1 Introduction

The World Health Organization (WHO) defines depression as “a common mental disorder, characterized by sadness, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite, feelings of tiredness and poor concentration” (WHO, 2019). Experiencing any of these feelings might results in long-lasting negative effects not only on the individual’s ability to behave and live a satisfying life but also on many other people the individuals interact daily with at home, school, work and in any other societal context (e.g., commuting, shopping, leisure related activities). Therefore, the cost of depression can be assessed in terms of long-lasting effects on living a satisfying life for both the individual, their family, and the society. However, many earlier studies focused on the assessment of the cost of depression in terms of treatment costs and lost productivity for the individual and methods related to these,Footnote 1 and found that most of the costs (up to 85%) of depression in adults on a societal level are due to work absenteeism and/or presenteeism (e.g., Smit et al., 2006 a, b). To our knowledge, very little is known about the indirect costs (calculated from a societal perspective) related to someone else’s depression. For example, a cost-of-illness study carried out in a sample of clinically depressed adolescents aged 12 to 21 years old reported that the annual societal costs of families with a clinically depressed adolescent are very high and higher than those of other psychological disorders (Bodden et al., 2018). Nevertheless, when assessing the willingness to pay for different treatments, it was found that even though respondents recognized that severe mental illnesses could dramatically lower quality of life, they were less willing to pay to avoid such illnesses than they were to pay to cure less burdensome general medical illnesses (e.g., Smith et al., 2012). Therefore, given the known high burden imposed by depression at individual, family and societal level, there is an increasing need for research that estimates the monetary value of public interventions designed not only to treat but also to prevent depression. The methods often used to value interventions, i.e., cost-effectiveness analysis, cost utility analysis and cost-benefit analysis, are complex and require significant time and effort to be implemented. Therefore, experience-based methods that are both easier and require significantly less time from the respondents are more attractive than preference-based methods. The attractiveness of the experience-based methods is the use of an a posteriori approach and take in consideration the respondents, experiences. For example, the well-being valuation method (WVM) uses only data about respondents’ experiences, their income and their subjective well-being (SWB) and compute the monetary trade-off between previous experiences and income that would leave SWB unchanged. Using the WVM, Powdthavee & van den Berg (2011) estimated that in the UK, an additional £44 237 in income per year would be needed for an individual to have the same level of life satisfaction if they did not suffer from depression or anxiety. The costs together with the estimated benefit from curing all depression and anxiety disorders using evidence-based psychological therapies would a boost GDP by about 4% in the UK (Layard, 2017), makes an even stronger case for the importance of preventing depression.

In this paper, we use, to our knowledge, for the first time Swedish data to estimate the WVM monetary equivalent cost for directly and/or indirectly experiencing depression. The Swedish case is relevant given that Sweden is one of the happiest countries in the world (Helliwell et al., 2020), where the government has been actively involved in providing national mental health strategies that are facilitating prevention and awareness, but the government has not yet an explicit plan how to boost the well-being of its citizens.

According to the OECD’s Better Life Index, Sweden outperforms the OECD average in income, jobs, education, health, environmental quality, social connections, civic engagement, safety, and life satisfaction (OECD, 2022), but the average life satisfaction (measured on a 0–10 scale) decreased from 7.6 to 2012 to 7.3 in 2021. During this period, the Swedish government was actively supporting prevention and treatment of mental illness, which might explain why the self-reported nervousness or anxiety has increased in the Swedish adult population from 31% to 2011 to 42% in 2021 (Socialstyrelsen 2022).

Our study aims to empirically test the relationship between depression experience and life satisfaction in Sweden. Our findings are meant to be used to start the debate about the need of actively considering how to boost citizens’ well-being. Along with using a less commonly used valuation method to value the monetary equivalent cost for suffering from depression, we also distinguish between two types of situations, depending on whether the respondents themselves directly had experienced depression and/or someone near, family or friends. Furthermore, we also combine information about the individuals’ depression experience and their concern for suffering of depression in the future, and compute the monetary compensation for eight definitions of depression experience and four model specifications for all individuals, and separately for women and men and three age-groups. In this way, we provide a spectrum of compensating variations that might be used as reference points when debating and/or designing new public interventions aimed to prevent depression. Our findings support and extend previous research that has established depression experiences as an important factor in relation to lower SWB by reporting the monetary compensation that would keep the individual’s life satisfaction unchanged when experiencing depression. The monetary compensation varies by type of experience, gender, and age. On average, women have more depression experiences than men, but their monetary compensation would be, on average, much lower than for men. Individuals aged 18–39 years have more depression experiences than middle-aged people, but the monetary compensation would be, much higher for individuals aged 40–65 years than for both younger and older individuals. In addition to provide new empirical evidence for the negative relationship between depression experience and life satisfaction in Sweden, our findings also suggest the need for a dialogue that can contribute to the development of policies that aim to boost citizens’ well-being.

In what follows, the next section briefly describes different alternatives of valuing non- financial costs of depression for individuals, presenting the well-being valuation method in comparison to the most used existing alternatives. The third section presents a short description of the survey questions used and a few descriptive statistics of the variables needed to assess the monetary value of depression. The fourth section discusses the results for the well-being equation and the marginal rate of substitution between income and experiencing depression, while the last section concludes discusses.

2 Valuing Non-Financial Costs of Depression

Theoretical models in health economics are often focused on finding an optimal solution under given constraints. However, when estimating the cost of any health intervention not all relevant choices can be observed, especially when externalities and non-market goods are involved. The extensive literature on health economics on valuation methods for non-financial costs of health builds predominantly on compensating surplus and equivalent surplus, two welfare measures introduced by Hicks & Allen (1934). In this context, the equivalent surplus is the amount of money, to be paid or received, that keep the individual welfare level unchanged in the absence of depression. The compensated surplus is defined as the amount of money needed to keep the individual at the welfare level after a change in her/his status quo. The cost of depression can therefore be defined as the amount of money the individual would need to receive to have the same welfare level as before the deterioration of her/his health. In this context, the monetary compensation is expected to allow the depressed individual to keep her/his overall level of welfare level before experiencing depression. The estimation of the needed monetary compensation for all affected individuals is a measure for the potential budgetary savings that interventions designed for preventing and decreasing depression implemented as early as possible in an individual’s life can bring. Therefore, an intervention that costs less than the compensated surplus might be considered effective. But how can we estimate the compensating surplus?

Preventing and decreasing depression require both monetary and non-monetary resources. The monetary compensation with respect the individual’s welfare is dependent on the individual’s preferences and experiences, which are often not easy to quantify. For example, the monetary values for services and goods which are not traded in the market and hence have no market price use to be evaluated by using the contingent valuation (CV) method. Using a CV-survey, respondents are directly asked how much they would be willing to pay for specific intervention designed to improve the individual’s health. The total monetary value of the intervention is estimation based on the average willingness to pay. An increasingly used alternative, based on significantly less demanding survey data are responses to subjective well- being (SWB) questions. Contrary to using the respondents’ willingness to pay, which is a priori approach, a growing literature uses data about respondents’ experiences, income and their subjective well-being (SWB), which is a posteriori approach, and compute the monetary trade-off between an experience and income that would leave SWB unchanged. This is called the subjective well-being valuation method, or well-being valuation method (WVM), or life satisfaction approach (LSA).

SWB is a direct measure of subjective experienced utility, whilst in the stated preferences approach, the estimations of the monetary values are based on choices and ex ante statements of preference that are not always consistent with ex post experiences. Therefore, WVM has been sometimes considered as preferable to the revealed and stated preference approaches in the valuation of non-market goods (e.g., Dolan et al., 2011).

While individual’s preferences are usually linked in economics to the individual’s utility, which is a relatively abstract concept, experiences can be linked to the individual’s well- being. One critical issue for evaluations of public policies is how the well-being should be measured (Kahneman & Deaton, 2010). Stiglitz-Sen-Fitoussi Commission recommended, for example, to focus on income and consumption (rather than production) when evaluating economic well-being, to focus on households, and to take in account the joint distribution of economic resources (Stiglitz et al., 2009). A growing literature has emerged on the use of global retrospective measures of individual’s well-being, such as evaluations of general life satisfaction and accounts of happiness. These measures have the advantage of providing information on appraisal of circumstances and feelings about them. SWB refers to the individual’s own valuation of their well-being and it is usually measured through a self-reported judgements about how the individual feels and thinks about her/his life, including their happiness, sadness, and life satisfaction, collected via surveys and/or interviews. However, in addition to trends on measurement of well-being focused on elevating the scientific standards and rigor that facilitate national and international comparisons of well-being, to our knowledge, there are only a few attempts of shifting the focus toward multidimensional approaches. For example, Ruggeri et al., (2020) using ten dimensions (referred collectively as the multidimensional psychological well-being) to compute a single value standardized to the population, reported insights that demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions.

The WVM implies that information about people’s experiences is collected directly without drawing attention towards the health condition in question and therefore suffers less from the cognitive pitfalls of the approach of stated preferences, which attempts to elicit people’s preferences over different hypothetical situations. WVM has proven to be useful to calculate shadow prices for non-market commodities such as, airport noise, air quality, different phenomena related to crime and safety and to evaluate health losses and/or gains by analyzing the impact of a change in health status; e.g., airport noise (Van Praag & Baarsma, 2005), air quality (Luechinger, 2009), crime and safety (Powdthavee, 2005; Frey et al., 2009) and social relationships (Powdthavee, 2008).

When using WVM to evaluate health, Ferrer- i-Carbonell and van Praag (2002) estimated, in a first step, the income equivalent of health satisfaction changes, i.e., the equivalent income change that would be necessary to change general life satisfaction to the same extent as a change in health satisfaction would do. In the next step, health satisfaction changes were linked to specific diseases to estimate the income equivalent for these diseases. However, there is no clear consensus on what the best measure of the individual’s experiences of different health conditions may be for the evaluation process. For example, Böckerman et al., (2011) explored how two well-established utility-based health-related quality-of-life measures, EQ-5D and 15D, capture the effects of various chronic conditions on SWB. Their results suggest that using health utility as a basis for resource allocation is likely to underfund the treatment of psychiatric disorders. This finding is of high policy relevance given that both SWB and health utility are usually aims of health policy and instruments in resource allocation. Additionally, valuing depression might be even more complex when taking in account the negative externalities of the individual’s depression on their family, their colleagues at school and/or at work and their friends in any social context. Regardless of all these important details, to use WVM to estimate the cost of depression requires data about the individual’s SWB, their experience of depression and their income. WVM involves estimating the well-being equation (1), which is explained by the non-market good being valued (D) and income (M), among other variables (X). This allows us to specify the well-being equation as it follows:

$${SWB}_{i}=\alpha +{{\beta }_{D}{D}_{i}+\beta }_{M}{M}_{i}+{\beta }_{X}{X}_{i}+{\epsilon }_{i},$$
(1)

where, SWB is a measure of subjective well-being like general life satisfaction or happiness, D is the event of experiencing directly or indirectly depression, M stands for household income; and X are other determinants of SWB, such as individual socio-demographic characteristics.

The well-being equation generally provides the estimated relationship between general life satisfaction, a set of domain satisfactions and a set of control variables, including the individual’s demographic and socio-economic characteristics. If the estimated parameter of interest βD is negative, it means that experiencing depression is associated with an average decrease of βD units in life satisfaction, but it does not imply that any individual that experienced depression (directly or indirectly) will cause her/his life satisfaction to decrease by βD units. However, when not all determinants of general life satisfactions can be observed and the unobservables are correlated with income and/or experience of depression, the estimates are biased.

As we already mentioned above, the WVM’s central ingredients are the estimates: the change on the individual’s SWB due to experiencing depression (here, βD) and the change on the individual’s SWB due the disposable household’s income (here, βM). The relative size of βD and βM reveal an implicit marginal rate of substitution between the household income and the experience of depression. In other words, the WVM provides information about how much income would be needed to keep the SWB unchanged when individuals are experiencing depression. Specifically, the marginal rate of substitution (MRS) between experiencing depression and money,

$$MRS=-\frac{{\beta }_{D}}{{\beta }_{M}},$$
(2)

provides the cost of depression.

3 Materials and Methods

3.1 Survey Design

Valuing non-financial cost of depression using the well-being valuation approach requires a randomly selected representative sample of individuals that are asked to rate their experiences such as their general life satisfaction or their momentary happiness and their experience of depression. During the autumn of 2017, 1753 individuals from the web panel Norstat (consisting at that time of about 67,000 individuals), were invited to participate in our survey. The sample was drawn using quota sampling that assures the representativeness of our sample with the Swedish population with respect gender, age and region. Out of these, 600 (or 34%) started to answer the survey and 500 (or 29%) completed the full survey. All respondents who completed the survey received a small amount of money (about 5 Euros), which they were asked if they would like to donate to charity. The selection criteria aiming to assure the representativeness of the sample by gender, age and four big geographical regions may potentially lead to self-selection bias. For example, persons with severe health problems are less likely to be part of a web panel, but they might suffer of depression and therefore using our sample to compute the monetary compensation for depression experience might produce underestimated values. Additionally, self-reported measures of depression can contain considerable measurement error, but we attempted to reduce this problem by providing the respondents with information about depression. The survey started by clearly informing the respondents about the difference between a real depression and temporary feelings of depression, anxiety and worry, as these feelings can be considered a natural part of life and are not something you get treatment for. We also provide the description of depression according to the Swedish online Healthcare guide 1177 and the Swedish Council on Health Technology Assessment in Health Care (See Box 1 in Appendix A1). In this way, we aimed to minimize the measurement errors of the self-reported measured of depression.

Additionally, the respondents answered a few questions about individual well-being, thoughts, and feelings about quality of life, and how they expect that their life quality would change if they will be affected by depression. They also answered questions about their satisfaction with their health, their access to health care services and their income. All these questions could be answered using a 0–10 scale and the alternative “I don’t know”.

The subjective well-being measure we used is general life satisfaction, elicited through the following question “Think about your life and personal circumstances, how satisfied are you with your life as a whole?”, using a scale 0–10 scale, 0 = not satisfied at all, …, 10 = completely satisfied. As already mentioned by Powdthavee & van den Berg (2011), there is no clear consensus on what the best measure of individual’s experience may be for the evaluation process. Furthermore, there it is not clear whether when people are assessing their “live as a whole” are they considering only their own well-being or also their family’s well-being in general.

Afterwards, to capture the respondents previous experience of depression, we asked if they ever had experienced depression, had met a specialist who evaluated their depression (Definition 2), if they had received a diagnose (Definition 1), if they know someone close to them (family and/or friend) who had/has depression (Definition 3), and if they are worried that they might get depressed.

3.2 Descriptive Statistics

Table 1 shows that approximately 64% of the respondents had experienced directly or indirectly some form a of depression (d4); 17.23% has/had a diagnose (d1); 22.24% were examined by a physician for depression (d2); 55.11% know someone who had/has depression (Definition d3).

Table 1 Depression experience, by definition (%)
Full size table

As we can see in Table 1, a few respondents did not want to answer the questions about their depression experience. This situation might be created by a combination of the rules of the web-panel and ethical requirements. According to the Norstat’s rules, the respondents must answer all questions of the web survey to be rewarded for their participation. Therefore, our data do not have missing values, but due to ethical requirements, a few questions were designed to give the respondents the alternative of answering explicitly that they do not want to answer. Except the question about the household income, which 71 of the 499 respondents did not want to answer, there are only four other variables used in our empirical analysis, including the depression experiences, that 1–14 respondents did not want to answer. Descriptive statistics reported in Table A1 in Appendix A2 suggest that there are not statistically significant differences between the mean values of the final sample (n = 425) and the original sample (n = 499), but there are a few statistically significant mean differences between the final sample and the sample of the respondents who did want to report their income (e.g., age, gender and the time used to answer the survey), and between women and men (e.g., more women than men experienced depression). Tables A1 suggests that the few non-responses are not random.

To get a sense of the correlation between life satisfaction of respondents and their experience of depression, Fig. 1 provides a graphical presentation of the life satisfaction of those who experienced depression (panel a) and those who did not experienced any form of depression (panel b). The plots show how the respondents answered to the question about their life satisfaction on a 0–10 scale, in percent. As expected, that the average life satisfaction scores of those who did not experienced depression are noticeably higher than those who experienced depression. The differences between the two groups suggest that the experience of any form of depression has impact on the individual’s well-being. Nonetheless, and important for our empirical analysis there are very small difference across the different ways we controlled for individual experience of depression.

Fig. 1
figure 1

Life satisfaction (%) by different experience of depression

Full size image

4 Estimation Results

In a detailed review of articles from mainstream economics journals that consider subjective well-being (SWB) and its determinants, Dolan et al., (2008), concluded that poor health, separation, unemployment, and lack of social contact are all strongly negatively associated with SWB. The review highlights a range of problems in drawing firm conclusions about the causes of SWB and the lack of certainty on the direction of causality. We did not have information about the municipality where the respondents live, and therefore we could not control for the local unemployment conditions, but we controlled for the individual’s unemployment. This section presents the estimation results for the general life satisfaction equations with focus on the estimated parameters needed to value depression: the estimates coefficients for depression experience and income. We estimate different model specifications separately for each type of depression experience the respondents had, with focus on estimating the additional income required to just compensate for the well-being losses experienced due to depression experienced directly by the individual or indirectly by living together with a person diagnosed with depression. In the first step, we estimate the impact of experienced depression, household income and other determinants on the individual’s life satisfaction. In the second step, we estimate how much money would be needed to give to people to keep the level of their life satisfaction unchanged when experiencing directly or indirectly depression. We are controlling for the sensitivity of the estimates by considering four model specifications (m1-m4) and eight definitions of depression experience (d1-d8) for whole sample (Tables A2a-A2h in Appendix A2) and separately for women (Tables A3a in Appendix A3) and men (Table A3b in Appendix A3) and by three age-groups (Tables A4a-A4d in Appendix A3).

The first model specification (m1) includes the traditional determinants of life satisfaction, including income and depression experience, the two variables needed to compute the monetary compensation for experiencing depression. Afterwards, we also control for the respondent’s intention to donate their survey remuneration to charity (model specification m2), the number of minutes used to complete the survey (model specification m3), and the respondent’s confidence that they can manage their life to avoid getting depression (model specification m4).

Table 2 presents the estimates for the four model specifications m1- m4 of the well-being equation for the case when the experience of depression refers to the respondents answer that they have/had a diagnosis for depression (referred as Definition 1 and denoted Def1). The estimates of the well-being equations are relatively stable across the four model specifications. The estimates for experiencing depression are statistically significant, and they are, as expected, negative. The estimates for income are not statistically significant, but they are, as expected, positive across all four specifications. Another estimate that is statistically significant across all four model-specifications is the dummy for being married or cohabitating, showing that the individuals with this marital status have on average a higher life satisfaction that those single. The parameter for the country of birth is statistically significant for only two of the model specifications, but is negative for all, suggesting that individuals who are not born in Sweden have on average a lower life satisfaction. The estimated parameters for donating their payment for answering the survey for preventing depression, the time in minutes for answering the survey and the self-assessed control for preventing own depression are statistically significant. These estimates suggest that people who are willing to donating their payment for answering the survey for preventing depression have on average a higher life satisfaction than those who do not want to donate. Individuals who are expecting that they know to manage their life to prevent own depression have on average a higher life satisfaction than those who expect that they cannot. The time spend to answer the survey is negatively correlated with the respondents’ life satisfaction.

To check the sensitivity of the estimated parameters, we re-run the regression for all four model specifications for different definitions of the depression experience, i.e., Definitions 2–4 presented above, and the Definitions 1–4 conditional on the worriedness of the respondent for getting depression. The estimated parameters for depression experience and income have the same sign and statistical significance, but their magnitudes vary across all eight definitions (d1) - (d8) and all four model specifications (m1) - (m4); see Tables A2a-A2h in the Appendix A2. Table 3 presents the estimates of the well-being equations for all eight definitions (d1) - (d8) of experiencing depression for model specification m4.

In the next step, we use the estimated parameters for depression and income to estimate how much money would be needed to compensate people to return their well-being level without having depression (see Table 4). The results suggest that the monetary compensation varies both by depression experience and model specification. The loss in life satisfaction for those who experienced depression corresponds to approximately Euro 5,000–17,000 depending on the definition of depression. The estimated values are on average higher when the individual is worried that he or she could become depressed. Regardless of being worried, the costs are almost double when the individual was experiencing depression themselves (i.e., (d1), (d2), (d5) and (d6)) compared to when they know someone near, family or friends who experienced depression. Perhaps, the most interesting result is that the average cost for those who were examined for depression is higher than the cost for those who were diagnosed with depression, which could be considered as a proxy for the individual need for help for other life-related problems and/or other health problems of those who were not diagnosed with depression.

Given the statistically significant mean differences between the samples of men and women and across the samples of young, middle-aged, and older respondents with respect depression experiences, we estimate the life satisfaction equations, specification m4, separately for each of these samples. The estimates for experiencing depression are, as expected, negative, for all samples and statistically significant for seven definitions for women, for six definitions for men (Tables A3a and A3b in Appendix A3), for all eight definitions for individuals aged 41–65 years old (Tables A3c and A3d in Appendix A3). The estimates for income are not statistically significant, but they are, as expected, positive across all eight definitions for all sub-samples.

Our computations of the monetary compensations based on the estimated coefficients of the depression experience and income using samples of approximately 200 individuals show that the compensation varies even more when the analysis was performed separately for women and men (Figure A1 in Appendix 3) and separately for three age-groups (Figure A2 in Appendix A3). The monetary compensation would be, on average, higher for men (4,000–12,500 Euros) than for women (2,350-5,660 Euros) and for middle-aged (9,000–45,500 Euros) than for both young (2,100-4,500) and older individuals (350 − 32,000 Euros). Even though these numbers should be used only as reference points, they suggest the importance of preventing depression.

Table 2 Well-being equation, by model specification (m1) - (m4). Diagnosed depression (Def 1)
Full size table
Table 3 Well-being equation estimates, by definition of experiencing depression. Specification 4 (m4)
Full size table
Table 4 The estimated cost of depression (in Swedish crowns), by definition (Def 1–4) and model specification (1)-(4)
Full size table

5 Discussion and Conclusions

In this paper, we use for the first time Swedish data to value the individual experience of depression in monetary terms by using the well-being valuation method. The well-being yearly cost of the respondents who knew someone who has experienced depression (€5000) is less than one third part of the wellbeing cost for those who experienced depression themselves (€17,000). These figures vary much more by gender (€2,350-€12,500) and age groups (€350-€45,000), suggesting a higher monetary compensation for men than women, and for middle-aged than younger and older individuals. Much more women than men experienced depression and their hypothetical monetary compensation is approximately half of men’s compensation. Similarly, much more individuals aged 18–39 than middle-aged experienced depression and their monetary compensation is much lower. But, given that the estimated coefficients for depression experience and income are sensitive with respect both definition of depression experience (eight definitions) and model specification (four specifications), the monetary compensation that would keep the individuals’ life satisfaction unchanged should be only used as relative reference points when no other information is available. However, the limitations of our study are driven by data availability and could be addressed in future studies. First, our results are produced using a representative sample for the Swedish adult population with respect to gender, age and four big geographical regions, which is subject to self-selection bias for created by self-selection for participation in a web-panel. Persons with severe mental health problems are less likely to be part of a web panel, and therefore our results are most likely applicable to individuals with less severe or well-managed mental health problems. Second, our study design was cross-sectional and therefore the findings are correlational. Although, theoretically, people with good mental health would seem to develop greater well-being, it is also possible that individuals with greater well-being may also have lower probability to develop depression. A longitudinal study with information about both the exact date and the type of depression experience will provide insight into a potential causal relationship.

In conclusion, our findings support and extend previous research that has established depression as an important factor in relation to lower SWB. Our results highlight that all types of depression experience lower the individuals’ life satisfaction, but both being diagnosed with depression and being screened for depression have much higher negative impact on the individual life satisfaction than knowing someone near, family or friends who suffer of depression. The compensation would be, on average, higher for individuals who experienced own depression compared to those who know someone near, family or friend, who experienced depression. This result is not unexpected but highlights the importance of allocating resources even for helping family and friends of the individual who suffers due to depression.

Our results also highlight that the monetary compensation for keeping the individual’s life satisfaction unchanged when suffering from depression varies by gender and age. The compensation would be, on average, higher for men than for women and for middle-aged individuals than for young and older people, respectively. These results make an even stronger case for examining how various aspects of mental health are developed and design preventive programs that support both the individuals and their family and friends.

In addition to add new empirical evidence for the negative relationship between depression experience and life satisfaction in Sweden, our findings also suggest the need for a dialogue that contributes to the development of policies that aim to boost citizens’ well-being. Based on evidence from UK (Layard, 2017), it is highly relevant, but not enough, that the Swedish government was and is actively involved in providing national mental health strategies that are facilitating prevention and awareness. Well-being is arguably the most important aspect of the human condition and therefore, the government should consider people’s individual well-being when designing and implementing public policies. However, to design practical, cost-effective policies to improve individuals’ and society’s wellbeing, resources must be invested to assure the existence of robust methods for measuring well-being, rigorous conceptual frameworks for understanding subjective well-being and empirical frameworks for estimating causal effects.