ancient wisdom and spiritual practices with contemporary knowledge from current scientific research in neuropsychology can be applied extensively and beneficially to improve the quality of their daily lives.

Specifically, The Art of Happiness was a 9-month program, with one program activity each month, either a weekend module or a retreat; there were two retreats—a mid-course retreat and a concluding retreat (for full details on the program, see Supplementary Material ). Each thematic module provided an opportunity to sequentially explore the topics presented in the core course text, The Art of Happiness by the Lama and Cutler (2008) .

In terms of the content of this program, as mentioned above, the material presented and explored has been drawn on the one hand from the teachings of Mahayana Buddhism and Western contemplative traditions, and current scientific research found in neuropsychology on the other hand. On the scientific side, topics included the effects of mental training and meditation, the psychology and neuroscience of well-being and happiness, neuroplasticity, mind–brain–body interactions, different areas of contemplative sciences, the placebo effects, the brain circuits of attention and mind wandering, stress and anxiety, pain and pleasure, positive and negative emotions, desire and addiction, the sense of self, empathy, and compassion (for a full list of the scientific topics, see Supplementary Material ).

The overall approach of the course was one of non-dogmatic exploration. Topics were presented not as undisputed truths, but instead as information to be shared, explored, examined, and possibly verified by one's own experience. Participants were heartily invited to doubt, explore, and test everything that was shared with them, to examine and experience firsthand whether what was being offered has validity or not.

The course was, essentially, an informed and gentle training of the mind, and in particular of emotions, based on the principle that individual well-being is inextricably linked to the development of inner human virtues and strengths, such as emotional balance, inner self-awareness, an open and caring attitude toward self and others, and clarity of mind that can foster a deeper understanding of one's own and others' reality.

The program provided lectures and discussions, readings, and expert videos introducing the material pertinent to each module's topic. Participants engaged with the material through listening, reading, discussing, and questioning. Participants were provided with additional learning opportunities to investigate each topic more deeply, critically, and personally, through the media of meditation, journaling, application to daily life, exercises at home, and contemplative group work with other participants in dyads and triads. Participants were then encouraged to reflect repeatedly on their insights and on their experiences, both successful and not, to apply their newly acquired understandings to their lives, by incorporating a daily reflection practice into their life schedule. The two program retreats also provided intensive contemplative experiences and activities, both individual and in dialogue with others.

On this basis, month after month in different dedicated modules, participants learned new ways to nurture their own happiness, to cultivate their openness to others, to develop their own emotional and social well-being, and to understand some of the scientific discoveries on these topics.

The specific topics addressed in corresponding modules and retreats, each in a different and consecutive month, were as follows: (1) The Purpose of Life: Authentic Happiness; (2) Empathy and Compassion; (3) Transforming Life's Suffering; (4) Working with Disturbing Emotions I: Hate and Anger; first retreat (intermediate); (5) Working with Disturbing Emotions II: The Self Image; (6) Life and Death; (7) Cultivating the Spiritual Dimension of Life: A Meaningful Life; second retreat (final). Full details of the entire program are reported in the Supplementary Material .

Participants were guided in the theory and practice of various contemplative exercises throughout the course pertaining to all the different themes. Recorded versions of all the various meditation exercises were made available to participants, enabling them to repeat these practices at home at their own pace.

Participants were encouraged to enter the program already having gained some basic experience of meditation, but this was not a strict requirement. In fact, not all participants in this experiment actually fulfilled this (only five), although each of the other participants had previous basic experiences of meditation (through personal readings, other video courses, etc.). In spite of this variety, by the end of the 9-month program, all participants were comfortable with contemplative practices in general and more specifically with the idea of maintaining a meditation practice in their daily lives.

During the various Art of Happiness modules, a variety of basic attentional and mindful awareness meditations were practiced in order to enhance attentional skills and cultivate various levels of cognitive, emotional, social, and environmental awareness.

Analytical and reflective contemplations are a form of deconstructive meditation ( Dahl et al., 2015 ), which were applied during the course in different contexts. On the one hand, these types of meditations were applied in the context of heart-opening practices—for example, in the cultivation of gratitude, forgiveness, loving-kindness toward self and others, self-compassion, and compassion for others. Analytical and reflective meditations were also practiced as a learning tool for further familiarization with some of the more philosophical subject matter of the course—engaging in a contemplative analysis of impermanence (for example, contemplating more deeply and personally the transitory nature of one's own body, of one's own emotions and thoughts, as well as of the material phenomena that surround us). These analytical meditations were also accompanied by moments of concentration (sustained attention) at the conclusion of each meditation focusing on what the meditator has learned or understood in the meditative process, in order to stabilize and reinforce those insights more deeply within the individual.

Additional contemplative activities were also included in the program: contemplative art activities, mindful listening, mindful dialogue, and the practice of keeping silence during the retreat. Participants were, in addition, encouraged to keep a journal of their experiences during their Art of Happiness journey, especially in relation to their meditations and the insights and questions that emerged within themselves, in order to enhance their self-awareness and cultivate a deeper understanding of themselves, their inner life and well-being, and their own inner development during the course and afterward.

During the two retreats, the previous topics were explored again (modules 1–4 for the intermediary retreat and modules 5–7 for the final retreat), but without discussing the theoretical aspects (i.e., the neuroscientific and psychological theories), instead only focusing on the contemplative practices, which were practiced extensively for the whole day, both individually and in group activities (for a full list of the contemplative practices and retreat activities, see Supplementary Material ).

We collected data at five-time points, always during the first day (either of the module or the retreat): at baseline (month 1 - T0), at pre (T1) and post (P1) of the mid-course retreat (month 5–Retreat 1), and at pre (T2) and post (R2) of the final retreat (month 9–Retreat 2), as shown in Figure 1 . Participants filled out the questionnaires on paper all together within the rooms of the Institute Lama Tzong Khapa at the beginning of each module or retreat, and at the end of the retreats, with the presence of two researchers. The order of the questionnaires was randomized, per person and each questionnaire session lasted less than an hour.

Figure 1 . The timing of the course and the experimental procedure, including the modules, the retreats, and the 5 data collections (from T0 to P2).

The adopted questionnaires were those commonly used in the literature to measure a variety of traits and states linked to well-being. An exhaustive description of the self-reported measures follows below.

Satisfaction With Life Scale (SWLS)

The SWLS ( Diener et al., 1985 ) was developed to represent cognitive judgments of life satisfaction. Participants indicated their agreement in five items with a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Scores range from 5 to 35, with higher scores representing higher levels of satisfaction. Internal consistency is very good with Cronbach's α = 0.85 [Italian version of the normative data in Di Fabio and Palazzeschi (2012) ].

Short Version of the Perceived Stress Scale (PSS-10)

The PSS ( Cohen et al., 1983 ) was designed to assess individual perception and reaction to stressful daily-life situations. The questionnaire consists of 10 questions related to the feelings and thoughts of the last month, with a value ranging from 0 (never) to 4 (very often) depending on the severity of the disturbance caused. Scores range from 0 to 40. Higher scores represent higher levels of perceived stress, reflecting the degree to which respondents find their lives unpredictable or overloaded. Cronbach's α ranges from 0.78 to 0.93 [Italian version of the normative data by Mondo et al. (2019) ].

State-Trait Anxiety Inventory (STAI)

The STAI ( Spielberger et al., 1983 ) was developed to assess anxiety. It has 40 items, on which respondents evaluate themselves in terms of frequency with a four-point Likert scale ranging from 1 (almost never) to 4 (almost always). The items are grouped in two independent subscales of 20 items each that assess state anxiety, with questions regarding the respondents' feelings at the time of administration, and trait anxiety, with questions that explore how the participant feels habitually. The scores range from 20 to 80. Higher scores reflect higher levels of anxiety. Internal consistency coefficients for the scale ranged from 0.86 to 0.95 [Italian version of the normative data by Spielberger et al. (2012) ].

Positive and Negative Affect Schedule (PANAS)

PANAS ( Watson et al., 1988 ) measures two distinct and independent dimensions: positive and negative affect. The questionnaire consists of 20 adjectives, 10 for the positive affect subscale and 10 for the negative affect scale. The positive affect subscale reflects the degree to which a person feels enthusiastic, active, and determined while the negative affect subscale refers to some unpleasant general states such as anger, guilt, and fear. The test presents a five-point Likert scale (1 = very slightly or not at all; 5 = extremely). The alpha reliabilities are acceptably high, ranging from 0.86 to 0.90 for positive affect and from 0.84 to 0.87 for negative affect [Italian version of the normative data by Terracciano et al. (2003) ].

Five Facet Mindfulness Questionnaire (FFMQ)

The FFMQ ( Baer et al., 2008 ) was developed to assess mindfulness facets through 39 items rated on a five-point Likert scale, ranging from 1 (never or very rarely true) to 5 (very often or always true). A total of five subscales are included: attention and observation of one's own thoughts, feelings, perceptions, and emotions ( Observe ); the ability to describe thoughts in words, feelings, perceptions, and emotions ( Describe ); act with awareness, with attention focused and sustained on a task or situation, without mind wandering ( Act-aware ); non-judgmental attitude toward the inner experience ( Non-Judge ); and the tendency to not react and not to reject inner experience ( Non-React ). Normative data of the FFMQ have demonstrated good internal consistency, with Cronbach's α ranging from 0.79 to 0.87 [Italian version by Giovannini et al. (2014) ].

State-Trait Anger Expression Inventory-2 (STAXI-2)

The STAXI-2 ( Spielberger, 1999 ) provides measures to assess the experience, expression, and control of anger. It comprises 57 items rated on a four-point Likert scale, ranging from 0 (not at all) to 3 (very much indeed). Items are grouped by four scales: the first, State Anger scale, refers to the emotional state characterized by subjective feelings and relies on three more subscales: Angry Feelings, Physical Expression of Anger, and Verbal Expression of Anger. The second scale is the Trait Anger and indicates a disposition to perceive various situations as annoying or frustrating with two subscales—Angry Temperament and Angry Reaction. The third and last scales are Anger Expression and Anger Control. These assess anger toward the environment and oneself according to four relatively independent subscales: Anger Expression-OUT, Anger Expression-IN, Anger Control-OUT, and Anger Control-IN. Alpha coefficients STAXI-2 were above 0.84 for all scales and subscales, except for Trait Anger Reaction, which had an alpha coefficient of 0.76 [Italian version by Spielberger (2004) ].

Statistical Analysis

The responses on each questionnaire were scored according to their protocols, which resulted in one score per participant and a time point for each of the 22 scale/subscale questionnaires examined. Missing values (<2%) were imputed using the median. Descriptive statistics for all variables were analyzed and are summarized in Table 1 and in the first panel (column) of Figures 2 – 5 . Prior to conducting primary analyses, the distribution of scores on all the dependent variables was evaluated. Because the data were not normally distributed, we used non-parametric tests. Permutation tests are non-parametric tests as they do not rely on assumptions about the distribution of the data and can be used with different types of scales and with a small sample size.

Table 1 . Descriptive statistics of the depended variables among time points.

Figure 2 . Results of the Satisfaction with Life Scale (SWLS), Perceived Stress Scale (PSS), State and Trait Anxiety Index (STAI), and Positive and Negative Affect Scales (PANAS). The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from the one-way ANOVA. Note that scores are on the y -axis and time is on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—retreat 1), pre (T2), and post (R2) of the final retreat (month 9—retreat 2). Statistical significance, * p < 0.05.

Figure 3 . Results for the Five Facet Mindfulness Questionnaire FFMQ (Observe, Describe, Act with Awareness, Non-judge, and Non-react). The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from one-way ANOVA. Note that scores are on the y -axis and time id on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—retreat 1), pre (T2), and post (P2) of the final retreat (month 9—retreat 2). Statistical significance, * p < 0.05.

Figure 4 . Results of the first part of the State Trait Anger Expression Inventory (STAXI-2): State Anger, State Anger Feelings, State Anger Physical, State Anger Verbal, Trait Anger, and Trait Anger Temperament. The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from one-way ANOVA. Note that scores are on the y -axis and time is on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—retreat 1), pre (T2), and post (R2) of the final retreat (month 9—retreat 2). Statistical significance, ** p < 0.01 and * p < 0.05.

Figure 5 . Results from the second part of the State Trait Anger Expression Inventory (STAXI-2): Trait Anger Reaction, Anger Expression-IN, Anger Expression-OUT, Anger Control-IN, and Anger Control OUT. The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from one-way ANOVA. Note that scores are on the y -axis and time is on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—Retreat 1), pre (T2), and post (R2) of the final retreat (month 9—Retreat 2). Statistical significance, * p < 0.05.

The longitudinal effects of the program were analyzed to determine whether scores changed between the start, mid-point (5 months), and the end (9 months) of the course. To achieve this, we compared the main effect of the program on the score , considering Time as a unique factor with three levels: at the baseline (T0), at the pre of the mid-retreat (T1), and at the pre of the final retreat (T2). Here, we used a one-way permutation Repeated Measures Analysis of Variance (RM ANOVA) with the aovperm() function from the Permuco package v. 1.0.2 in R ( Frossard and Renaud, 2018 ), which implements a method from Kherad-Pajouh and Renaud (2014) . The difference between the traditional and the permutation ANOVA is that, while the traditional ANOVA tests the equality of the group mean, the permutation version tests the exchangeability of the group observations. In this study, the number of permutations was set to 100,000 and the alpha level was set to 0.05; therefore, the p -value was computed as the ratio between the number of permutation tests that have an F value higher than the critical F value and the number of permutations performed. Effect size estimates were calculated using partial eta squared. Post hoc testing used pairwise permutational t -tests with the “pairwise.perm.t.test” function from the “RVAideMemoire” package in R ( Hervé and Hervé, 2020 ). To account for Type I errors introduced by multiple pairwise tests and Type II errors introduced by small sample size, we applied the false discovery rate (FDR) correction method of Benjamini and Hochberg (1995) and set statistical significance at p = 0.05. Results are summarized in Table 2 and in the third panel (column) of Figures 2 – 5 .

Table 2 . One-way ANOVA and pairwise comparison results with 100,000 permutations.

The short-term effects of the contemplative program on each retreat were analyzed to determine whether scores changed post-retreats and whether these changes occurred in both retreats. Thus, we used a two-way permutation RM ANOVA, with the score of each scale/subscale as the dependent variable and the within-subject factors Retreat (1, 2) and Condition (Pre T1/T2, Post P1/P2) as independent variables. Results are summarized in Table 3 and in the second panel (column) of Figures 2 – 5 .

Table 3 . Results of the two-way permutation RM ANOVAs.

In addition, we explored differences attributed to the course and to the retreats using a paired permutation t test with the “perm.t.test()” function in R. We compare those psychological measures at the beginning of the course (T0) with its very end (P2), which coincided with the end of the second retreat. In this way, we illustrate a summary of changes due both to the second retreat and to the whole course. The results are summarized in Table 4 and depicted in a radar plot in Figure 6 .

Table 4 . Overall changes between the start (T0) and the end of the course (P2).

Figure 6 . Results of the permutation t -test between the start and the end of the course. All values ranged from 0 to 1. Variables: SWLS, Satisfaction with Life Scale; S-Ang/F, Feeling Angry; S-Ang/V, Feel like Expressing Anger Verbally; S-Ang/P, Feel like Expressing Anger Physically; T-Ang/T, Angry Temperament; T-Ang/R, Angry reaction; AX-O, Anger Expression-OUT; AX-I, Anger Expression-IN; AC-O, Anger Control-OUT; AC-I, Anger Control-IN; PSS, Perceived Stress Scale; STAI-Y1, State-Trait Anxiety Inventory—State; STAI-Y2, State-Trait Anxiety Inventory—Trait; PA and NA, Positive and Negative Affect Scales, respectively; OBS, Observe; DES, Describe; AWA, Act with awareness, Njudge, Non-judge; NReact, Non-react. To make consistent that an increase of the specific scale corresponds to an improvement in well-being, negative scales were reversed, namely: PSS, STAI-Y1, STAI-Y2, PANAS-NA, S-Ang, S-Ang/F, S-Ang/P, S-Ang/V, T-Ang, T-Ang/T, S-Ang/R, AX-O, AX-I. Concerning the statistical significance, *** p < 0.001, ** p < 0.01, and * p < 0.05.

Effects of the Program

Results from one-way permutation RM ANOVA showed a statistically significant effect of the program on SWLS at the p = 0.008 level over the Time course factor with a large effect size (ηp 2 = 0.16). Post hoc analysis revealed that the SWLS score was significantly higher at T2 with respect to T2 (mean difference = 2.48; p = 0.016). Similarly, SWLS was higher T2 as compared to T1 (mean difference = 1.38; p = 0.032).

Results also provided statistically significant evidence of changes in the PSS over the Time course ( p = 0.009), showing a large effect size (ηp 2 = 0.16). Post-hoc results showed a difference between T0 and T1, revealing that the PSS was significantly lower at T1 (mean difference = −2, p = 0.02).

Results revealed a significant effect of the Time course for Trait Anxiety ( p = 0.009, ηp 2 = 0.16). Post-hoc tests revealed a reduction in Trait Anxiety from the start of the course (T0) to the first day of the second retreat (T2) (M diff. = −3.21, p = 0.25).

Results also showed a significant effect of the Time course for negative affect ( p = 0.004, ηp 2 = 0.19). Post hoc analysis revealed that contemplative practice led to a reduction in negative affect from the baseline (T0) to the first day of the first retreat (T1) (mean difference = −2.42) and between T0 and first day of the second retreat (T2) (mean difference = −2.92), which differed significantly with p = 0.021 and p = 0.012, respectively.

Moreover, a significant effect of the Time course was found for several subscales of the FFMQ. First, the observe scale was found at the p = 0.023 level showing a large effect size (ηp 2 = 0.13). Post-hoc comparisons revealed an increasing capacity to observe one's own thoughts, from the middle of the course (T1) to the first day of the second retreat (T2) (mean difference = 1.58, p = 0.038). Likewise, there was a significant difference for the capacity to Act with Awareness ( p = 0.036, ηp 2 = 0.12). Post hoc comparisons revealed an increased level at T2 as compared to T1 (mean difference = 2.07, p = 0.043). The Time course had a significant effect on the Non-Judge subscale with a large effect size ( p = 0.002, ηp 2 = 0.20). Post hoc analysis indicated a significant increase from T0 to T1 (mean difference = 2.07, p = 0.013), as well as from T0 to T2 (mean difference = 3.31, p = 0.013).

In regard to the STAXI-2, we found Time course significant effects on Trait Anger ( p = 0.001, ηp 2 = 0.23) and its subscales, Trait Anger Temperament ( p = 0.001, ηp 2 = 0.22) and Trait Anger Reaction ( p = 0.016, ηp 2 = 0.14). Post-hoc comparisons revealed a significance difference on the Trait Anger Scale, which decreased from the beginning of the course (T0) to 5 months later (T1) (mean difference = −1.83, p = 0.041) and also from T0 to the end of the course (T2) (mean difference = −3.24, p = 0.002). Similarly, State Anger Temperament significantly decreased from T0 to T1 (mean difference = −0.79, p = 0.016) and from T0 to T2 (mean difference = −1.38, p = 0.008). Additionally, Trait Anger Reaction decreased from T0 to T2 (mean difference = −1.24, p = 0.023). Finally, the longitudinal effect of the course on the STAXI-2 led to significant results in the Anger Control-IN subscale over the Time course ( p = 0.03, ηp 2 = 0.12). Here, post-hoc comparisons showed a statistically significant difference between T0 and T2, which increased (mean difference = 1.76, p =.044). For more details, see Table 2 and the third panel (column) of Figures 2 – 5 .

Effects of the Retreats

Two-way permutation RM ANOVAs showed a significant main effect for Retreat on SWLS ( p = 0.002, ηp 2 = 0.16), Trait Anxiety ( p = 0.001, ηp 2 = 0.19), positive affect ( p = 0.044, ηp 2 = 0.07), Observe ( p = 0.008, ηp 2 = 0.12), Act with awareness ( p ≤ 0.001, ηp 2 = 0.22), Non-Judge ( p = 0.045, ηp 2 =.07), Non-React ( p = 0.02, ηp 2 = 0.10), Trait Anger ( p = 0.008, ηp 2 = 0.12), Trait Anger Temperament ( p = 0.022, ηp 2 = 0.09), Trait Anger Reaction ( p = 0.019, ηp 2 = 0.10), and Anger Control-IN ( p = 0.029, ηp 2 = 0.08). A main effect of the Condition (Pre vs. Post) was found only for the State Anxiety scale with p = 0.004 and a large effect size (ηp 2 = 0.14). Analysis results including F statistics are summarized in Table 3 ; a visual representation of the data is presented in the second panel (column) of Figures 2 – 5 .

Overall Effects of the Course and Retreats

As predicted, permutation t -test analysis revealed that participants increased their reported level of SWLS from the start (T0) to the end (P2) of the course (mean difference = 2.83, p = 0.008). Two subscales from the FFMQ, namely, the capacity to observe one's own thoughts (mean difference = 1.86, p = 0.039) and non-judgmental attitude toward the inner experience (mean difference = 3.24, p = 0.006), also significantly increased from the start to the end of the course. On the other hand, the affect linked to the progression from the start (T0) to the very end of the course (P2) was related to a significant decrease in the negative affect (mean difference = −3.62, p = 0.001). In the same way, the average level of stress of the sample decreased significantly (mean difference = −1.9, p = 0.033) along with a significant decrease of Trait Anxiety (M diff = −3.97, p ≤ 0.001). Participants also decreased on almost all STAXI-2 subscales. Here, the results from permutation paired t -test reveal a significant difference in scores, which decreased from T0 to P2 on all the subscales of Trait Anger (mean difference = −3.55, p ≤ 0.001; Trait Anger Temperament: mean difference = −1.34, p ≤ 0.001; Trait Anger Reaction: mean difference = −1.52, p ≤ 0.001), with an increased value for the subscales Anger Control-OUT (mean difference = 1.93, p ≤ 0.009) and Anger Control-IN (mean difference = 1.93, p = 0.017). For more details, see Table 4 and Figure 6 .

The aim of this study was to examine the effectiveness of an integrated 9-month mental training program called The Art of Happiness , which was developed to increase well-being in a general population. By a range of well-established psychometric assessment tools, we quantified how several psychological well-being variables changed with course attendance. We took into account both the trait effects of the course acting at a long timescale (over the 9-month duration of the full course) and the state effects of intensive retreat experiences acting at a short time scale (over the course of each of the two retreats). Several psychological well-being measures related to states and—more importantly—traits gradually improved as participants progressed from the beginning to the end of the course.

On the one hand, the program produced a significant longitudinal effect (9 months) revealing a progressive increase in the volunteer's levels of life satisfaction and of the capacities to reach non-judgmental mental states, to act with awareness, to non-react to inner experience, and to exercise control over attention to the internal state of anger, in line with other contemplative interventions ( Fredrickson et al., 2008 ; Keng et al., 2011 ; Baer et al., 2012 ; Kong et al., 2014 ). Conversely, after the completion of the program, there were decreases in levels of trait anxiety, trait anger (including both the anger temperament and reaction subscales), and negative affect, showing a progressive reduction during the intervention. These results support prior research that demonstrated the longitudinal positive effects of a multitude of contemplative practices on well-being measures linked to—among others—decreased trait anxiety, trait anger, and negative affect ( Fix and Fix, 2013 ; Khoury et al., 2015 ; Gotink et al., 2016 ). Such findings highlight the gradual development of mental states related to subjective well-being in parallel with ongoing contemplative practices over a time scale of months, with a gradual increase of wholesome mental states, and a gradual decrease of unwholesome mental states. Notably, as in other mindfulness interventions ( Khoury et al., 2015 ; Gotink et al., 2016 ), there was a significant reduction in the level of perceived stress already in the first few months of the program (T0–T1).

Additionally, these results show the specific effects between retreat experiences within the program as an intervention for fostering happiness. Specifically, the retreats had a positive effect on the participants' perceived well-being, which improved between the two retreats (with a 4-month interval). Among other assessed dimensions, between the retreats, there were significantly increased levels of life satisfaction, positive affect, and mindful abilities to act with awareness, to observe, non-react, and non-judge inner experience and the capacity to control anger toward oneself. Conversely, there were significantly lower levels of trait anxiety and trait anger (including both the anger temperament and reaction subscales) between the retreats (over a period of 4 months).

Regarding the very short effects of the course, we highlight significant changes within the first part of the training and prior to the first retreat (T0–T1). Here, some variables related to happiness changed most, suggesting their independence from retreat. Particularly, PSS notably decreased along with negative affect and Trait Anger (the subscale of Angry Temperament), while the capacity of non-judgmental attitude toward the inner experience significantly increased, providing useful information for future interventions.

Moreover, participants' state anxiety significantly decreased in a very short time (5 days), between pre and post of both retreats. These findings are consistent with previous studies, which demonstrated the positive effects of contemplative training and practices on these measures in retreats ( Khoury et al., 2017 ; Howarth et al., 2019 ; McClintock et al., 2019 ). In Figure 6 , we make a general and integrated comparison between the various psychological measures, comparing the very beginning of the course with its very end, which also coincided with the end of the second retreat. In this way, we illustrate both state changes (due to the second retreat) and trait changes (due to the whole course). This representation allows an integrated view of all the changes that took place at different time scales. This graph might suggest that the only measures that did not change significantly from the beginning to the end of the course are those in which the participants already had a score strongly oriented toward well-being, and therefore with little room for a change. Thus, future studies could take into account individual differences when evaluating happiness programs.

Although the present findings are promising, this study presents several limitations that need to be taken into consideration. The two main limitations rely on the absence of a randomized control group and in the fact that participants were self-selected. This lack of verification makes it difficult to determine whether the results are attributable to the program or to other factors, for example, simply arising due to spending time in a happiness-oriented activity. It is also important to note that despite examining several assessments within persons, the sample size was restricted to 29. Furthermore, responses to the questionnaires may have been biased toward the socially desirable response as the course's staff administered them, and another active group could have controlled for these effects. Consequently, it is recommended to conduct future studies with larger samples and a well-designed and controlled trial, in order to achieve more conclusive findings. Another limitation is that, while all the participants attended the whole course with a comparable (coherent) level of commitment to the practices (including the retreats), we did not verify their course-related activity and practices at home, and therefore, we have no way to check whether they actually did the practice activities at home as suggested during the modules.

Possible new directions of exploration of this study concern the age range of the participants, which, in our case, was limited to middle-aged individuals (39–66), and therefore, the effects on younger or older individuals remain currently unexplored. Another interesting direction would be to conduct follow-up measurements to assess the stability of the longitudinal effects months or years after the end of the program. Finally, while well-being and happiness are individual and subjective narratives of one's life as good and happy ( Bauer et al., 2008 ), and therefore self-assessments through questionnaires are a valid and common tool of investigation, in interventions such as The Art of Happiness , it would be appropriate to also explore individual differences, more objective psychophysiological effects, as well as cultural and social aspects influencing the inner model of happiness.

Despite these methodological limitations and still unexplored directions of research, the results described here suggest that The Art of Happiness may be a promising program for fostering well-being in individuals, improving mental health and psychological functioning. Longitudinal integrated contemplative programs with retreats offer a unique opportunity for the intensive development of the inner attitudes related to the capacity to be happy, reducing mental health symptoms and improving a more stable eudemonic well-being in healthy adults.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Nicola De Pisapia, upon reasonable request.

Ethics Statement

The studies involving human participants were reviewed and approved by Ethics Committee of the Sapienza University of Rome. The participants provided their written informed consent to participate in this study.

Author Contributions

ND, CM, and AR designed the study. ND, CM, LC, and AR collected the data. CR analyzed the data. CR and ND wrote the original draft. All authors edited and reviewed the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank the Institute Lama Tzong Khapa (Pomaia, Italy) for the support in various phases of this experiment. We also wish to express our gratitude to the reviewers for their thoughtful comments and efforts toward improving the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.600982/full#supplementary-material

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Keywords: meditation, wisdom, happiness, well–being, mindfulness

Citation: Rastelli C, Calabrese L, Miller C, Raffone A and De Pisapia N (2021) The Art of Happiness: An Explorative Study of a Contemplative Program for Subjective Well-Being. Front. Psychol. 12:600982. doi: 10.3389/fpsyg.2021.600982

Received: 31 August 2020; Accepted: 11 January 2021; Published: 11 February 2021.

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*Correspondence: Nicola De Pisapia, nicola.depisapia@unitn.it

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• Published: 20 July 2023

A systematic review of the strength of evidence for the most commonly recommended happiness strategies in mainstream media

• Dunigan Folk   ORCID: orcid.org/0000-0001-6728-2776 1 &
• Elizabeth Dunn   ORCID: orcid.org/0000-0003-1214-7512 1  

Nature Human Behaviour volume  7 ,  pages 1697–1707 ( 2023 ) Cite this article

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We conducted a systematic review of the evidence underlying some of the most widely recommended strategies for increasing happiness. By coding media articles on happiness, we first identified the five most commonly recommended strategies: expressing gratitude, enhancing sociability, exercising, practising mindfulness/meditation and increasing nature exposure. Next, we conducted a systematic search of the published scientific literature. We identified well-powered, pre-registered experiments testing the effects of these strategies on any aspect of subjective wellbeing (that is, positive affect, negative affect and life satisfaction) in non-clinical samples. A total of 57 studies were included. Our review suggests that a strong scientific foundation is lacking for some of the most commonly recommended happiness strategies. As the effectiveness of these strategies remains an open question, there is an urgent need for well-powered, pre-registered studies investigating strategies for promoting happiness.

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Acknowledgements

We thank S. Lyubomirsky and H. Passmore for comments on a previous version of this manuscript, and M. Smith for providing guidance on conducting the systematic literature search. We also thank J. Tan, P. Ramachadran, R. Li, R. Kaur, C. Peretz and C. Cardle for assistance with the media and scholarly literature searches. Our work was supported by grant #GR012572 from the Social Sciences and Humanities Research Council of Canada (SSHRC; E.D.) and the SSHRC Doctoral Award #6567 (D.F.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Folk, D., Dunn, E. A systematic review of the strength of evidence for the most commonly recommended happiness strategies in mainstream media. Nat Hum Behav 7 , 1697–1707 (2023). https://doi.org/10.1038/s41562-023-01651-4

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• Published: 19 June 2020

Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries

• Kai Ruggeri 1 , 2 ,
• Eduardo Garcia-Garzon 3 ,
• Áine Maguire 4 ,
• Sandra Matz 5 &
• Felicia A. Huppert 6 , 7  

Health and Quality of Life Outcomes volume  18 , Article number:  192 ( 2020 ) Cite this article

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Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP).

To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB).

The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions.

Conclusions

We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being.

What is well-being?

Well-being has been defined as the combination of feeling good and functioning well; the experience of positive emotions such as happiness and contentment as well as the development of one’s potential, having some control over one’s life, having a sense of purpose, and experiencing positive relationships [ 23 ]. It is a sustainable condition that allows the individual or population to develop and thrive. The term subjective well-being is synonymous with positive mental health. The World Health Organization [ 45 ] defines positive mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community”. This conceptualization of well-being goes beyond the absence of mental ill health, encompassing the perception that life is going well.

Well-being has been linked to success at professional, personal, and interpersonal levels, with those individuals high in well-being exhibiting greater productivity in the workplace, more effective learning, increased creativity, more prosocial behaviors, and positive relationships [ 10 , 27 , 37 ]. Further, longitudinal data indicates that well-being in childhood goes on to predict future well-being in adulthood [ 39 ]. Higher well-being is linked to a number of better outcomes regarding physical health and longevity [ 13 ] as well as better individual performance at work [ 30 ], and higher life satisfaction has been linked to better national economic performance [ 9 ].

Measurement of well-being

Governments and researchers have attempted to assess the well-being of populations for centuries [ 2 ]. Often in economic or political research, this has ended up being assessed using a single item about life satisfaction or happiness, or a limited set of items regarding quality of life [ 3 ]. Yet, well-being is a multidimensional construct, and cannot be adequately assessed in this manner [ 14 , 24 , 29 ]. Well-being goes beyond hedonism and the pursuit of happiness or pleasurable experience, and beyond a global evaluation (life satisfaction): it encompasses how well people are functioning, known as eudaimonic, or psychological well-being. Assessing well-being using a single subjective item approach fails to offer any insight into how people experience the aspects of their life that are fundamental to critical outcomes. An informative measure of well-being must encompass all the major components of well-being, both hedonic and eudaimonic aspects [ 2 ], and cannot be simplified to a unitary item of income, life satisfaction, or happiness.

Following acknowledgement that well-being measurement is inconsistent across studies, with myriad conceptual approaches applied [ 12 ], Huppert and So [ 27 ] attempted to take a systematic approach to comprehensively measure well-being. They proposed that positive mental health or well-being can be viewed as the complete opposite to mental ill health, and therefore attempted to define mental well-being in terms of the opposite of the symptoms of common mental disorders. Using the DSM-IV and ICD-10 symptom criteria for both anxiety and depression, ten features of psychological well-being were identified from defining the opposite of common symptoms. The features encompassed both hedonic and eudaimonic aspects of well-being: competence, emotional stability, engagement, meaning, optimism, positive emotion, positive relationships, resilience, self-esteem, and vitality. From these ten features an operational definition of flourishing, or high well-being, was developed using data from Round 3 of the European Social Survey (ESS), carried out in 2006. The items used in the Huppert and So [ 27 ] study were unique to that survey, which reflects a well-being framework based on 10 dimensions of good mental health. An extensive discussion on the development and validation of these measures for the framework is provided in this initial paper [ 27 ].

As this was part of a major, multinational social survey, each dimension was measured using a single item. As such, ‘multidimensional’ in this case refers to using available measures identified for well-being, but does not imply a fully robust measure of these individual dimensions, which would require substantially more items that may not be feasible for population-based work related to policy development. More detailed and nuanced approaches might help to better capture well-being as a multidimensional construct, and also may consider other dimensions. However, brief core measures such as the one implemented in the ESS are valuable as they provide a pragmatic way of generating pioneering empirical evidence on well-being across different populations, and help direct policies as well as the development of more nuanced instruments. While this naturally would benefit from complementary studies of robust measurement focused on a single topic, appropriate methods for using sprawling social surveys remain critical, particularly through better standardization [ 6 ]. While this paper will overview those findings, we strongly encourage more work to that end, particularly in more expansive measures to support policy considerations.

General approach and key questions

The aim of the present study was to develop a more robust measurement of well-being that allows researchers and policymakers to measure well-being both as a composite construct and at the level of its fundamental dimensions. Such a measure makes it possible to study overall well-being in a manner that goes beyond traditional single-item measures, which capture only a fraction of the dimensions of well-being, and because it allows analysts to unpack the measure into its core components to identify strengths and weaknesses. This would produce a similar approach as the most common reference for policy impacts: Gross Domestic Product (GDP), which is a composite measure of a large number of underlying dimensions.

The paper is structured as follows: in the first step, data from the ESS are used to develop a composite measure of well-being from the items suggested by Huppert & So [ 27 ] using factor analysis. In the second step, the value of the revised measure is demonstrated by generating insights into the well-being of 21 European countries, both at the level of overall well-being and at the level of individual dimensions.

The European social survey

The ESS is a biannual survey of European countries. Through comprehensive measurement and random sampling techniques, the ESS provides a representative sample of the European population for persons aged 15 and over [ 38 ]. Both Round 3 (2006–2007) and Round 6 (2012–2013) contained a supplementary well-being module. This module included over 50 items related to all aspects of well-being including psychological, social, and community well-being, as well as incorporating a brief measure of symptoms of psychological distress. As summarized by Huppert et al. [ 25 ], of the 50, only 30 items relate to personal well-being, of which only 22 are positive measures. Of those remaining, not all relate to the 10 constructs identified by Huppert and So [ 27 ], so only a single item could be used, or else the item that had the strongest face validity and distributional items were chosen.

Twenty-two countries participated in the well-being modules in both Round 3 and Round 6. As this it within a wider body of analyses, it was important to focus on those initially. Hungary did not have data for the vitality item in Round 3 and was excluded from the analysis, as appropriate models would not have been able to reliably resolve a missing item for an entire country. To be included in the analysis and remain consistent, participants therefore had to complete all 10 items used and have the age, gender, employment, and education variables completed. Employment was classified into four groups: students, employed, unemployed, retired; other groups were excluded. Education was classified into three groups: low (less than secondary school), middle (completed secondary school), and high (postsecondary study including any university and above). Using these criteria, the total sample for Round 6 was 41,825 people from 21 countries for analysis. The full sample was 52.6% female and ranged in age from 15 to 103 (M = 47.9; SD = 18.9). Other details about participation, response rates, and exclusion have been published elsewhere [ 38 ].

Huppert & So [ 27 ] defined well-being using 10 items extracted from the Round 3 items, which represent 10 dimensions of well-being. However, the items used in Round 3 to represent positive relationships and engagement exhibited ceiling effects and were removed from the questionnaire in Round 6. Four alternatives were available to replace each question. Based on their psychometric properties (i.e., absence of floor effects and wider response distributions), two new items were chosen for positive relationships and engagement (one item for each dimension). The new items and those they replaced can be seen in Table  1 (also see Supplement ).

Development of a composite measure of psychological well-being (MPWB)

A composite measure of well-being that yields an overall score for each individual was developed. From the ten indicators of well-being shown in Table 1 , a single factor score was calculated to represent MPWB. This overall MPWB score hence constitutes a summary of how an individual performs across the ten dimensions, which is akin to a summary score such as GDP, and will be of general value to policymakers. Statistical analysis was performed in R software, using lavaan [ 40 ] and lavaan.survey [ 35 ] packages. The former is a widely-used package for the R software designed for computing structural equation models and confirmatory factor analyses (CFA). The latter allows introducing complex survey design weights (combination of design and population size weights) when estimating confirmatory factor analysis models with lavaan, which ensures that MPWB scoring followed ESS guidelines regarding both country-level and survey specific weights [ 17 ]. Both packages have been previously tested and validated in various analyses using ESS data (as explained in detail in lavaan.survey documentation).

It should be noted that Round 6 was treated as the focal point of these efforts before repeating for Round 3, primarily due to the revised items that were problematic in Round 3, and considering that analyses of the 2006 data are already widely available.

Prior to analysis, all items were coded such that higher scores were more positive and lower scores more negative. Several confirmatory factor analysis models were performed in order to test several theoretical conceptualizations regarding MPWB. Finally, factor scores (expected a posteriori [ 15 ];) were calculated for the full European sample and used for descriptive purposes. The approach and final model are presented in supplemental material .

Factor scores are individual scores computed as weighted combinations of each person’s response on a given item and the factor scoring coefficients. This approach is to be preferred to using raw or sum scores: sum or raw scores fail to consider how well a given item serves as an indicator of the latent variable (i.e., all items are unrealistically assumed to be perfect and equivalent measures of MPWB). They also do not take into account that different items could present different variability, which is expected to occur if items present different scales (as in our case). Therefore, the use of such simple methods results in inaccurate individual rankings for MPWB. To resolve this, factor scores are both more informative and more accurate, as they avoid the propagation of measurement error in subsequent analyses [ 19 ].

Not without controversy (see Supplement ), factor scores are likely to be preferable to sum scores when ranking individuals on unobservable traits that are expected to be measured with noticeable measurement error (such as MPWB [ 32 ];). Similar approaches based on factor scoring have been successfully applied in large international assessment research [ 21 , 34 ]. With the aim of developing a composite well-being score, it was necessary to provide a meaningful representation of how the different well-being indicators are reflected in the single measure. A hierarchical model with one higher-order factor best approximated MPWB along with two first-order factors (see supplement Figure S 1 ). This model replicates the factor structure reported for Round 3 by Huppert & So [ 27 ]. The higher-order factor explained the relationship between two first-order factors (positive functioning and positive characteristics showed a correlation of ρ = .85). In addition, modelling standardized residuals showed that the items representing vitality and emotional stability and items representing optimism and self-esteem were highly correlated. The similarities in wording in both pairs of items (see Table 1 ) are suspected to be responsible for such high residual correlations. Thus, those correlations were included in the model. As presented in Table  2 , the hierarchical model was found to fit the data better than any other model but a bi-factor model including these correlated errors. The latter model resulted in collapsed factor structure with a weak, bi-polar positive functioning factor. However, this bi-factor model showed a problematic bi-polar group factor with weak loadings. Whether this group factor was removed (resulting in a S-1 bi-factor model, as in [ 16 ]), model fit deteriorated. Thus, neither bi-factor alternative was considered to be acceptable.

To calculate the single composite score representing MPWB, a factor scoring approach was used rather than a simplistic summing of raw scores on these items. Factor scores were computed and standardized for the sample population as a whole, which make them suitable for broad comparison [ 8 ]. This technique was selected for two reasons. First, it has the ability to take into account the different response scales used for measuring the items included in the multidimensional well-being model. The CFA model, from which MPWB scores were computed, was defined such that the metric of the MPWB was fixed, which results in a standardized scale. Alternative approaches, such as sum or raw scores, would result in ignoring the differential variability across items, and biased individual group scores. Our approach, using factor scoring, resolves this issue by means of standardization of the MPWB scores. The second reason for this technique is that it could take account of how strongly each item loaded onto the MPWB factor. It should be noted that by using only two sub-factors, the weight applied to the general factor is identical within the model for each round. This model was also checked to ensure it also was a good fit for different groups based on gender, age, education and employment.

Separate CFA analyses per each country indicate that the final model fit the data adequately in all countries (.971 < CFI < .995; .960 < TFI < .994; .020 < RMSEA < .05; 0,023 < SRMR < 0,042). All items presented substantive loadings on their respective factors, and structures consistently replicated across all tested countries. Largest variations were found when assessing the residual items’ correlations (e.g., for emotional stability and vitality correlation, values ranged from 0,076 to .394). However, for most cases, residuals correlations were of similar size and direction (for both cases, the standard deviation of estimated correlations was close of .10). Thus, strong evidence supporting our final model was systematically found across all analyzed countries. Full results are provided in the supplement (Tables S 2 -S 3 ).

Model invariance

In order to establish meaningful comparisons across groups within and between each country, a two-stage approach was followed, resulting in a structure that was successfully found to be similar across demographics. First, a descriptive comparison of the parameter estimates unveiled no major differences across groups. Second, factor scores were derived for the sample, employing univariate statistics to compare specific groups within country and round. In these analyses, neither traditional nor modern approaches to factor measurement invariance were appropriate given the large sample and number of comparisons at stake ([ 8 ]; further details in Supplement ).

From a descriptive standpoint, the hierarchical structure satisfactorily fit both Round 3 and Round 6 data. All indicators in both rounds had substantial factor loadings (i.e., λ > .35). A descriptive comparison of parameter estimates produced no major differences across the two rounds. The lack of meaningful differences in the parameter estimates confirms that this method for computing MPWB can be used in both rounds.

As MPWB scores from both rounds are obtained from different items that have different scales for responses, it is necessary to transform individual scores obtained from both rounds in order to be aligned. To do this between Round 3 and Round 6 items, a scaling approach was used. To produce common metrics, scores from Round 3 were rescaled using a mean and sigma transformation (Kolen & Brennan 2010) to align with Round 6 scales. This was used as Round 6 measures were deemed to have corrected some deficiencies found in Round 3 items. This does not change outcomes in either round but simply makes the scores match in terms of distributions relative to their scales, making them more suitable for comparison.

As extensive descriptive insights on the sample and general findings are already available (see [ 41 ]), we focus this section on the evidence derived directly from the proposed approach to MPWB scores. For the combined single score for MPWB, the overall mean (for all participants combined) is fixed to zero, and the scores represent deviation from the overall mean. In 2012 (Round 6), country scores on well-being ranged from − 0.41 in Bulgaria to 0.46 in Denmark (Fig.  1 ). There was a significant, positive relationship between national MPWB mean scores and national life satisfaction means ( r =  .56 (.55–.57), p  < .001). In addition, MPWB was negatively related with depression scores and positively associated with other well-being measurements (see Supplement ).

Distribution of national MPWB means and confidence intervals across Europe

Denmark having the highest well-being is consistent with many studies [ 4 , 18 ] and with previous work using ESS data [ 27 ]. While the pattern is typically that Nordic countries are doing the best and that eastern countries have the lowest well-being, exceptions exist. The most notable exception is Portugal, which has the third-lowest score and is not significantly higher than Ukraine, which is second lowest. Switzerland and Germany are second and third highest respectively, and show generally similar patterns to the Scandinavian countries (see Fig. 1 ). It should be noted that, for Figs.  1 , 2 , 3 , 4 , 5 , countries with the lowest well-being are at the top. This is done to highlight the greatest areas for potential impact, which are also the most of concern to policy.

Well-being by country and gender

Well-being by country and age

Well-being by country and employment

Well-being by country and education

General patterns across the key demographic variables – gender, age, education, employment – are visible across countries as seen in Figs.  1 , 2 , 3 , 4 , 5 (see also Supplement 2 ). These figures highlight patterns based on overall well-being as well as potential for inequalities. The visualizations presented here, though univariate, are for the purpose of understanding broad patterns while highlighting the need to disentangle groups and specific dimensions to generate effective policies.

For gender, women exhibited lower MPWB scores than men across Europe (β = −.09, t (36508) = − 10.37; p  < .001). However, these results must be interpreted with caution due to considerable overlap in confidence intervals for many of the countries, and greater exploration of related variables is required. This also applies for the five countries (Estonia, Finland, Ireland, Slovakia, Ukraine) where women have higher means than men. Only four countries have significant differences between genders, all of which involve men having higher scores than women: the Netherlands (β = −.12, t (1759) = − 3.24; p  < .001), Belgium (β = −.14, t (1783) = − 3.94; p  < .001), Cyprus (β = −.18, t (930) = − 2.87; p  < .001) and Portugal (β = −.19, t (1847) = − 2.50; p  < .001).

While older individuals typically exhibited lower MPWB scores compared to younger age groups across Europe (β 25–44  = −.05, t (36506) = − 3.686, p  < .001; β 45–65  = −.12, t (36506) = − 8.356, p  < .001; β 65–74  = −.16, t (36506) = − 8.807, p  < .001; β 75+  = −.28, t (36506) = − 13.568, p  < .001), the more compelling pattern shows more extreme differences within and between age groups for the six countries with the lowest well-being. This pattern is most pronounced in Bulgaria, which has the lowest overall well-being. For the three countries with the highest well-being (Denmark, Switzerland, Germany), even the mean of the oldest age group was well above the European average, while for the countries with the lowest well-being, it was only young people, particularly those under 25, who scored above the European average. With the exception of France and Denmark, countries with higher well-being typically had fewer age group differences and less variance within or between groups. Only countries with the lowest well-being showed age differences that were significant with those 75 and over showing the lowest well-being.

MPWB is consistently higher for employed individuals and students than for retired (β = −.31, t (36506) = − 21.785; p  < .00) or unemployed individuals (β = −.52, t (36556) = − 28.972; p  < .001). Unemployed groups were lowest in nearly all of the 21 countries, though the size of the distance from other groups did not consistently correlate with national MPWB mean. Unemployed individuals in the six countries with the lowest well-being were significantly below the mean, though there is little consistency across groups and countries by employment beyond that. In countries with high well-being, unemployed, and, in some cases, retired individuals, had means below the European average. In countries with the lowest well-being, it was almost exclusively students who scored above the European average. Means for retired groups appear to correlate most strongly with overall well-being. There is minimal variability for employed groups in MPWB means within and between countries.

There is a clear pattern of MPWB scores increasing with education level, though the differences were most pronounced between low and middle education groups (β = .12, t (36508) = 9.538; p  < .001). Individuals with high education were significantly higher on MPWB than those in the middle education group (β = .10, t (36508) =11.06; p  < .001). Differences between groups were noticeably larger for countries with lower overall well-being, and the difference was particularly striking in Bulgaria. In Portugal, medium and high education well-being means were above the European average (though 95% confidence intervals crossed 0), but educational attainment is significantly lower in the country, meaning the low education group represents a greater proportion of the population than the other 21 countries. In the six countries with the highest well-being, mean scores for all levels of education were above the European mean.

Utilizing ten dimensions for superior understanding of well-being

It is common to find rankings of national happiness and well-being in popular literature. Similarly, life satisfaction is routinely the only measure reported in many policy documents related to population well-being. To demonstrate why such limited descriptive approaches can be problematic, and better understood using multiple dimensions, all 21 countries were ranked individually on each of the 10 indicators of well-being and MPWB in Round 6 based on their means. Figure  6 demonstrates the variations in ranking across the 10 dimensions of well-being for each country.

Country rankings in 2012 on multidimensional psychological well-being and each of its 10 dimensions

The general pattern shows typically higher rankings for well-being dimensions in countries with higher overall well-being (and vice-versa). Yet countries can have very similar scores on the composite measure but very different underlying profiles in terms of individual dimensions. Figure  7 a presents this for two countries with similar life satisfaction and composite well-being, Belgium and the United Kingdom. Figure 7 b then demonstrates this even more vividly for two countries, Finland and Norway, which have similar composite well-being scores and identical mean life satisfaction scores (8.1), as well as have the highest two values for happiness of all 21 countries. In both pairings, the broad outcomes are similar, yet countries consistently have very different underlying profiles in individual dimensions. The results indicate that while overall scores can be useful for general assessment, specific dimensions may vary substantially, which is a relevant first step for developing interventions. Whereas the ten items are individual measures of 10 areas of well-being, had these been limited to a single domain only, the richness of the underlying patterns would have been lost, and the limitation of single item approaches amplified.

a Comparison of ranks for dimensions of well-being between two different countries with similar life satisfaction in 2012: Belgium and United Kingdom. b Comparison of ranks for dimensions of well-being between two similar countries with identical life satisfaction and composite well-being scores in 2012: Finland and Norway

The ten-item multidimensional measure provided clear patterns for well-being across 21 countries and various groups within. Whether used individually or combined into a composite score, this approach produces more insight into well-being and its components than a single item measure such as happiness or life satisfaction. Fundamentally, single items are impossible to unpack in reverse to gain insights, whereas the composite score can be used as a macro-indicator for more efficient overviews as well as deconstructed to look for strengths and weaknesses within a population, as depicted in Figs.  6 and 7 . Such deconstruction makes it possible to more appropriately target interventions. This brings measurement of well-being in policy contexts in line with approaches like GDP or national ageing indexes [ 7 ], which are composite indicators of many critical dimensions. The comparison with GDP is discussed at length in the following sections.

Patterns within and between populations

Overall, the patterns and profiles presented indicate a number of general and more nuanced insights. The most consistent among these is that the general trend in national well-being is usually matched within each of the primary indicators assessed, such as lower well-being within unemployed groups in countries with lower overall scores than in those with higher overall scores. While there are certainly exceptions, this general pattern is visible across most indicators.

The other general trend is that groups with lower MPWB scores consistently demonstrate greater variability and wider confidence intervals than groups with higher scores. This is a particularly relevant message for policymakers given that it is an indication of the complexity of inequalities: improvements for those doing well may be more similar in nature than for those doing poorly. This is particularly true for employment versus unemployment, yet reversed for educational attainment. Within each dimension, the most critical pattern is the lack of consistency for how each country ranks, as discussed further in other sections.

Examining individual dimensions of well-being makes it possible to develop a more nuanced understanding of how well-being is impacted by societal indicators, such as inequality or education. For example, it is possible that spending more money on education improves well-being on some dimensions but not others. Such an understanding is crucial for the implementation of targeted policy interventions that aim at weaker dimensions of well-being and may help avoid the development of ineffective policy programs. It is also important to note that the patterns across sociodemographic variables may differ when all groups are combined, compared to results within countries. Some effects may be larger when all are combined, whereas others may have cancelling effects.

Using these insights, one group that may be particularly important to consider is unemployed adults, who consistently have lower well-being than employed individuals. Previous research on unemployment and well-being has often focused on mental health problems among the unemployed [ 46 ] but there are also numerous studies of differences in positive aspects of well-being, mainly life satisfaction and happiness [ 22 ]. A large population-based study has demonstrated that unemployment is more strongly associated with the absence of positive well-being than with the presence of symptoms of psychological distress [ 28 ], suggesting that programs that aim to increase well-being among unemployed people may be more effective than programs that seek to reduce psychological distress.

Certainly, it is well known that higher income is related to higher subjective well-being and better health and life expectancy [ 1 , 42 ], so reduced income following unemployment is likely to lead to increased inequalities. Further work would be particularly insightful if it included links to specific dimensions of well-being, not only the comprehensive scores or overall life satisfaction for unemployed populations. As such, effective responses would involve implementation of interventions known to increase well-being in these groups in times of (or in spite of) low access to work, targeting dimensions most responsible for low overall well-being. Further work on this subject will be presented in forthcoming papers with extended use of these data.

This thinking also applies to older and retired populations in highly deprived regions where access to social services and pensions are limited. A key example of this is the absence in our data of a U-shaped curve for age, which is commonly found in studies using life satisfaction or happiness [ 5 ]. In our results, older individuals are typically lower than what would be expected in a U distribution, and in some cases, the oldest populations have the lowest MPWB scores. While previous studies have shown some decline in well-being beyond the age of 75 [ 20 ], our analysis demonstrates quite a severe fall in MPWB in most countries. What makes this insight useful – as opposed to merely unexpected – is the inclusion of the individual dimensions such as vitality and positive relationships. These dimensions are clearly much more likely to elicit lower scores than for younger age groups. For example, ageing beyond 75 is often associated with increased loneliness and isolation [ 33 , 43 ], and reduction in safe, independent mobility [ 31 ], which may therefore correspond with lower scores on positive relationships, engagement, and vitality, and ultimately lower scores on MPWB than younger populations. Unpacking the dimensions associated with the age-related decline in well-being should be the subject of future research. The moderate positive relationship of MPWB scores with life satisfaction is clear but also not absolute, indicating greater insights through multidimensional approaches without any obvious loss of information. Based on the findings presented here, it is clearly important to consider ensuring the well-being of such groups, the most vulnerable in society, during periods of major social spending limitations.

Policy implications

Critically, Fig.  6 represents the diversity of how countries reach an overall MPWB score. While countries with overall high well-being have typically higher ranks on individual items, there are clearly weak dimensions for individual countries. Conversely, even countries with overall low well-being have positive scores on some dimensions. As such, the lower items can be seen as potential policy levers in terms of targeting areas of concern through evidence-based interventions that should improve them. Similarly, stronger areas can be seen as learning opportunities to understand what may be driving results, and thus used to both sustain those levels as well as potentially to translate for individuals or groups not performing as well in that dimension. Collectively, we can view this insight as a message about specific areas to target for improvement, even in countries doing well, and that even countries doing poorly may offer strengths that can be enhanced or maintained, and could be further studied for potential applications to address deficits. We sound a note of caution however, in that these patterns are based on ranks rather than actual values, and that those ranks are based on single measures.

Figure 7 complements those insights more specifically by showing how Finland and Norway, with a number of social, demographic, and economic similarities, plus identical life satisfaction scores (8.1) arrive at similar single MPWB scores with very different profiles for individual dimensions. By understanding the levers that are specific to each country (i.e. dimensions with the lowest well-being scores), policymakers can respond with appropriate interventions, thereby maximizing the potential for impact on entire populations. Had we restricted well-being measurement to a single question about happiness, as is commonly done, we would have seen both countries had similar and extremely high means for happiness. This might have led to the conclusion that there was minimal need for interventions for improving well-being. Thus, in isolation, using happiness as the single indicator would have masked the considerable variability on several other dimensions, especially those dimensions where one or both had means among the lowest of the 21 countries. This would have resulted in similar policy recommendations, when in fact, Norway may have been best served by, for example, targeting lower dimensions such as Engagement and Self-Esteem, and Finland best served by targeting Vitality and Emotional Stability.

Targeting specific groups and relevant dimensions as opposed to comparing overall national outcomes between countries is perhaps best exemplified by Portugal, which has one of the lowest educational attainment rates in OECD countries, exceeded only by Mexico and Turkey [ 36 ]. This group thus skews the national MPWB score, which is above average for middle and high education groups, but much lower for those with low education. Though this pattern is not atypical for the 21 countries presented here, the size of the low education group proportional to Portugal’s population clearly reduces the national MPWB score. This implies that the greatest potential for improvement is likely to be through addressing the well-being of those with low education as a near-term strategy, and improving access to education as a longer-term strategy. It will be important to analyze this in the near future, given recent reports that educational attainment in Portugal has increased considerably in recent years (though remains one of the lowest in OECD countries) [ 36 ].

One topic that could not be addressed directly is whether these measures offer value as indicators of well-being beyond the 21 countries included here, or even beyond the countries included in ESS generally. In other words, are these measures relevant only to a European population or is our approach to well-being measurement translatable to other regions and purposes? Broadly speaking, the development of these measures being based on DSM and ICD criteria should make them relevant beyond just the 21 countries, as those systems are generally intended to be global. However, it can certainly be argued that these methods for designing measures are heavily influenced by North American and European medical frameworks, which may limit their appropriateness if applied in other regions. Further research on these measures should consider this by adding potential further measures deemed culturally appropriate and seeing if comparable models appear as a result.

A single well-being score

One potential weakness remains the inconsistency of scaling between ESS well-being items used for calculating MPWB. However, this also presents an opportunity to consider the relative weighting of each item within the current scales, and allow for the development of a more consistent and reliable measure. These scales could be modified to align in separate studies with new weights generated – either generically for all populations or stratified to account for various cultural or other influences. Using these insights, scales could alternatively be produced to allow for simple scoring for a more universally accessible structure (e.g. 1–100) but with appropriate values for each item that represents the dimensions, if this results in more effective communication with a general public than a standardized score with weights. Additionally, common scales would improve on attempts to use rankings for presenting national variability within and between dimensions. Researchers should be aware that factor scores are sample-dependent (as based on specific factor model parameters such as factor loadings). Nevertheless, future research focused on investigating specific item differential functioning (by means of multidimensional item response functioning or akin techniques) of these items across situations (i.e., rounds) and samples (i.e., rounds and countries) should be conducted in order to have a more nuanced understanding of this scale functioning.

What makes this discussion highly relevant is the value of a more informed measure to replace traditional indicators of well-being, predominantly life satisfaction. While life satisfaction may have an extensive history and present a useful metric for comparisons between major populations of interest, it is at best a corollary, or natural consequence, of other indicators. It is not in itself useful for informing interventions, in the same way limiting to a single item for any specific dimension of well-being should not alone inform interventions.

By contrast, a validated and standardized multidimensional measure is exceptionally useful in its suitability to identify those at risk, as well as its potential for identifying areas of strengths and weaknesses within the at-risk population. This can considerably improve the efficiency and appropriateness of interventions. It identifies well-understood dimensions (e.g. vitality, positive emotion) for direct application of evidence-based approaches that would improve areas of concern and thus overall well-being. Given these points, we strongly argue for the use of multidimensional approaches to measurement of well-being for setting local and national policy agenda.

There are other existing single-score approaches for well-being addressing its multidimensional nature. These include the Warwick-Edinburgh Mental Well-Being Scale [ 44 ] and the Flourishing Scale [ 11 ]. In these measures, although the single score is derived from items that clearly tap a number of dimensions, the dimensions have not been systematically derived and no attempt is made to measure the underlying dimensions individually. In contrast, the development approach used here – taking established dimensions from DSM and ICD – is based on years of international expertise in the field of mental illness. In other words, there have long been adequate measures for identifying and understanding illness, but there is room for improvement to better identify and understand health. With increasing support for the idea of these being a more central focus of primary outcomes within economic policies, such approaches are exceptionally useful [ 13 ].

Better measures, better insights

Naturally, it is not a compelling argument to simply state that more measures present greater information than fewer or single measures, and this is not the primary argument of this manuscript. In many instances, national measures of well-being are mandated to be restricted to a limited set of items. What is instead being argued is that well-being itself is a multidimensional construct, and if it is deemed a critical insight for establishing policy agenda or evaluating outcomes, measurements must follow suit and not treat happiness and life satisfaction values as universally indicative. The items included in ESS present a very useful step to that end, even in a context where the number of items is limited.

As has been argued by many, greater consistency in measurement of well-being is also needed [ 26 ]. This may come in the form of more consistency regarding dimensions included, the way items are scored, the number of items representing each dimension, and changes in items over time. While inconsistency may be prevalent in the literature to date for definitions and measurement, the significant number of converging findings indicates increasingly robust insights for well-being relevant to scientists and policymakers. Improvements to this end would support more systematic study of (and interventions for) population well-being, even in cases where data collection may be limited to a small number of items.

The added value of MPWB as a composite measure

While there are many published arguments (which we echo) that measures of well-being must go beyond objective features, particularly related to economic indicators such as GDP, this is not to say one replaces the other. More practically, subjective and objective approaches will covary to some degree but remain largely distinct. For example, GDP presents a useful composite of a substantial number of dimensions, such as consumption, imports, exports, specific market outcomes, and incomes. If measurement is restricted to a macro-level indicator such as GDP, we cannot be confident in selecting appropriate policies to implement. Policies are most effective when they target a specific component (of GDP, in this instance), and then are directly evaluated in terms of changes in that component. The composite can then be useful for comprehensive understanding of change over time and variation in circumstances. Specific dimensions are necessary for identifying strengths and weaknesses to guide policy, and examining direct impacts on those dimensions. In this way, a composite measure in the form of MPWB for aggregate well-being is also useful, so long as the individual dimensions are used in the development and evaluation of policies. Similar arguments for other multidimensional constructs have been made recently, such as national indexes of ageing [ 7 ].

In the specific instance of MPWB in relation to existing measures of well-being, there are several critical reasons to ensure a robust approach to measurement through systematic validation of psychometric properties. The first is that these measures are already part of the ESS, meaning they are being used to study a very large sample across a number of social challenges and not specifically a new measure for well-being. The ESS has a significant influence on policy discussions, which means the best approaches to utilizing the data are critical to present systematically, as we have attempted to do here. This approach goes beyond existing measures such as Gallup or the World Happiness Index to broadly cover psychological well-being, not individual features such as happiness or life satisfaction (though we reiterate: as we demonstrate in Fig.  7 a and b, these individual measures can and should still covary broadly with any multidimensional measure of well-being, even if not useful for predicting all dimensions). While often referred to as ‘comprehensive’ measurement, this merely describes a broad range of dimensions, though more items for each dimension – and potentially more dimensions – would certainly be preferable in an ideal scenario.

These dimensions were identified following extensive study for flourishing measures by Huppert & So [ 27 ], meaning they are not simply a mix of dimensions, but established systematically as the key features of well-being (the opposite of ill-being). Furthermore, the development of the items is in line with widely validated and practiced measures for the identification of illness. The primary adjustment has simply been the emphasis on health, but otherwise maintains the same principles of assessment. Therefore, the overall approach offers greater value than assessing only negative features and inferring absence equates to opposite (positives), or that individual measures such as happiness can sufficiently represent a multidimensional construct like well-being. Collectively, we feel the approach presented in this work is therefore a preferable method for assessing well-being, particularly on a population level, and similar approaches should replace single items used in isolation.

While the focus of this paper is on the utilization of a widely tested measure (in terms of geographic spread) that provides for assessing population well-being, it is important to provide a specific application for why this is relevant in a policy context. Additionally, because the ESS itself is a widely-recognized source of meaningful information for policymakers, providing a robust and comprehensive exploration of the data is necessary. As the well-being module was not collected in recent rounds, these insights provide clear reasoning and applications for bringing them back in the near future.

More specifically, it is critical that this approach be seen as advantageous both in using the composite measure for identifying major patterns within and between populations, and for systematically unpacking individual dimensions. Using those dimensions produces nuanced insights as well as the possibility of illuminating policy priorities for intervention.

In line with this, we argue that no composite measure can be useful for developing, implementing, or evaluating policy if individual dimensions are not disaggregated. We are not arguing that MPWB as a single composite score, nor the additional measures used in ESS, is better than other existing single composite scoring measures of well-being. Our primary argument is instead that MPWB is constructed and analyzed specifically for the purpose of having a robust measure suitable for disaggregating critical dimensions of well-being. Without such disaggregation, single composite measures are of limited use. In other words, construct a composite and target the components.

Well-being is perhaps the most critical outcome measure of policies. Each individual dimension of well-being as measured in this study represents a component linked to important areas of life, such as physical health, financial choice, and academic performance [ 26 ]. For such significant datasets as the European Social Survey, the use of the single score based on the ten dimensions included in multidimensional psychological well-being gives the ability to present national patterns and major demographic categories as well as to explore specific dimensions within specific groups. This offers a robust approach for policy purposes, on both macro and micro levels. This facilitates the implementation and evaluation of interventions aimed at directly improving outcomes in terms of population well-being.

Availability of data and materials

The datasets analysed during the current study are available in the European Social Survey repository, http://www.europeansocialsurvey.org/data/country_index.html

Abbreviations

Diagnostic and Statistical Manual of Mental Disorders

European Social Survey

Gross Domestic Product

International Classification of Disease

Multidimensional psychological well-being

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Acknowledgements

The authors would like to thank Ms. Sara Plakolm, Ms. Amel Benzerga, and Ms. Jill Hurson for assistance in proofing the final draft. We would also like to acknowledge the general involvement of the Centre for Comparative Social Surveys at City University, London, and the Centre for Wellbeing at the New Economics Foundation.

This work was supported by a grant from the UK Economic and Social Research Council (ES/LO14629/1). Additional support was also provided by the Isaac Newton Trust, Trinity College, University of Cambridge, and the UK Economic and Social Research Council (ES/P010962/1).

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KR is the lead author and researcher on the study, responsible for all materials start to finish. FH was responsible for the original grant award and the general theory involved in the measurement approaches. ÁM was responsible for broad analysis and writing. EGG was responsible for psychometric models and the original factor scoring approach, plus writing the supplementary explanations. SM provided input on later drafts of the manuscript as well as the auxiliary analyses. The authors read and approved the final manuscript.

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. Hierarchical approach to modelling comprehensive psychological well-being. Table S1 . Confirmatory Factor Structure for Round 6 and 3. Figure S2 . Well-being by country and gender. Figure S3 . Well-being by country and age. Figure S4 . Well-being by country and employment. Figure S5 . Well-being by country and education. Table S2 . Item loadings for Belgium to Great Britain. Table S3 . Item loadings for Ireland to Ukraine.

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Ruggeri, K., Garcia-Garzon, E., Maguire, Á. et al. Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries. Health Qual Life Outcomes 18 , 192 (2020). https://doi.org/10.1186/s12955-020-01423-y

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January 31, 2024

Here’s the Happiness Research that Stands Up to Scrutiny

From meditation to smiling, researchers take a second look at studies claiming to reveal what makes us happy

By Amber Dance & Knowable Magazine

Yagi Studio/Getty Images

We all want to be happy — and for decades, psychologists have tried to figure out how we might achieve that blissful state. The field’s many surveys and experiments have pointed to a variety of approaches, from giving stuff away to quitting Facebook to forcing one’s face into a toothy grin.

But psychology has undergone serious upheaval over the last decade, as researchers realized that many studies were unreliable and unrepeatable. That has led to a closer scrutiny of psychological research methods, with the study of happiness no exception. So — what  really  makes us happy? Under today’s more careful microscope, some routes to happiness seem to hold up, while others appear not to, or have yet to re-prove themselves. Here’s what we know so far, and what remains to be reassessed,  according to a new analysis  in the  Annual Review of Psychology .

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One long-standing hypothesis is that smiling makes you feel happier. In a classic 1988 study, researchers asked 92 Illinois undergraduates to hold a felt tip pen in their mouth either with their teeth, forcing an unnatural grin, or with their lips, making them pout. The students then looked at four examples of  The Far Side  comics . On average, those with the forced smiles  found the one-panel comics slightly funnier  than those with the forced pouts.

But when 17 different research labs got together to retest the pen-clench smile’s effects on 1,894 new participants, the finding  failed to hold up , the researchers reported in 2016.

The repetition study was part of a broader effort to counter  psychology’s reproducibility crisis , which in part has been attributed to the variety of ways in which researchers could examine and reanalyze their data until they arrived at publishable results. “It’s kind of like shooting a bunch of arrows at the wall and drawing the bullseye on after,” says Elizabeth Dunn, a social psychologist at the University of British Columbia in Vancouver and coauthor of the new  Annual Review of Psychology  paper.

One solution has been for scientists to publicly declare, or preregister, their analysis plans before they conduct their experiments. In other words, they draw the bullseye first. Dunn and her graduate student, Dunigan Folk, homed in on such preregistered studies in their analysis, which narrowed the vast field of happiness research to just 48 published papers. Even that small number is encouraging, says Brian Nosek, a psychologist at the University of Virginia in Charlottesville and executive director of the Center for Open Science, which aims to improve research reproducibility. “I was actually surprised that there were as many papers that qualified,” he says. “That really demonstrates that this area of research has adopted a lot of these new rigor-enhancing practices.”

Preregistration alone doesn’t guarantee that results will be correct, nor does it solve all of psychology’s reproducibility problems. Quality studies also require sound methods and large and diverse sets of participants, for example. And indeed, most of the papers reviewed were high quality in those features beyond just preregistration, Dunn says. Even under the regimen of renewed scrutiny, some of the paths to happiness held up, the researchers found — including practicing gratitude, acting sociable and spending money on other people.

Take gratitude. In one of the recent studies, researchers asked hundreds of parents to either write about how they spent their week, or pen a gratitude letter to someone they knew. Expressing gratitude  resulted in more positive moods . In another recent study, scientists asked more than 900 undergraduates to express gratitude in letters, texts or social media, or to list their daily activities. Those in the gratitude group  scored as happier and more satisfied  with their lives the following day. In both cases, it’s unclear how long these effects would persist.

Three different preregistered studies pointed to sociability as beneficial. In one, scientists assigned 71 adults to act extroverted — “bold, talkative, outgoing, active and assertive” — for a week, and another 76 to be “unassuming, sensitive, calm, modest and quiet.” Participants in the extroverted condition  reported better moods  during the study week, though the benefits were less for those who were naturally introverted.

And surprise! Smiling to promote happiness was also supported by new, preregistered research — once scientists switched to more natural grins. About two dozen labs from 19 different countries worked together to test the instruction to grip a pen in the teeth or to mimic the expression of a smiling person in nearly 4,000 subjects. The pen clenching still didn’t work, but people who were told to copy a smile  did report better moods . Remarkably, this was true  even if the subjects didn’t believe it would work , another team reported in 2023.

Researchers have also found that external agencies can promote people’s happiness. Giving people cash promoted life satisfaction, as did workplace interventions such as naps.

Dunn cautions, however, that participation in preregistered studies tends to yield small effects on happiness overall, in part because scientists can’t massage the data to get bigger numbers. If the interventions were a diet program, she says, users might drop about four pounds.

Nice ideas, poor results

Other well-known happiness approaches haven’t measured up to Dunn and Folk’s standards — at least, not yet. The researchers didn’t find clear evidence of benefits for volunteering, performing random acts of kindness or meditation. For example, a recent, preregistered study asked participants to perform acts of kindness for others, or for themselves, or simply to list what they did each day. Being kind to others over a four-week period  made no difference to well-being .

Dunn and Folk didn’t find any preregistered studies at all on  exercising  or spending  time in nature , two  oft-recommended strategies . That doesn’t mean those strategies don’t or can’t work, Dunn says — just that as the preregistered landscape now stands, research hasn’t weighed in. The pair considered only two preregistered studies on meditation, and did not include meditation research on people with diagnosed mental health problems.

Such rigor is admirable, but it also means one can miss things, says Simon Goldberg, a psychologist at the University of Wisconsin-Madison. He studies the effects of meditation, including research among people who have  psychological problems such as depression and anxiety . He noted that because of Dunn and Folk’s strict criteria, they omitted hundreds of studies on  meditation’s benefits . “It’s, in the spirit of rigor, throwing lots of babies out with the bathwater,” he says. “It’s really very obvious that meditation training reduces symptoms of anxiety and depression.”

Dunn agrees that the review only covered the tip of the iceberg of happiness research. But that tip should expand as more psychologists preregister their science as part of  what some call a renaissance  in the field. As Dunn and Folk conclude, “happiness research stands on the brink of an exciting new era.”

This article originally appeared in Knowable Magazine , an independent journalistic endeavor from Annual Reviews. Sign up for the newsletter .

Psychology of Happiness: A Summary of the Theory & Research

Little did I know the overwhelming depth of this topic! I found myself asking questions – can science explain happiness?

Can happiness be measured? What is happiness, anyway?

Arguably, a lot has been written on the topic of happiness , including on this website. The following provides an exploration of happiness, and, importantly, it provides you with links to further resources on this important topic.

Keep reading to discover a range of topics including the main theories of happiness, and a fascinating look at the neuroscience of happiness, as well as an interesting discussion on topics such as subjective wellbeing (the more scientific term for happiness), what positive psychology has to say about happiness, success and happiness, and more. Hopefully, it will answer some questions about happiness. Please enjoy!

Before you continue, we thought you might like to download our three Happiness & Subjective Wellbeing Exercises for free . These detailed, science-based exercises will help you or your clients identify sources of authentic happiness and strategies to boost wellbeing.

This Article Contains:

A scientific explanation of happiness, a look at the theory and science of happiness, the psychology of happiness, happiness and positive psychology, interesting research and studies, the happiness research institute, the happiness professor, other well-known researchers, articles on success and happiness, 16 most important happiness articles, other recommended journal and scholarly articles (pdf), a take-home message.

What exactly do we mean when we talk about a scientific explanation of happiness? What, in fact, is the science of happiness?

Put very simply, the science of happiness looks at “ what makes happy people happy ” (Pursuit of Happiness, 2018). If you think about it, the subjective nature of happiness makes it incredibly difficult to define and also challenging to measure (Kringelbach & Berridge, 2010).

Let’s look into this further …

In the past

Happiness has been the topic of discussion and debate since the ancient Greek times. Hedonism has a long history (Ryan & Deci, 2001). Science has looked closely at happiness as ‘hedonically’ defined – or, in other words, happiness is the outcome of the pursuit of pleasure over pain (Ryan & Deci, 2001).

Aristippus, a Greek philosopher from the 4th century BC claimed happiness was the sum of life’s ‘hedonic’ moments (Ryan & Deci, 2001). Hedonic enjoyment is a state whereby an individual feels relaxed, has a sense of distance from their problems and, can be said to feel ‘happy’ (Ryan & Deci, 2001).

Since the days of Aristotle, happiness has been conceptualized as being composed of at least 2 aspects – hedonia (or, pleasure) and eudaimonia (a sense that life is well-lived) (Kringelbach & Berridge, 2010).

In the present

What does science say about this? Well, research has shown that, whilst these two aspects are definitely distinct and that, in ‘happy’ people, both hedonic and eudaimonic components of happiness correspond (Kringelbach & Berridge, 2010).

A study by Kesebir and Diener (2008) report that in happiness surveys , more than 80% of interviewees rated their overall ‘eudaimonic’ life satisfaction as “pretty to very happy” and, at the same time, 80% of people interviewed also rate their current, hedonic ‘mood’ as positive (e.g. giving a rating of 6-7 on a 10-point valence scale, where 5 is ‘hedonically neutral’).

Neuroscientists have made substantial progress into investigating the functional neuroanatomy of pleasure (which, according to Kringelbach and Berridge 2010, makes an important contribution to our experience of happiness and plays a key role in our sense of wellbeing).

Pleasure has, for many years in the discipline of psychology, been closely associated with happiness (Kringelbach & Berridge, 2010).

According to Sigmund Freud (1930), people: ‘ strive after happiness; they want to become happy and to remain so. This endeavor has two sides, a positive and a negative aim. It aims, on the one hand, at an absence of pain and displeasure, and, on the other, at the experiencing of strong feelings of pleasure ’ (p. 76).

Kringelbach and Berridge (2010) argue that the neuroscience of both pleasure and happiness can be found by studying hedonic brain circuits. This is because, according to most modern perspectives, pleasure is an important component of happiness.

Does this provide the opportunity to ‘measure’ happiness, therefore providing a scientific explanation of happiness?

In fact, work of neuroscientists has found that pleasure is not merely a sensation, or thought, but rather an outcome of brain activity in dedicated ‘hedonic systems’ (Kringelbach & Berridge, 2010).

All pleasures, from the most fundamental (food, sexual pleasure) right through to higher-order pleasures (e.g. monetary, medical, and altruistic pleasures) seem to involve the same brain systems (Kringelbach & Berridge, 2010).

Some of the hedonic mechanisms are found deep within the brain (the nucleus accumbens, ventral pallidum, and brainstem) and others are located in the cortex (orbitofrontal, cingulate, medial prefrontal and insular cortices) (Kringelbach & Berridge, 2010).

In the future

It can be said, then, that pleasure activated brain networks are widespread. Despite this exciting finding – a brain network for happiness – Kringelbach and Berridge (2010) say that further research is needed to fully comprehend the functional neuroanatomy of happiness.

As well as the findings from neuroscience supporting an anatomical basis to happiness, another component of a scientific explanation of happiness is the issue of measurement.

Can happiness be measured?

Some individuals argue that maybe happiness should not be the subject of scientific explanation because it is impossible to objectively measure it (Norrish & Vella-Brodrick, 2008).

Perhaps, though, as argued by Ed Diener, happiness is subjective. According to Ed Diener, people are happy if they think they are, and each person is the best judge of whether they are, in fact, happy or not (Norrish & Vella-Brodrick, 2008).

He introduced a term to describe this ‘measure’ of happiness: Subjective wellbeing .

Having the measure of subjective wellbeing makes a scientific explanation of happiness possible… by asking questions such as:

• Are you happy?
• How would you rate your happiness on a scale of 1 – 10

Controlled experiments can be devised to determine what can be done to raise/lower these responses.

The Experience Sampling Method (ESM) has been valuable in the assessment of subjective wellbeing. It has been a positive development in the science of happiness.

ESM provides an overall indication of wellbeing over time, based on the total balance of measurement of positive and negative affect at different times (Norrish & Vella-Brodrick, 2008).

Diener provided evidence that subjective wellbeing has “construct validity” meaning that, yes, it is measuring something ‘real’! This is because Diener showed that subjective wellbeing is constant over time, is highly correlated with some personality traits and has the capacity to predict future outcomes.

Diener and colleagues suggest that it is possible to measure happiness using valid, reliable methods including using instruments, looking at observable indicators of happiness such as smiling behavior, and objective reports from one’s friends and family (Norrish & Vella-Brodrick, 2008).

Nevertheless, many critics have opposed the concept of subjective wellbeing, including psychologist Michael Argyle (2001). Argyle states

“the main weakness of subjective measure is that they are affected by cognitive biases such as the effects of expectation and adaptation so that we don’t know how far to believe the scores”

However, other researchers have developed several well-validated scales for measuring happiness, supporting its’ validity as a scientific construct.

The Steen Happiness Index (Seligman, Steen, Park & Peterson, 2005)

Consists of twenty items. Participants read a series of statements and select the one that best describes how they are at the present time. Items indicate three kinds of ‘happy life’ – the pleasant life, the engaged life, and the meaningful life.

These dimensions will be explored closely very soon!

Subjective Happiness Scale (Lyubomirsky & Lepper, 1999)

Consists of four items to assess global subjective happiness. The participants read four statements, including ‘In general, I consider myself…’ and the individual then selects an item from 1 to 7 from, for example, ‘not a very happy person’ to ‘a very happy person’.

Test-retest and self-peer correlations have suggested good to excellent reliability, and construct validation studies of convergent and discriminant validity have confirmed the use of this scale to measure the construct of subjective happiness.

Happiness Scale (Fordyce, 1977)

This scale is also referred to as the Emotion Questionnaire as it assesses emotional wellbeing as an indication of perceived happiness. It is comprised of two items. The first is a scale measuring happiness/unhappiness by participants ranking descriptive phrases on a 0 – 10 scale.

The other item making up the test requires participants to give an approximate percentage of time that he/she feels happy, unhappy and neutral. The test has shown to have adequate reliability and validity.

Therefore, evidence from neuroscience, paired with evidence from the measurement of subjective wellbeing, or, happiness, suggest that a scientific explanation of happiness is, in fact, possible.

It is overwhelming to consider what happiness is… where to begin?! Happiness has been the topic of discussion and debate since the ancient Greek times.

In 1973, ‘Psychology Abstracts International’ began listing happiness as an index term (Diener, 1984). However, because happiness is a term that is used widely and frequently, it has various meanings and connotations (Diener, 1984).

The construct of happiness is still evolving, and although challenging to define, it is a construct that can be empirically evaluated through qualitative and quantitative assessment (Delle Fave, Brdar, Freire, Vella-Brodrick & Wissing, 2011). Delle Fave and colleagues (2011) noted that happiness is also an ambiguous term which can have a number of meanings:

• A transient emotion (that is synonymous with joy)
• An experience of fulfillment and accomplishment (characterized by a cognitive evaluation)
• A long-term process of meaning-making and identity development through achieving one’s potential and the pursuit of subjectively relevant goals.

Historically, since the days of Aristotle, happiness has been conceptualized as being composed of at least 2 aspects – hedonia (or, pleasure) and eudaimonia (a sense that a life is well-lived) (Kringelbach & Berridge, 2010).

Research has shown that, whilst these two aspects are definitely distinct, that in ‘happy’ people, both hedonic and eudaimonic components of happiness correspond (Kringelbach & Berridge, 2010).

A study by Kesebir and Diener (2008) report that in happiness surveys, more than 80% of interviewees rated their overall ‘eudaimonic’ life satisfaction as “pretty to very happy” and, at the same time, 80% of the people interviewed also rate their current, hedonic ‘mood’ as positive (e.g. giving a rating of 6-7 on a 10-point valence scale, where 5 is ‘hedonically neutral’).

Moving forward into the modern era, there is some agreement about the aspects that make up theories of happiness. There are, according to Haybron (2003), when looking at theories of happiness, 3 basic views:

• Hedonism – in other words, to be happy is to experience, on the whole, a majority of pleasure. Hedonia.
• Life-satisfaction view – to be happy is to have a favorable attitude about one’s life as a whole, either over its entirety or just over a limited period of time. Eudaimonia.
• Affective state theory – that happiness depends on an individual’s overall emotional state.

Other theories of happiness are so-called ‘hybrid’ theories that combine the life satisfaction theory with other hedonistic or affective-state theories (Haybron, 2003). One of these hybrid theories is the one that is the most widely accepted theory of happiness: subjective wellbeing (Haybron, 2003). Subjective wellbeing is considered to be a more scientific term than happiness.

A closer look at hedonia

Hedonism has a long history (Ryan & Deci, 2001). Science has looked closely at happiness as ‘hedonically’ defined – or, in other words, the pursuit of pleasure over pain (Ryan & Deci, 2001). Aristippus, a Greek philosopher from the 4th century BC claimed happiness was the sum of life’s ‘hedonic’ moments (Ryan & Deci, 2001).

Hedonic enjoyment is a state whereby an individual feels relaxed, has a sense of distance from their problems and, can be said to feel ‘happy’ (Ryan & Deci, 2001).

Hedonia refers, in simple terms, to the pursuit of pleasure. It was argued by Hobbes that happiness is found in the successful pursuit of our human appetites, and DeSade went on to say that the pursuit of sensation and pleasure is the ultimate goal of life (Ryan & Deci, 2001).

The Utilitarian philosophers, including Bentham, put forth the argument that a good society is one which is developed out of individuals attempting to maximize pleasure and pursue self-interest (Ryan & Deci, 2001).

It should be clarified that hedonia, in respects to happiness, does not have the same meaning as physical hedonism: happiness can come not only from short-term pleasure, but can also arise from achieving goals or other valued outcomes (Ryan & Deci, 2001). So-called hedonic psychologists are of the belief that happiness can include the preferences and pleasures of the mind, as well as the body (Ryan & Deci, 2001).

Kahneman (1999) defined hedonic psychology as the study of “what makes experiences and life pleasant and unpleasant” (p. ix). Within the framework of hedonic psychology, the terms wellbeing and hedonism are used interchangeably (Ryan & Deci, 2001). Hedonic psychology explains wellbeing in terms of pleasure versus pain, and it, therefore, becomes the center of much research and also interventions that principally aim to enhance human happiness (Ryan & Deci, 2001).

Hedonic psychology has been a focus of the theory of happiness, in part, due to the links between hedonia and other dominant theories. For example, hedonia ties in with behavioral theories of reward and punishment, as well as theories that focus on the cognitive expectations of the outcomes of reward and punishment (Ryan & Deci, 2001).

Despite there being a variety of ways to consider the human experience of pleasure/pain, the majority of research in hedonic psychology looks into the assessment of subjective wellbeing. To introduce the term, briefly, subjective wellbeing (or ‘happiness’) consists of three components (Ryan & Deci, 2001):

• Life satisfaction
• The presence of a positive mood
• The absence of a negative mood

Elsewhere in this website, you can read more about eudaimonia and the Aristotelian view of happiness . For the purpose of exploring theories of happiness, I will briefly look at eudaimonia now:

What is eudaimonia? (The life satisfaction view of happiness)

Aristotle argued that, because of man’s unique capacity to reason, pleasure alone cannot achieve happiness – because animals are driven to seek pleasure, and man has greater capacity than animals (The Pursuit of Happiness, 2018).

In striving for happiness, the most important factor is for a person to have ‘complete virtue’ – in other words, to have good moral character (Pursuit of Happiness, 2018).

Eudaimonia was, according to Aristotle, “activity expressing virtue” that will therefore lead to a happy life. Aristotle proposed that happiness was neither virtue, or pleasure, but rather the exercise of virtue.

The argument taken by the Aristotelian view is that happiness, per se, is not the principal criterion of wellbeing (Ryan & Deci, 2001). Proponents of this view see wellbeing as achieved by people living in accordance with the ‘daimon’ (true self). (Ryan & Deci, 2001). Eudaimonic theories of happiness argue that rather than the pursuit of pleasure, happiness is the result of the development of individual strengths and virtues (Norrish & Vella-Brodrick, 2008).

The theory of eudaimonic happiness has its basis in the concept of the self-actualising individual (proposed by Maslow ) and the concept of the ‘fully functioning person’ (Rogers) (Norrish & Vella-Brodrick, 2008). Many modern scientific explanations of happiness are conducive with the theory of eudaimonic happiness.

For example, Waterman suggested that happiness is enhanced by people acting in accordance with their most deeply held values (Norrish & Vella-Brodrick, 2008). Waterman also introduced the term ‘personal expressiveness’ to describe the state of authenticity that occurs when people’s activities reflect their values.

The eudaimonic theory of happiness adopts the Self-Determination Theory to conceptualize happiness (Deci & Ryan, 2000). This theory argues that fulfillment in the areas of autonomy and competence will enhance happiness. In other words, this view suggests that subjective wellbeing (i.e. happiness) can be achieved through engaging in eudaimonic pursuits (Norrish & Vella-Brodrick, 2008).

Affective state theory

To recap, this theory of happiness proposes that happiness is the result of one’s overall emotional state. Bradburn (1969) put forward the argument that happiness is made up of two separate components that are quite independent and uncorrelated: positive affect and negative affect. According to Bradburn, happiness is a global judgment people make by comparing their negative affect and positive affect (Diener, 1984).

This led to the development of the Affect Balance Scale (Diener, 1984). The Bradburn Affect Balance Scale is a self-report measure of the quality of life. The scale is made up of descriptions of ten mood states (for example, item one is feeling “particularly excited or interested in something”), and the subject reflects upon whether they have been in that mood state during the last week.

A measure of the quality of life, as an indication of happiness, is derived by the sum of the ‘negative’ items are taken away from the sum of the ‘positive’ items (Diener, 1984).

Affect state theory also takes the view that the absence of negative affect is not the same thing as the presence of positive affect (Diener, 1984).

Theories developed by positive psychologists

The discipline of positive psychology has developed some unique theories of happiness. For example, Seligman (2002) introduced the Authentic Happiness theory. This theory is based around the notion that authentic happiness results from a person living according to their ‘signature strengths’ which develop as people become aware of their own personal strengths and take ownership of them (Seligman, 2002).

Another theory of happiness is Csikszentmihalyi’s ‘flow’ theory. Flow may be defined as “ the state of engagement, optimal happiness, and peak experience that occurs when an individual is absorbed in a demanding and intrinsically motivating challenge ” (Norrish & Vella-Brodrick, 2008, p. 395). This state of engagement has been proposed to be a pathway to happiness (Norrish & Vella-Brodrick, 2008).

Some psychologists suggest that perhaps, in fact, happiness is relative – or, in other words, it is an evaluation of subjective judgments about one’s situations, comparing others’ situations to one’s own or even one’s earlier situations, goals or aspirations (Norrish & Vella-Brodrick, 2008). This argument has, however, been refuted.

Veenhoven explains that comparison may affect the cognitive or life-satisfaction aspects of happiness, but that the affective component results from hedonic experience (meeting one’s fundamental needs) and is therefore quite separate of any comparisons (Norrish & Vella-Brodrick, 2008).

To summarise these related topics – the scientific explanation of happiness and the theory and science of happiness – there are a number of theories conceptualizing happiness and in keeping with these theories, the term can have slightly different meanings.

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Way back in 1929, Walter A. Pitkin wrote ‘ The Psychology of Happiness ’ and in this book, he differentiated between happiness and related emotions including pleasure and enjoyment (Samuel, 2019). He argued that achieving happiness was not merely the result of luck or chance. Since this time, psychologists have continued to try and define happiness.

According to psychology, happiness is about more than simply the experience of a positive mood. In order to describe happiness, psychologists commonly refer to subjective wellbeing (Kesebir & Diener, 2008). In other words, happiness is “ people’s evaluations of their lives and encompasses both cognitive judgments of satisfaction and affective appraisals of moods and emotions ” (Kesebir & Diener, 2008, p. 118).

The psychological inquiry into happiness is important because happiness is not only associated with improved physical health and even longevity, but it is also a priority for people – across the world, happiness has been rated as being more important than other desirable outcomes including living a meaningful life or making a lot of money (Psychology Today, 2019).

There are three ways that psychologists study happiness:

1. Need and goal satisfaction theories

These theories suggest that happiness results from striving to achieve appropriate goals and meeting one’s fundamental human needs (Nelson, Kurtz & Lyubomirsky, in press). Deci and Ryan (2000) for example, proposed Self-determination Theory, which stipulates that wellbeing is achieved when one meets their basic human needs including autonomy, competence, and relatedness.

2. Genetic and personality predisposition theories

These propose that wellbeing is influenced by genes, and is associated with the personality traits of extraversion and neuroticism (Nelson et al., in press). This, in turn, implies that wellbeing does not change much over time.

3. Process/activity theories

Process/activity theories argue that wellbeing may be improved by participating in activities that are engaging and require effort (Nelson et al., in press).

Psychologists ask the question, ‘is it possible to increase one’s happiness?’. Some psychologists claim that making an attempt to enhance happiness is pointless because happiness levels are predetermined and stable over time (Norrish & Vella-Brodrick, 2008).

Consistent with this argument is the happiness set point. The happiness set point argues that a person’s state of happiness will be constant over time, regardless of changes in circumstances (Norrish & Vella-Brodrick, 2008).

Adapting to environmental changes is termed ‘the hedonic treadmill ’ or ‘homeostatic control’ (Norrish & Vella-Brodrick, 2008). This notion of adaptation (leading to relatively stable levels of happiness) is supported by findings in research that individuals who may be high in either positive or negative affect (e.g. lottery winners, paralysis victims) demonstrate that their happiness levels revert to their ‘usual’ range after a period of time (Norrish & Vella-Brodrick, 2008).

Some psychologists argue that the happiness set point provides evidence that happiness cannot be enhanced (Norrish & Vella-Brodrick, 2008). There is a perspective taken by some psychologists that happiness is a ‘trait’ or a personal disposition to experience a certain affect.

This perspective suggests that happiness is relatively stable over time, and therefore efforts to increase happiness are futile (Norrish & Vella-Brodrick, 2008). However, research has shown that although subjective wellbeing may be associated with personality traits (e.g. extraversion), that differences in reports of happiness levels over time suggest that, in fact, happiness is not a trait (Norrish & Vella-Brodrick, 2008).

Thus, happiness has been an important area of focus for psychologists. What, then, about the more recent science of happiness…positive psychology?

Positive psychology can be described as a psychology of potential, and what ‘could be’ as compared to what ‘is’ (Seligman & Csikszentmihalyi, 2000). It aims to shift what has historically been the predominant focus of psychology – pathology – to examining the development of positive qualities in individuals and communities (Seligman & Csikszentmihalyi, 2000).

In other words, Positive Psychology aims to understand and cultivate the factors that put individuals, communities, and societies in a position where they are able to ‘flourish’ (Fredrickson, 2001).

What does it mean to ‘flourish’? Put simply, it is a state of optimal wellbeing (Fredrickson, 2001). Fredrickson (2001) asked the question “ What role do positive emotions play in positive psychology? ”

Well, as it turns out, happiness can be thought of as experiencing predominantly positive emotions , or affective states, rather than negative ones (Tkach & Lyubomirsky, 2006). Thus, positive emotions are a sign of flourishing, or, in other words, happiness (Fredrickson, 2001). Happiness is central to the assumptions of positive psychology.

Seligman (2011) described the PERMA model of flourishing. This model defines psychological wellbeing in terms of 5 domains:

• P ositive emotions
• E ngagement
• R elationships
• A ccomplishment

For more detail on flourishing and how to achieve it, check out our article on Seligman’s PERMA+ model .

Let’s look at some interesting happiness research! In a large random-assignment experiment, Seligman and colleagues (2005) operationalized then evaluated 5 different happiness interventions.

They found that two of the interventions – writing about three good things the person had experienced each day and why they occurred, and using ‘signature strengths’ in a novel way – made people happier, and less depressed up to six months later! Compared to participants who engaged in the intervention, those in the placebo control group returned to the baseline levels of happiness and depression symptoms after just one week!

Lyubomirsky and colleagues (2006) conducted three studies examining the effects of writing, talking and thinking about significant life events – ‘triumphs and defeats’. While the majority of psychological research has focused on the way in which negative life circumstances are processed and managed, this unique study looked at the processing a positive life experience (Lyubomirsky, Sousa & Dickerhoof, 2006). This aspect of the study involved participants reflecting on their happiest day.

The researchers found that when participants thought while ‘replaying’ their happiest moment, it resulted in enhanced personal growth, improvements in general health and physical functioning, as well as lower pain levels, compared to the outcomes if the person was writing while analyzing their happiest moments.

The findings of the study suggest that people should be advised against over-analyzing or trying to make sense of a happy experience. Rather, Lyubomirsky and associates suggest that individuals should feel content in reliving and savoring happy experiences rather than trying to understand their meanings or causes.

Even though the experience of happiness is related to greater wellbeing and psychological health, in fact, some studies have shown that the desire to feel happy in an extreme form, or even simply placing a high value on happiness, can be detrimental in terms of wellbeing. In fact, in a research study by Ford and colleagues (2014), it was found that the emphasis placed upon attaining happiness can present a risk factor for symptoms and even a diagnosis of depression.

In a study of 181 participants, Sheldon et al. (2010) conducted a 6-month longitudinal experiment that sought to increase the happiness levels of those in the ‘treatment’ condition. The treatment group set goals to increase their feelings of autonomy, competence or relatedness in life while the comparison group set out to improve their life experiences.

In fact, it was found that those individuals in the treatment group had sustained increases in happiness (Sheldon et al., 2010). However, this gain lasted only while the individuals were actively engaged with the goals.

Interestingly, those who initially had a positive attitude towards change in happiness experienced greater benefits from the treatment! (Sheldon et al., 2010).

What, do you ask, is the Happiness Research Institute ? Well, it is an independent ‘think tank’ developed to investigate the reasons that some societies are happier than others.

The Happiness Research Institute aims to provide relevant parties with up-to-date information about the origins and effects of happiness, as well as to draw attention to subjective wellbeing as an important area for public policy debate. Furthermore, the Institute aims to improve the quality of life of all people.

The Happiness Research Institute provides knowledge, consultancy, and presentations. An example of the knowledge-building activities carried out by the Institute was that, in 2018, the Happiness Research Institute, in conjunction with the Nordic Council of Ministers compiled a study that was called ‘In the shadow of happiness’.

The study examined the reasons why some people living in Nordic countries are happy whilst others are suffering or struggling. The research also involved an analysis of why some groups within this cluster are struggling more often, and the impact this has on society.

In terms of consultancy, the Happiness Research Institute has also worked with groups including the Danish government, the Minister of State for Happiness in the United Arab Emirates (UAE) and the city of Goyang in South Korea. The aim of these partnerships is to improve quality of life and wellbeing of citizens.

Presentations by the Happiness Research Institute have taken place globally and featured at more than 1000 international events to share knowledge about what drives happiness, wellbeing, and quality of life.

The Happiness Research Institute analyses the somewhat separate components of the different cognitive, affective and eudaimonic dimensions of happiness, wellbeing and quality of life in order to explore these complex concepts. As previously explained, the cognitive dimension refers to the appraisal of overall life satisfaction, while the affective dimension focuses on the emotions that people experience on a daily basis.

Finally, the eudaimonic dimension looks at Aristotle’s perception of the ‘good life’ and is centered on purpose and meaning.

The reason that the Happiness Research Institute measures happiness is in order to shift policy priorities and therefore try and improve quality of life in societies, that will facilitate, in turn, the achievement of goals such as longevity and productivity. The Institute focuses not on the factors that cannot be changed (i.e. genetics, biology) but rather policies (that can be changed over time) and behavior (that can be changed immediately).

By examining the policies related to overall life satisfaction (i.e. the cognitive dimension of happiness) the Happiness Research Institute can explain 75% of the variance between more than 150 countries which were included in the 2018 World Happiness Report. The Institute also hopes to highlight the overlooked dimension of inequality in wellbeing, and increase the awareness and understanding of this inequality. The Happiness Research Institute is accessible via Twitter, Facebook, and LinkedIn, and Meik Wiking is the CEO.

Professor Paul Dolan was coined ‘the happiness professor’ in The Telegraph in July, 2018. Professor Dolan is the Professor of Behavioural Science at the London School of Economics and Political Science. He is a leading expert in the fields of human behaviour and happiness.

Prof Dolan wrote the best-selling book , Happiness by Design and, more recently, Happy Ever After . His work is centred around two themes:

• The development of measures of happiness and subjective wellbeing that can then be used in policy, and by individuals who are looking to be happier.
• Utilising work from behavioural science that can be used to understand and change individual behaviour, and contribute more to this evidence base.

What would positive psychology be without its founding fathers , and other famous contributors?

Martin Seligman:

Dr. Seligman was born in 1942, and is credited as being the ‘father of Positive Psychology’ (The Pursuit of Happiness, 2018). Seligman suggests that there are three kinds of happiness:

• Pleasure and gratification
• Embodiment of strengths and virtues
• Meaning and purpose

One can remember that, as discussed earlier, happiness – or, subjective wellbeing – had three similar, distinct components like Seligman suggested. In his book , Authentic Happiness: Using the new positive psychology to realize your potential for lasting fulfillment , Seligman (2002) says:

‘[Positive Psychology] takes you through the countryside of pleasure and gratification, up into the high country of strength and virtue, and finally to the peaks of lasting fulfillment: meaning and purpose’

Seligman also wrote a book titled Learned Optimism: How to Change Your Mind and Your Life . He is an acclaimed author, and psychologist, also known for his work on ‘learned helplessness’ which has been popular within the discipline of psychology.

Michael W. Fordyce

Fordyce (December 14, 1944 – January 24, 2011) was a pioneer in the subject of happiness research (Friedman, 2013). In 1977, in the journal Social Indicators Research, the Fordyce Happiness Scale was published. In his multitude of research, Fordyce demonstrated that happiness can be measured statistically, and that also, by engaging in ‘volitional behavior’, happiness can also be deliberately increased (Friedman, 2013).

Diener was born in 1946, and is also known as ‘Dr. Happiness’ (Pursuit of Happiness, 2018). He is a leading researcher in the field of positive psychology. Diener is perhaps best known for coming up with the term “subjective wellbeing”, which is the component of happiness that can be empirically measured (Pursuit of Happiness, 2018). Diener believes that happiness has a strong genetic component, and thus is relatively stable. He also developed the Satisfaction with Life Scale.

Sonja Lyubomirsky

Lyubomirsky is a research psychologist who writes the Psychology Today blog titled ‘ The How of Happiness ’ (Sonja Lyubomirsky, 2019). She is a professor and vice chair at the University of California, Riverside. Lyubomirsky is the author of two books : The How of Happiness , and The Myths of Happiness .

Daniel Gilbert

Gilbert, a social psychologist, is also referred to as Professor Happiness at Harvard University (Dreifus, 2008). He is in charge of a laboratory that has been set up to investigate the nature of happiness. Gilbert’s main work centres around the fact that relationships with family and friends, and that the time spent investing in these social relationships contribute more to happiness than material possessions (Dreifus, 2008).

He suggests that more pleasure can be found in experiences, rather than goods or objects – perhaps, he argues, because experiences can be shared with others whereas possessions are generally not shared (Dreifus, 2008).

The psychology of happiness – WOBI

Research has suggested that there might be a causal relationship between positive affect and success … that not only does success bring happiness but, interestingly, that a happy person is more likely to achieve success (Psychology of Happiness, 2019). These three articles provide an account of success and happiness:

• Boehm, J. K., & Lyubomirsky, S. (2008). Does happiness promote career success? Journal of Career Assessment, 16 , 101–116.
• Lyubomirsky, S., King, L., & Diener, E. (2005). The benefits of frequent positive affect: Does happiness lead to success? Psychological Bulletin, 131 , 803–855.
• Uusiautti, S. (2013). On the positive connection between success and happiness. International Journal of Research Studies in Psychology , 1–12.

[Reviewer’s update:

Since this post was originally published, additional research has come out suggesting that the original theory at the heart of Uusiautti’s (2013) research doesn’t seem to hold true. As a replacement, you may want to check out the article by Okabe-Miyamoto et al. (2021), who recently found that increasing the variety of experiences to escape the hedonic treadmill may actually result in smaller boosts in wellbeing – not larger ones.]

In recent times, a wealth of research has been published into the topic of happiness, such as:

• Diener, E., Heintzelman, S. J., Kushlev, K., Tay, L., Wirtz, D., Lutes, L. D., & Shigehiro, O. (2017). Findings all psychologists should know from the new science on subjective well-being. Canadian Psychologist, 58 , 87 – 104
• Oerlemans, W. G. M., & Bakker, A. B. (2018). Motivating job characteristics and happiness at work: A multilevel perspective. Journal of Applied Psychology, 103 , 1230 – 1241.
• Kaufman, M., Goetz, T., Lipnevich, A. A., & Pekrun, R. (2018). Do positive illusions of control foster happiness? Emotion, September 20, no pagination specified .
• Hoffman, J., Gander, F., & Ruch, W. (2018). Exploring differences in well-being across occupation type and skill. Translational Issues in Psychological Science, 4 , 290 – 303.
• Piff, P. K., & Moskowitz, J. P. (2018). Wealth, poverty, and happiness: Social class is differentially associated with positive emotions. Emotion, 18 , 902 – 905.
• McGuirk, L., Kuppens, P., Kingston, R., & Bastian, B. (2018). Does a culture of happiness increase rumination over failure? Emotion, 18 , 755 – 764.
• Warr, P. (2018). Self-employment, personal values, and varieties of happiness-unhappiness. Journal of Occupational Health Psychology, 23 , 388 – 401.
• Liao, K Y-H, & Weng, C-Y. (2018). Gratefulness and subjective well-being: Social connectedness and presence of meaning as mediators. Journal of Counseling Psychology, 65 , 383 – 393.
• Blanke, E. S., Riediger, M., & Brose, A. (2018). Pathways to happiness are multidirectional: Association between state mindfulness and everyday affective experience. Emotion, 18 , 202 – 211.
• Fuochi, G., Veneziani, C. A., & Voci, A. (2018). Differences in the way to conceive happiness relate to different reactions to negative events. Journal of Individual Differences, 39 , 27 – 38.
• Weber, S., & Hagmayer, Y. (2018). Thinking about the Joneses? Decreasing rumination about social comparison increases well-being. European Journal of Health Psychology, 25 , 83 – 95.
• Felsman, P., Verduyn, P., Ayduk, O., & Kross, E. (2017). Being present: Focusing on the present predicts improvements in life satisfaction but not happiness. Emotion, 17 , 1047 – 1051.
• Tamir, M., Schwartz, S. H., Oishi, S., & Kim, M. Y. (2017). The secret to happiness: Feeling good or feeling right? Journal of Experimental Psychology: General, 146 , 1448 – 1459.
• Phillips, J., De Freitas, J., Mott, C., Gruber, J., & Knobe, J. (2017). True happiness: The role of morality in the folk concept of happiness. Journal of Experimental Psychology: General , 165 – 181.
• Chopik, W. J., & O’Brien, E. (2017). Happy you, healthy me? Having a happy partner is independently associated with better health in oneself. Health Psychology, 36 , 21 – 30.
• Gross-Manos, D., & Ben-Arieh, A. (2017). How subjective well-being is associated with material deprivation and social exclusion on Israeli 12-year-olds. American Journal of Orthopsychiatry, 87 , 274 – 290.

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Follow the links below to some intriguing research in PDF form!

• How Do Simple Positive Activities Increase Well-Being? – Sonja Lyubomirsky, Kristin Layous (Access here )
• The How, Why, What, When and Who of Happiness: Mechanisms Underlying the Success of Positive Activity Interventions – Kristin Layous & Sonja Lyubomirsky (Access here )
• Variety is the Spice of Happiness: The Hedonic Adaptation Prevention (HAP) Model – Kennon M. Sheldon, Julia Boehm, Sonja Lyubomirsky (Access here )
• Pursuing happiness: The architecture of sustainable change – Lyubomirsky, S, Sheldon, K M, Schkade, D (Access here )
• A measure of subjective happiness: Preliminary reliability and construct validation – Lyubomirsky, S, Lepper, HS (Access here )
• Will raising the incomes of all increase the happiness of all? – Richard A. Easterlin (Access here )
• Lottery Winners and Accident Victims: Is Happiness Relative? – Philip Brickman, Dan Coates, Ronnie Janoff-Bulman (Access here )

This article provides a snapshot of a huge topic which is, in fact, the overarching focus of positive psychology: happiness. It has been shown that subjective wellbeing is the closest thing to a scientific equivalent to happiness, which can be measured. The main feature of this article is that it has provided a range of resources which you can refer to in the future, including 16 key papers published in the last two years.

So, happiness… an elusive phenomenon, which we all seem to strive for. Hopefully this article has provided an overview of what is, undoubtedly, a very important issue. We all strive to be happier.

What is your understanding of happiness? What do you think makes happy people happy? Do you think that happiness can be measured, or, like some argue, do you think it is purely subjective?

What do you think about the recent articles shared? Please feel free to discuss this interesting topic further! I hope you have claimed some important take-home messages on happiness. Thanks for reading!

We hope you enjoyed reading this article. Don’t forget to download our three Happiness Exercises for free .

• Argyle, M. (2001). The Psychology of Happiness . Routledge.
• Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behaviour. Psychological Inquiry, 11 , 227 – 268.
• Delle Fave, A., Brdar, I., Freire, T., Vella-Brodrick, D., & Wissing, M. P. (2011). The eudaimonic and hedonic components of happiness: Qualitative & quantitative findings. Social Indicators Research, 100 , 185 – 207.
• Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95 , 542 – 575.
• Dreifus, C. (2008). The smiling professor. New York Times. Retrieved from https://www.nytimes.com/2008/04/22/science/22conv.html
• Ford, B. Q., Shallcross, A. J., Mauss, I. B., Floerke, V. A., & Gruber, J. (2014). Desperately seeking happiness: Valuing happiness is associated with symptoms and diagnosis of depression. Journal of Social and Clinical Psychology, 33 , 890 – 905.
• Fordyce, M. W. (1977). Development of a program to increase personal happiness. Journal of Counseling Psychology, 24 , 511 – 521.
• Fredrickson, B. L. (2001). The role of positive emotions in positive psychology. American Psychologist, 56 , 218 – 226
• Freud, S., & Riviere, J. (1930). Civilization and Its Discontents . New York: J Cape & H Smith.
• Friedman, H. L. (2013). The legacy of a pioneering happiness researcher: Michael W. Fordyce (Dec 14, 1944 – Jan 24, 2011). Journal of Happiness Studies, 14 , 363 – 366
• Happiness (2019). In Psychology Today. Retrieved from https://www.psychologytoday.com/au/basics/happiness
• Haybron, D. M. (2003). What do we want from a theory of happiness? Metaphilosophy, 34 , 305 – 329
• Kahneman, D. (1999). Objective happiness. In Well-being: The foundations of hedonic psychology. D. Kahneman, E. Diener, & N. Schwartz (Eds). USA: Russell Sage Foundation.
• Kesebir, P., & Diener, E. (2008). In pursuit of happiness: Empirical answers to philosophical questions. Perspectives on Psychological Science, 3 , 117 – 125.
• Kringelbach, M. L., & Berridge, K. C. (2010). The Neuroscience of Happiness and Pleasure. Social Research (New York) , 77, 659 – 678.
• Lyubomirsky, S. (2019). Sonja Lyubomirsky. Retrieved from http://www.sonjalyubomirsky.com/
• Lyubomirsky, S., & Lepper, H. S. (1999). A measure of subjective well-being: Preliminary reliability and construct validation. Social Indicators Research, 46 , 137 – 155.
• Lyubomirsky, S., Sousa, L., & Dickerhoof, R. (2006). The costs and benefits of writing, talking, and thinking about life’s triumphs and defeats. Journal of Personality and Social Psychology , 90, 692 – 708.
• Nelson, S. K., Kurtzy, J. L., & Lyubomirsky, S. (in press). What psychological science knows about happiness . In S. J. Lynn, W. O’Donohue & S. Lilienfeld (Eds.) Better, stronger, wiser: Psychological science and well-being. New York: Sage
• Norrish, J. M., & Vella-Brodrick, D. A. (2008). Is the study of happiness a worthy scientific pursuit? Social Indicators Research, 87 , 393 – 407.
• Okabe-Miyamoto, K., Margolis, S., & Lyubomirsky, S. (2021). Is variety the spice of happiness? More variety is associated with lower efficacy of positive activity interventions in a sample of over 200,000 happiness seekers.  The Journal of Positive Psychology.
• Psychology of Happiness (2019). Psychologist World. Retrieved from https://www.psychologistworld.com/emotion/psychology-of-happiness-positive-affect
• Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52 , 141 – 166.
• Samuel, L. R. (2019). The Psychology of Happiness (Circa 1929). Psychology Today. Retrieved from https://www.psychologytoday.com/au/blog/psychology-yesterday/201901/the-psychology-happiness-circa-1929
• Seligman, M. E. P. (2002). Authentic Happiness: Using the new Positive Psychology to realize your potential for lasting fulfillment . New York, NY: Free Press.
• Seligman, M. E. P. (2011). Flourish . New York, NY: Simon & Schuster.
• Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive Psychology: An introduction. American Psychologist, 55 , 5 – 14.
• Seligman, M. E. P., Steen, T. A., Park, N., & Peterson, C. (2005). Positive psychology progress: empirical validation of interventions. American Psychologist, 60 , 410 – 421
• Sheldon, K. M., Abad, N., Ferguson, Y., Gunz, A., Houser-Marko, L., Nichols, C. P., & Lyubomirsky, S. (2010). Persistent pursuit of need-satisfying goals leads to increased happiness: A 6-month experimental longitudinal study. Motivation and Emotion, 34 , 39 – 48.
• The Pursuit of Happiness (2019). Retrieved from https://www.pursuit-of-happiness.org
• Tkach, C., & Lyubomirsky, S. (2006). How do people pursue happiness? Relating personality, happiness-increasing strategies and well-being. Journal of Happiness Studies, 7 , 183 – 225.

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What our readers think.

I am impressed by the organization of ideas and materials on happiness. I would be interested to get more materials on happiness if you can supply me, with or refer me to some articles or books.

Hi Mohammad,

Thank you for your kind words and interest in learning more about happiness. I’m glad to hear that you found our resources helpful.

In addition to the article you mentioned, we have a wealth of resources on the psychology of happiness. Here are some additional articles that you may find useful:

– “ The Science of Gratitude: How It Improves Your Health and Happiness “: This article explores the benefits of practicing gratitude, including improved relationships, better physical health, and increased happiness. It also includes practical tips for cultivating gratitude in your daily life. – “ The Power of Positive Self-Talk: How It Can Improve Your Mental Health “: This article explores the benefits of positive self-talk, including increased self-esteem and reduced anxiety. It also provides practical tips for cultivating positive self-talk.

And here are some additional book recommendations on happiness: – “ The How of Happiness: A Scientific Approach to Getting the Life You Want ” by Sonja Lyubomirsky: This book is based on years of scientific research on the psychology of happiness and provides evidence-based strategies for increasing happiness and life satisfaction. – “ Stumbling on Happiness ” by Daniel Gilbert: This book explores the science of happiness and why humans often struggle to predict what will make them happy. Gilbert provides insight into the psychological processes that influence our happiness and offers practical tips for living a more fulfilling life.

Hope this helps! Kind regards, Julia | Community Manager

Were you happy while typing this article? How did you feel throughout the entire writing process?

in precise… PHENOMENAL SNAPSHOT.

Thank you for the snapshot on the concepts and theories of happiness . It is really helpful for my thesis writing.

Nice article, but incomplete . You should have discusses ed neurobiochemistry. How dopamine , endorphins serotonin & oxytocin are invested by nature in happiness circuitry. How have we evolved to incorporate release of these chemicals through daily activities

Thank you for this article. I’m sure that it’ll help me to defining happiness in my research.

This article is a really informative overview of Happiness, the subject that I believe is the most important driver of life advancement. Focusing on happiness and its pursuit as a positive discipline instead of focusing on ailments and pathologies that need to be “treated” or “cured” to find some happiness is the best approach. I recently published my book, “America, The Happy” addressing the pursuit happiness and its role in American life. I would have liked to have found this piece earlier, but I’ll reference it in my next one. Very good work.

Thank you so much for this overview it’s contributing greatly to my research into happiness.

A Happiness ‘Recipe’ In its rudiments a neuro-anatomy of happiness maps positive affective states of attentive arousal and pleasure to neurological processes, respectively the activity of dopamine and opioid systems. These systems can be hijacked by addictive drugs, but I submit that they can also be conjointly activated by simple cognitive protocols detailed below. This is achieved through opioid/dopamine interactions induced from concurrent contingencies that induce relaxation and attentive arousal. This simple, innocuous, and easily falsifiable procedure is in short a ‘recipe’ for happiness that conforms with commonplace notions that happiness is coextensive with a committed and meaningful life. My work is largely based on the latest iteration of incentive or discrepancy-based models of motivation representative of the work of Dr. Kent Berridge of the University of Michigan. Berridge is a renowned bio-behaviorist and neuroscientist who has contributed significantly to the neuroscience of happiness (see link below) and was kind to vet and endorse the little book I have linked below. My explanation and argument are tiered into three parts, for a lay audience (pp.7-52), an expanded academic version (pp.53-86), and a formal journal article published on the topic in the International Journal of Stress Management. The procedure is a variant of mindfulness practice but entails a new definition of mindfulness based on affective neuroscience. Still, all is moot if the procedure is ineffective. A brief summary of my argument In discrepancy models of motivation (or bio-behaviorism), affect is schedule dependent. VR (variable-ratio) schedules of reinforcement or reward (gaming, gambling, creative behavior) are characterized by moment to moment positive act-outcome discrepancy or uncertainty between what is expected and what actually happens, which parallels the release of the neuro-modulator dopamine that is felt a state of attentive arousal, but not pleasure. However, heightened pleasurable affect as well as heightened attentive arousal is also reported while performing under VR schedules, but only when the musculature is in a state of inactivity or relaxation. Relaxation induces the activity of mid-brain opioid systems and is felt as pleasure. Because dopamine and opioid systems can co-activate each other, concurrent contingencies which induce relaxation (mindfulness protocols) and attentive arousal (purposive or meaningful behavior) will result in a significant spike in affective tone as both dopaminergic and opioid activity will be much higher due to their synergistic effects. The procedure to do this, outlined on pp. 47-52, has several important characteristics. Behavior Analytic- no appeal to events outside of objective behavior. Simple – explained in five minutes, and refutable as quickly. Cognitive Behavioral – coheres to CBT principles, and is structured, brief, and rational. Also, as a layman (though academically trained in behavioral psychology, I am an executive for a tech company in New Orleans), I am most curious to see if this procedure is effective. Formal test is not at first necessary, but informal exposure is since the procedure is simple in aspect but possibly very useful in practice. (But again, I may be wrong!) https://www.scribd.com/doc/284056765/The-Book-of-Rest-The-Odd-Psychology-of-Doing-Nothing https://www.scribd.com/doc/121345732/Relaxation-and-Muscular-Tension-A-bio-behavioristic-explanation Berridge, Kringelbach article on the neuro-anatomy of happiness https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3008353/ And Holmes’ Article on Meditation and Rest from ‘The American Psychologist’ https://www.scribd.com/document/291558160/Holmes-Meditation-and-Rest-The-American-Psychologist

I am amazed at no mention of BROADEN AND BUILT THEORY by Barb Frederickson, nor of DR PAUL WONG’S POSITIVE PSYCHOLOGY 2. Thank you for your amazing work.

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From economic wealth to well-being: exploring the importance of happiness economy for sustainable development through systematic literature review

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• Published: 23 May 2024

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• Shruti Agrawal   ORCID: orcid.org/0000-0002-1620-9429 1 , 5 ,
• Nidhi Sharma 1 , 5 ,
• Karambir Singh Dhayal   ORCID: orcid.org/0000-0002-0000-4330 2 &
• Luca Esposito   ORCID: orcid.org/0000-0001-5983-6898 3 , 4  

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The pursuit of happiness has been an essential goal of individuals and countries throughout history. In the past few years, researchers and academicians have developed a huge interest in the notion of a ‘happiness economy’ that aims to prioritize subjective well-being and life satisfaction over traditional economic indicators such as Gross Domestic Product (GDP). Over the past few years, many countries have adopted a happiness and well-being-oriented framework to re-design the welfare policies and assess environmental, social, economic, and sustainable progress. Such a policy framework focuses on human and planetary well-being instead of material growth and income. The present study offers a comprehensive summary of the existing studies on the subject, exploring how a happiness economy framework can help achieve sustainable development. For this purpose, a systematic literature review (SLR) summarised 257 research publications from 1995 to 2023. The review yielded five major thematic clusters, namely- (i) Going beyond GDP: Transition towards happiness economy, (ii) Rethinking growth for sustainability and ecological regeneration, (iii) Beyond money and happiness policy, (iv) Health, human capital and wellbeing and (v) Policy push for happiness economy. Furthermore, the study proposes future research directions to help researchers and policymakers build a happiness economy framework.

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1 Introduction

Happiness is considered the ultimate goal of human beings (Ikeda, 2010 ; Lama, 2012 ). All economic, social, environmental and political human activities are aligned towards achieving this goal. This fundamental pursuit of human life introduces a new scope of research, namely the ‘happiness economy’ (Agrawal and Sharma 2023 ). The happiness economy is an emerging economic domain wherein many countries are working to envision and implement a happiness-oriented framework by expanding how they measure economic success, which includes wellbeing and sustainability (Cook and Davíðsdóttir 2021 ; Forgeard et al., 2011 ). The investigation of happiness, life-satisfaction and subjective well-being has witnessed increasing research interest across the disciplines- from psychology, philosophy, psychiatry, and cognitive neuroscience to sociology, economics and management (Diener 1984 ; Hallberg and Kullenberg, 2019 ).

In the post-Covid era, the world seeks an enormous transformation shift in the public system (Costanza 2020 ). However, public authorities need more time to realize such needs. To experience the ‘policy transformation’ within the coming few years, we require a paradigm shift that helps warm peoples’ hearts and minds. The new economic paradigm can penetrate the policy processes in advanced economies and every part of the world affected by the epidemic with the support of intellectuals, researchers, entrepreneurs and professionals.

OECD ( 2016 ) proposed a well-being economy framework to measure living conditions and people’s well-being. In 2020, developed countries like Finland, New Zealand, Iceland, Scotland and Wales have become members of the Wellbeing Economy Government (WEGo) (Abrar 2021 ). Since then, the network of government and international authorities across the globe has gained a quick momentum concerning an increasing tendency about a growing tendency to concentrate governmental decisions around human well-being rather than wealth and economic growth (Coscieme et al. 2019 ; Costanza et al. 2020 ).

In light of these circumstances, the purpose of this article is to describe the concept of a “happiness economy” or one that seeks to give everyone fair possibilities for growth, a sense of social inclusion, and stability that can support human resilience (Coyne and Boettke 2006 ). It provides a promising route towards improved social well-being and environmental health and is oriented towards serving individuals and communities (Skul’skaya & Shirokova, 2010 ). Moreover, the happiness economy paradigm is a transition from material production and consumption of commodities and services as the only means to economic development towards embracing a considerable variety of economic, social, environmental and subjective well-being dynamics that are considered fundamental contributors to human happiness (Atkinson et al., 2012 ; King et al., 2014 ; Agrawal and Sharma 2023 ). In following so, it reflects the ‘beyond growth’ approach that empathizes with the revised concept of growth, which is not centred around an increase in income or material production; instead it is grounded in the philosophy of achieving greater happiness for more people (Fioramonti et al. 2019a ).

Whereas the other critiques of economic growth emphasize contraction, frugality and deprivation, the happiness economy relies on a cumulative approach of humanity, hope and well-being, with a perceptive to build a ‘forward-looking’ narrative of ways for humans to live a happy and motivated life by inspiring the cumulative actions and encouraging policy-reforms in the measuring growth of an economy (Stucke 2013 ). Agrawal et al. ( 2023a , b ) explore the domain of happiness economics through a review of the various trends coupled with the future directions and highlight why it needs to be supported for a well-managed economic system and a happy society.

In this paper, we define a “happiness economy as an economy that aims to achieve the well-being of individuals in a nation, promoting human happiness, environmental up-gradation, and sustainability. Alternatively, as an economy where the wellbeing of people counts more than the goals of production and income”. Moreover, we have examined the existing body of research on the happiness economy and analyzed the emerging research themes related to rethinking the conventional approach to economic growth. We conclude by discussing how the happiness economy concept has been accepted so far and realizing its importance by triggering policy reforms at the societal level, by outlining potential future directions that might be included into the current national post-growth policies.

Various researchers and experts in the field of happiness economy support the idea that there is a lack of thorough studies related to the concept, definitions, and themes of the happiness economy model in the nations. This gap has motivated us to conduct a SLR in order to identify the evolution in the domain of happiness economy and to identify the emerging themes in this context. Therefore, this present study seeks to offer a holistic outline of the emerging research area of the happiness economy and helps to understand how the happiness economy can accelerate sustainable development. With the following research questions, this study seeks to give an all-encompassing review of this subject.

What is the annual publication trend in this domain and the most contributing authors, journals, countries etc?

Which themes and upcoming research areas are present in this field?

What directions will the happiness economics study field go in the future?

The SCOPUS database was used to achieve the above research objectives. We have selected 257 articles for examination by hand-selecting the pertinent keywords and going over each one. In the methods section, a thorough explanation of the procedures for gathering, reviewing, and selecting documents is provided.

The remainder of this paper is structured as follows; A thorough survey of the literature on the happiness economy is provided in Sect.  2 . The research approach employed in the study is presented in Sect.  3 . A thorough data analysis of the research findings is given in Sect.  4 . After discussing the results in Sect.  5 , Sect.  6 suggests areas for further research in this field. The study is summarised with a conclusion in Sect.  7 . Section  8 outlines the study’s limitation.

2 Literature review

The supporters of conventional economic growth proclaim that the material production of goods and services and consumption is vital to enhancing one’s living standards. The statement is true to some degree, mainly in countries of enormous deprivation. Some studies have found significantly less correlation between growth and happiness after fulfilling minimum threshold needs (Easterlin 1995 ; Kahneman and Krueger, 2006 ; Inglehart et al., 2008 ). These studies recommend that rather than concentrating solely on economic growth, governmental policy should give priority to non-economic aspects of human existence above a particular income level. According to some researchers, it is challenging to distinguish between the use and emissions of natural resources and economic growth (absolute decoupling) because of the interdependence between socioeconomic conditions and their biophysical basis (Wiedenhofer et al. 2020 ; Wang and Su, 2019 ; Wu et al., 2018 ). However, a shred of increasing evidence shows that it could be possible for humans to maintain a quality of life and a decent standard of living inside the ecological frontier of the environment, given that a contemporary perspective on the production and use of materials are adopted in conjunction with more fair wealth distribution (Millward-Hopkins et al. 2020 ; Bengtsson et al., 2018 ; Ni et al., 2022 ).

The scholarly discourse and institutional framework on the relationship between happiness and economic progress are synthesised in the happiness economy (Frey and Gallus 2012 ; Sohn, 2010 ; Clark et al., 2016 ; Easterlin, 2015 ; Su et al., 2022 ). From a happiness economy perspective, extreme materialism is unsustainable as it significantly impacts natural resources and hinders social coherence and individuals psychological and physical well-being (Fioramonti et al. 2022a ). Additionally, inequalities within countries have grown, while psychological suffering has increased, especially during accelerated growth (Vicente 2020 ; Galbraith, 2009 ). The modern world is witnessing anxiety, depression, wars, reduction of empathy, climate change, pandemics, loss of social bonds and other psychological disorders (Brahmi et al., 2022 ; Santini et al., 2015 ).

It has been scientifically proven that cordial human relations, care-based activity, voluntary activities and the living environment immensely impact a person’s health and societal well-being (Bowler et al. 2010 ; Keniger et al., 2013 ). Ecological economists demonstrated that free ecosystem services have enhanced human well-being (Fang et al. 2022 ). Social epidemiologists have long argued that an increase in inequalities has a negative influence on society while providing equality tends to improve significant objective ways of well-being, from healthier communities to happier communities, declining hate and crime and enhancing social cohesion, productivity, unity and mutual trust (Aiyar and Ebeke 2020 ; Ferriss, 2010 ).

From moving beyond materialistic growth, the happiness economy promotes, appreciates, and protects the environmental, societal, and human capital contributions that lead to cummalative well-being. In a happiness economy framework, a multidimensional approach is needed to evaluate the level of development based on the environmental parameters, health outcomes, as well as public trust, hope, value-creating education and social bonds (Agrawal and Sharma 2023 ; Bayani et al. 2023 ; Lavrov, 2010 ). Such factors have consistently been excluded from any traditional concept or assessment of economic growth. As a result, countries have promoted more industrial activities that deteriorate the authentic ways of human well-being and, hence, the foundations of economic progress.

An excess of production can create a detrimental effect on climate and people’s health, thereby creating a negative externality for society (Fioramonti et al. 2022b ). Moderation of output may be more efficient and desirable than hyper/over-production, as the former can reduce negative environmental externalities (e.g. waste, climate change) and create positive externalities (e.g. employment of the local resources and community) (Kim et al. 2019 ; Kinman and Jones, 2008 ). Moreover, people can also be productive in other contexts outside of the workplace, such as as volunteers, business owners, artists, friends, or members of the community (Fioramonti et al. 2022a ).

Various scholars and scientific research have established that the essential contributions to happiness in one’s life are made by natural surroundings, green and blue spaces, eco-friendly environment, healthy social relations, spirituality, good health, responsible consumption and value-creating education (Helliwell et al. 2021 ; Francart et al., 2018 ; Armstrong et al., 2016 ; Gilead, 2016 ; Giannetti et al., 2015 ). Unfortunately, existing conventional growth theories have ignored all these significant contributions. For example, GDP considers natural ecosystems as economically helpful only up until they are mined and their products are traded (Carrero et al. 2020 ). The non-market benefits they generate, such as natural fertilization, soil regeneration, climate regulation, clean air and maintenance of biodiversity, are entirely ignored (Boyd 2007 ; Hirschauer et al., 2014). The quality time people spend with their families and communities for leisure, educating future generations and making a healthy communal harmony is regarded meaningless, even in the event that they are important to enhance people’s well-being and, hence, to assist any dimension of economic engagement (Griep et al. 2015 ; Agrawal et al., 2020 ). Similarly, if an economy is focusing on people’s healthy lifestyle (for example, by providing comfortable working hours, improving work-life balance, emphasizing mental health, focusing on healthy food, reducing pollution, and promoting sustainable consumption), it is not considered in sync with the growth paradigm (Roy 2021 ; Scrieciu et al., 2013; Shrivastava and Zsolnai 2022 ; Lauzon et al., 2023 ).

Among the latest reviews, Bayani et al. ( 2023 ) highlight that the economics of happiness helps reduce the country’s financial crime by providing a livelihood that reduces financial delinquency. Chen ( 2023 ) highlights that smart city performance enhances urban happiness by adopting green spaces, reusing and recycling products, and controlling pollution. The study by (Agrawal and Sharma 2023 ) proposed a conceptual framework for a happiness economy to achieve sustainability by going beyond GDP. Similarly, Fioramonti et al. ( 2019b ) explored going beyond GDP for a transition towards a happy and well-being economy. The article by Laurent et al. ( 2022 ) has intensively reviewed the well-being indicators in Rome and proposed a conceptual framework for it.

Table  1 provides a thorough summary of the prior review studies about the happiness economy and its contribution to public policy and sustainable development.

3 Research methodology

In the current study, we have adopted an integrative review approach of SLR and bibliometric analysis of the academic literature to get a detailed knowledge of the study, which could also help propose future research avenues. The existing scientific production’s qualitative and quantitative context must be incorporated for a conclusive decision. The study by Meredith ( 1993 ) defines that SLR enables an “integrating several different works on the same topic, summarising the common elements, contrasting the differences, and extending the work in some fashion”. In the present study, the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) is applied to perform the SLR to follow systematic and transparent steps for the research methodology, as shown in Fig.  1 . The PRISMA technique includes the identification, screening, eligibility, and exclusion criteria parts of the review process.

Additionally, examples of the data abstraction and analysis processes are provided (Mengist et al. 2020 ; Moher et al., 2015 ). The four main phases of the PRISMA process are eligibility, identification, screening, and data abstraction and analysis. Because the PRISMA technique employs sequential steps to accomplish the study’s purpose, it benefits SLR research. Moreover, the bibliometric analysis helps summarise the existing literature’s bibliographic data and determine the emerging condition of the intellectual structure and developing tendencies in the specified research domain (Dervis 2019 ).

3.1 Identification

The step to conduct the PRISMA is the identification of the relevant keywords to initiate the search for material. Next, search strings for the digital library’s search services are created using the selected keywords. The basic search query is for digital library article titles, keywords, and abstracts. Next, a Boolean AND or OR operator is used to generate the search string (Boolean combinations of the operators may also be used).

There are different search databases to conduct the review studies, such as Scopus, Sage, Web of Science, IEEE, and Google Scholar. Among all the available search databases, we have used the Scopus database to identify the articles; since 84% of the material on Web of Science (WoS) overlaps with Scopus, very few authors have addressed the benefits of adopting Scopus over WoS (Mongeon and Paul-Hus 2016 ). Scopus is widely used by academicians and researchers for quantitative analysis (Donthu et al. 2021 ). It is the biggest database of scientific research and contains citations and abstracts from peer-reviewed publications consisting of journal research articles, books and conference articles (Farooque et al., 2019 ; Dhayal et al., 2022 ; Brahmi et al., 2022 ). The following search term was used: (TITLE-ABS-KEY (“happiness economy” OR “economics of happiness” OR “happiness in economy” OR “economy of happiness” OR “economy of wellbeing” OR “wellbeing economy” OR “wellbeing in economy” OR “beyond growth”). This process yields 380 artciles in the initial phase.

3.2 Screening

The second phase is completed by all identified articles from the Scopus database obtained from the search string in the identification phase. The publications are either included or excluded throughout the screening process based on the standards established by the authors and with the aid of particular databases. Exclusion and inclusion criteria are shown during the screening phase to identify pertinent articles for the systematic review procedure. The timeline of this study’s selected articles is from 1995 to 2023. The first article related to the research domain was published in 1995. The second criterion for the inclusion includes the types of documents. In the present research, the authors have regarded only peer-reviewed journals and review articles. Other types of articles, such as books, book chapters, conference articles, notes, and editorials, are excluded to maintain the quality of the review. The third inclusion and exclusion criterion is based on language. All the non-English language documents are excluded to avoid translation confusion; hence, only the English language articles are considered for the final review. After the screening process, 297 articles are obtained.

3.3 Eligibility

Articles are manually selected or excluded depending on specific criteria specified by the authors during the eligibility process. During the elimination process, the authors excluded the articles that did not fit into the scope of review after manual screening of the articles. Two hundred fifty-seven articles were selected after the eligibility procedure. These selected articles are carefully reviewed for the study by reviewing the titles, abstracts, and standards from earlier screening processes.

3.4 Data abstraction and analysis

Analysis and abstraction of data are part of the fourth step. Finally, 257 papers were taken into account for final review. After that, the studies are culled to identify pertinent themes and subthemes for the current investigation by thoroughly reviewing each article’s text. An integrative review is a form of study that combines mixed, qualitative, and quantitative research procedures. It is carried out as shown in Fig.  1 . R-studio Bibliometrix and VOSviewer version 1.6.18 were used to evaluate the final study dataset corpus of 257 articles. Since the Bibliometrix software package is a free-source tool programmed in the R language. It is proficient of conducting comprehensive scientific mapping. It also contains several graphical and statistical features with flexible and frequent updates (Agrawal et al. 2023a , b ).

Extraction of articles and selection process

This section provides an answer to the first research question, RQ1, by indicating the main information of corpus data, research publication trends, influential prolific authors, journals, countries and most used keywords, etc. (Refer to Tables  2 , 3 and 4 ) and (Refer to Figs.  2 , 3 , 4 , 5 and 6 ).

4.1 Bibliometric analysis

Table  2 shows the relevant information gathered from the publication-related details. It presents the cognitive knowledge of the research area, for instance, details about authors, annual average publication, average citations and collaboration index. By observing the rate of document publishing, the study illustrates how much has already been done and how much remains to be investigated.

The annual publication trend is shown in Fig.  2 . It is reflected that the first article related to happiness in an economy was released in the year 1995 when (Bowling 1995 ) published the article “What things are important in people’s lives? A survey of the public’s judgements to inform scales of health related quality of life” where the article discussed “quality of life” and “happiness” as an essential component of a healthy life. Oswald ( 1997 ) brought the concept of happiness and economics together and raised questions such as “Does money buy happiness?” or “Do you think your children’s lives will be better than your own?”. Eventually, the gross national product of the past year and the coming year’s exchange rate was no longer the concern; instead, happiness as the sublime moment became more accurate (Schyns 1998 ; Easterlin, 2001; Frey and Stutzer, 2005 ). Post-2013, we can see exponential growth in the publication trend, and the reason behind the growth is the report published by the “ Stiglitz-Sen-Fitoussi” Commission, which has identified limitations of GDP and questioned the metric of wealth, economic and societal progress. The affirmed questions have gained the attention of researchers and organizations, and thus, they have explored the alternatives to GDP. As a result, the “Organization for Economic Co-operation and Development” (OECD) have proposed a wellbeing framework. Some research work has significantly impacted that time, contributing to the immense growth in this research area (Sangha et al. 2015 ; Spruk and Kešeljević, 2015 ; Nunes et al., 2016 ).

Publication trend

Table  3 shows the top prolific journals concerning the topmost publications in the domain of happiness economy for the corpus of 257 articles, namely “International Journal of Environmental Research and Public Health”, “Ecological Economics”, “Ecological Indicators”, “Sustainability” and “Journal of Cleaner Production” with 5, 4, 4,4 and 4 articles respectively (Refer to Table  4 ). Moreover, the most influential journals with maximum citations are “Nature Human Behavior”, “Quality of Life Research”, “Journal of Applied Behavior Analysis”, “Journal of Cleaner Production” and “Ecological Economics”, with 219, 205, 186, 154 and 142 citations, respectively. “Journal of Cleaner Production” and “Ecological Economics” are highly prolific and the most influential journals in the happiness economy research domain.

Table  4 shows the most influential authors. Baños, R.M. and Botella, C. are the two most contributing authors with maximum publications. For the maximum number of citations, Zheng G. and Coscieme L. are the topmost authors for their research work. The nations were sorted according to the quantity of publications, and Fig.  3 showed where the top ten countries with the highest number of publications are listed originated. It can be seen from the figure that the United Stated has contributed the maximum publications, 66, followed by the United Kingdom with 41 articles, followed by Germany with 32 articles. It is worth noting that emerging nation such as India and China have also made significant contributions.

Top ten contributing countries

Figure  4 shows semantic network analysis in which the relationships between words in individual texts are performed. In the present study, we have identified word frequency distributions and the co-occurrences of the authors’ keywords in this study. We employed co-word analysis to find repeated keywords or terms in the title, abstract, or body of a text. In Fig.  5 , the circle’s colour represents a particular cluster, and the circle’s radius indicates how frequently the words occur. The size of a keyword’s node indicates how frequently that keyword appears. The arcs connecting the nodes represent their co-occurrence in the same publication. The greater the distance between two nodes, the more often the two terms co-occur. It can be seen that “happiness” is linked with “growth” and “life satisfaction”. The nodes of “green economy”, “ecological economics”, and “climate change” are in a separate cluster that shows they are emerging areas, and future studies can explore the relationship between happiness economy with these keywords.

Co-ocurrance of author’s keyword (Author’s compilation)

4.2 Thematic map analysis through R studio

The thematic analysis map, as shown in Fig.  5 , displays, beneath the author’s keywords, the visualisation of four distinct topic typologies produced via a biblioshiny interface. The thematic map shows nine themes/clusters under four quadrants segregated in “Callon’s centrality” and “density value”. The degree of interconnectedness between networks is determined by Callon’s centrality, while Callon’s density determines the internal strength of networks. (Chen et al. 2019 ). The rectangular boxes in Fig.  5 represent the subthemes under each topic or cluster that are either directly or indirectly connected to the major themes, based on the available research. In the upper-right quadrant, four themes have appeared, namely “circular economy”, “well-being economy”, “depression”, and “sustainable development”, they fall under the category of motor themes since they are extremely pertinent to the research field, highly repetitious, and well-developed. When compared to other issues with internal linkages but few exterior relations, “urban population” in the upper-left quadrant is seen as a niche concern since it is not as significant. This cluster may have affected the urban population’s happiness (Knickel et al. 2021 ). “Social innovation” is categorised as an emerging or declining subject with low centrality and density, meaning it is peripheral and undeveloped. It is positioned in the lower-left quadrant. Last but not least, the transversal and fundamental themes “happiness economy”, “subjective well-being”, and “climate change” in the lower-right quadrant are seen to be crucial to the happiness economy study field but are still in the early stages of development. As a result, future research must place greater emphasis on the quantitative and qualitative growth of the study area in light of the key themes that have been identified.

Thematic map analysis

4.3 Science mapping through cluster analysis

In the study, science mapping was conducted to examine the interrelationship between the research domains that could be intellectual (Aria and Cuccurullo 2017 ; Donthu et al. 2021 ). It includes various techniques, such as co-authorship analysis, co-occurrence analysis, bibliographic coupling, etc. We have used R-Studio for the study’s temporal analysis by cluster analysis. To answer RQ2, the authors have performed a qualitative examination of the emerging cluster themes through the science mapping of the existing research corpus of 257 articles by performing bibliographic coupling of documents. Bibliographic coupling analysis helps identify clusters reflecting the most recent research themes in the happiness economy field to illuminate the field’s current areas of interest.

The visual presentation of science mapping relied on VoSviewer version 1.6.18 (refer to Fig.  6 ). Five significant clusters emerged in this research domain (refer to Table  5 ). Going beyond GDP: Transition towards happiness economy, rethinking growth for sustainability and ecological regeneration, beyond money and happiness policy, health, human capital and wellbeing and Policy-Push for happiness economy. A thorough examination identified cluster analyzes has also assists us in identifying potential future research proposals. (Franceschet 2009 )

4.4 Cluster 1: Going beyond GDP: transition towards happiness economy

It depicts from the green colour circles and nodes, where seven research articles were identified with a common theme of beyond GDP that can be seen in Fig.  6 . Cook and Davíðsdóttir ( 2021 ) investigated the linkages between the alternative measure of the beyond growth approach such as a well-being economy prespective and the SDGs. They proposed a conceptual model of a well-being economy consisting of four capital assets interrelated with SDGs that promote well-being goals and domains. To extend the concept of going beyond GDP, various economic well-being indicators are being aligned with the different economic, environmental, and social dimensions to target the set goals of SDG. It is found that the “Genuine Progress Indicator” (GPI) is consider as the most extensive method that covers the fourteen targets among the seventeen’s SDG’s. Cook et al. ( 2022 ) consider SDGs to represent the classical, neoclassical and growth-based economy model and as an emerging paradigm for a well-being economy. The significance of GDP is more recognized within the goals of sustainable development.

GPI is considered an alternative indicator of economic well-being. On this basis, excess consumption of high-quality energy will expand macro-economic activity, which GDP measures. For such, a conceptual exploration of the study is conducted on how pursuing “Sustainable Energy Development” (SED) that can increase the GPI results. As the study’s outcome, according to the GPI, SED will have a significant advantage in implementing energy and environment policy and will also contribute to the advancement of social and economic well-being. Coscieme et al. ( 2020a ) explored the connection between the unconditional growth of GDP and SDG. The author considered that policy coherence for sustainable development should lessen the damaging effects of cyclic manufacturing on the ecosystem. Thus, the services considered free of charge in the GDP model should be valued as a component of society. Generally, such services include ecosystem services and a myriad of “economic” functions like rainfall and carbon sequestration. To work for SDG 8, defined by the “United Nations Sustainable Development Goals” (UNSDGs), a higher GDP growth rate would eventually make it more difficult to achieve environmental targets and lessen inequality. Various guidelines were proposed to select alternative variables for SDG-8 to enhance coherence among all the SDG and other policies for sustainability.

Fioramonti et al. ( 2019a ) state their focus is to go beyond GDP toward a well-being economy rather than material output with the help of convergence reforms in policies and economic shifts. To achieve the SDG through protecting the environment, promoting equality, equitable development and sharing economy. The authors have developed the Sustainable Well-Being Index (SWBI) to consolidate the “Beyond GDP” streams as a metric of well-being matched with the objectives to achieve SDG. The indicators of well-being for an economy have enough possibility to connect current transformations in the economic policies and the economy that, generally, GDP is unable to capture.

Fioramonti et al. ( 2022a ) investigate the critical features of the Wellbeing Economy (WE), including its various parameters like work, technology, and productivity. Posting a WE framework that works for mainstream post-growth policy at the national and international levels was the study’s primary goal. The authors have focused on building a society that promotes well-being that should be empowering, adaptable, and integrative. A well-being economic model should develop new tools and indicators to monitor all ecological and human well-being contributors. A multidimensional approach including critical components for a well-being economy was proposed that creates value to re-focus on economic, societal, personal, and natural aspects. Rubio-Mozos et al. ( 2019 ) conducted in-depth interviews with Fourth Sector business leaders, entrepreneurs, and academicians to investigate the function of small and medium-sized businesses and the pressing need to update the economic model using a new measure in line with UN2030. They have proposed a network from “limits to growth” to a “sustainable well-being economy”.

4.5 Cluster 2: Rethinking growth for sustainability and ecological regeneration

Figure  6 depicts it from blue circles and nodes, wherein four papers were identified. Knickel et al. ( 2021 ) proposed an analytical approach by collecting the data from 11 European areas to examine the existing conditions, difficulties, and anticipated routes forward. The goal of the study is to define the many ideas of a sustainable well-being economy and territorial development plans that adhere to the fundamental characteristics of a well-being economy. A transition from a conventional economic viewpoint to a broader view of sustainable well-being is centred on regional development plans and shifting rural-urban interactions.

Pillay ( 2020 ) investigates the new theories of de-growth, ecosocialism, well-being and happiness economy to break the barriers of traditional economic debates by investigating ways to commercialise and subjugate the state to a society in line with non-human nature. The significant indicator of Gross National Happiness (GNH) is an alternative working indicator of development; thus, the Chinese wall between Buddha and Marx has been built. They questioned the perspective of Buddha and Marx, whether they were harmonized or became a counter-hegemonic movement. In order to determine if the happiness principle is grounded in spiritual values and aligns with the counter-hegemonic ecosocialist movement, the author examined the ecosocialist perspective. Shrivastava and Zsolnai ( 2022 ) have investigated the theoretical and practical ramifications of creative organisations for well-being rooted in the drive for a well-being economy. Wellbeing and happiness-focused economic frameworks are emerging primarily in developed countries. This new policy framework also abolishes GDP-based economic growth and prioritizes individual well-being and ecological regeneration. To understand its application and interpretation, Van Niekerk ( 2019 ) develops a conceptual framework and theoretical analysis of inclusive economics. It contributes to developing a new paradigm for economic growth, both theoretically and practically.

4.6 Cluster 3: ‘Beyond money’ and happiness policy

It depicts pink circles and nodes, wherein five articles were identified, as shown in Fig.  6 . According to Diener and Seligman ( 2004a ) economic indicators are critical in the early phases of economic growth when meeting basic requirements is the primary focus. However, as society becomes wealthier, an individual’s well-being becomes less dependent on money and more on social interactions and job satisfaction. Individuals reporting high well-being outperform those reporting low well-being in terms of income and performance. A national well-being index is required to evaluate well-being variables and shape policies systematically. Diener and Seligman ( 2018 ) propounded the ‘Beyond Money’ concept in 2004. In response to the shortcomings of GDP and economic measures, other quality-of-life indicators, such as health and education, have been created. The national account of well-being has been proposed as a common path to provide societies with an overall quality of life metric. While measuring the subjective well-being of people, the authors reasoned a societal indicator of the quality of life. In this article, the authors have proposed an economy of well-being model by combining subjective and objective measures to convince policymakers and academicians to enact policies that enhance human welfare. The well-being economy includes quality of life indicators and life satisfaction, subjective well-being and happiness.

Frey and Stutzer ( 2000 ) perceived the microeconomic well-being variables in countries. In the study, survey data was used from 6000 individuals in Switzerland and showed that the individuals are happier in developed democracies and institutions (government federalization). They analyzed the reported subjective well-being data to determine the function of federal and democratic institutions on an individual’s satisfaction with life. The study found a negative relationship between income and unemployment. Three criteria have been employed in the study to determine happiness: demographic and psychological traits, macro- and microeconomic factors, and constitutional circumstances. Thus, a new pair of determinants reflects happiness’s effect on individuals’ income, unemployment, inflation and income growth.

Happiness policy, according to Frey and Gallus ( 2013b ), is an intrinsic aspect of the democratic process in which various opinions are collected and examined. “Happiness policy” is far more critical than continuing a goal such as increasing national income and instead considered an official policy goal. The article focuses on how politicians behave differently when they believe that achieving happiness is the primary objective of policy. Frey et al. ( 2014 ) explored the three critical areas of happiness, which are positive and negative shocks on happiness, choice of comparison and its extent to derive the theoretical propositions that can be investigated in future research. It discussed the areas where a more novel and comprehensive theoretical framework is needed: comparison, adaptation, and happiness policy. Wolfgramm et al. ( 2020 ) derived a value-driven transformation framework in Māori economics of wellbeing. It contributes to a multilevel and comprehensive review of Māori economics and well-being. The framework is adopted to advance the policies and implement economies of well-being.

4.7 Cluster 4: Health, human capital and wellbeing

It is depicted as a red colour circle and nodes in Fig.  6 , and only three papers on empirical investigations were found. Laurent et al. ( 2022 ) investigated the Health-Environment Nexus report published by the “Wellbeing Economy Alliance”. In place of increased production and consumption, they suggested a comprehensive framework for human health and the environment that includes six essential paths. The six key pathways are well-being energy, sustainable food, health care, education, social cooperation and health-environment nexus. The proposed variables yield the co-benefits for the climate, health and sustainable economy. Steer clear of the false perception of trade-offs, such as balancing the economy against the environment or the need to save lives. McKinnon and Kennedy ( 2021 ) focuses on community economics of well-being that benefits entrepreneurs and employees. They investigated the interactions of four social enterprises that work for their employees inside and within the broader community. Cylus et al. ( 2020 ) proposed the opportunities and challenges in adopting the model of happiness or well-being in an economy as an alternative measure of GDP. Orekhov et al. ( 2020 ) proposed the derivation of happiness from the World Happiness Index (WHI) data to estimate the regression model for developed countries.

4.8 Cluster 5: Policy-push for happiness economy

It is depicted as an orange circle and nodes in Fig.  6 , and only five papers on empirical and review investigations were found. Oehler-Șincai et al. ( 2023 ) proposed the conceptual and practical perspective of household-income-labour dynamics for policy formulation. It discusses the measurement of well-being as a representation of various policies focusing on health, productivity, and longevity. It focuses on the role of policy in building the subjective and objective dimensions of well-being, defines the correlation between well-being, employment policies, and governance, is inclined to the well-being performance of various countries, and underscores present risks that jeopardize well-being. Musa et al. ( 2018 ) have developed a “community happiness index” by incorporating the four aspects of sustainability—economic, social, environmental, and urban governance—as well as the other sustainability domains, such as human well-being and eco-environmental well-being. From then onwards, community happiness and sustainable urban development emerged. Chernyahivska et al. ( 2020 ) developed strategies to raise the standard of living for people in countries undergoing economic transition by using the quality of life index. The methods uncovered are enhancing employment opportunities and uplifting the international labour market in urban and rural areas, prioritizing human capital, eliminating gender inequality, focusing on improving the individual’s health, and enhancing social protection. Zheng et al. ( 2019 ) investigated the livelihood and well-being index of the population that makes liveable conditions and city construction in society based on people’s happiness index. The structure of a liveable city should be emphasised on sustainable development. The growth strategy in urban areas is an essential aspect of building a liveable city. Frey and Gallus ( 2013a ) criticised the National Happiness Index as a policy goal in a country because it cannot be measured and thus fails to measure the true happiness of people. To measure real happiness, the government should establish living conditions that enable individuals to become happy. The rule of law and human rights must support the process.

The structure of a liveable city should be emphasized in sustainable development. The growth strategy in urban areas is an essential aspect of building a liveable city. Frey and Gallus ( 2013a ) criticized the National Happiness Index as a policy goal in a country because it cannot be measured and thus fails to identify the true individuals happiness. To measure real happiness, the government should establish living conditions that enable individuals to be happy. The process needs to be supported by human rights and the rule of law.

Visualization of cluster analysis

5 Discussion of findings

Concerns like the improved quality-of-life and a decent standard of living within the ecological frontier of the environment have various effects on individuals overall well-being and life satisfaction. The ‘beyond growth’ approach empathized with the revised concept of growth, which is based on the idea of maximising happiness for a larger number of people rather than being driven by a desire for financial wealth or production. In that aspect, the notion of happiness economy is designed that prioritizes serving both people and the environment over the other. This present article has focused on the beyond growth approach and towards a new economic paradigm by doing bibliometric and visual analysis on the dataset that was obtained from Scopus, helping to determine which nations, publications, and authors were most significant in this field of study.

In this field of study, developed nations have made significant contributions as compared to the developing nations. In total, 59 countries have made the substantial contribution to the beyond growth approach literature an some of them have proposed their respective national well-being economy framwework. Among 59 countries the United States and the United Kingdom have been crucial to the publishing. With the exception of five of the top 10 nations, Europe contributes the most to scientific research. The existing research shows the inclination of developed and developing countries to build a new economic paradigm that goes beyond growth by prioritizing the happiness level at individual as well as at collective level.

The most prolific journals in this research domain are the “International Journal of Environmental Research” and “Public Health” with the total publication of 5 and 4. The top two cited journals were the “ Nature Human Behavior” with 219 citations and the “Quality of Life Research” with 205 citations. Due to various economic and non-economic factors, these journals struggled to strike a balance between scientific accuracy and timeliness, and it became vital to spread accurate and logical knowledge. For, example, discussing the relationship between inequality and well-being, exploring the challenges and opportunites of happiness economy in different countries, assessing the role of health in all policies to support the transition to the well-being economy. Visualization of semantic network analysis of co-ocurrance of authors keywords from the VOSviewer showed the future research scope to explore the association between happiness economy along with green economy, climate change, spirituality and sustainability. However, in the thematic mapping, the motor themes denotes the themes that are well-developed and repetative in research, such as, well-being economy, depression, sustainable development and circular economy. The basic themes depicts the developing and transveral themes such as happiness economy, subjective well-being and climate condition. As a result, future research must place greater emphasis on the theoretical and practical expansion of the research field in view of the determined major subjects.

The present study have performed the cluster analysis to identify the emerging research themes in this domain through VOSviewer that helps to analyze the network of published documents. Based on published papers, the author can analyse the interconnected network structure with the use of cluster analysis. We have identified the top five clusters from the study. Each cluster denote the specific and defined theme of the research in this domain. In cluster 1, the majorly of the authors are working in the area of going beyond GDP and transition towards happiness economy, which consists of empirical and review studies. Cluster 2 represents that authors are exploring the relationship between rethinking growth for sustainability and ecological regeneration to evaluate the transition from a conventional economic thought to a broader view of sustainable well-being which is centred on regional development plans and shifting rural-urban interactions. In cluster 3, the authors are exploring the beyond money and happiness policy themes and identified the shortcomings of GDP and economic measures, other quality-of-life indicators, such as health and education. They have proposed the well-being index to evaluate the well-being variables and shape socio-economic policies systematically. The authors have proposed an economy of well-being model by combining subjective and objective measures to convince policymakers and academicians to enact policies that enhance human welfare. The well-being economy includes quality of life indicators and life satisfaction, subjective well-being and happiness. In cluster 4, the authors are working of related theme of Health, human capital and wellbeing, whereby they have put up a comprehensive framework for health and the environment that includes several important avenues for prioritising human and ecological well-being over increased production and consumption. In cluster 5, the authors have suggested the policy-push for happiness economy in which they have identified the conceptual and practical perspective of household-income-labour dynamics for policy formulation. Majorly of the authors in this clutster have focused on the role of policy in building the subjective and objective dimensions of well-being, defines the correlation between well-being, employment policies, and governance, is inclined to the well-being performance of various countries, and underscores present risks that jeopardize well-being. Hence, the present study will give academics, researchers, and policymakers a thorough understanding of the productivity, features, key factors, and research outcomes in this field of study.

6 Scope for future research avenues

The emergence of a happiness economy will transform society’s traditional welfare measure. Such changes will generate more reliable and practical means to measure the well-being or welfare of an economy. After a rigorous analysis of the existing literature, we have proposed the scope for future research in Table  6 .

7 Conclusion

In 2015, the United Nations proposed the pathbreaking and ambitious seventeen “Sustainable Development Goals” (SDGs) for countries to steer their policies toward achieving them by 2030. In reality, economic growth remains central to the agenda for SDGs, demonstrating the absence of a ground-breaking and inspirational vision that might genuinely place people and their happiness at the core of a new paradigm for development. As this research has reflect, there are various evidence that the happiness economy strategy is well-suited to permeate policies geared towards sustainable development. In this context, ‘happiness’ may be a strong concept that ensures the post-2030 growth will resonate with the socioeconomic and environmental traits of everyone around the world while motivating public policies for happiness.

The current research has emphasized the many dynamics of the happiness economy by using a bibliometric analytic study of 257 articles. We have concluded that the happiness economy is an emerging area that includes different dimensions of happiness, such as ecological regeneration, circular economy, sustainability, sustainable well-being, economic well-being, subjective well-being, and well-being economy. In addition to taking into consideration the advantages and disadvantages of human participation in the market, a happiness-based economic system would offer new metrics to assess all contributions to human and planetary well-being. In terms of theoretical ramifications, we suggest that future scholars concentrate on fusing the welfare and happiness theory with economic policy. As countries are predisposed to generate disharmony and imbalance, maximizing societal well-being now entails expanding sustainable development. Since the happiness economy is still a relatively novel field, it offers numerous potential research opportunities.

8 Limitations

Similar to every other research, this one has significant restrictions as well. We are primarily concerned that all our data were extracted from the Scopus database. Furthermore, future research can utilize other software like BibExcel and Gephi to expound novel variables and linkages. Given the research limitations, this article still provides insightful and relevant direction to policymakers, scholars, and those intrigued by the idea of happiness and well-being in mainstream economics.

The study offers scope for future research in connecting the happiness economy framework with different SDGs. Future studies can also carry empirical research towards creating a universally acceptable ‘happiness economy index’ with human and planetary well-being at its core.

Data availability

Data not used in this article.

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Luca Esposito

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All authors contributed to the study conception and design. Shruti Agrawal: Conceptualization, Material preparation, Data Collection, Formal analysis, Methodology, Writing - Original Draft, Review and Editing. Nidhi Sharma: Validation, Project Administration, Supervision, and Writing - Review & Editing. Karambir Singh Dhayal: Validation, Formal analysis, Methodology, Writing - Review and Editing. Luca Esposito: Validation, Writing - Review and Editing. The first draft of the manuscript was written by Shruti Agrawal and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript

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Agrawal, S., Sharma, N., Dhayal, K.S. et al. From economic wealth to well-being: exploring the importance of happiness economy for sustainable development through systematic literature review. Qual Quant (2024). https://doi.org/10.1007/s11135-024-01892-z

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Tracking happiness in times of covid-19: a bibliometric exploration.

1. Introduction

2. materials and methods, 2.1. dataset extraction, 2.2. bibliometric analysis, 3. dataset analysis, 3.1. dataset overview, 3.2. sources, 3.3. authors, 3.4. analysis of the literature, 3.4.1. top 10 most cited papers—overview, 3.4.2. top 10 most cited papers—review, 3.4.3. words analysis, 3.5. mixed analysis, 4. discussions, 5. limitations, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Share and Cite

Jigani, A.-I.; Delcea, C.; Florescu, M.-S.; Cotfas, L.-A. Tracking Happiness in Times of COVID-19: A Bibliometric Exploration. Sustainability 2024 , 16 , 4918. https://doi.org/10.3390/su16124918

Jigani A-I, Delcea C, Florescu M-S, Cotfas L-A. Tracking Happiness in Times of COVID-19: A Bibliometric Exploration. Sustainability . 2024; 16(12):4918. https://doi.org/10.3390/su16124918

Jigani, Adina-Iuliana, Camelia Delcea, Margareta-Stela Florescu, and Liviu-Adrian Cotfas. 2024. "Tracking Happiness in Times of COVID-19: A Bibliometric Exploration" Sustainability 16, no. 12: 4918. https://doi.org/10.3390/su16124918

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• DOI: 10.60079/ahrmr.v2i1.246
• Corpus ID: 270052432

Exploring Comprehensive Approaches to Employee Health and Happiness

• Oki Hartanto
• Published in Advances in Human Resource… 31 January 2024
• Business, Psychology
• Advances in Human Resource Management Research

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27 Essential Employee Recognition Statistics [2023]: Need To Know Facts For Managers And HR

• Time Management Statistics
• Employee Wellness Statistics
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Research Summary. Almost everyone wants to be recognized for their work, whether they get paid to do it or not. Because of this, companies are realizing that recognizing employees is a vital part of keeping their workers happy, productive, and committed to the organization. Here are the key statistics on employee recognition:

29% of employees haven’t received recognition for good work in over a year, if at all.

80% of employees would work harder if they felt better appreciated.

Strong employee recognition programs reduce turnover rates by 31%.

Employees who are recognized are almost six times more likely to stay at their jobs than those who aren’t.

37% of employees report that the best way to improve their engagement is for their superiors to give them recognition.

92% of employees are likely to repeat a specific action if given recognition for it.

General Employee Recognition Statistics

37% of employees report that employee recognition is the most important thing their manager or company can do to motivate them to produce great work.

Recognition is by far the most popular motivator to do great work that employees listed, and it is followed by 13% of employees saying they are self-motivated, 12% saying they need inspiration to do great work, and 12% saying that autonomy is their biggest motivator.

80% of employees say they’re motivated to work harder when they’re recognized for their work by their superiors.

Employees who think they’ll be recognized are 2.7 times more likely to have a high engagement at work.

Employee Recognition Statistics By Retention

44% of surveyed employees who said they were planning to leave their jobs cited a lack of recognition as their main reason.

69% of employees planning to quit their jobs said that receiving recognition and rewards would cause them to choose to stay at their current positions.

A lack of recognition is the number three reason why employees leave their jobs.

Employees who don’t feel recognized are five times more likely to say they’re “extremely likely” to look for work elsewhere than employees who feel recognized.

Employee Recognition Statistics By Employee Opinions

Employees who feel recognized at work are 2.6 times more likely to say that they believe promotions at their workplace are fair.

39% of surveyed employees say they aren’t recognized enough at work.

29% of employees say they don’t receive recognition for their work.

Over a quarter of employees haven’t been recognized for their work, and 17% say they receive it annually, which isn’t much better.

20% say they’re recognized quarterly, 11% say they receive recognition weekly, and 2% say they do daily.

28% of employees say recognition from their manager is the most memorable.

This is followed by 24% who say the same about the recognition from a high-level company leader or CEO and 12% who say that from their manager ’s manager. Others say their most memorable recognition came from a customer (10%), their peers (12%), or another source altogether (17%).

Employee Recognition Statistics By Employer Benefits

The top 20% of companies that have strong cultures of employee recognition have 31% lower voluntary turnover rates than their competitors.

Just improving recognition by 15% in a company can increase its margins by 2%.

Organizations with employee recognition programs see employee performance, engagement, and productivity increase by 14% more than those that don’t have these programs.

Happy workers are 13% more productive than unhappy workers.

Companies with good employee recognition strategies are 48% more likely to have high employee engagement rates.

Employee Recognition Statistics By Methods

On average, companies budget 2% of payroll for employee recognition and reward programs.

Annually, businesses spend about $3.25 billion on merchandise for employees.

Tenure-based recognition makes up 87% of companies’ recognition programs.

65% of employees prefer non-cash incentives from their employers.

It should be noted that these employees answered this way when their incentives and awards were personally meaningful to them, so they aren’t just saying that they would rather have a $50 Bluetooth speaker than $50 in cash.

46% of employees say that an unexpected reward like a snack, lunch, or thank you note from their bosses would make them feel more appreciated.

Employee Recognition Statistics FAQs

What percentage of employees feel recognized at work?

60% of employees feel recognized at work. Of these, 15% say they’re recognized almost frequently enough, 42% say they’re recognized just the right amount, and 3% say they’re recognized too frequently.

Even though 60% is more than half of all employees, just 45% say they feel they’ve received enough or too much recognition from their workplaces . This is too low, especially since such a large percentage of employees don’t feel recognized.

For employers who are worried about going overboard with their recognition efforts, having just 3% of employees think that you do too much to recognize their work is a fair price to pay for having the remaining 97% feel appreciated and motivated to do more good work.

How do you measure employee recognition?

You measure employee recognition by asking employees if they feel recognized. While you can measure some amount of employee recognition by looking at how many programs the company has in place to show appreciation to its employees, those numbers don’t matter if the employees still feel unseen.

A company could spend millions on gifts, parties, and awards for its employees, and while that is a measure of how seriously the company takes employee recognition, it doesn’t do any good if employees don’t feel recognized and appreciated.

What percentage of companies have recognition programs?

80% of companies have recognition programs. The type of recognition these programs offer can vary greatly, as each company will recognize their employees for different actions and reward them in different ways.

For example, 87% of recognition programs involve rewarding employees for tenure. Others recognize employees for innovation, excellent work, or breaking records.

Why is employee recognition important?

Employee recognition is important because it helps employees feel valued, increasing engagement and productivity and decreasing voluntary turnover.

Companies in the top 20% of those with strong employee recognition programs have voluntary turnover rates that are 31% lower than their competitors’. In addition, companies that have these programs in any capacity often see employee performance , engagement, and productivity go up by 14%.

Even a little effort in this area goes a long way, as companies that improve their recognition efforts by 15% often see their margins increase by 2%.

Do employee recognition programs work?

Yes, employee recognition programs work. Companies that have these programs see employee performance, engagement, and productivity rates that are 14% higher than those in companies that don’t have recognition programs.

However, it’s important that these programs recognize employees in a way that is meaningful to them. Otherwise, they won’t have much of an impact.

For example, many employee recognition programs give out cash rewards to their workers, but in reality, 65% of employees would rather have a personalized, non-cash award instead.

Note that personalized doesn’t necessarily mean getting a Bluetooth speaker with their name on it instead of a plain one, but it does mean finding something that they’d personally enjoy and presenting it to them in a meaningful way.

How does employee recognition affect productivity?

Employee recognition affects productivity by increasing it. Companies that recognize their employees see productivity, performance, and engagement levels 14% higher than their competitors, who don’t have employee recognition programs.

Companies often forget to recognize their employees’ efforts, as 29% of employees say they haven’t received recognition for good work in over a year, if at all. By not making an effort to recognize their employees, companies are harming themselves more than they might think.

Those with employee recognition programs see employee productivity, engagement, and performance go up, and voluntary turnover rates go down.

Employees themselves admit that they do better work when they receive recognition, as 80% say they work harder and 37% say they produce better work when they know their superiors will recognize their efforts.

Haiilo. “ 8 Employee Engagement Statistics You Need To Know in 2022 [Infographic]. ” Accessed on March 15, 2022.

Great Place To Work. “ Creating a Culture of Recognition. ” Accessed on March 15, 2022.

HuffPost. “ Appreciation Motivates Employees To Work Harder, Study Says. ” Accessed on March 15, 2022.

Quantum Workplace. “ What are the Top Drivers of Employee Engagement? ” Accessed on March 15, 2022.

Achievers. “ Achievers Survey Finds Without Recognition, Expect Employee Attrition in 2018. ” Accessed on March 15, 2022.

15Five. “ Top 10 Reasons Why Employees Leave Their Jobs (Infographic). ” Accessed on March 15, 2022.

SurveyMonkey. “ Can Employee Recognition Help You Keep Them Longer? ” Accessed on March 15, 2022.

Authentic Recognition. “ How Frequently Should You Give Recognition? ” Accessed on March 15, 2022.

Gallup. “ Employee Recognition: Low Cost, High Impact. ” Accessed on March 15, 2022.

Forbes. “ New Research Unlocks the Secret of Employee Recognition. ” Accessed on March 15, 2022.

Deloitte. “ Recognition Programmes: Are They Important? ” Accessed on March 15, 2022.

University of Oxford. “ Happy Workers are 13% More Productive. ” Accessed on March 15, 2022.

Jostle. “ Improve Employee Engagement Using Recognition and Reward. ” Accessed on March 15, 2022.

Incentra. “ Budgeting for Employee Engagement and Recognition Programs. ” Accessed on March 15, 2022.

Statista. “ Annual Spend on Incentives by Businesses in the United States as of December 2015, by Business Sector and Type. ” Accessed on March 15, 2022.

Northstar Meetings Group. “ Cash Isn’t King: A Major Study Conducted by the IMA and IRF Refutes Myth That Cash is the Number One Motivator. ” Accessed on March 15, 2022.

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Abby is a writer who is passionate about the power of story. Whether it’s communicating complicated topics in a clear way or helping readers connect with another person or place from the comfort of their couch. Abby attended Oral Roberts University in Tulsa, Oklahoma, where she earned a degree in writing with concentrations in journalism and business.

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Money Does Not Always Buy Happiness, but Are Richer People Less Happy in Their Daily Lives? It Depends on How You Analyze Income

Laura kudrna.

1 Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom

Kostadin Kushlev

2 Department of Psychology, Georgetown University, Washington, DC, United States

Associated Data

Publicly available datasets were analyzed in this study. These data can be found at: https://www.atusdata.org (The ATUS extract builder was used to create the ATUS dataset, see Hofferth et al., 2017 ). GSOEP data were requested from https://www.diw.de/en/diw_02.c.222516.en/data.html , see Richter and Schupp, 2015 .

Do people who have more money feel happier during their daily activities? Some prior research has found no relationship between income and daily happiness when treating income as a continuous variable in OLS regressions, although results differ between studies. We re-analyzed existing data from the United States and Germany, treating household income as a categorical variable and using lowess and spline regressions to explore nonlinearities. Our analyses reveal that these methodological decisions change the results and conclusions about the relationship between income and happiness. In American and German diary data from 2010 to 2015, results for the continuous treatment of income showed a null relationship with happiness, whereas the categorization of income showed that some of those with higher incomes reported feeling less happy than some of those with lower incomes. Lowess and spline regressions suggested null results overall, and there was no evidence of a relationship between income and happiness in Experience Sampling Methodology (ESM) data. Not all analytic approaches generate the same results, which may contribute to explaining discrepant results in existing studies about the correlates of happiness. Future research should be explicit about their approaches to measuring and analyzing income when studying its relationship with subjective well-being, ideally testing different approaches, and making conclusions based on the pattern of results across approaches.

Introduction

Does having more money make someone feel happier? The answer to this longstanding question has implications for how individuals live their lives and societies are structured. It is often assumed that more income brings more happiness (with happiness broadly defined herein as hedonic feelings, while recognizing closely related constructs, including satisfaction and eudaimonia; Tiberius, 2006 ; Angner, 2010 ; Dolan and Kudrna, 2016 ; Sunstein, 2021 ). In many aspects of policy, upward income mobility is encouraged, and poverty can result in exclusion, stigmatization, and discrimination by institutions and members of the public. More income provides people with opportunities and, sometimes, capabilities to consume more and thus satisfy more of their preferences, meet their desires and obtain more of what they want and need ( Harsanyi, 1997 ; Sen, 1999 ; Nussbaum, 2008 ). These are all reasons to assume that higher income will bring greater happiness—or, at least, that low income will bring low happiness.

Some research challenges the assumption that earning more should lead to greater happiness. First, because people expect that more money should make them happier, people may feel less happy when their high expectations are not met ( Graham and Pettinato, 2002 ; Nickerson et al., 2003 ) and they may adapt more quickly to more income than they expect ( Aknin et al., 2009 ; Di Tella et al., 2010 ). Second, since the 1980s in many developed countries, the well-educated have had less leisure time than those who are not ( Aguiar and Hurst, 2007 ) and people living in high-earning and well-educated households report feeling more time stress and dissatisfaction with their leisure time ( Hamermesh and Lee, 2007 ; Nikolaev, 2018 ). The quantity of leisure time is not linearly related to happiness, with both too much and too little having a negative association ( Sharif et al., 2021 ). Evidence also shows that people with higher incomes spend more time alone ( Bianchi and Vohs, 2016 ). The lower quality and quantity of leisure and social time of people with higher incomes may, in turn, negatively impact their happiness, especially given there are strong links between social capital or “relational goods” and well-being ( Helliwell and Putnam, 2004 ; Becchetti et al., 2008 ).

At the same time, some—but not all—evidence suggests that working class individuals tend to be more generous and empathetic than more affluent individuals ( Kraus et al., 2010 ; Piff et al., 2010 ; Balakrishnan et al., 2017 ; Macchia and Whillans, 2022 ), and such kindness toward others has been associated with higher well-being ( Dunn et al., 2008 ; Aknin et al., 2012 ). Relatedly, psychological research suggests that people with lower socioeconomic status have a more interdependent sense of self ( Snibbe and Markus, 2005 ; Stephens et al., 2007 ). It is, therefore, possible that people high in income have lower well-being because they experience less of the internal “warm glow” ( Andreoni, 1990 ) benefit that comes along with valuing social relationships and group membership. In theory, therefore, there are reasons to suppose that high income has both benefits and costs for well-being, and empirical evidence can inform the debate about when and whether these different perspectives are supported.

Empirical Evidence on Income and Happiness

The standard finding in existing literature is that higher income predicts greater happiness, but with a declining marginal utility ( Dolan et al., 2008 ; Layard et al., 2008 ): that is, higher income is most closely associated with happiness among those with the least income and is least closely associated with happiness for those with the most income. Recently, this finding has been qualified by studies showing that the relationship between income and happiness depends on how happiness is conceptualized and measured: as an overall evaluation of one’s life or as daily emotional states ( Kahneman and Deaton, 2010 ; Killingsworth, 2021 ). In this vein, authors Kushlev et al. (2015) found no relationship between income and daily happiness in the American Time Use Survey (ATUS), which has recently been found for other happiness measures, too ( Casinillo et al., 2020 , 2021 ) The finding from Kushlev et al. (2015) was replicated in the German Socioeconomic Panel Survey (GSEOP) by Hudson et al. (2016) , and in another analysis of the ATUS by Stone et al. (2018) .

Some research has focused specifically on the effect of high income on happiness. Kahneman and Deaton (2010) conducted regression analyses using a Gallup sample of United States residents, finding that annual income beyond ~$75K was not associated with any higher daily emotional well-being. Income beyond ~$75K, however, predicted better life evaluations. Using a self-selecting sample of experiential data in the United States, Killingsworth (2021) conducted piecewise regressions and found no evidence of satiation or turning points. Jebb et al. (2018) fit regression spline models to global Gallup data, showing that the satiation point in daily experiences found by Kahneman and Deaton (2010) was also apparent in other countries. Unlike Kahneman and Deaton (2010) , however, Jebb et al. (2018) also found evidence of satiation in people’s life evaluations, and even some evidence for “turning points”—whereby richer people evaluated their lives as worse than some of those with lower incomes. A satiation point in life evaluations was also found in European countries at around €28K annually ( Muresan et al., 2020 ).

This pattern of findings could partly depend on the choice of analytic strategy. In analyses of the same dataset as Jebb et al. (2018) but using lowess regression, researchers found no evidence of satiation or turning points in the relationship between income and people’s life evaluations ( Sacks et al., 2012 ; Stevenson and Wolfers, 2012 ). These conflicting results suggest that the effect of analytic strategy on results deserves a closer examination.

The Research Gap

While there has been much research on income and happiness, including according to how happiness is defined and measured, we are not away of any studies that have compared the relationship between income and happiness according to how income is defined and measured. We propose that the relationship between income and happiness may depend not only on how happiness is measured, but also on how income is measured and analyzed. To improve our knowledge of the relationship between income and happiness, this paper, we focus on nonlinearities in the relationship between income and happiness and re-analyze the ATUS data used by Kushlev et al. (2015) and Stone et al. (2018) , as well as the GSOEP data used by Hudson et al. (2016) . Specifically, while Kushlev et al. (2015) analyzed income as a continuous variable in the ATUS, we treat income the way it was measured: as a categorical variable. We compare these results to GSOEP data where we re-code the original continuous measure of income into categorical quantiles. To further explore nonlinearities in the relationship between income and happiness, we also conduct local linear “lowess” and spline regression analyses.

We chose to re-analyze these data to address the question of differences in the relationship between income and happiness according to the measurement and analysis of income because the ATUS and GSOEP provide nationally representative data on people’s feelings as experienced during specific “episodes” of the day after asking them to reconstruct what they did during the entire day. Thus, compared to data from Gallup, which measures affect “yesterday,” measurements in the ATUS are more grounded in specific experiences, and therefore, less subject to recall bias ( Kahneman et al., 2004 ). And unlike Gallup, which uses more crude, dichotomous (“yes-no”) response scales, ATUS measures happiness along a standard seven-point Likert-type scale. In the GSOEP, we were also able to analyze data from the Experience Sampling Methodology (ESM), which asks people how they are feeling during specific episodes during the day and, as such, is even more grounded in specific experiences.

Measuring and Analyzing Income

The original ATUS income variable—family income—contains 16 uneven categories (see Table 1 ). For example, Category 11 has a range of ~$10K, whereas Category 14 has a range of ~$25K. The increasingly larger categories are designed to reflect declining marginal utility as an innate quality of income. Based on this, Kushlev et al. (2015) analyzed income as a continuous variable using the original uneven categories. Continuous scales, however, assume equal intervals between scale points—a strong assumption to make for the relatively arbitrary rate of change in the category ranges. Is increasing one’s income from $20,000 to $25,000 really equidistant to increasing it from $35,000 to $40,000 ( Table 1 )? And can we really assume, for example, that adding $5,000 of additional income to $35,000 is the same as adding $10,000 of additional income to $40,000? Recognizing this issue, income researchers have adopted alternative strategies. For example, Stone et al. (2018) took the midpoints of each category of income, and then log-transformed it. Thus, they transformed the categorical measure of income into a continuous measure. This approach produced results for happiness consistent with the findings of Kushlev et al. (2015) .

The original categories of income in the ATUS family income measure with number of individuals in each income category in the ATUS 2010, 2012, and 2013 well-being modules.

Complete cases only for all variables analyzed.

Both the increasing ranges of the income scale itself and its log-transformations reflect an assumed declining marginal utility of income: They treat a given amount of income increase at the higher end of the income distribution as having less utility than the same amount at the lower end of the distribution. But by subsuming income’s declining utility in its very measurement (or transformation thereof), it becomes difficult to interpret a null relationship with happiness. In other words, we might not be seeing a declining marginal utility of income reflected on happiness because the income variable itself reflects its declining utility.

Even when the income variable itself does not reflect its declining utility, a null relationship between income and daily experiences of happiness has been observed. Hudson et al. (2016) used GSOEP, which contains a measure of income that is continuous in its original form. Whether analyzing this income measure in its raw original form or in transformed log and quadratic forms, a null relationship with happiness was observed. This approach, however, does not consider whether there might be nonlinear/log/quadratic turning or satiation points at higher levels of income—an issue also applicable to previous analyses of ATUS ( Kushlev et al., 2015 ; Stone et al., 2018 ). This is important because there are theoretically both benefits and costs to achieving higher levels of income that could occur at various levels of income; however, this possibility has not yet been fully explored in ATUS or GSOEP data.

In sum, past research using ATUS has treated categorically measured income as a continuous variable, either assuming equidistance between scale points or attempting to create equidistance through statistical transformations. By doing so, however, researchers may have statistically accounted for the very utility of income for happiness that they are trying to test. In both ATUS and GSOEP, the question of whether there might be satiation and/or turning points at higher levels of income has not been fully considered. The present research explores whether treating income as a categorical variable in both ATUS and GSOEP would replicate past findings or reveal novel insights, focusing on possible nonlinearities in the relationship between income and happiness.

Materials and Methods

We used data from ATUS well-being modules in 2010, 2012, and 2013. To facilitate future replications of this research, the ATUS extract builder was used to create the dataset ( Hofferth et al., 2017 ). 1 The ATUS is a repeated cross-sectional survey and is nationally representative of United States household residents aged 15 years and older. Its sampling frame is the Current Population Survey (CPS), which was conducted 2–5 months prior to the ATUS. Some items in the ATUS come from the CPS, including the household income item that we analyze.

Data from the GSOEP come from the Innovation Sample (IS), which is a subsample of the larger main GSOEP ( Richter and Schupp, 2015 ). The main GSOEP and the IS are designed to be nationally representative. The IS contains information on household residents aged 17 years of age and older. We used two modules from these data: the 2012–2015 DRM module, which is a longitudinal survey, and the 2014–2015 ESM module.

Outcome Measures

In ATUS, participants were called on the phone and asked how they spent their time yesterday: what activities they were doing, for how long, who they spent time with and where they were located. This information was used to create their time use diary. A random selection of three activities were taken from these diaries and participants were asked how they felt during them. The feelings items were tired, sad, stressed, pain, and happy. Participants were also asked how meaningful what they were doing felt.

In GSOEP, participants were interviewed face to face for the DRM questions and through smartphones for the ESM questions. In the DRM, as in the ATUS, they were asked how they spent their time yesterday and, for a random selection of three activities, they were asked further details about how they felt. In the ESM, participants were randomly notified on mobile phones at seven random points during the day for around 1 week. As in the DRM, they were asked how they were spending their time at the point of notification, as well as how they felt. Participants in both ESM and DRM samples were asked about whether they were feeling happy, as well as other emotions such as sadness, stress, and boredom.

The focus of this research is on the happiness items from both the ATUS and GSOEP to highlight differences according to the treatment of the independent measure of income rather than differences according to the dependent outcome of emotional well-being.

Data were analyzed in STATA 15 and jamovi. The Supplementary Material S1 file contains the STATA command file for the main commands written to analyze the data. In both ATUS and GSOEP, OLS regressions were conducted with happiness as the outcome measure and income as the explanatory measure. Following Kushlev et al. (2015) and Hudson et al. (2016) , the average happiness across all activities each day was taken to create an individual-level measure. Because the GSOEP DRM sample contained multiple observations across years, the SEs were clustered at the individual level for models using this dataset.

The treatment of income differed according to the dataset because income was collected differently in each dataset. In the ATUS, income was first analyzed in continuous, log, and quadratic forms in OLS regressions, as in other research ( Kushlev et al., 2015 ; Hudson et al., 2016 ). Next, it was analyzed as a categorical variable with 16 categories, preserving the identical format that it was originally collected in from the CPS questionnaire.

In GSOEP, the income variable in the dataset is provided in continuous form because participants reported their monthly income as an integer. To compare to the ATUS results, 16 quantiles of income were created and analyzed in GSOEP DRMs (see Table 2 - note that there were insufficient observations to conduct these analyses with GSOEP ESMs). This income variable was also analyzed in continuous, log, and quadratic forms.

The range and number of person-year observations of the GSOEP Income 4 variable divided into 16 quantiles.

Omnibus F -tests and effect sizes ( n 2 ) are also reported to compare the categorical, continuous, log, and quadratic approaches.

We conducted lowess and spline regressions to further investigate possible nonlinearities in the relationship between income and happiness. For the lowess regressions, the smoothing parameter was set at of 0.08. For the regression splines, we fitted knots at four quartiles and five quantiles of income. We also used the results of OLS regressions treating income as a categorical variable, as well as the results of the lowess regression treating income as continuous, to fit knots at pre-specified values of income (where these analyses suggested there could be turning and/or satiation points).

Complete case analyses were conducted with 33,976 individuals in ATUS, 6,766 individuals in German DRMs, and 249 individuals in German ESMs. There was item-missing data in some samples (ATUS, 1.7% missing; GSOEP DRMs, 8.2% missing; GSOEP ESMs data, and 6.0% missing). We make analytical and not population inferences and therefore do not use survey weights ( Pfeffermann, 1996 ).

Results are presented without and with controls for demographic and diary characteristics. Following Kushlev et al. (2015) , Hudson et al. (2016) , and Stone et al. (2018) , these controls were age, gender, marital status, ethnic background, 2 health, 3 employment status, children, 4 and whether the day was a weekend. We also control for the year of the survey in ATUS DRM data to address the issue that our results are not due to new data but rather how we treat the income variable.

The list of variables we use in analyses are in Table 3 .

List of variables used in analyses in ATUS and GSOEP.

In both ATUS and GSOEP, daily happiness was analyzed using a 0–6 scale (in GSOEP scale points 1–7 were recoded to 0–6 to match ATUS). The ATUS mean happiness was 4.38 (SD = 1.33). The GSOEP DRM mean happiness was 2.91 (SD = 1.46), and the GSOEP ESM mean happiness was 2.65 (SD = 1.03).

The magnitude of our results can be considered in the context of effect sizes from other research on demographic characteristics and daily happiness ( Kahneman et al., 2004 ; Stone et al., 2010 ; Luhmann et al., 2012 ; Hudson et al., 2019 ). For example, the effect size for the relationship between age and daily experiences of happiness was 0.16 in Stone et al. (2010) . Our effect sizes range from 0.06 to 0.37. Throughout, we focus on coefficients, their 95% CIs, and visualizations of these coefficients and CIs, rather than on their statistical significance ( Lakens, 2021 ). The purpose of this is to highlight how analytic treatments of income affect the magnitude and precision of the relationship between income and happiness.

When treating the 16-category family income variable as continuous in OLS regressions, there was no substantive relationship between income and happiness as in other prior research ( Kushlev et al., 2015 ; Hudson et al., 2016 ; Stone et al., 2018 ). Out of the linear, squared, and log coefficients without and with controls, the largest and most precise coefficients were with controls; for linear income it was ( b  = −0.006, 95% CI = −0.01, −0.002), squared income ( b  = −0.0001, 95% CI = 0.0003, 0.00006), and log income ( b  = −0.03, 95% CI = −0.05, 0.001). The omnibus F -test (without controls) for linear income was F  = 0.28, n 2  = 0.000008 (95% CI = 0.00, 0.0002), for income squared was F  = 1.60, n 2  = 0.00005 (95% CI = 0.00, 0.0003), and for log income was F  = 0.23, n 2  = 0.000006 (95% CI = 0.00,0.0002).

The categorization of income focused attention on those with incomes of $35–40K, who appeared substantively happier than some of those with higher incomes (and lower incomes; see Figure 1 ). For example, with controls, those with incomes of $35–40K appeared happier relative to those with incomes of $150K+ ( b  = 0.16, 95% CI: 0.08, 0.24) and $100–150K ( b  = 0.14, 95% CI: 0.07, 0.221). The omnibus test for categorical income was F  = 1.61, n 2  = 0.007 (95% CI = 0.00, 0.0009).

Predicted values of average individual happiness in the American Time Use Survey (ATUS) at the 16 values of the family income variable without and with controls. Covariates at means. 95% CI.

Results from regression splines and a lowess regression suggested null results overall (see Figure 2 ). Further details of the analyses are in Supplementary Material S2 .

Line graph of predicted values from lowess regressions explaining variance in happiness from income treated as a continuous variable in ATUS.

When treating the continuous household income variable as continuous (in €10,000s) in OLS regressions, there was no substantive relationship between income and happiness as in other prior research ( Kushlev et al., 2015 ; Hudson et al., 2016 ; Stone et al., 2018 ). The association with the largest magnitude and most precision was for log income with controls ( b  = −0.08, 95% CI = −0.18, 0.01). 5

As in ATUS, treating the variable as categorical suggested some relationships between income and happiness. These results drew attention to those third quantile (~€14–18K), who seemed happier than those both higher and lower in income (see Figure 3 ). For example, with controls, they were happier than those in quantiles 13 (€42.6–48K, b  = 0.46, 95% CI = 0.25, 0.67), seven (~€24–27K, b  = 0.34, 95% CI = 0.13, 0.56), and one (€2.40–11,520K, b  = 0.28, 95% CI = 0.05, 0.51). The omnibus test for categorical income was F  = 4.00, n 2  = 0.009 (95% CI = 0.003, 0.01), whereas the omnibus test for linear income was F  = 0.09, n 2  = 0.00001 (95% CI = 0.00, 0.0007). The omnibus for log income was F  = 1.42, n 2  = 0.0002 (95% CI = 0.00, 0.0001) and for income squared it was F  = 0.96, n 2  = 0.0001 (95% CI = 0.00, 0.001).

Predicted values of average person-year happiness from GSOEP DRMs at 16 quantiles of income (Income 4) without and with controls. Covariates at means. 95% CI.

The lowess and spline regressions suggested null results overall, as the coefficients were small in magnitude (see Figure 4 ). Further details of the analyses are in Supplementary Material S3 .

Line graph of predicted values from lowess regressions explaining variance in happiness from income treated as a continuous variable in GSOEP DRMs at 16 quantiles of income.

There was no evidence to suggest any substantive association between income and happiness in ESM data for linear income, income squared, log income, in the lowess regressions, or regression splines. A visualization of the lowess results are in Figure 5 and further details of the analyses are in Supplementary Material S4 .

Results of local linear “lowess” regression from GSOEP Experience Sampling Methodology (ESM) data with happiness as the outcome and continuous annual income as the explanatory variable.

The omnibus F -test for linear income was F  = 0.53, n 2  = 0.002 (95%CI = −0.00, 0.03), and for log income it was F  = 0.12, n 2  = 0.0005, 95%CI = 0.00, 0.02. For income squared it was F  = 0.63, n 2  = 0.003, 95%CI = 0.00 0.03.

Is income creating a signal in these data on daily experiences of happiness, or is it all simply noise? The present results suggest that whether income can be concluded as being associated with daily experiences of happiness may depend on how income is analyzed. When income in ATUS is analyzed in its original, categorical form, there is some evidence that some people with higher incomes feel somewhat less happy than some of those with lower incomes. When the continuous income variable in GSOEP is split into categories, a similar pattern is observed. This is not inconsistent with the findings of Kushlev et al. (2015) , Hudson et al. (2016) , and Stone et al. (2018) , who found no relationship between income and daily feelings of happiness in the same data when income was analyzed as a continuous variable. It simply illustrates that a relationship between income and happiness could be interpreted when treating income categorically rather than continuously.

There are at least three possible interpretations to our overall results. One interpretation tends toward conservative. We conducted multiple comparisons of many transformations of income, which might inspire some to question whether we should have accounted for this in some way by adjusting for multiple comparisons. Although we found some evidence of differences in happiness according to income, such an adjustment might lead to an overall null conclusion when characterizing the relationship between income on happiness. A second interpretation is more generous. Within this perspective, one might emphasize the fact that because our income measures were correlated, no correction for multiple comparisons was required. It could then be argued that because we found some evidence for the relationship between income on happiness, there is good evidence that the overall effect is not null. A more moderate perspective, and the one adopted in this paper, is that because the overall pattern of our results showed mixed null and nonnull results, we can make an overall conclusion of some differences in happiness according to income. We also noticed that equivalizing income in the German data strengthened the relationship of income and happiness, further supporting the conclusion of some differences—and that the analytic treatment of income matters.

Based on the moderate perspective, we conclude that there is very little evidence of any relationship between income and daily experiences of happiness—and any relationship that does exist would suggest higher income could be associated with less happiness. The results do not support the results of Sacks et al. (2012) or Killingsworth (2021) , where a greater income was associated with greater happiness, and there were no satiation or turning points (see also Stevenson and Wolfers, 2012 ). These results are more aligned with Kahneman and Deaton (2010) , who found a satiation point in the relationship between income daily experiences of happiness, researchers finding no association between income and happiness ( Kushlev et al., 2015 ; Jebb et al., 2018 ; Casinillo et al., 2020 , 2021 ), who found that higher income can be associated with worse evaluations of life. We suggest the analytic strategy for income could contribute to explaining discrepant results in existing literature, and researchers should be clear about the approaches they have tested, although we acknowledge that sampling differences could play a role, too.

Overall, the results were broadly consistent between countries because there was no substantive relationship between income and happiness when income was treated continuously but there appeared to be relationships when treating income categorically. Despite a similar overall pattern in the income results, there were other difference between countries. German residents rated their happiness as lower than United States residents (a difference of ~1.5 scale points out of seven). This could be because of different interpretations of the word “happiness” in Germany and the United States. The word for happiness in German used in the survey— glück —can mean something more akin to lucky or optimistic—which is different from the meaning of word “happy” in the United States. Despite this linguistic difference, those with higher incomes were still less happy than some of those with lower incomes in both samples.

Limitations

One limitation to our results is the representativeness of the income distribution. Household surveys like those that we used do not tend to capture the “tails” of the income distribution very well: People in institutions and without addresses are excluded from these sample populations, which omits populations such as those living in nursing homes and prisons, as well as the homeless. Moreover, people do not always self-report their income accurately due to issues such as social desirability bias ( Angel et al., 2019 ). Existing studies that have focused on those with very low incomes do tend to find that low income is associated with low happiness ( Diener and Biswas-Diener, 2002 ; Clark et al., 2016 ; Adesanya et al., 2017 ). In ATUS, the highest household income value available was $150K, whereas in GSOEP it was €360K. Thus, it is not always clear whether the very affluent, such as millionaires, are represented in these samples ( Smeets et al., 2020 ). Overall, our results cannot be taken as representative of people who are very poor or rich and should not be interpreted as such.

Another limitation is that the present results cannot be interpreted casually because there has been no manipulation of income in these data nor exploration of mechanisms and there was no longitudinal data in ATUS. As discussed by Kushlev et al. (2015) , there are issues such as reverse causality. Here, however, some of our results potentially suggest an alternative reverse causality pathway, whereby less happy people may select into earning more income. Because the counterfactual is not apparent—we do not know how happy people with high incomes would be without their higher income—it could also be that those with high incomes would be even less happy than they currently are if they had not attained their current level of income. In other words, people with high incomes may have started out as less happy in the first place and be even less happy if they did not have high incomes.

A further limitation is the time period of the data, especially that they were collected prior to the COVID-19 pandemic. This could be an issue because it is possible that the relationship between income and daily experiences of happiness has changed, such as due to the exacerbation of health inequalities and restrictions on freedom of movement due to nationwide lockdowns. Our study does not provide any information on the longer-term and health and well-being consequences of both COVID-19 itself and the policy response to COVID-19 ( Aknin et al., 2022 ). As one example, access to green space, which has health and well-being benefits, is lower among those with low income, and this mechanism between income and happiness may have become more salient during COVID-19 ( Geary et al., 2021 ). Overall, it is important to consider the regional, political, and socioeconomic contexts in which income is attained to understand its relationship with well-being, including levels of income in reference groups such as neighbors, friends, and colleagues ( Luttmer, 2005 ; De Neve and Sachs, 2020 ). It would be important to replicate the results in this research with more recent data to address the limitation that the data we used are not recent, considering our broader point that the measurement and analysis of income should be considered as carefully as the measurement and analysis of happiness.

Future Directions

This research points to several directions for future research. One direction relates to data and measures: Nonlinearities in the relationship between income and happiness could be examined using time use data from other countries, considered between countries and/or within countries over time ( Deaton et al., 2008 ; De Neve et al., 2018 ), and investigated for measures of emotional states other than happiness ( Piff and Moskowitz, 2018 ). In general, our results suggest that researchers should pay attention to how income is measured and analyzed when considering how it is related to happiness, which complements findings from other research that the way happiness is measured and analyzed is important ( Kahneman and Deaton, 2010 ; Jebb et al., 2018 ).

Future research could also explore mechanisms that may explain our findings. In addition to those mentioned in the Introduction—expectations ( Graham and Pettinato, 2002 ; Nickerson et al., 2003 ), time use ( Aguiar and Hurst, 2007 ; Hamermesh and Lee, 2007 ; Bianchi and Vohs, 2016 ; Nikolaev, 2018 ; Sharif et al., 2021 ); generosity ( Dunn et al., 2008 ; Kraus et al., 2010 ; Piff et al., 2010 ; Aknin et al., 2012 ; Balakrishnan et al., 2017 ; Macchia and Whillans, 2022 ), and sense of self ( Snibbe and Markus, 2005 ; Stephens et al., 2007 )—another is the identity-related effect of transitioning between socioeconomic groups. Though one might expect upward mobility to be associated with greater happiness, research suggests that some working class people do not wish to become upwardly mobile because it could lead to a loss of identity and change in community ( Akerlof, 1997 ; Friedman, 2014 ). Indeed, upward intergenerational mobility is associated with worse life evaluations in the United Kingdom—though not in Switzerland ( Hadjar and Samuel, 2015 ), although recent findings show substantial negative effects of downward mobility, too ( Dolan and Lordan, 2021 ). Over time, therefore, the degree of mobility in a population could influence the relationship between income and happiness in both positive and negative directions.

Additionally, social comparisons could drive the effects of higher income on happiness. Higher income might not benefit happiness if one’s reference group—that is, the people to whom we compare or have knowledge of in some form ( Hyman, 1942 ; Shibutani, 1955 ; Runciman, 1966 )—changes with higher socioeconomic status. As income increases, people might compare themselves to others who are also doing similarly or better to them, and then not feel or think that they are doing any better by comparison—or even feel worse ( Cheung and Lucas, 2016 ). This is one of the explanations for the well-known “Easterlin Paradox” ( Easterlin, 1974 ), which suggests that as national income rises people do not become happier because they compare their achievements to others. The paradox is debated ( Sacks et al., 2012 ). Additionally, some research shows that it is possible to view others’ greater success as one’s own future opportunity and for upward social comparisons to then positively impact upon well-being ( Senik, 2004 ; Davis and Wu, 2014 ; Ifcher et al., 2018 ). As with the role of mobility in the relationship between income and happiness, it is unclear whether the role of social comparisons would create a positive or negative impact over time and future research could explore this.

Final Remarks

Overall, our results provide some evidence that individual attainment in terms of income may not equate to the attainment of individual happiness—and could even be associated with less daily happiness, depending upon how income is measured and analyzed. These results suggest that how income is associated with happiness depends on how income is measured and analyzed. They provide some support to the idea that financial achievement can have both costs and benefits, potentially informing normative discussions about the optimal distribution of income in society.

Data Availability Statement

Ethics statement.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author Contributions

LK and KK contributed to conception and design of the study. LK organized the data, performed the statistical analysis in STATA, and wrote the first draft of the manuscript. KK performed additional statistical analysis in jamovi and wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.

LK was supported by a London School of Economics PhD scholarship during early work and later by the National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) West Midlands. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

LK thanks Professor Paul Dolan and Dr Georgios Kavetsos for their support early on in conducting this research, as well as Professor Richard Lilford for insights about multiple comparisons.

1 https://www.atusdata.org

2 In the ATUS this was Hispanic and Black, in GSOEP this was German origin.

3 In the ATUS this was whether the respondent had any physical or cognitive difficulty (yes/no), in GSOEP this was self-rated general health (bad, poor, satisfactory, good, and very good).

4 In the ATUS this was presence of children <18 years in the household, in GSOEP this was number of children.

5 This association was stronger and more precise when equivalizing income (dividing by the square root of household size), b  = −0.16, 95%CI = −0.06, −0.27, underscoring the importance of transparency in the treatment of income.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.883137/full#supplementary-material

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