economic development and happiness thesis

Examining the Empirical Relationship Between Happiness and Human Development in Emerging Economies

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• Md Ataul Gani Osmani   ORCID: orcid.org/0000-0003-0207-2596 6 ,
• Laeeq Razzak Janjua   ORCID: orcid.org/0000-0002-1948-5859 7 ,
• Mirela Panait   ORCID: orcid.org/0000-0002-5158-753X 8 , 9 &
• Vikas Kumar Singh Tomar   ORCID: orcid.org/0000-0003-2435-667X 10  

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This study explores the empirical relationship between happiness and human development in seven emerging economies. The sample emerging economies are selected from the World Bank list, such as China, Russia, India, Indonesia, Brazil, Mexico, and Turkey. by using panel data from UNDP and our world in data from 2005 to 2020, this study first applies the panel cointegration test to determine whether there is a long-run relationship or not and to find that no long-run relationship between happiness and human development in emerging economies is manifested. Second, the ultimate application of panel VAR modeling entails that a one-way short-run causal relationship running from human development to happiness in emerging economies exists. This means that in the short-run, in emerging economies, human development causes happiness but not vice versa. Therefore, for building significant long-run relationships, those economies should focus on long-term strategies of economic freedom and mental health development.

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Department of Economics, Varendra University, Rajshahi, Bangladesh

Md Ataul Gani Osmani

Faculty of Applied Sciences, WSB University, Dąbrowa Górnicza, Poland

Laeeq Razzak Janjua

Petroleum-Gas University of Ploiesti, Ploiești, Romania

Mirela Panait

Institute of National Economy, Bucharest, Romania

Alma Mater Studiorum – Università di Bologna, Rimini, Italy

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Osmani, M.A.G., Janjua, L.R., Panait, M., Tomar, V.K.S. (2024). Examining the Empirical Relationship Between Happiness and Human Development in Emerging Economies. In: Chivu, L., Ioan-Franc, V., Georgescu, G., De Los Ríos Carmenado, I., Andrei, J.V. (eds) Constraints and Opportunities in Shaping the Future: New Approaches to Economics and Policy Making. ESPERA 2022. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-47925-0_7

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Economic Growth Evens Out Happiness: Evidence from Six Surveys

Andrew e. clark.

† PSE, 48 Boulevard Jourdan, 75014 Paris, France. Tel.: + 33-1-43-13-63-29. [email protected]

Sarah Flèche

§ Centre for Economic Performance, London School of Economics and Political Science, Houghton Street, London WC2A 2AE, UK. [email protected]

Claudia Senik

‡ PSE, 48 Boulevard Jourdan, 75014 Paris, France. Tel.: + 33-1-43-13-63-12. rf.sne.esp@kines

Associated Data

In spite of the great U-turn that saw income inequality rise in Western countries in the 1980s, happiness inequality has fallen in countries that have experienced income growth (but not in those that did not). Modern growth has reduced the share of both the “very unhappy” and the “perfectly happy”. Lower happiness inequality is found both between and within countries, and between and within individuals. Our cross-country regression results argue that the extension of various public goods helps to explain this greater happiness homogeneity. This new stylised fact arguably comes as a bonus to the Easterlin paradox, offering a somewhat brighter perspective for developing countries.

I. Introduction

“ Will raising the incomes of all increase the happiness of all ?” Richard Easterlin asked somewhat ironically in 1995 ( Easterlin, 1995 ), having shown that average self-declared happiness generally does not increase over the long run, even during episodes of sustained economic growth ( Easterlin, 1974 ). This finding has helped inspire a vast empirical and theoretical literature on social comparisons and adaptation (as surveyed in Clark et al ., 2008 ). Easterlin’s original finding has more recently been called into question, with it being suggested that in some countries there is a positive time-series correlation between per capita GDP and average levels of subjective well-being (a well-known contribution to this extent is Stevenson and Wolfers, 2008 a ). At the same time as this ongoing debate about the relationship between the average happiness level and GDP growth, a striking new stylised fact has recently emerged regarding the distribution of happiness or “happiness inequality”. As documented in Clark et al . (2014) , there is strong evidence across a wide variety of datasets that GDP growth is associated with systematically lower levels of happiness inequality (where this latter is picked up by the coefficient of variation). This article contributes to this happiness-inequality literature.

We provide evidence that economic growth is systematically correlated with a more even distribution of subjective well-being. This correlation for the most part holds despite the associated rise in income inequality, does not seem to be the result of any statistical artefact, is found in almost all domains of satisfaction, but is not found in placebo tests on other subjective variables. We suggest the extension of the provision of public goods as one likely candidate explanation for the lowering of happiness inequality.

None of our analyses of countries over time reveal a significant relationship between GDP growth and average happiness. This classic finding has attracted great attention, as it suggests that economic growth may serve no (happiness) purpose. Outside of a utilitarian world, however, we may be more Rawlsian and give a certain weight to the avoidance of misery: here higher GDP does seem to chalk up points. The value we attach to GDP growth will then depend on the particular social-welfare function that we have in mind.

The remainder of the paper is organised as follows. Section 2 shows that higher income is associated with a tighter distribution of subjective well-being across countries, within countries, across individuals, and within individuals. Section 3 then presents a regression analysis emphasising the key roles of income inequality and public goods in determining happiness inequality. Section 4 considers a number of additional tests regarding the measurement of dispersion, the issue of time in panel (for our panel data results), and some placebo tests. Last, Section 5 concludes.

2. Income Growth and Happiness Inequality

A great many pieces of work have used country-level data in order to show a relationship, or the lack of one, between GDP growth and average levels of satisfaction or happiness over time. However, until very recently, little attention was paid to inequality in subjective well-being as economies grew. Knowing that average happiness scores remained broadly unchanged does not tell us anything about the distribution of well-being: flat happiness time profiles can be associated with a stable distribution of happiness, rising inequality, or lower inequality.

Two papers, Stevenson and Wolfers (2008 b ) and Dutta and Foster (2013) , have explicitly addressed this issue. Both consider American General Social Survey (GSS) data from the early 1970s onwards (although their analytical approach differs); both underline a general fall in the inequality of happiness in the US up to around 2000, followed by an inversion of the trend (i.e. rising happiness inequality). Veenhoven (2005) found falling happiness inequality in EU countries (surveyed in the Eurobarometer) over the years 1973–2001, in spite of rising income inequality. He also notes a tighter distribution of happiness in “modern nations” rather than more traditional countries.

Clark et al . (2014) then looked at this issue systematically, using a wide variety of different datasets and a long time period (1970–2010). The crux of their argument is that countries with growing GDP per capita have also experienced falling happiness inequality. The data used there come from the World Values Survey (WVS), the German Socio-Economic Panel (SOEP), the British Household Panel Survey (BHPS), the American GSS and the Household, Income and Labour Dynamics in Australia survey (HILDA). Happiness inequality was picked up by the coefficient of variation (the standard deviation of happiness divided by the sample happiness mean).

It is difficult to know what the correct measure of the distribution of subjective well-being is. One worry is that higher happiness levels will mechanically lead to lower dispersion as reflected in the coefficient of variation. If higher income produces greater happiness, on average, then the correlation between GDP per capita and the coefficient of variation of happiness will mechanically turn out to be negative. We here avoid the possibility of any artificial relationship by using the simple standard deviation of happiness as our measure of distribution. As we will show, this makes no difference to the main result that economic growth evens out the distribution of happiness (as we find no evidence in country panels that higher GDP per capita does actually raise average happiness). We will also test a number of other measures of the spread of happiness.

Most countries with growing GDP per capita exhibit a tighter distribution of happiness, even though the US, with its U-shape of happiness inequality in GSS data, is an outlier in this respect. We provide a unified explanation of the experience of all countries, via a regression framework that encompasses the effect of GDP per capita, income inequality, and the role of public goods.

The following sub-sections consider the relationship between income and happiness inequality, first in country cross-sections and panels, and then in individual cross-sections and panels. It finishes by asking whether the correlation is a statistical artefact, and considering the relationship with domain satisfaction variables, rather than a single measure of overall well-being.

2.1 WVS Country Cross-Section

Our first piece of evidence follows on from Clark et al . (2014) by looking at country cross-sections from the last available wave of the WVS (in the 2000s). Figure 1 shows the results. The left-hand panel reveals that the standard deviation of happiness is lower in richer countries. This relationship is significant at the one per cent level, as shown by the regression equation at the foot of the figure. The right-hand side panel (taken from Clark et al ., 2014 ) shows the equivalent relationship using the coefficient of variation. The slope may look flatter, but of course the dependent variable is not on the same scale. In both cases, richer countries have tighter happiness distributions.

Happiness Inequality and GDP per capita. WVS Cross-Section, Last available year (2000s)

2.2 WVS Country Panel

The next piece of evidence comes from changes over time within countries. We first consider WVS countries. These are a priori interviewed five years apart, although the gap may be somewhat shorter or somewhat longer. In Figure 2A we include WVS countries that are observed at least twice, with the gap between observations being at least five years. We also only include countries that recorded positive real GDP per capita growth (where these figures come from the Penn World Tables) in all of the intervening years between the two consecutive WVS observations. 1 What happened to happiness inequality in these growth periods?

A. Happiness Inequality within Countries with Uninterrupted GDP Growth . WVS Panel. Selected Western countries

B. Happiness Inequality within Country in Countries with Falling GDP . WVS Panel.

Figure 2A answers this question using data from a selection of Western countries. Although there is some sample variability here, happiness inequality falls over time as countries grow richer. The average relationship over time in these countries is given by the grey line in the Figure. Again, it makes little difference whether we consider the standard deviation or the coefficient of variation. The trend line slopes downwards in both panels of Figure 2A : as countries become richer, their inequality in happiness falls. This is formalised in the regression equation at the foot of the figure, where “year” attracts a negative and significant coefficient. 2

While suggestive, neither Figure 1 nor Figure 2A arguably provides evidence of a clean causal relationship. In the cross-section analysis, there could be some innate country characteristic that both makes a country rich and reduces the spread of its happiness distribution; in Figure 2A there has perhaps been a generic move towards reduced happiness inequality across all countries, which has nothing to do with GDP growth per se .

One obvious rejoinder to the latter is to consider countries that did not experience such periods of continuous GDP growth, and show that the relationship is different for them. This is what we do in Figure 2B , where we select countries that experienced some years with falling real GDP per capita between the two WVS observations (although there are fewer of these). Figure 2B reveals a substantial rise in happiness inequality in these countries. Hence, there is no evidence of a general trend towards a tighter distribution of subjective well-being over time in the WVS: this tightening is only found for countries which have systematically become richer. It is worth noting that this finding is not particular to the WVS. We can reproduce Figure 2A using 40 years of annual data from the Eurobarometer: these results are depicted in Appendix A Figure 1 .

2.3 BHPS, SOEP, HILDA and GSS: Country Panels

One drawback of the WVS is its small time-series dimension. 3 We therefore now turn to single-country datasets, which contain many more waves of data. We use four popular long-running single-country datasets, covering the United Kingdom (BHPS), Germany (SOEP), Australia (HILDA), and the United States (GSS). We summarise the way in which happiness and GDP co-move over time in the regressions in Table 1 . The first three columns refer to regressions for our three dependent variables: the log of real GDP per capita, average life satisfaction, and the standard deviation of life satisfaction. We carry out separate regressions for each of the four countries. The control variables in each regression include standard demographics (as listed in the note at the foot of the table), the year (divided by 10) and (if significant) year-squared divided by 100. We include the latter to detect non-linear movements over time. We might in particular suspect that inequality in life satisfaction in the US is U-shaped over time, following the findings of Stevenson and Wolfers (2008 b ) and Dutta and Foster (2013) .

Time Trends in GDP per capita, Happiness and Happiness Inequality. Single-country Panels.

Note : Happiness and life satisfaction questions are administered consistently over time within countries for all of these surveys, although the surveys use different scales: 1-3 in the GSS, 0-10 in the SOEP and HILDA, and 1-7 in the BHPS. We select people aged between 18 and 65 years old; we also drop observations corresponding to a declared income of below 500$ per year. GDP per capita figures are taken from Heston, Summers and Aten – the Penn World Tables. The other controls include gender, age, age-squared, marital status, labour-force status and education. Robust standard errors are in parentheses.

The results in column 1 show that real GDP per capita has trended upwards in all four countries, with no evidence of any non-linearities. Column 2 suggests that average happiness is flat in all four countries.

We are most interested in the results in column 3, which show the trend in happiness inequality. In the UK and Australia, this trend is negative and linear. However, in both the US (as previously found) and Germany there is evidence of a U-shaped relationship. Column 4 calculates the turning point in happiness inequality in these two countries. These turn out to be 1993 for the US, 4 and rather later in 2007 for Germany. We will below suggest that the U-shapes in these two countries are at least partly driven by developments in income inequality. 5

2.4 BHPS, SOEP, HILDA and GSS: Individual Cross-Sections

At the individual level, within each country, happiness dispersion is also smaller amongst the rich than amongst the less well-off ( Figure 3 ): the richer seem to be more insulated against various kinds of shocks. In particular, higher income allows consumption to be protected from movements in income. 6 And it is in general likely that higher income allows the hedonic impact of various life shocks (job loss, divorce, etc.) to be smoothed.

Income as a Buffer Stock: The Standard Deviation of Happiness is Lower in Higher Income Deciles

2.5 BHPS, SOEP and HILDA: Individual Panels

Figure 3 referred to pooled data. We can also use our three panel datasets (we thus drop the GSS here) to look at the same correlation within-individual. The within-individual volatility of life satisfaction over time is lower for those in higher income deciles, as illustrated in Figure 4 (where the income decile we assign to the individual in the panel data is defined as the average rounded income decile over the period during which the individual is observed in the panel).

Income as a Buffer Stock: The Standard Deviation of Happiness within Individual over Time is Lower for Individuals in Higher Income Deciles

2.6 A Statistical Artefact?

All of our results so far suggest that higher income goes hand in hand with less happiness inequality. However, we may worry that this is a statistical artefact. In particular, if higher incomes make people happier, and happiness is measured using a bounded scale, then inequality will fall as more people become right-censored on the top rung of the happiness ladder. At the extreme, as everyone reports the top subjective well-being score, inequality will be zero.

A first response to this worry is that there is actually no evidence in Table 1 that higher income over time does go hand-in-hand with higher happiness. We can nonetheless directly address the changing distribution of subjective well-being via histograms of happiness at the beginning and end of the periods under consideration. This is what Clark et al . (2014) do. Their striking finding is that, as GDP grows, fewer people report the lowest happiness scores. But far from becoming more heavily populated, the top category is becoming increasingly deserted in all of the BHPS, GSS, HILDA and SOEP. As a consequence, the middle happiness categories are increasingly popular. The fall in happiness inequality then actually seems to have come from a mean-preserving contraction, as the percentage reporting the lowest happiness levels is also falling. 7

A further formal test, if required, of the role of censoring comes from dropping the top and bottom scores in the panel datasets. 8 The test here is to look at happiness answers in year t+1 of those who reported happiness scores of between 2 and 6 (on a 1 to 7 scale) in the BHPS data (say) at year t . All of these individuals can report either higher or lower happiness when re-interviewed in year t+1 . As in Table 1 , the results in Figure 3 in Appendix A show that there continues to be a trend to lower happiness inequality as GDP grows in the three panels.

2.7 Well-being Domains

Is there something strange about the specific distribution of overall satisfaction scores? Figure 5 suggests mostly not. We here look at the trends in inequality in various domain satisfactions. Satisfaction with health, income and job all exhibit falling inequality in the UK, Germany and Australia. As was the case for life satisfaction, the US is an outlier. Job satisfaction inequality is (slightly) falling, but not that for health and income. We suspect that income inequality may play a crucial role here: this is the topic of the next section.

Trends in Satisfaction Inequality by Domain United Kingdom (BHPS)

3. Regression Analysis: Income Inequality and Public Goods

The results above come from analyses without any controls other than basic demographics. Can we now identify variables that help to explain the fall in happiness inequality over time? We here consider the role of income inequality and public goods. Recent years have seen rising incomes accompanied by rising income inequality 9 in many countries (which we would suppose to increase happiness inequality). 10 We do indeed find rising income inequality over time in the BHPS, GSS, HILDA and SOEP datasets.

At the same time, we may also reasonably expect income growth in general to produce public goods (like education, health, public infrastructure and social protection). Modern growth, in addition, comes with non-material public goods, such as lower violence and crime, greater freedom of choice in private life, political freedom, transparency and pluralism, better governance, and so on ( Inglehart, 1997 , and Inglehart et al ., 2008 ). These public goods are, by definition, available to everyone (although, of course, their marginal benefit may differ across individuals). We then suspect that their provision will reduce happiness inequality.

The role of income inequality and public goods in determining the standard deviation of life satisfaction is explored in Table 2 . The data here is the WVS and these are OLS regressions. 11 The “public-good”-type variables we consider are Government expenditure, Life expectancy, the under-five Mortality rate, the Control of Corruption, Civil Liberties, Trust and Religious Fractionalisation. While we group these together under the heading “Public Goods”, they are not all 100% publicly-provided. Amongst the variables in Table 2 , health is probably a publicly-provided public good, whereas something like trust is at least partly privately-produced.

OLS Estimates of the Determinants of SD Life Satisfaction: WVS-EVS (1981–2008)

Notes : The other controls include country fixed effects, time trends, age-category dummies, sex, labour-force status, and marital status. The errors are clustered at the country-year level. See Appendix B for the detailed variable descriptions.

We also control separately for the level of GDP per capita and income inequality by country-year. All of the explanatory variables are described in Appendix B . The four separate regressions in Table 2 are balanced, in that they are estimated only on the sample of observations with no missing values for the most complete specification (that in column 4).

The changes over time in our seven public-goods variables over our broad analysis period appear in Appendix C . Government expenditure as a percentage of GDP has been on an upward trend over the past 20 years, and Life expectancy has risen sharply since the early 1980s, at the same time as the under-five Mortality rate has been falling. On the contrary the upward trend in Trust (from the WVS) is only weak, and there is no discernible trend in the Control of Corruption and Civil Liberties. The last figure here shows the rise in income inequality since 1970, using data from the Luxembourg Income Study (LIS).

The first column of Table 2 confirms, as expected from Figure 1 , that happiness inequality does indeed fall with the log of GDP per capita. 12 Column 2 then adds income inequality, as measured by the Mean Log Deviation in Income, and our measure of Government expenditure. These attract insignificant coefficients, and their addition only mildly affects the estimated coefficient on log GDP per capita. 13 Column 3 adds the Life expectancy, under-five Mortality, Control of Corruption and Civil Liberties. Their inclusion turns the coefficient on GDP per capita insignificant, while the estimated coefficients on these “public-good” variables are of the expected sign (better outcomes reduce happiness inequality). Economic growth thus seems to reduce happiness inequality because it provides public goods that tighten the distribution of subjective well-being: the correlation between GDP per capita and Life expectancy, Mortality and the Control of Corruption are all around 0.8 in absolute value, while that with Civil Liberties is 0.67. Last, column 4 adds measures of Trust and Religious fractionalisation to the regression. Neither of these is significant 14 (although religious fractionalisation varies only little within countries over time, and we here include country fixed effects). Income inequality is positively correlated with happiness inequality in column (4).

Income growth would therefore seem to reduce happiness inequality because it allows for the greater provision of public goods. However, if this growth is accompanied by too large a rise in income inequality, then it is entirely possible that happiness inequality will actually rise as a result: this is arguably what we have observed in the US data above, and perhaps to a lesser extent in Germany.

4. Additional Tests

All of our analysis above uses the standard deviation in respondents’ self-declared satisfaction as our key dispersion measure. Such a measure assumes that happiness is continuous and cardinal, with equal distances between the steps. Although such an assumption is common in the field, we have no way of knowing whether it is actually true. To be on the safe side, we have reproduced our results using the Index of Ordinal Variation ( Berry and Mielke, 1992 ), a measure of variation specifically designed for ordinal measures. 15 As shown in Clark et al . (2014) , the movement over time in the IOV is practically identical to that in the standard deviation of happiness, so it is perhaps unsurprising that they produce such similar results.

One common cardinal measure of dispersion is the Gini coefficient. The movements over time in the Gini coefficient of subjective well-being are depicted in Appendix A Figure 4 . As was the case for the standard deviation, this falls over time, with a sharp upturn towards the end of the period in the US, and a smaller upturn for West Germany. A regression such as that in column 1 of Table 2 with the Gini of well-being as the dependent variable produces a negative significant coefficient; adding the controls in column 4 renders this coefficient insignificant.

Our empirical results in Section 3 come from both repeated cross-section and panel data. One potential problem in panel data is that of “panel conditioning”. Landua (1992) underlined that individuals seem to become used to satisfaction scales after a while in SOEP data. He, in particular, noted a tendency away from the end values. This finding was reproduced on more recent SOEP data by Frick et al . (2006) . Wooden and Li (2014) analyse HILDA data and find a clear narrowing in the dispersion of satisfaction scores over the first few waves in which the individual responds. We have checked what happens to the trend in life satisfaction when we drop the first two participation waves per individual. The results appear in Appendix A Figure 5 for our three panel data sets, and continue to show a downward trend in dispersion, with again the exception of the final few years in West Germany. 16

Last, it might be suspected that there is something inherent about self-reported variables, particularly in a panel-data context, which produces more homogenous responses over time. We here run a placebo test by looking at the changing standard deviation over time in other variables available in our four single-country datasets. For the BHPS, SOEP and GSS, we use self-reported interest in politics, which is measured on a one-to-four ordinal scale; in HILDA we consider the self-reported number of hours per week spent volunteering.

The results appear in Appendix A Figure 6 . There is little evidence of any particular trend in the dispersion of these measures over time, and certainly no evidence of a downward trend. The shrinking dispersion of subjective well-being over time in our datasets appears to be specific to this question, rather than a general feature of self-reports.

5. Conclusions

In spite of the great U-turn that saw income inequality rise in Western countries in the 1980s, happiness inequality has notably fallen in countries that have experienced consistent income growth (but not in those that did not). Modern growth has reduced the share of both the “very unhappy” and the “perfectly happy”. The extension of public amenities may have contributed to this greater happiness homogeneity by reducing the insecurity faced by the worst-off groups in the population.

We believe that higher national income goes hand-in-hand with a reduced spread in happiness. It can always be countered that there are potential common factors that drive both income/GDP and the inequality of happiness. One example, at the country level, might be the quality of institutions. Higher quality institutions (including educational institutions) might lead to both higher GDP and a smaller spread in happiness. Any omitted variable which explains all of our empirical findings would however have to vary across countries and within countries over time, and across individuals and within individuals over time, in the same way that income does: no obvious candidates come to mind.

The question of why the top of the happiness scale is increasingly deserted is perhaps a more difficult one to answer. One salient point is that the extension of public goods and government expenditure is costly, and the happier may have borne the brunt of the necessary taxation. A second explanation, which is far more difficult to test, is that growth has enlarged the world of possibilities of those previously at the top of the well-being distribution, raising their aspirations and reducing their satisfaction. Comparisons may be made not only within country, but also across countries (as suggested by Becchetti et al ., 2013 ), and the worldwide integration of the elite is likely an integral part of globalization ( Goldthorpe and McKnight, 2004 ). It is also possible that some of the by-products of economic growth have rendered comparisons to others easier to make and more salient. This is very likely the case with the internet. Clark and Senik (2010) show that those who have internet access at home are also more likely to report that they compare their income to others (although they do not have data on exogenous changes in internet access, for which see Lohmann, 2014 ). Over the course of economic growth, this dampening effect will be concentrated amongst the richer, and perhaps also amongst those who were previously among the happiest.

As in much of this literature, we find no evidence that economic growth has been accompanied by higher mean levels of subjective well-being. But we show that it has come along with reduced happiness inequality. This new “augmented” Easterlin paradox therefore offers a somewhat brighter perspective: economic growth is a useful tool for policy-makers who care not only about average subjective well-being, but also its distribution.

Supplementary Material

Supp appendix.

* We are very grateful to two anonymous referees for pertinent and helpful suggestions. We thank participants at very many conferences and seminars, as well as Dan Benjamin, François Bourguignon, Daniel Cohen, Jan-Emmanuel De Neve, Richard Easterlin, Yarine Fawaz, Francisco Ferreira, Ada Ferrer-i-Carbonell, Paul Frijters, Sergei Guriev, Ori Heffetz, Tim Kasser, John Knight, Richard Layard, Sandra McNally, Andrew Oswald, Nick Powdthavee, Bernard van Praag, Eugenio Proto, Martin Ravallion, Paul Seabright, Conal Smith and Mark Wooden. We thank CEPREMAP and the French National Research Agency, through the program Investissements d'Avenir, ANR-10-LABX_93-01, for financial support; this work was also supported by the US National Institute on Aging (Grant R01AG040640) and the Economic & Social Research Council.

1 This Figure differs from that in Clark et al . (2014) , which only considered the coefficient of variation and which included countries with both positive growth in all years and positive growth in some years.

2 We restrict the sample to Western countries here only to avoid cluttering the figure excessively. The same shape appears if we include all WVS countries: the regression for the left-hand panel of Figure 2A then becomes SD(Satisfaction)=0.008***year+19.33.

3 The WVS started in 1981 and has been repeated every five years since 1990–1991. The sixth wave is currently in the field.

4 This turning point is somewhat earlier than that in Stevenson and Wolfers (2008 b ) and Dutta and Foster (2013) , which undoubtedly comes from our use of control variables in Table 1 .

5 Analogous relations between GDP and happiness inequality are found in Eurobarometer data. We show a number of the relevant single-country graphs in Appendix A Figure 2 . Happiness inequality trends downwards in all eight of the countries shown there.

6 Which is why consumption varies less over time than does income: see Krueger and Perri (2006) .

7 In general, objective happiness is converted into reported happiness via a “reporting function”. It is possible that reporting functions change as income grows. For this to explain our results, such reporting functions would have to operate both in the cross-section and in time series, and operate in something of the same way across countries. Equally, the fact that the richer seem more insulated against various kinds of shocks can be explained by a concave reporting function. We arguably know only little about reporting functions, and how they may change over time, and differ between individuals.

8 We cannot do this in the GSS, of course, as it is not panel.

9 See Atkinson et al . (2011) .

10 Becchetti et al . (2014) find that education reduces and unemployment increases happiness inequality. They however do not find any role for income inequality. Their measure of the latter is the share of the poor, which is different from ours.

11 We find the same results in ordered probit analysis.

12 The errors are clustered at the country-year level here (which is the aggregation level of GDP per capita), to avoid under-estimating the standard errors: see Moulton (1990) .

13 We might worry about reverse causality, with subjective well-being affecting productivity at work, and thus income (see Ostroff, 1992 , for survey analysis and Oswald et al ., 2015 , for an experimental approach). Our argument throughout this paper is a second-moment one, relating the level of income to the spread of the subjective well-being distribution. We actually find no relationship between average income and average well-being in our individual-country panels (see column 2 of Table 1 ). The only way in which movements in life satisfaction could then drive the level of income would be if the rising life satisfaction of the previously dissatisfied increases productivity more than the falling life satisfaction of the previously satisfied reduces productivity. We know of no evidence for such non-linear effects.

14 It is not always easy to have good measures of social capital at the cross-country level. Although trust is insignificant in this regression, some other measure of social capital may do better. The difficulty is in finding consistent measures across countries and over time.

15 Dutta and Foster (2013) also propose an ordinal measure of happiness inequality.

16 We have also taken the opposite tack, and kept only the first five responses in the panel. The same result pertains.

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Illinois Business Law Journal

Happiness and its Effect on Economic Development and Business Profitability

Introduction

“Policy decisions at the organizational, corporate, and governmental levels should be more heavily influenced by issues related to well-being––people’s evaluations and feelings about their lives.” [1]   This recent trend in economic development literature, that policy decisions at the government and corporate level should be influenced not by profit maximization but their effect on people’s subjective well-being, is gaining acceptance in the real world.  Empirical research and analysis shows that policy aimed at improving workplace happiness not only has an impact on employees subjective feelings of well-being, but also improves worker productivity and by extension corporate profitability.      

How Do Happiness Studies Work?  

Because “happiness” is not a quantifiable variable, there are numerous ways in which researchers control for the subjective aspect of one’s reported happiness.  A typical happiness survey consists of simply asking respondents, “all things considered, how happy are you with your life?”. [2]   To control for the fact that a respondent might have gotten into a car accident on the way to work, or that a respondent got a parking ticket that day, which may skew the believability of a response, researchers readminister the survey at random intervals over a period of time. [3] What they find is that respondents generally report the same level of happiness over a long period of time.  Another way to control for the problems associated with happiness studies is to ask the friends and family of the respondents how happy they feel the respondent is with their lives.  The idea is that friends and family will have an insight into how happy a respondent is, and researchers find that friends and family tend to report the same level of happiness as did the respondent. [4]   Finally, researchers also study things like suicide rates for example, and find that only those respondents reporting a low level of happiness will commit suicide in the future. [5]   These findings support the conclusion that respondents give truthful and insightful responses to the question “how happy are you?”.    

Important Findings of Happiness Studies  

People with a higher education report significantly higher levels of happiness than those with less education. [6]   People over 60 report being happier than people of middle age. [7]   And married couples are happier than singles. [8]     

But for the purposes of this paper, one of the most important finding is that although higher levels of income are positively correlated with happiness, this positive relationship has it limits.  After an income of approximately $20,000, the statistical relationship between income and happiness is negligible. [9]   In other words, marginal increases in income after $20,000 only slightly increases happiness. [10]   Also, research in this field has found that although GDP per capita has increased 400% since 1960, the average American does not report high levels of happiness than Americans in the 1960’s, even though Americans today are four times as wealthy in real terms. [11] These findings suggest that striving to be the wealthiest nation in the world should not be the basis of our policy decisions if our ultimate goal is happiness and life satisfaction.  Other countries are taking note of this phenomenon, and France for example has created The Commission on the Measure of Economic Performance and Social Progress which is engaged in researching levels of happiness and developing policy initiatives aimed at creating sustainable national well-being. [12]    

Happiness and Workplace Productivity  

Looking on the bright side, the implication of this literature is not that we must choose between being rich and unhappy versus poor and happy.  In fact, implementing workplace policies that foster happiness may increase productivity thereby increasing wealth.  Policies aimed at profit maximization may be counterintuitive in that they decrease productivity and limit a business’s ability to grow and be profitable.  “One academic study found that managers with average salaries of about $65,000 cost their organizations roughly $75 a week per person in lost productivity if they are “psychologically distressed.” [13]   Providing treatment to depressed workers has been shown to increase productivity, which added up to an estimated annual value of $2,601 per depressed full-time employee. [14]   This treatment also resulted in a 28% lower rate of absenteeism. [15]   Also, a national survey of 1,792 adults conducted by Peter D. Hart Research found that 63 percent trust employers “just some” or “not much” when it comes to treating employees fairly. [16]   The bottom line is that unhappy workers who feel they are not treated fairly have no incentive to be productive and will only do just enough to not get fired.    

How Can Happiness and Productivity Be Improved?

Employee morale can be increased in the most obvious ways, yet are sometimes ignored by managers and higher level executives.  For example, treating an employee with respect, getting to know employees, creating an employee recognition program, and having regular meetings to address questions or concerns an employee may have significantly improve morale and happiness, and therefore improve productivity. [17]   The problem in the business community is that these methods are not seen as profit maximizing tools, but rather side issues dealt with on a human resources level.  If corporations made these sorts of programs as important as their other profit maximization business dealings, they could increase workplace productivity and profitability.  “ Staff that enjoy good working relationships, receive proactive career development, feel valued by the organization and well treated in times of change, are likely to be contributing the most to a business.  Furthermore, they will be ambassadors for the organization, sending out positive messages to the outside community and enhancing the employer brand.” [18]

Conclusion  

Happiness is more than just an ideal emotion with no place in the business world.  Research has shown that happiness at work dramatically effects productivity.  Policies centered on strict rules, output, and profit maximization may be counterintuitive in that they displace employee happiness, negating the possible benefits of such programs.  In addition, improving employee happiness is inexpensive and can be attained simply by treating employees with respect and recognizing their successes.  By implementing policies aimed at promoting workplace happiness, corporations and other businesses will not only improve the workplace environment, but also their bottom line.    

[5] Daly, Mary C., Daniel J. Wilson. “Happiness, Unhappiness, and Suicide: An Empirical Assessment.”

Federal Reserve Bank of San Francisco Working Paper, 2008-18.

http://www.frbsf.org/publications/economics/papers/2008/wp08-19bk.pdf  

http://www.iew.uzh.ch/wp/iewwp015.pdf

[12] J oseph E. Stiglitz, Amartya Sen, Jean-Paul Fitoussi, “Report of the Commission on the Measurement of Economic Performance and Social Progress”. 2009.

[13] Diane Stafford, “Study Finds That Happy Workers Are More Productive Workers”. St. Petersburg Times . April 10, 2009.

http://bx.businessweek.com/health-and-wellness/view?url=http%3A%2F%2Fc.moreover.com%2Fclick%2Fhere.pl%3Fr1916735415%26f%3D9791

[14] Lloyd deVries, “Happy Workers, Better Workers”.  2004. CBSNews.com

http://www.cbsnews.com/stories/2004/11/24/health/webmd/main657624.shtml

[15] John Reh, “Which are More Productive, Happy Workers or Sad Workers?”.  November 27, 2004. 

http://management.about.com/b/2004/11/27/which-are-more-productive-happy-workers-or-sad-workers.htm

[16] Kent Hoover, “Survey Finds Number of Unhappy Workers on the Rise”.  Washington Business Journal.  September 7, 2001.

http://washington.bizjournals.com/washington/stories/2001/09/10/newscolumn3.html

[17] Lea Hartog,”15 Ways to Boost Employee Moral”.  HRWorld.com. April 28, 2008.

http://www.hrworld.com/features/15-ways-boost-employee-morale/

[18] “Happiness at Work Index – Research Report 2007.” Chiumenta London.

http://www.arboraglobal.com/documents/Happiness%20at%20Work%20Index%202007.pdf

Happiness: the real purpose of economic development?

Professorial Fellow, The University of Melbourne

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John Wiseman attended the Conference on Happiness and Economic Development as a guest of the Centre for Bhutan Studies, Thimphu, Bhutan

University of Melbourne provides funding as a founding partner of The Conversation AU.

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In recent weeks, while global financial markets threatened to implode, looters rampaged through the streets of London , and the British Prime Minister David Cameron reflected darkly on the dangers of a “broken society”, Bhutan’s Prime Minister, Jigme Y. Thinley hosted a landmark international conference on Happiness and Economic Development .

The gathering of eminent scholars was co-hosted by Professor Jeffrey Sachs , head of the Colombia University Earth Institute and senior adviser to UN Secretary General, Ban Ki Moon.

Participants included influential economists, philosophers and politicians such as Princeton’s Professor Peter Singer, Lord Richard Layard from the London School of Economics and Digvijaya Singh, General Secretary, Congress Party of India.

UN resolution on happiness and economic development

The springboard for the conference was the recent passage by the UN General Assembly of the Bhutanese sponsored resolution calling for happiness and wellbeing to become a central goal of global social and economic development policies.

The conference began with an overview of the path breaking work being undertaken in Bhutan to promote and measure “Gross National Happiness” (GNH), defined as “a harmonious balance between material wellbeing and the spiritual, emotional and cultural needs of the society.”

The nine domains used to measure GNH in Bhutan include psychological well-being; time use; community vitality; culture diversity; environmental diversity and good governance, as well as the more traditional measures of living standards, health and education.

Happiness, economic development and poverty

The most striking message from the conference was the conclusion that the core goal of economic development should indeed be to maximise the happiness and wellbeing of current and future generations.

While material wealth is clearly an essential precondition for poverty reduction, there is mounting empirical evidence that economic growth alone is an insufficient foundation for building a good society in which all citizens have the opportunities and capabilities to live fulfilling lives of dignity, creativity – and happiness.

There was also strong support for the view that the achievement of this goal will require a heightened awareness of the interdependence between human beings, other species and our shared environmental heritage.

A renewed commitment to valuing and promoting human happiness and wellbeing is crucial both as a way of staying focused on a more, balanced, equitable approach to human development and as a necessary foundation for action to address the escalating ecological, economic and social challenges of climate change.

Conference delegates were also clear that the promotion of happiness and wellbeing is not an alternative to eradicating poverty and swiftly reducing inequalities within and between nations.

It is therefore vital to ensure that the inclusion of subjective happiness measures in global frameworks such as the Millenium Development Goals does not dilute the accountability of governments for delivering on core commitments to meet basic material needs.

A growing international movement to rethink GDP

As the 2009 Sarkozy Report on the Measurement of Economic Performance and Social Progress , authored by Joseph Stiglitz, Amartya Sen and Jan-Paul Fitoussi noted: “the time is ripe for our measurement system to shift emphasis from measuring economic production to measuring people’s wellbeing.”

At a global level the Millenium Development Goals , the Human Development Index and the OECD Better Life Index provide valuable examples of the role which carefully considered happiness and wellbeing indicators can play as tools for engaging citizens and policy makers in informed reflection and debate about social, economic and ecological goals and priorities.

There are also many inspiring and creative national and local approaches to understanding and measuring wellbeing – including Australian initiatives such as Community Indicators Victoria and the ABS work on Measuring Australia’s Progress .

More than a slogan

For the promotion of happiness and wellbeing to be more than a slogan, happiness and wellbeing goals and indicators need to inform and drive policy making and resource allocation.

Citizens and communities need to see tangible achievements as well as fine words.

This will require concerted programs of public and media education as well as action to assist policy makers make best use of broader conceptual frameworks and improved data sets.

Questions as well as answers

Like all good conferences, this event led to as many questions as answers.

For instance, how do we avoid the very real risk of concepts like “happiness” and “wellbeing” being trivialised, co-opted or assumed to refer to a narrow individualstic agenda of unconstrained accumulation and consumption?

How do we build shared global agreements about happiness and wellbeing goals, domains and indicators - while respecting differences in culture and language?

And what needs be done to ensure the development of the development of happiness and wellbeing indicators is informed by broad public engagement and does not remain solely the province of technical experts, economists and statisticians?

Finally, perhaps most importantly, how can this most timely conference and this growing movement help us navigate an ethical pathway to a just and sustainable, safe climate future?

Towards a new global debate about the purpose of economics

The outcomes of the conference will inform future UN discussions on the implementation of the General Assembly resolution on the core goals of economic and future priorities for the Millenium Development Goals.

More broadly there is significant potential for these ideas to drive a revitalised public debate about the purpose of economic and social policy.

The recent London riots are a powerful wake up call about the consequences of the loss of a shared moral compass and of the corrosion of shred values of reciprocity and common good.

This conference provided an important opportunity to reflect on the ways in which we might reset our ethical compass to navigate an increasingly threatening global ecological and economic environment.

• Happiness Index
• Economic development

Business Development Manager, MDHS

Newsletter and Deputy Social Media Producer

College Director and Principal | Curtin College

Head of School: Engineering, Computer and Mathematical Sciences

Educational Designer

The Reporter

Happiness Economics

Despite Thomas Carlyle's claim, when he was arguing that slavery was morally superior to the market, economics is no longer the dismal science . 1 A growing body of literature in the economics of happiness and mental well-being has emerged. It is now fashionable to try to understand the pursuit of happiness, and, after a long delay, the ideas promoted originally by Richard Easterlin are attracting worldwide attention. 2 There is even a World Database of Happiness.

Anyway, I came to the topics of happiness and well-being as a labor economist who had mostly worked on wages, and who early on was struck by the stability of the Mincerian earnings function across time and space. The basic structure of a log earnings equation, no matter what dataset was used and what country it is estimated for, has a similar structure. It turns out that there are patterns in the well-being data. I am struck by the fact that there is a great deal of stability in happiness and life-satisfaction equations, no matter what country we look at, what dataset or time period, whether the question relates to life satisfaction or happiness, and how the responses are coded whether in three, four, five or even as many as ten categories.

In general, economists have focused on modeling three fairly simple questions on life satisfaction and happiness, and that is what I have done mostly in my research, primarily with Andrew Oswald at the University of Warwick, but also with David Bell at the University of Stirling, Chris Shadforth at the Bank of England, and Richard Freeman from Harvard.

Typical questions are: 1) Happiness - for example from the General Social Survey, Taken all together, how would you say things are these days - would you say that you are very happy, pretty happy, or not too happy? 2) Life satisfaction - for example from the European Eurobarometer Surveys, On the whole, are you very satisfied, fairly satisfied, not very satisfied, or not at all satisfied with the life you lead? 3) Psychological health and mental strain - for example from the British Household Panel Survey, Such as the GHQ score, which amalgamates answers to questions about how well people have been sleeping, their level of confidence, feelings of depression, among others. 3

The micro data on happiness are easily obtainable from most data archives including ICPSR for the GSS and the Eurobarometers , the Data Archive at the University of Essex and ZACAT in Germany for the Eurobarometers, ISSP, European Social Survey, BHPS, GSOEP, European Quality of Life Survey, European Social Surveys and so on. Life satisfaction data are also now available annually from the Latinobarometers , while happiness data are also available annually in the Asianbarometers . Several of the data series extend back at least to the early 1970s. Some are panels ( BHPS, GSOEP ).

Economists have had longstanding reservations about the reliability of interpersonal comparisons of well-being. Psychologists, however, view it as natural that a concept such as happiness should be studied in part by asking people how they feel. One definition of happiness is the degree to which an individual judges the overall quality of his or her life as favorable. As a validation of the answers to recorded happiness levels, it turns out that answers to happiness and life satisfaction questions are correlated with: 1) objective characteristics such as unemployment; 2) assessments of the person's happiness by friends and family members; 3) assessments of the person's happiness by his or her spouse; 4) heart rate and blood-pressure measures of response to stress; 5) the risk of coronary heart disease; 6) duration of authentic or so-called Duchenne smiles (a Duchenne smile occurs when both the zygomatic major and obicularus orus facial muscles fire, and human beings identify these as "genuine" smiles); 7) skin-resistance measures of response to stress; 8) electroencephelogram measures of prefrontal brain activity.

It is apparent that most people are happy (or, more precisely, mark themselves fairly high up on a scale). This finding has subsequently been replicated in many datasets over many time periods and for numerous countries. For example, in the United States in 2006 only 13 percent of people in the GSS said they were not very happy, 56 percent were pretty happy, and 31 percent very happy. In Eurobarometer 67.2 for April-May 2007 (ICPSR#21160) for the European Union (EU) 15 in 2007, for example, 3 percent said they were not at all satisfied, while 12 percent were not very satisfied, 60 percent fairly satisfied, and 24 percent very satisfied. In the 2005 Latinobarometers , which also asked the same 4-step life satisfaction question in eighteen Latin American countries, 4.6 percent said they were not at all satisfied, 25.4 percent were not very satisfied, 39.7 percent were fairly satisfied, and 30.3 percent very satisfied.

There are a number of common patterns in the determinants of happiness, which have been replicated in a number of other papers. Happiness and life satisfaction tends to be higher among a) women, b) people with lots of friends, c) the young and the old, d) married and cohabiting people, e) the highly educated, f) the healthy, g) those with high income, h) the self-employed, i) people with low blood pressure, j) those who have sex at least once a week with the same partner, k) right-wing voters, l) the religious, m) members of non-church organizations, n) volunteers, o) those who take exercise, and p) those who live in western countries. 4 The self-employed especially value their independence. 5 It turns out that the main findings from responses on both happiness and life satisfaction are also broadly replicated in data on unhappiness, hypertension, stress, depression, anxiety, and pain from a considerable number of cross-country data sources. 6 Happy people are less likely to commit suicide. 7

There is also evidence of adaptation. Good and bad life events wear off, at least partially, as people get used to them. Oswald and Powdthavee provide longitudinal evidence that people who become disabled go on to exhibit considerable recovery in mental well-being. 8 In fixed-effects equations they estimate the degree of hedonic adaptation at -- depending on the severity of the disability -- approximately 30 percent to 50 percent.

I recently compared the results using data on life satisfaction and happiness with those from evaluated time use 9 and in particular the U-index (for "unpleasant" or "undesirable") as propounded by Krueger et al for use in National Time Accounting 10 . The U-index is designed to measure the proportion of time an individual spends in an unpleasant state. Encouragingly, there are many similarities in the findings. For example, both the U-index and conventional measures show higher levels of well-being among wealthier, higher educated and older individuals. One attraction of the evaluated time use data, though, is that it can be used to understand why some groups are happier than others.

In the United States happiness has trended downward over time, but the picture is rather more mixed in Europe: using the Eurobarometer life satisfaction questions, when an ordered logit is estimated with controls for education, age, gender, schooling, marital status, and labor market status, nine countries have positive time trends (Denmark; Finland; France; Italy: Luxembourg; Netherlands; Spain; Sweden; and the United Kingdom.) Austria and Ireland have no significant time trend while Belgium, Germany, Greece, and Portugal have significant downward trends. There is evidence from developing countries of a steeper upward trend in happiness. This is especially apparent in Latin America and among the eight Eastern European countries that joined the European Union in 2004. 11

There is evidence though of a downward trend in happiness for women and an upward trend for men in the United States 12 . Betsey Stevenson and Justin Wolfers confirmed this finding for the United States and also found that women's declining relative well-being is found in multiple countries, datasets, and measures of subjective well-being, and is pervasive across demographic groups. 13 Relative declines in female happiness have eroded a gender gap in happiness in which women in the 1970s typically reported higher subjective well-being than did men. These declines have continued and a new gender gap may be emerging, one with higher subjective well-being for men.

A small literature, begun by Rafael Di Tella and his co-authors, has found that both unemployment and inflation lower happiness. 14 This is true even after controlling for country fixed effects and year effects. My paper extends the literature by looking at more countries over a longer time period. It also considers the impacts on happiness of GDP per capita and interest rates. 15 I find, conventionally, that both higher unemployment and higher inflation lower happiness. Interest rates are also found to enter happiness equations negatively. Changes in GDP per capita have little impact on more economically developed countries, but do have a positive impact in the poorest countries -- consistent with the Easterlin hypothesis. I find that unemployment depresses well-being more than inflation. The least educated and the old are more concerned about unemployment than inflation. Conversely, the young and the most educated are more concerned about inflation. An individual's experience of high inflation over their adult lifetime lowers their current happiness over and above the effects from current inflation and current unemployment. Unemployment appears to be more costly than inflation in terms of its impact on wellbeing.

Oswald and I found that the well-being of the young had risen both in the United States and Europe. 16 Explaining why was difficult. This is still the case: in the GSS the time trend is positive for those aged under 30 and flat for those aged 30 and higher. In Europe using pooled Eurobarometers there is a significant upward time trend for both but much stronger for the young. We ruled out that it was explained by a decline in the chance of war with the Eastern bloc, falling discrimination, changing education and work, or the rise in youth-oriented consumer goods. The paper demonstrated that most of the increase is to be found in the group who were unmarried.

In a 2004 paper, Oswald and I estimated the dollar values of events like unemployment and divorce. 17 They are large. For the typical individual, a doubling of salary makes a lot less difference than life events like marriage or unemployment. A lasting marriage (compared to widowhood as a "natural" experiment), for example, is estimated to be worth $100,000 a year. Further calculation suggests that to "compensate" men exactly for unemployment would take a rise in income of $60,000 per annum, and to "compensate" for being black would take $30,000 extra per annum. Oswald and Powdthavee find that using GHQ mental distress as the measure of well-being, the hedonic compensation annual amount in the first year for the death of a child might be of the order of £100,000 ($200,000). 18 These are all large sums, and in a sense reflect the low (happiness) value of extra income. Money, it seems, does not buy more sexual partners or more sex. 19

The data show that richer people are happier and healthier, but can we be sure about causality? As in other areas of economics, this is both of central importance and difficult to establish beyond all doubt. One attempt, by Jonathan Gardner and Oswald, found that Britons who receive lottery wins of between £1,000 and £120,000 go on, compared to those who receive tiny wins, to exhibit better psychological health. 20 But Oswald and I found that individuals in the United States were found to be less happy if their incomes are far above those of the poorest people. However, people do appear to compare themselves more with well-off families, so that perhaps they get happier the closer their income comes to that of rich people around them. Relative income certainly appears to matter. Erzo Luttmer finds, for the United States, that higher earnings of neighbors are associated with lower levels of self-reported happiness, controlling for an individual's own income. 21 Alberto Alesina and his co-authors find, using sample of individuals across the United States (1981-96) and Europe (1975-92) that individuals have a lower tendency to report themselves as happy when inequality is high, even controlling for individual income. The effect is stronger in Europe than in the United States. 22

Life satisfaction scores predict reasonably well the scale of the flow of workers coming to the United Kingdom from the eight Accession countries of the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, and Slovenia. These countries joined the European Union in 2004 and only the United Kingdom and Ireland gave them the right to work. In these countries the levels of happiness are low. 23 The propensity to migrate is more highly correlated with life satisfaction scores than it is with GDP per capita, the unemployment rate, or the employment rate. 24

Consistent with other research, Oswald and I recently documented, using evidence on over two million people, that psychological well-being is U-shaped through life. 25 A difficulty with research on this issue is that there are likely to be omitted cohort effects (earlier generations may have been born in, say, particularly good or bad times). First, using data on 500,000 randomly sampled Americans and West Europeans, the paper designs a test that can control for cohort effects. Holding other factors constant, we show that a typical individual's happiness reaches its minimum on both sides of the Atlantic and for both males and females in middle age. Second, evidence is provided for the existence of a similar U-shape through the life-course in East European, Latin American, and Asian nations. Third, a U-shape in age is found in separate well-being regression equations in 72 developed and developing nations. Fourth, using measures that are closer to psychiatric scores, we document a comparable well-being curve across the life cycle in two other datasets: 1) in GHQ-N6 mental health levels among a sample of 16,000 Europeans, and 2) in reported depression-and-anxiety levels among one million UK citizens. Fifth, we discuss some apparent exceptions, particularly in developing nations, to the U-shape. Sixth, we note that American male birth-cohorts seem to have become progressively less content with their lives. More, truly longitudinal research on this topic would be valuable.

In surveys of well-being, countries such as Denmark and the Netherlands emerge as particularly happy while nations like Germany and Italy report lower levels of happiness. But are these kinds of findings credible? Oswald and I provide some evidence suggesting that the answer is yes. 26 Using data on 16 countries, we show that happier nations report systematically lower levels of hypertension. As well as potentially validating the differences in measured happiness across nations, this suggests that blood-pressure readings might be valuable as part of a national well-being index.

In a pair of articles Oswald and I studied well-being in Australia along with a published response by Andrew Leigh and Justin Wolfers. 27 According to the HDI, Australia now ranks third in the world. That places the country above all the other English-speaking nations. This article raises questions about that assessment. It reviews the new work on happiness economics, considers implications for policymakers, and examines where Australia lies in international subjective well-being rankings. Using new ISSP data on approximately 50,000 randomly sampled individuals from 35 nations, the article shows that Australia lies close to the bottom of an international ranking of job satisfaction levels. Among a sub-sample of English-speaking nations, where a common language should help subjective well-being measures to be reliable, Australia performs fairly poorly on a range of happiness indicators. Moreover, Australia has the highest overall suicide rate. This appears to be a paradox.

The literature on the economics of well-being is currently growing at a remarkable rate. If one takes the view that human happiness is ultimately the most important topic in social science, perhaps -- after decades where economists lagged behind other social scientists -- this should not be surprising. However, we still have a great deal to learn. Is the Easterlin Paradox - that happiness at a national level does not increase with wealth once basic needs are fulfilled -- correct or only an approximation to the truth? Are relative-wage effect comparisons always harmful to people? How should we think about the connections between mental health and what economists have traditionally called utility? What are the deep links between money and happiness? The next decade is likely to see a great deal of work on these important topics.

1. "...I should say, like some we have heard of, no, a dreary, desolate, and indeed, quite abject and distressing one, what we might call, by way of eminence, the dismal science" Thomas Carlyle, 1849.

2. R.A. Easterlin, "Does Economic Growth Improve The Human Lot? Some Empirical Evidence" in Nations and Households in Economic Growth: Essays in Honor of Moses Abramowitz , P. A. David and M. W. Reder, eds. New York: Academic Press (1974), and R.A. Easterlin, "Will Raising the Incomes of All Increase the Happiness of All?" Journal of Economic Behavior and Organization , 27 (June1995), pp.35-48.

3. For economists who want a readable way into the literature, introductions to it can be found in sources such as A.J. Oswald, "Happiness And Economic Performance", Economic Journal , 107 November1997, pp. 1815-31; B.S. Frey and A. Stutzer, Happiness and Economics , Princeton and Oxford: Princeton University Press, (2002); and A.E. Clark, P Frijters, and M.A. Shields, "Relative Income, Happiness, and Utility: An Explanation for the Easterlin Paradox And Other Puzzles", Journal of Economic Literature , 46(1), (2008), pp.95-144.

4. D.G. Blanchflower and A.J. Oswald, "Well-Being Over Time in Britain and the United States", Journal of Public Economics , Volume 88, Issues 7-8, July 2004, pp. 1359-86; and P. Dolan, T. Peasgood, and M. White, "Do We Really Know What Makes Us Happy? A Review of the Economic Literature on the Factors Associated with Subjective Well-Being", Journal of Economic Psychology , 29, (2008), pp. 94-122.

5. D.G. Blanchflower and A.J. Oswald, "What Makes An Entrepreneur?" Journal of Labor Economics , January 1998, 16(1), pp. 26-60; D.G. Blanchflower, "Self-Employment: More May Not Be Better", Swedish Economic Policy Review , 11(2), Fall 2004, pp. 15-74; and D.G. Blanchflower and C. Shadforth, "Entrepreneurship in the UK", Foundations and Trends in Entrepreneurship , 3(4), (2007), pp. 1-108.

6. D.N.F. Bell and D.G. Blanchflower, "The Scots May Be Brave But They Are Neither Healthy Nor Happy", Scottish Journal of Political Economy , 54(2), pp. 151-307, May 2007; and D.G. Blanchflower, "International Evidence on Well-being", presented to an NBER conference, forthcoming in National Time Accounting and Subjective Well-Being , A.B. Krueger, ed. University of Chicago Press.

7. H.H. Koivuma, R. Honkanen, H. Viinamaeki, K. Heikkalae, J. Kaprio, and M. Koskenvuo, "Life Satisfaction and Suicide: a 20-Year Follow-Up Study", American Journal of Psychiatry , 1589(3), 2001, pp. 433-9.

8. A.J. Oswald and N. Powdthavee, "Does Happiness Adapt? A Longitudinal Study Of Disability with Implications for Economists and Judges", Journal of Public Economics , 92, (2008), pp.1061-77.

9. D.G. Blanchflower, "International Evidence on Well-Being."

10. A.B. Krueger, D. Kahneman, D. Schkade, N. Schwarz, and A.A. Stone, "National Time Accounting: The Currency of Life", in National Time Accounting and Subjective Well-Being , A.B. Krueger, ed. University of Chicago Press, forthcoming

11. D.G. Blanchflower and C. Shadforth, "Fear, Unemployment, and Migration", NBER Working Paper No. 13506 , September 2007; and D.G. Blanchflower, "International Evidence on Well-Being."

12. D.G. Blanchflower and A.J. Oswald "Well-Being Over Time in Britain and the United States."

13. B. Stevenson and J. Wolfers, "The Paradox Of Declining Female Happiness", mimeo, University of Pennsylvania, 2007.

14. R. Di Tella, R.J. MacCulloch, and A.J. Oswald, "Preferences Over Inflation and Unemployment: Evidence from Surveys of Happiness", American Economic Review , 91, 2001, pp. 335-41. See also J. Wolfers, "Is Business Cycle Volatility Costly? Evidence from Surveys of Subjective Wellbeing", International Finance , 6:1, 2003, pp.1-26.

15. D.G. Blanchflower, "Is Unemployment More Costly Then Inflation?" NBER Working Paper No. 13505 , September 2007.

16. D.G. Blanchflower and A.J. Oswald, "The Rising Well-Being Of The Young", in Youth Employment and Joblessness in Advanced Countries , D.G. Blanchflower and R. B. Freeman, eds, University of Chicago Press, 2000.

17. D.G. Blanchflower and A.J. Oswald "Well-Being Over Time in Britain and the United States."

18. A.J. Oswald and N. Powdthavee, "Death, Happiness, and the Calculation of Compensatory Damages", Journal of Legal Studies , forthcoming.

19. D.G. Blanchflower and A.J. Oswald, "Money, Sex and Happiness", Scandinavian Journal of Economics , 106(3), 2004, pp 393-415.

20. J. Gardner and A.J. Oswald, "Money And Mental Well-being: A Longitudinal Study of Medium Sized Lottery Wins", Journal of Health Economics , 26, 2007, pp.49-60.

21. E. Luttmer, "Neighbors As Negatives; Relative Earnings and Well-Being", Quarterly Journal of Economics , August 2005, 120(3), pp. 963-1002.

22. A. Alesina, R.Di Tella, and R.J. MacCulloch, "Inequality and Happiness: Are Europeans and Americans Different?" Journal of Public Economics , 88, 2004, pp. 2009-42.

23. D.G. Blanchflower, "Unemployment, Well-Being and Wage Curves in Eastern and Central Europe", Journal of Japanese and International Economies , 15(4), December 2001, pp. 364-402.

24. D.G. Blanchflower and C. Shadforth "Entrepreneurship in the UK".

25. D.G. Blanchflower and A.J. Oswald, "Is Well-Being U-Shaped over the Life Cycle?" Social Science and Medicine , 66(8), pp. 1733-1749, April 2008.

26. D.G. Blanchflower and A.J. Oswald, "Happiness and Hypertension Across Nations", Journal of Health Economics , 27(2), March 2008, pp. 218-33.

27. D.G. Blanchflower and A.J. Oswald, "Happiness and the Human Development Index: the Paradox of Australia," The Australian Economic Review , 38(3), September 2005, pp. 307-18; D.G. Blanchflower and A.J. Oswald, "On Leigh-Wolfers and Well-being in Australia", The Australian Economic Review , 39(2), June 2006, pp. 185-6; and A. Leigh and J. Wolfers, "Happiness and the Human Development Index: Australia is not a Paradox", Australian Economic Review , 39(2), June 2006, pp. 176-84.

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The Relationship between Happiness and Economic Development in KSA: Study of Jazan Region

• February 2018
• Asian Social Science 14(3):78

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