Is the price of happiness different around the world? : a meta-analysis of 615 correlation findings between income and happiness, in rich and poor countries

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Is the price of happiness different around the world?

A meta-analysis of 615 correlation findings between income and happiness, in rich

and poor countries.

Bachelor thesis by Chloé Ajamlou, 6076076 Supervised by Ieva Rozentale

University of Amsterdam, Faculty of Economics and Business August 20, 2014

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Index

§1 Introduction

§1.1 Context topic 4

§1.2 Problem examined and aim of research 4

§1.3 Research method 5

§1.4 Value of thesis 5

§1.5 Outline thesis 5

§2 Literature review

§2.1 Measuring happiness 6

§2.2 Relation income and happiness 7

§2.3 Differences in correlation magnitudes 8

§2.4 Hypothesis 10 §3 Research method §3.1 Research method 10 §3.2 Data description 11 §4 Results §4.1 Statistics 12

§4.2 Separate studies reviewed 13

§4.3 Comparision statistical results with literature review 15 §5 Conclusion

§5.1 Conclusion 16

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§5.2 Discussion and recommendations 16 Reference list 17 Appendix 1 20 Appendix 2 21 Appendix 3 50 3

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§1 Introduction §1.1 Context topic

A higher income is in general associated with a higher level of happiness. With a higher income, it is not only easier to satisfy ones need regarding consumption, but income and wealth are also essential for people’s freedom to make choices and therefore happiness (OECD, 2011).At the same time, when one has more money, this could have a negative effect on his/her happiness level. For example, think of the phenomenon obesity; a disease which typically arises together with income and it has a negative effect on happiness. Still, many researchers agree upon the fact that there is a positive correlation between income and happiness, which means happiness, is higher in rich countries than poor countries (Easterlin and Angelescu, 2012).

Although most studies found a positive relation between income and happiness, it is simplistic to guide economic policy based on only the traditional macroeconomic variable GDP, assuming a higher income results in higher happiness. Namely, one might argue that income could be more important for countries with low income than for countries with high income. This because in countries with high income, certain basic needs are already fulfilled for most

inhabitants, so more income would not result as much increase in happiness as it would in poor countries. The question arises if income is of more importance regarding happiness for poor countries than rich.

§1.2 Problem examined and aim research

To examine the difference in influence of income on happiness between poor and rich countries, we need to study different correlation findings on this topic. Professor Veenhoven of the

Erasmus University Rotterdam, Happiness Economics Research Organization, directs the World Database of Happiness (WDH). This database (1994) contains 603 studies to happiness in 69 countries of which 755 findings on the correlation between income and happiness. Although it contains many findings on the correlation between income and happiness, these findings are not yet ranked on place, so we cannot compare the results across different countries. In this thesis, we address this gap.

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The aim of this thesis is to range the correlation findings between income and happiness, of the WDH, and investigate if these findings differ between rich and poor countries. The research question is: To what extend we can see a difference in correlation findings between happiness

and income, regarding rich and poor countries?

The overall objective of the thesis is to analyze if there is any difference in correlation findings in the WDH when comparing rich and poor countries.

§1.3 Research method

In order to make a comparison between the income and happiness correlation of rich and poor countries, all the correlation studies of the WDH are ranged at their place of study. Then these findings are subdivided into two categories: rich or poor. When a finding is divided into the category rich, it means the correlation findings were found in a rich country and the same reasoning holds for a poor country. To distinguish rich and poor countries, the same distinction will be used as Easterlin (2013) did when comparing the influence of income on happiness over time between rich and poor countries.

Second, the means of the correlation findings of both groups are compared. Then, is examined if there is a significance difference between rich and poor countries regarding the happiness and income correlation.

§1.4 Value of thesis

Analyzing any difference in correlation findings (of the WDH) between rich and poor countries has not been done yet, but it is a useful comparison. This comparison helps us to expend our knowledge about the relation between income and happiness. Nowadays government

expenditure is controlled too often for the amount of expenditure, instead of the effect of this expenditure (Frey and Stutzer, 2002), whereas happiness can been seen as a potential effect. If it appears that with lower government expenditure, the same happiness level can be reached, this is very useful information and it can save any government a lot of money.

§1.5 Outline thesis

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The following paragraph consists of a literature overview. This literature overview describes the concept of measuring happiness and the studies on the relation between income and happiness will be illustrated. This paragraph concludes with a hypothesis of the research question, based on the literature. In the third paragraph, we go over the research method and the results. We turn to the literature conclusions to compare the results of the regression analysis done in this research and the existing literature. The last paragraph consists of the conclusion of this thesis and the value of the conclusion. Also, the limitations of this research are discussed as well as the recommendations for in the future.

§2 Literature review

In this paragraph, the concept of measuring happiness will be explained and an overview of the most important literature regarding the relation between income and happiness is clarified. We will conclude with a hypothesis on the research question, based on the discussed literature. §2.1 Measuring happiness

When measuring happiness, we measure how one feels, how one evaluates his/her life (Diener, 2000). Nobody can evaluate somebody’s happiness/satisfaction level, except that person itself. So asking people themselves how they feel, is the only way to know how happy they are; that is why happiness is also called subjective well-being. As one might expect, there is no official way of measuring happiness or subjective well-being (OECD, 2011), since it is no objective variable. In all kinds of happiness researches, different questions might have been used to understand how happy people are. Despite the fact that different questions or measurement methods are used in multiple researches, we are able to compare happiness levels within and between countries, even over time. It seems that happiness is a universal understanding all over the world (Veenhoven and Timmermans, 1998).

Basically, there are two common ways of measuring happiness. Both are based on this, or an equivalent, question: ‘How happy do you feel at the moment, all things considered?’ Within the first method people have to report their happiness level according to a number, mostly on a scale from 0 to 10. Within the second method people have to indicate one of the following, or some similar, options: 1) very happy, 2) quit happy 3) not to happy. The results of both measures can be plotted against other variables, like income. Also, these results of both measures can

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compared with each other, despite the fact the procedures of both measures differ (Easterlin, 1974).Of course, when the topic of happiness is more researched, and a national index is developed, this will help us in the future to make even better comparisons (Diener, 2000). §2.2 Relation income and happiness

Several studies have been done on the relationship between income and happiness, both cross-sectional and time-series. This thesis focuses on cross-cross-sectional data, since we want to know if there is any difference in happiness between rich and poor countries, at any given moment, not through time.

Various studies proved there is a positive correlation between income and happiness. In 1974 and 1994 Easterlin found that within countries, higher income groups are happier than lower income groups. Income is measured by GDP per capita. Easterlin used happiness data from the Gallup World Poll and the Cantrill ladder. In the Gallup World Poll people are

questioned how happy they feel: very happy, fairly happy or not very happy. The Cantril ladder used the Self-Anchoring Striving Scale, which means people are first asked to set up boundary points regarding happiness, and then they are asked to determine how happy they are within these boundaries.

Sacks et al (2010) agrees with Easterlin that higher income groups within a certain country are happier than lower income groups, but they extended this study by investigating the income/happiness correlation between countries. The result is that they found people in countries with a higher real GDP per capita tend to be happier than people in countries with a lower real GDP per capita. This relationship is proved by regressing happiness information of the Gallup World Poll, World Values Survey and the Pew Global Attitudes against GDP per capita. From the plot of life satisfaction, whereas information is retrieved from the World Values Survey, against GDP per capita, we can learn there is a positive linear log relationship. Life satisfaction was measured by asking the respondents how satisfied they were with their life these days, all things considered. A plot of the data gathered by the Pew Global Attitudes shows the same results: satisfaction grows with log-income at about the same rate for rich and poor countries. The Pew Global Attitudes measured happiness based on a ladder of life. Questioned people had to point on which scale on the ladder they valued their life at this moment, while imagining at the

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bottom their worst possible life and at the top their best possible life. The Gallup World Poll used the same method to obtain satisfaction numbers. Since the Gallup World Poll is the widest

research regarding the wide range of GDP levels and countries (131 countries), these results are the most convincing. The correlation of average satisfaction in a country and its log of GDP per capita are, according to a plot using Gallup’s numbers, 0, and 8. It also states that happiness is indeed higher in rich countries than in poor countries.

Diener and Biswas-Diener (2001) agree with the positive correlation between income and subjective wellbeing, but they argue this correlation is just small. Describe how SWB is

measured and why they think this correlation is just small. Deaton (2008) describe how Deaton investigated the relationship. On page 60 he describes that this relationship is steeper for poor countries, but he also gives some reasons why this is a false statement?

One might argue that when having a higher income, it is easier to fulfil some basic needs and to have a good quality of life. The findings of Diener and Diener (1995) confirm this

argumentation: they found a high correlation between GDP per capita and quality of life in 101 countries, both rich and poor. Quality of life was measured by the mean of the following standardized quality of life measures: mastery, affective autonomy, intellectual autonomy, egalitarian commitment, harmony, conservationism and hierarchy. Although they found a strong correlation with between the mean of the quality life measures and GDP per capita, they also found that economic prosperity (high GDP per capita) goes together with more suicide and CO2 emissions, which means there aren’t only positive effects of a higher GDP.

As we can see several studies found a positive relation between income and happiness, but Frey and Stutzer (2002) warn for the fact that correlations between happiness and income don’t per se mean that richer people are happier, but is could also mean that happier people earn more. Diener and Biswas-Diener state about the same in ‘Will money increase subjective well-being?’ A high subjective well-being might increase people’s changes for a high income, so the causality is not very clear.

§2.3 Differences in correlation magnitudes

Seeing that most studies showed a positive relationship between income and happiness, in this section, we investigate its nuances.

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Diener and Oishi (2000), Veenhoven and Timmermans (1998) and Frey and Stutzer (2002) agree all upon the statement that having more income is more important when having a low income level than a high income level, because when having a higher income, universal needs are

fulfilled already. There is a certain boundary point up to what extend people gain more happiness when income grows, but after that boundary, any increase happiness will diminish compared to income. This implies that the correlation between income and happiness is lower in for rich countries than for poor countries. Veenhoven and Timmermans (1998) state that this boundary point is 10.000 dollar per capita per year.

Stevenson and Wolfers (2008) do not agree with a certain boundary or satiation point and are not sure if there is a difference in income/happiness correlation, between rich and poor countries. They used data from the World Bank to determine income, real GDP per capita, and for happiness data the same information as Veenhoven and Timmermans and Diener and Oishi. The reason that they did not found a satiation point is they analyzed well-being as a function of log GDP per capita, where previous studies focused on the absolute level. Stevenson and Wolfers think it is better to focus on the log GDP per capita instead of the absolute level, because this yields a better fit.

Easterlin (1974) states that people within a country compare themselves to others and their feelings of happiness are partly based at their compared situation. Easterlin (1995) also mentions that happiness feelings are partly based on expectations. Therefore, if we would raise the income of a whole country, it will not increase the happiness of that country. Based on the assumption that people tend to compare themselves with people around them, Easterlin (1974) argues that there is no difference in correlation between income and happiness when a rich and poor county are compared. Contrary to Easterlin’s findings, Sacks et al (2010) state that there is nil to very minor evidence that relative income and expectations play a role when determining life satisfaction. These statements are based on a statistical analysis of the life satisfaction plots. But, although Sacks et al and Easterlin don’t agree about the comparison and adaptation

principle, they do agree on the fact that the degree of impact (income on happiness), is the same in rich and poor countries. Veenhoven (1991) reassessed the findings of Easterlin and he found that the richer you are, the less important income is, regarding happiness. This means the correlation between income and happiness is higher in poor countries than in rich countries.

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To summarize: Diener and Oishi, Veenhoven and Timmermans and Frey and Stutzer believe in a satiation point of happiness, which implies the correlation of income and happiness is higher in rich countries; Stevenson and Wolters did not found prove for such a satiation point. Furthermore, Easterlin thinks that there is no difference in correlation findings when comparing rich and poor countries, owing the fact that people tend to compare themselves to others, so income is relative. Although Sacks et all do not believe in the comparing principle, they do agree with Easterlin that the correlation level between happiness and income is the same in rich and poor countries.

§2.4 Hypothesis

Not all researchers agree upon the fact that there is a positive relation between income and happiness, but most do. And several researchers agree upon the statement that income is more important, with respect to happiness, for low income groups/countries than for high income groups/countries. Based on this information the hypothesis on the research question is that there is a difference in correlation findings between happiness and income, when comparing rich and poor countries. We are not able yet to form a hypothesis to what extend the correlations differ. §3 Research method

§3.1 Research method

The correlation findings between happiness and income of the WDH will be used for our

calculations. The correlation findings of studies based on research done in a mix of rich and poor countries will be eliminated, since we want to make a distinction between rich and poor countries and that is not possible in those researches. The findings found in either a rich or a poor country will be labelled. The labelling will go as following: if the correlation finding is found in a rich country, it will get label 1 and if the correlation finding is found in a poor country, it will get label 2. All countries with a high income according the World Bank (2014) will be classified as rich country. The countries which are not classified as a high income country will be classified as poor country. But, there are a few exceptions. Latvia, Russia, Lithuania, Estonia, Czech Republic, Poland and the Slovak Republic are labelled as high income countries according to the World Bank, but in this research these countries are labelled as a poor country, because an extra criterium applies: these countries are considered as transition countries. Transition countries

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typically deal with constraints on long term development, like for example rising inflation, inequality and corruption. Therefore these countries cannot be compared with rich countries yet. The distinction between rich and poor countries can be found in appendix 1. Please note that Easterlin (2013) applies the same distinction in countries in his research.

Then, the mean of the correlation findings of both rich and poor countries is being calculated. These means are tested against each other. The hypotheses are as following:

H0: µ(r1) = µ(r2)

H1: µ(r1) ≠ µ(r2)

µ(r1) is the mean of the correlation findings of rich countries and µ(r2) is the mean of correlation findings of poor countries.

In fact, we do a meta analysis. We put together a lot of correlation findings between happiness and income together, to retrieve one outcome. As mentioned before, the findings are retrieved from several different researches, which means a comparison of happiness and income

correlations can be biased. In paragraph 4.2 some separate studies are reviewed, where we can see that there is homogeneity between the studies.

§3.2 Data description

All the correlation findings between happiness and income are retrieved from the WDH. This database contains over 700 correlation findings, obtained by over 200 researches. For this thesis a selection of these findings is made, based on three criteria: measurement of income, area of study and correlation measurement.

The dataset contains correlation findings between happiness/life satisfaction and several ways of income. For this thesis only the findings with a certain type of income are selected: income career and current income level. The reason that these findings are selected for our analysis is because these types of income match best with the literature about income. Most literature is about national GDP or GDP per capita; income career and current income level are closely related to that. The correlation findings between happiness and following types of income

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are left out: attitudes to own income, relative income and source of income. These findings are left out because it does not match well with the use of the term income in literature.

After this selection regarding the income label we have made another selection of the data, based on the place of study. Since a comparison between a rich and poor country is made, we need correlation findings that are retrieved either in rich or poor countries. It cannot be the case that the correlation finding is obtained by a field study in a area which contains rich and poor countries. So when a research was held in for example South-East Asia, this research was left out in our analysis, since there are both rich and poor countries in the area of South-East Asia, like for example Japan and India.

The last selection of the data was based on the way how correlation was stated in the dataset. The following correlation findings were included in our dataset: Beta, Goodman &Kruskal's gamma, Kendall's tau-c, ordered probit regression coefficient, Kendall's rank correlation coefficient tau-b, R^2. The following findings were excluded from our dataset: ordered logit regression coefficient, difference of means after transformation, Tschuprow’s T.

After the selection, 615 correlation findings, retrieved from 131 studies, were left. The reason that there are more correlation findings than studies, is that most studies deliver more than one correlation finding between happiness and income, owing the fact some slightly different measurement or regression is used. For this thesis, it is decided to keep all these findings, even though one research delivers more than one correlation findings. Namely, it is hard to make a distinction which correlation to use and which not, so for a more unbiased result the dataset is kept as large as possible.

§4 Results §4.1Statistics

There were 362 findings on the relation between income and happiness in rich countries and 253 findings in poor countries. These findings arose from 131 individual articles. In section 3.2 is the selection process described. When comparing µ(r1) = 0,138089 and µ(r2) = 0,1192134 on first sight, we can see that µ(r1) is higher than µ(r2). This is a surprising comparison at first sight, since we learned from our literature review that the correlation between income and happiness is

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the same in rich and poor countries. Some researchers argue that if there is any difference in correlation, this correlation might be slightly higher in poor countries, because in poor countries one will benefit more from some extra income, because of the satiation of basic needs.

The calculated Z statistic, according to the formula to compare two means, is 1,3259367, which is smaller than 1,96. This means we do not have enough statistical evidence to state that there is any difference in the income/happiness correlation between rich and poor countries.

§4.2 Separate studies reviewed

In this section we go into depth of several researches, which are used in our meta-analysis. We will describe how happiness was measured in this researches and also some trends.

Timmermans researched correlates of happiness and income as GDP per capita in 42 nations. He did this based on an analysis of the World Values Survey 2. Together with Veenhoven he

discusses in Welvaart en geluk (1998) the results of this analysis. They write that it is proven we can measure happiness very well by asking people, and that we can compare these statistics between countries. In a certain way happiness is universal, and people from whole over the world have a pretty good understanding of this variable, and the interpretations of this variable seem to be similar between countries. In their analysis they write that there is a correlation between GDP per capita and happiness, but they also state that this relation is not perfect, because the unhappy countries are not the poorest. And a country like Ireland is not the richest country, but quit happy. So happiness is not only a matter of a nation’s GDP; happiness depends on so much more. Furthermore their analysis shows a satiation point exists. This point means that above an income of 10.000 dollar per capita, additional income will not provide additional happiness. So the conclusion of this research is: there is a correlation between happiness and income, but this correlation is a bit stronger in poor countries than in rich countries.

In ‘Can money buy happiness’ Dunn et al. write that the correlation between a person’s income and his/her happiness level is weak (r = 0,18). The people national questioned people in the US, who scored high on the question ‘How happy do you feel in general?’, where not per se the ones with a high income, but the ones who spend their money on others. It is argued that when

somebody spends his/her money on others, it will make that person happier than spending that money on his/her self. If we reason that that developed countries have more money to spend on

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other things than just the basics needs to survive, one might expect that developed countries spend more on others. But, surprisingly, the way of spending, on self or on others, is not related to any income group, high or low, so rich countries do not spend more on others than poor countries.

Shin and Johnson (1978) used data from the National Opinion Research Centre for their study. Happiness was measured by the question ‘Taken all things together, how you would say things are these days? – Very happy, pretty happy or not too happy. Income was measured as family income before taxes. A standardized partial regression coefficient was calculated between income and happiness and a weak coefficient between happiness and income was found. They state that happiness is the sum of all positive assessment of someone’s life situation. And this assessment is based on others around a person and his/her past experience. So resources, in this case income, plays a role for someone’s happiness level, but this role is relative with respect to time and surroundings. This is known as the comparing principle. Although this study is only done in the USA, the reasoning that the relation between income and happiness might probably be relative, applies for all countries in the world.

In ‘Does happiness pay?’ of Graham et al (r = 0,03) we can read that after a minimum level of GDP per capita, an increase in wealth does not increase happiness. They also hypotheses that happiness is a cause of a good income, so they question themselves: do happier people earn more money, or does their money make them happy? Happiness is questioned by the following

question: ‘To what extend are you satisfied with your life, at the present time?’ Income is

measured as real household income. In their research they found that Russian people with higher levels of happiness are more likely to increase their income in the future. This causality is

especially found for lower income groups. In terms of the impact of income on happiness; this effect seems to be more visible at higher levels of income.

DiTella and MacCulloch (2006) used information from the United States General Social Survey, in which happiness was questioned by the following question: Taken all together, how would you say things are these days – would you say you are very happy, pretty happy or not too happy? The outcome is plotted against real household income of Americans. We can learn from this regression that the correlation between happiness and income is diminishing as income rises. Also German Socio-Economic Panel data is studied. DiTella and MacCulloch made a distinction

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between the poor half of the people and the rich half of the people. When comparing the

correlation numbers between happiness and personal income. Happiness was measured by asking the people how satisfied they were with their life, all things considered, at a scale from 0 to 10. The correlation of happiness with personal income is 15 times as high for the poor half of the people.

Frey and Stutzer argue that counties with higher GDP per capita tend to have a more stable political situation and that (stable politics), instead of certain level of income might be the reason for a certain level of happiness. Also, the higher ones income is, the higher ones probability on a good health and basic human’s rights. They argue the happier people are, the more willing they are to work hard, the more likely they are to have a higher income. The effects of income and subjective well-being are small and diminishing.

This research synthesis produces some clarities. It appears that there is a relation between

income (GDP per capita) and happiness, although this correlation seems to be weak according to several studies. As we speak of any correlation differences between rich and poor countries or groups, Veenhoven and Timmermans, Frey and Stutzer and DiTella and MacCulloch agree upon the statement that the correlation between income (GDP) and happiness is stronger for poor groups than rich groups. Happiness is measured differently in several researches, but as

mentioned earlier these happiness results are well comparable across nations (Timmermans and Veenhoven).

But this research synthesis also carries some information which are not trends. Graham et al and Frey and Stutzer mention that the causality between income and happiness is not clear, which is not mentioned in the other researches. Also different causes on the happiness level of people are mentioned, besides income, like they way of spending ones income, the political situation in a country, which is most of the times linked to a country’s GDP, and the comparing principle.

§4.3 Comparison statistical results with literature review

As stated in §4.1, we did not find evidence that there is any difference in the income/happiness correlation if we compare rich and poor countries. This matches the idea of Easterlin (1974), but this contradicts the ideas of Veenhoven (1991), Diener and Oishi (2000), Veenhoven and

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Timmermans (1998) and Frey and Stutzer (2002), who suggest income might be more important for lower income groups than higher income groups.

§5 Conclusion

§5.1 Conclusion thesis

In this thesis we attempted to analyze if there is any difference in the income happiness

correlation between rich and poor countries. From the literature research, we learnt that there is a correlation between income and happiness, applying for different income groups around the world, albeit that the correlation is not very strong. We also learnt that there is no consensus yet about a possible difference in this correlation, when comparing rich and poor countries. Some researchers argue that income is of greater importance for lower income groups than for higher income groups, because of fulfilling basic needs; the phenomenon of a satiation point seems to describe this difference. Other researches state that the correlation between income and

happiness is the same in every income group/country, because of the comparing principle. Neither our meta-analysis – based on data of the WDH - resulted in any significance prove to state there is a difference in correlation magnitude, when comparing rich and poor countries. §5.2 Discussion and recommendations

Since, in existing literature, there is no consensus about a difference in correlation magnitude between the two different income groups (countries), the findings of the meta-analysis do match one part of existing knowledge: the part that claims the correlation is the same in rich and poor countries. At the same time these results contradicts the existence of a satiation point, because if that would exist we would find a difference in correlation magnitude between the two groups. Timmermans and Veenhoven (1998) state that happiness measures, measured at several different ways, can be compared with each other. But, to improve future research, A more extended review of all the studies and data (also measurements) is necessary.

Also, as Frey and Stutzer, Graham et al and Diener and Biswas-Diener argue, causality of

income and happiness is not clearly researched yet. This causality is a very interesting and useful continuation in this field of study.

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In this thesis a distinction between rich and poor countries was based according to the income classification of the World Bank. High income countries were classified as rich countries and all the other countries were labelled as poor. An exception was made for some transition countries, since these countries are currently dealing with some important issues, among some economical. A recommendation for in the future would be to make three different classification of countries, namely rich, poor and transition countries.

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Reference list

Deaton, A. (2008), Income, Health, and Well-Being around the World: Evidence from the Gallup World Poll. Journal of Economic Perspectives, Vol. 22, No.2, pp 53–72

Diener, E., &Oishi, S. (2000). Money and happiness: Income and subjective well-being across nations. In E. Diener& E. M. Suh (Eds.), Culture and subjective well-being (pp. 185-218). Cambridge, MA: MIT Press.

Diener, E. &Diener, C. (1995). The Wealth of nations revisited: income and quality of life.

Social Indicators Research, Vol. 36, Issue 3, pp 275-286

Diener, E. (2000). Subjective well-being: the science of happiness and a proposal for a national index. American psychologist, Vol 55(1), Jan 2000, 34-43. Doi: 10.1037/0003-066X.55.1.34 Diener, E, & Biswas-Diener, R. (2001), Will money increase subjective well-being? Social

Indicators Research 57(2002), pp. 119–169

DiTella, R. & MacCulloch, R. (2006). Some uses of happiness data in economics. The Journal of

Economic Perspectives, Vol. 20, pp. 25-46

Dunn et all (2008). Spending money on others promotes happiness. Science, Vol. 319 no.

5870 pp. 1687-1688.

Easterlin, R. (1974). Does economic growth improve human lot? Some empirical evidence.

Nations and Households in Economic Growth, 89

Easterlin, R. (1994). Will raising the incomes of all increase the happiness of all? Journal of

Economic Behavior and Organization, Vol. 27 (1995) 35-47

Easterlin, R &Angelescu, L. (2012). Modern Economic Growth and Quality of Life:

Cross-Sectional and Time Series Evidence. Netherlands: Springer

Easterlin, R. (2013). Happiness and economic growth: The evidence (Discussion Paper Series, ForschungsinstitutezurZukunft der Arbeit No. 7187). Retrieved from

http://hdl.handle.net/10419/69363

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Frey, B. & Stutzer, A. (2002). What Can Economists Learn from Happiness Research? Journal

of Economic Literature, Vol. 40, No. 2 (Jun., 2002), pp. 402-435

Graham, C., Eggers, A. & Sukhtankar, S. (2004). Does happiness pay?: An exploration based on panel data from Russia. Journal of Economic Behavior & Organization, Vol. 55, pp 319–342 OECD (2011), How’s life?: Measuring well-being, OECD Publishing. Retrieved from

http://dx.doi.org/10.1787/9789264121164-en

Sacks, D., Stevenson, B. &Wolfers, J. (2010). Subjective well-being, income, economic

development and growth (NBER Working Paper No. 16441). Retrieved from

http://www.nber.org/papers/w16441

Shin, D.C. & Johnson, D.M. (1978). Avowed happiness as an overall assessment of the quality of life. Social Indicators Research, Vol. 5, pp.475-492

Stevenson, B. &Wolfers, J., (2008). Economic Growth and Subjective Well-Being: Reassessing

the Easterlin Paradox. (CEPR, CESifo, NBER and IZA. Discussion Paper No. 3654)

Veenhoven, R. (1991). Is happiness relative? Social Indicators Research, Vol. 24, pp 1-34 Veenhoven, R. (1994). Correlates of happiness, 7838 findingsfrom 603 studies in 69 nations

1911-1994, Erasmus University, RISBO, Rotterdam Netherlands

Veenhoven. R., Timmermans, D. (1998), Welvaart en geluk, Economisch Statistische Berichten

(ESB) 28-8-1998, pp. 628-631

World Bank (2014). Countries and Economies. Retrieved from: http://data.worldbank.org/country

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Appendix 1

Distinction between rich and poor countries

Rich countries

GNP per capita in dollar (source: World

Bank 2014) Poor countries

GNP per capita in dollar (source: World Bank 2014)

Greece 22530 Mexico 9940 (upper middle)

Denmark

61110

Argentina

5090, 118 (upper middle)

Netherlands 47440 South Africa 7190 (upper middle)

Canada 52200 Brazil 11690 (upper middle)

Belgium 45210 Turkey 10950 (upper middle)

Germany 46100 Colombia 7560 (upper middle)

USA 53670 Bulgaria 7030 (upper middle)

UK 39100 Romania 9060 (upper middle)

Italy 34400 China 6560 (upper middle)

France 42250 India 1570 (lower middle)

Australia 65520 Belarus 6720 (upper middle)

Norway 102610 Hungary 12410 (upper middle)

Spain 29180 Latvia 14060

Portugal 20670 Russia 13860

Luxemburg 71810 Lithuania 13820

Ireland 39110 Estonia 17370

New Zealand 35520 Czech Republic 18060

Finland 47110 Poland ` 12960 Austria 48590 Slovak Republik 17200 Japan 46140 South Korea 25920 Chili 15230 20

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Appendix 2

Data set for meta-analysis

Who Place

Correlation finding

# Times cited + where it is published Zautra (1975): study US Utah 1970 Salt Lake County, USA 0.34 ?, University of Utah Idem Salt Lake County, USA 0.32 Headey& Krause (1994): study XZ

Germany West 1984 West Germany 0.14

4, ?

Idem West Germany 0.19

Krause (2013): study DE 2007 Germany 0.49 3, ? Bartolini et al. (2010): study DE 1988 Germany -0.03 15, Springer Idem Germany -0.08 Knabe&Rätzel (2007): study DE 1992 Germany 0.36 7, SSRN Idem Germany 0.33 Idem Germany 0.39 21

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Martin &Lichter (1983): study US Michigan 1973 Michigan/US 0.09 2, ? Chiriboga (1982a): study US 1969 USA 0.02 13, Cambridge Journals Idem USA -0.13 Headey et al. (1984a): study AU 1978 Melbourne, Australia 0.06 88, Social Indicators Research, Springer Hoopes&Lounsbury (1989): study US 1985 USA 0.44 15, Journal of CommunictyPsuchology, psychnet.apa.org Idem USA 0.49 Idem USA 0.45 Idem USA 0.32 Nickerson et al. (2003): study US 1976 USA -0.04 191, Psychological Science Idem USA -0.04 Gerlach& Stephan (1996): study XZ

Germany West 1984 Germany 0.22

185, Economics Letters, Elsevier Idem Germany 0.15 Idem Germany 0.04 Idem Germany 0.11 Idem Germany 0.21 Idem Germany -0.06 Dunn et al. (2008): study US 2008 /1 USA 0.18 460, sciencemag.org Idem USA 0.03 22

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Idem USA 0

Appleton & Song (2008): study CN 2002 China -0.11 101, World development, Elsevier Kainulainen (1998): study FI 1991 Finland -0.28 18 Idem Finland -0.26 Kainulainen (1998): study FI 1991 Finland -0.12 18 Idem Finland -0.13 Idem Finland -0.07 Idem Finland -0.06 Idem Finland -0.06 Idem Finland -0.08

Evans & Huxley (2005): study GB 1999 UK 0.2 12, Springer Idem UK -0.18 Davis (1984): study US 1972 USA 0.2 63, Social indicators research, Springer Maxwell (1985): study US 1966 USA 0.04 62, Journal of family issues Bradburn (1969): study US 1963 USA 0.49 4813, psychnt.apa.org Idem USA 0.53 Idem USA 0.5 Kennedy &Mehra (1985): study CA Edmonton CMA 1977 Canada -0.1 14, Social indicators research, Springer 23

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Idem Canada -0.07

Idem Canada -0.13

Shin & Johnson (1978): study US 1975 USA 0.22 668, Social indicators research, Springer Kainulainen (1998): study FI 1991 Finland 0.19 18 Idem Finland 0.2 Idem Finland 0.17 Idem Finland 0.16 Headey et al. (1984a): study AU 1978 Australia 0.11 136, uni-wuerzburg.de Headey& Wearing (1981): study AU AU Victoria 1978 /1 Australia 0.11 104, Springer Idem Australia -0.11 Headey& Krause (1988): study XZ

?, University of Mannheim Antonides (2004): study NL 2004 Netherlands 0.14 2, Wageningen University Idem Netherlands 0.19 Idem Netherlands 0.08 Idem Netherlands 0.11 Idem Netherlands 0.06 Idem Netherlands 0.08 Idem Netherlands 0.12 Idem Netherlands 0.17

Ormel (1980): study Netherlands 0.08 0, Groningen University

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NL 1970 Idem Netherlands 0.03 Graham et al. (2004): study RU 1995 Russia 0.03 252, Journal of Economic Behavior, Elsevier Idem Russia 0.03 Idem Russia 0.32 Idem Russia 0.19

Zhang & Hwang (2007): study RU 1994 Russia 0.04 4, Social indicators research, Springer Idem Russia 0.04 Idem Russia 0.04 Idem Russia 0.06

Oishi et al. (2007a):

study GB 1996 UK -0.04

169, Perspectives on Psychological, pps.sagepub.com

Hawkins & Booth (2005): study US 1980 USA 0.03 166, Social forces, sf.oxfordjournals.org Idem USA 0.08 Idem USA 0 Nickerson et al. (2003): study US 1976 USA 0.3 191, Psychological science, pss.sagepub.com Idem USA 0.26 Moon &Mikami (2007): study JP 2004 Japan 0.17

4, Wiley Online Library

Idem Japan 0.09

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Idem Japan -0.11 Headey&Veenhoven (1989): study AU AU Victoria 1981 Australia -0.06 25, repub.eur.nl Idem Australia 0.17 Hoopes&Lounsbury (1989): study US 1985 USA 0.44 15, psychnet.apa.org Idem USA 0.49 Idem USA 0.45 Idem USA 0.32 Wang et al. (2013) Id=8161: study CN 2008 China 0 3, sagepub.com Idem China 0 Idem China 0 Idem China 0

Chan & Lee (2006):

study CN 2000 China 0.19 67, Journal of Happiness studies, Springer Idem China 0.14 Idem China 0.09 Idem China 0.09 Prizmic-Larsen et al. (2009): study HR 2005 Croatia 0.14 2, bib.irb.hr Idem Croatia 0.08 Ventegodt (1995): study DK 1993 Denmark 0.05 ? Idem Denmark 0.04 Idem Denmark 0.04 26

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Ventegodt (1996): study DK 1993 Denmark 0.14 ? Idem Denmark 0.14 Idem Denmark 0.1 DiTella&MacCulloch (2006): study XZ Germany West 1985 /1 Germany 0.12 546, The journal of Economic Perscpectives, JSTOR Idem Germany 0.01 Krause (2013): study DE 2007 Germany 0.02 3, Journal of Economic Behavior & Organization, Elsevier Eriksson et al. (2007): study DE 1995 Germany 0.02 21, Social indicators research, Springer Rose &Ozcan (2007): study ZZ EU 15 2003 Turkey 0.15 12, ? Mollenkopf et al. (2004): study ZZ Europe 2000 Netherlands 0.35 13, International Journal of aging Stillman et al. (2014): study NZ 2005 New-Zealand -0.01 8, Elsevier

Moller (1988a): study

ZA 1983 South Africa 0.39

13, JSTOR

Idem South Africa 0.27

Frey &Stutzer

(2000b): study CH Switzerland 0.05

164, German Economic Review, Wiley Online

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1992 Library Idem Switzerland 0.14 Idem Switzerland 0.23 Frey &Stutzer (2000b): study CH 1992 Tonga -0.04 164, German Economic Review, Wiley Online Library Nettle (2005b): study GB 2000 UK 0.03 1, EUR Dunn et al. (2008): study US 2008 USA 0.11 460, sciencemag.org Idem USA 0.11 Dunn et al. (2008): study US 2008 /1 USA 0.38 460, sciencemag.org Idem USA 0.4 Idem USA -0.03 Forrester (1980): study US San Diego County, California 1980 USA 0.19 ? Idem USA 0.14 Timmermans (1997): study AR 1991 Argentina 0.13 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Argentina 0.11 Idem Argentina 0.16 Idem Argentina 0.07 Idem Argentina 0.13 Idem Argentina 0.11 28

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Idem Argentina 0.14 Idem Argentina 0.12 Idem Argentina 0.1 Cummins et al. (2003a): study AU 2003 Australia 0.48 34, Australian center of quality of life Idem Australia 0.032 Headey et al. (2008): study AU 2002 Australia .005 41, econstor.eu Idem Australia .017 Idem Australia 0.07 Idem Australia 0.04 Idem Australia 0.05 Idem Australia 0.04 Idem Australia 0.05 Shields et al. (2009): study AU 2001 Australia 0.04 88, Journal of Population Economics, Springer Idem Australia 0.06 Idem Australia 0.03 Idem Australia 0.06 Idem Australia 0.05 Idem Australia 0.05 Idem Australia 0.03 Idem Australia 0.01 ~Leisure Development Cen (1980): study AU 1979 Japan 0.5 ? Idem Japan 0.02 Idem Japan 0.03 29

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Idem Japan 0.01 Idem Austria 0.14 Idem Austria 0.16 Idem Austria 0.07 Idem Austria 0.12 Idem Austria 0.1 Idem Austria 0.07 Idem Austria 0.11 Idem Austria 0.04 Idem Austria 0.1 Fessel (1985): study AT 1985 Austria 0.12 ? Idem Austria 0.1 Timmermans (1997): study BY 1990 Belarus (1990) 0.06 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Belarus (1990) 0.09 Idem Belarus (1990) 0.06 Idem Belarus (1990) 0.05 Idem Belarus (1990) 0.06 Idem Belarus (1990) 0.06 Idem Belarus (1990) 0.06 Idem Belarus (1990) 0.06 Idem Belarus (1990) 0.08 Senik (2011): study BE 2002 France 0.29 10, econstor.eu Timmermans (1997): study BE 1990 Belgium 0.11 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Belgium 0.14 30

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Idem Belgium 0.12 Idem Belgium 0.14 Idem Belgium 0.11 Idem Belgium 0.12 Idem Belgium 0.1 Idem Belgium 0.06 Idem Belgium 0.09 Idem Belgium 0.09 Idem Belgium 0.06 Idem Belgium 0.1 Idem Belgium 0.12 Idem Belgium 0.1 Idem Belgium 0.1 Idem Belgium 0.12 Idem Belgium 0.1 Idem Belgium 0.1 Timmermans (1997): study BR 1990 Brazil 0.02 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Brazil 0.09 Idem Brazil 0.2 Idem Brazil 0.18 Idem Brazil 0.08 Idem Brazil 0.09 Idem Brazil 0.13 Idem Brazil 0.05 Idem Brazil -0.04 Idem Brazil 0.13 Idem Brazil 0.16 Idem Brazil 0.17 31

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Idem Brazil 0.18 Idem Brazil 0.18 Idem Brazil 0.08 Idem Brazil -0.01 Idem Brazil 0.08 ~Leisure Development Cen (1980): study BR 1979 Japan -0.1

Report not available

Idem Japan 0.04 Idem Japan 0.03 Idem Japan -0.07 Timmermans (1997): study BG 1990 Bulgaria 0.25 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Bulgaria 0.33 Idem Bulgaria 0.24 Idem Bulgaria 0.2 Idem Bulgaria 0.33 Idem Bulgaria 0.25 Senik (2011): study CA 2000 Canada 0.01 10, econstor.eu

Jones (2002a): study

CA 1998 Canada 0.29

Idem Canada 0.14 Idem Canada 0.11 Idem Canada 0.04 Idem Canada 0.15 Idem Canada 0.03 Idem Canada 0.34 32

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Idem Canada 0.16 Idem Canada 0.24 Idem Canada 0.1 Idem Canada 0.21 Idem Canada 0.07 Michalos&Zumbo (2000): study CA Prince George 1997 British Colombia 0.13 201, Social indicators research, Springer Idem British Colombia 0.09 Timmermans (1997): study CA 1990 Canada 0.14 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Canada 0.06 Idem Canada 0.16 Idem Canada 0.05 Idem Canada 0.06 Idem Canada 0.11 Idem Canada 0.18 Idem Canada 0.17 Idem Canada 0.07 Austrom (1984): study CA Ontario 1982 USA 0.2 28, secure.peterlang.com Timmermans (1997): study CL 1990 Chili 0.19

Idem Chili 0.16

Idem Chili 0.06

Idem Chili 0.15

Idem Chili 0.12

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Idem Chili 0.02 Idem Chili 0.17 Idem Chili 0.08 Idem Chili 0.19 Jiang et al. (2012): study CN 2002 China 0.27 33, World development, Elsevier Knight &Gunatilaka (2010) Id=6428: study CN 2002 China 0.2 144, World Development, Elsevier

Appleton & Song (2008): study CN 2002 China 0.18 101, World development, Elsevier Timmermans (1997): study CN 1990 China 0.06 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem China -0.09 Idem China 0.01 Idem China -0.07 Idem China 0.06 Idem China -0.09 Xu & Wu (1988): study CN 1986 China -0.39 ? Idem China -0.26 Timmermans (1997): study CZ 1990 Czechoslovakia 0.09 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Czechoslovakia 0.04 Idem Czechoslovakia 0.05 Idem Czechoslovakia 0.03 Idem Czechoslovakia 0.08 34

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Idem Czechoslovakia 0.02 Idem Czechoslovakia 0.05 Idem Czechoslovakia 0.05 Idem Czechoslovakia 0.07 Timmermans (1997): study DK 1990 Denmark 0.2 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Denmark 0.18 Idem Denmark 0.21 Idem Denmark 0.14 Idem Denmark 0.14 Idem Denmark 0.12 Idem Denmark 0.2 Idem Denmark 0.2 Idem Denmark 0.17 Timmermans (1997): study EE 1990 Estonia 0.07 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Estonia 0.07 Idem Estonia 0.08 Idem Estonia 0.05 Idem Estonia 0.05 Idem Estonia 0.05 Idem Estonia 0.07 Idem Estonia 0.08 Idem Estonia 0.08 Kainulainen (1998): study FI 1991 Finland 0.24 18 Idem Finland 0.18

Timmermans (1997): Finland 0.13 8, discussion in

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study FI 1990 ‘Welvaart en geluk’ (seereference list) Idem Finland 0.08 Idem Finland 0.24 Idem Finland -0.02 Idem Finland -0.05 Idem Finland -0.03 Idem Finland 0.1 Idem Finland 0.03 Idem Finland 0.16 Idem Finland -0.02 Idem Finland -0.03 Idem Finland -0.05 Idem Finland 0.26 Idem Finland 0.11 Idem Finland 0.19 Idem Finland -0.02 Idem Finland -0.07 Idem Finland -0.02 Stutzer& Frey (2006): study XZ

349, Journal of Socio-Economics, Elsevier Idem Germany 0.17 Idem Germany 0.35 Headey& Krause (1994): study XZ

44, Social science & medicine, Elsevier

Idem Germany 0.19

Idem Germany 0.08

Idem Germany 0.11

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Idem Germany 0.06 Idem Germany 0.08 Idem Germany 0.12 Idem Germany 0.17 ~Leisure Development Cen (1980): study XZ

Idem Germany 0.21 Idem Germany 0.13 Idem Germany 0.07 Noelle-Neumann (1977a): study XZ Germany West 1976 /2 Germany 0.3 16, Springer Idem Germany 0.18 Noelle-Neumann (1977a): study XZ

16, Springer

Idem Germany 0.14

Noelle-Neumann (1977a): study XZ

16, Springer Idem Germany 0.17 Timmermans (1997): study FR 1990 France 0.23 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem France 0.18 Idem France 0.23 Idem France 0.17 37

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Idem France 0.13 Idem France 0.17 Idem France 0.22 Idem France 0.19 Idem France 0.23 Knabe et al. (2009): study DE 2008 Germany 0.62 74, econstor.eu Idem Germany 0.01 Dittmann& Goebel (2010) Id=7660: study DE 2004 Germany 0 23, Social indicators research, Springer Idem Germany 0 Headey et al. (2008): study DE 2002 Germany 0.17 41, econstor.eu Idem Germany 0.07 Idem Germany 0.09 Koch et al. (2005): study DE 2002 Germany 0.12

?, IAB discussion Paper

Idem Germany 0.07 Eriksson et al. (2007): study DE 1995 Germany 0.25 21, Social indicators, research, Springer Berger (2009): study DE 1994 Germany 0.31 18, econstor Idem Germany 0.15 Obucina (2013): study DE 1994 Germany 0 0, Social indicators research, Springer Idem Germany 0 Knabe&Rätzel (2007): study DE Germany 0.44 7, papers.ssrn.com 38

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1992

Idem Germany 0.1

Timmermans (1997): study DE West

Germany 1990 Germany. West 0.18

8, discussion in ‘Welvaart en geluk’ (seereference list)

Idem Germany. West 0.2

Timmermans (1997): study DE East

Germany 1990 Germany, East 0.1

Idem Germany, East 0.16

Headey (2008): study DE 1990 Germany 0.02 118, Social indicators research, Springer Idem Germany 0.01 Bartolini et al. (2010): study DE 1988 Germany 0.39 15, Social indicators research, Springer 39

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Idem Germany 0.42

Sing (2009a): study

HK 2006 China 0.24 ?, Social indicators research Idem China 0.18 Idem China 0.1 Mitchell (1972): study HK 1967 China 0.24 ? Idem China 0.16 Headey et al. (2008): study HU 1996 Hungary 0.042 41, econstor.eu Idem Hungary 0.049 Idem Hungary 0.07 Headey et al. (2008): study HU 1996 Hungary 0.2 41, econstor.eu Idem Hungary 0.04 Idem Hungary 0.06 Timmermans (1997): study HU 1990 Hungary 0.19 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Hungary 0.18 Idem Hungary 0.25 Idem Hungary 0.18 Idem Hungary 0.13 Idem Hungary 0.14 Idem Hungary 0.23 Idem Hungary 0.18 Idem Hungary 0.16 Brinkerhoff et al. (1997): study IN

Other place in India India 0.11

21, social indicators research, Springer

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1996 /2 Idem India 0.02 Timmermans (1997): study IN 1990 India 0.08 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem India 0.2 Idem India 0.24 Idem India 0.21 Idem India 0.09 Idem India 0.24 Idem India 0.17 Idem India 0.13 Idem India 0.15 Idem India 0.08 Idem India 0.1 Idem India 0.13 Idem India 0.1 Idem India 0.05 Idem India 0.17 Idem India 0.18 Idem India 0.07 Idem India 0.08 Idem India 0.16 Idem India 0.03 Idem India 0.1 Idem India 0.11 Idem India 0.16 Idem India 0.03 Idem India 0.26 Idem India 0.11 41

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Idem India 0.19 Idem India 0.1 Idem India 0.21 Idem India 0.2 Idem India 0.26 Idem India 0.19 Idem India 0.11 Idem India 0.16 Idem India 0.17 Idem India 0.1 ~Leisure Development Cen (1980): study IN 1979 Japan 0.38

Idem Japan 0.24 Idem Japan 0.15 Idem Japan 0.25 Timmermans (1997): study IE 1990 Ireland 0.25 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Ireland 0.13 Idem Ireland 0.13 Idem Ireland 0.24 Idem Ireland 0.13 Idem Ireland 0.08 Idem Ireland 0.13 Idem Ireland 0.13 Idem Ireland 0.13 Idem Ireland 0.13 Idem Ireland 0.08 42

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Idem Ireland 0.24 Idem Ireland 0.25 Idem Ireland 0.17 Idem Ireland 0.08 Idem Ireland 0.1 Idem Ireland 0.03 Idem Ireland 0.16 Idem Ireland 0.09 Idem Ireland 0.03 Idem Ireland 0.1 Idem Ireland 0.16 Idem Ireland 0.08 Idem Ireland 0.09 Idem Ireland 0.17 Idem Ireland 0.11 Idem Ireland 0.1 Idem Ireland 0.26 Idem Ireland 0.22 Idem Ireland 0.16 Idem Ireland 0.13 Idem Ireland 0.26 Idem Ireland 0.11 Idem Ireland 0.19 Timmermans (1997): study IT 1990 Italy 0.03 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Italy 0.07 Idem Italy 0.07 Idem Italy 0.05 Idem Italy 0.01 43

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Idem Italy 0.05 Idem Italy 0.02 Idem Italy 0.03 Idem Italy 0.05 ~Leisure Development Cen (1980): study IT 1979 Italy 0.24

Idem Italy 0.23 Idem Italy 0.17 Idem Italy 0.16 Inoguchi&Fujii (2009): study JP 2009 Japan 0.13 ? Idem Japan 0.08 Idem Japan 0.13 Timmermans (1997): study JP 1990 Japan 0.09 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Japan 0.07 Idem Japan 0.07 Idem Japan 0.03 Idem Japan 0.15 Idem Japan 0.18 Idem Japan 0.05 Idem Japan -0.01 Idem Japan 0.05 Idem Japan 0.11 Idem Japan 0.14 Idem Japan 0.05 44

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Idem Japan 0.16 Idem Japan 0.09 Idem Japan 0.19 Idem Japan 0.05 Idem Japan 0.03 Idem Japan 0.02 ~Leisure Development Cen (1980): study JP 1979 Japan 0.17

Idem Japan 0.18 Idem Japan 0.11 Idem Japan 0.11 Lee (1998): study KR 1996 South Korea 0.8 9, isdpr.org Timmermans (1997):

study KR 1990 South Korea 0.23

Idem South Korea 0.15

Shin (1986): study

KR 1980 South Korea 0.28

?, Oxford Journals

~Leisure

Development Cen

(1980): study KR South Korea 0.24

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1979

Timmermans (1997): study LV 1990 Latvia 0.06 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Latvia 0.04 Idem Latvia 0 Idem Latvia 0.02 Idem Latvia 0.04 Idem Latvia 0 Idem Latvia -0.01 Idem Latvia 0.06 Idem Latvia 0.04 Timmermans (1997): study LT 1990 Lithuania 0.12 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Lithuania 0.19 Idem Lithuania 0.08 Idem Lithuania 0.13 Idem Lithuania 0.07 Idem Lithuania 0.06 Idem Lithuania 0.11 Idem Lithuania 0.07 Idem Lithuania 0.13 Timmermans (1997): study MX 1990 Mexico 0.15 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Mexico 0.16 46

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Idem Mexico 0.11 Idem Mexico 0.07 Idem Mexico 0.1 Idem Mexico 0.12 Idem Mexico 0.15 Idem Mexico 0.15 Idem Mexico 0.11 Rose &Ozcan (2007): study ZZ EU 15 2003 Turkey 0.15 12, ?

~Gallup & Kettering (1976d): study ZZ East Asia 1976 /2 Far East countries 0.49 Gallup international research institute Idem Far East countries 0.39 Idem Far East countries 0.49

~Gallup & Kettering (1976e): study ZZ Latin America 1976

/1 Latin America 0.22

Gallup international research institute

Idem Latin America 0.2

Idem Latin America 0.22

~Eurobarometer (1975a): study ZZ EU 9 1975 Belgium 0.24 ?, European commission Idem Belgium 0.21 Dekker &DeHart (2007): study NL 2006 Netherlands 0.24 0 Idem Netherlands 0.32 47

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Idem Netherlands 0.4 Idem Netherlands 0.28 Wetsteijn (2008): study NL 2004 Netherlands 0.2 0, EUR Cornelisse-Vermaat (2005): study NL 2001 Netherlands 0.05 9, agris, fao.org Timmermans (1997): study NL 1990 Netherlands 0.16 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Netherlands 0.16 Idem Netherlands 0.1 Idem Netherlands 0.11 Idem Netherlands 0.06 Idem Netherlands 0.1 Idem Netherlands 0.16 Idem Netherlands 0.11 Idem Netherlands 0.16

Jol (1985a): study

NL 1974 Netherlands -0.11 ?, CBS Boelhouwer& Stoop (1999): study NL 1974 Netherlands 0.26 49, Social indicators research, Springer Idem Netherlands 0.25 Idem Netherlands 0.24 Idem Netherlands 0.26 Idem Netherlands 0.3 Idem Netherlands 0.32 Idem Netherlands 0.36 Idem Netherlands 0.36 48

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Idem Netherlands 0.33 Idem Netherlands 0.31 ~Philips Nederland (1966): study NL 1964 Netherlands 0.21 ? Timmermans (1997): study NG 1990 Netherlands 0.2 8, discussion in ‘Welvaart en geluk’ (seereference list) Idem Netherlands 0.2 Idem Netherlands 0.18 Idem Netherlands 0.14 Idem Netherlands 0.16 Idem Netherlands 0.14 Idem Netherlands 0.2 Idem Netherlands 0.14 Idem Netherlands 0.17

- A question markt indicates that the information was not available.

- The empty boxes in the right column indicate is has the same value of the first filled box above it. This is because the information is retrieved from the same research.

- Source: WDH, see reference list. No excel file was available, so all information is manually over typed.

- It is possible to click on each separate research.

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Appendix 3 Statistics meta-analysis Rich Poor N 362 253 Mean µ 0,138089 0,1192134 Variation 0,016263 0,012461 Standard deviation 0,127528 0,111627 50