Faculty of Economics and Business
Bachelor Thesis Economics and Business
The journey to happiness
Life satisfaction, wellbeing and happiness different words same meaningBy
Maarten van lieshoud
Student number: 10291253
Supervisor: Stephan Jagau
6/29/2016
Abstract
This thesis is about the effects of gross domestic product per capita controlled for the
demographic variables age, highest educational level attained, marital and employment status of 46 non-developed and 14 developed countries on self-reported life satisfaction. The
method used to estimate the effects is Ordinary Least Square regression where life satisfaction is the dependent variable and the other demographic and macroeconomic variables are explanatory. The data is collected from World Values Survey, Trading
Economics and the World Bank. The main finding is that gross domestic product per capita has a positive effect on the self-reported life satisfaction of the residents from the non-developed and non-developed countries, that all the explanatory variables are significant and contribute to improve the model.
Statement of Originality
This document is written by Student Maarten van Lieshoud who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Contents
Introduction ... 3
Literature review ... 5
Methodology and Empirical findings ... 5
Average life satisfaction in Developed Countries ... 10
Average life satisfaction in Non-Developed Countries ... 10
Research Methodology ... 12
Hypothesis... 13
Conclusion ... 18
Introduction
The question that everyone asks himself during his or her life is whether an increase in
income is accompanied by an increase in happiness. The constant debate over if rich and poor countries will be happier with an increase in gross domestic product of capita is not
conclusive.
How could it be that this topic is so hard to get to grips with for people? It has to do with the concept of happiness that contains more than just a feeling. People who are happier during their lifetime are motivated to take productive action. This action manifest in a persons’ preferences for socially-active and work-related choices. (Easterlin, 1973).
Holding that thought about happiness it is interesting to first examine what the relevance of income is for happiness a little deeper. When income increases it can be used to increase ones’ happiness. The higher income can be redistributed by individuals who earned the now increased incomes to gain material prosperity. This has the purpose to enrich the feelings of wellbeing also referred to as happiness for individuals (Oswald, 1997).
What is interesting is to keep in mind not only the assumed direct impact of material prosperity on happiness, but also to question the effects it could possibly bring forward that are distractive. Those directed attention towards gaining material prosperity might redirect attention from what people might see as more important aims. These aims are for example love, religion, spirituality and/or self-development. So income can work as a distraction that will cause a replacement of focus away from means that will increase human wellbeing as well. The pursuit to a wealthy life will continue and might be frustrating and endless. This because it is difficult to determine when a person’s feeling of happiness is satisfied. Material prosperity can help increase someone’s happiness due to the fact income helps people provide for the satisfaction of their needs and desires more easily (Diener, 1999).
Introduced that happiness not only depends on someone’s material wellbeing, defining happiness seems to be a difficult case. It is easily seen that depending on what one values in life, infinitely many definitions are possible. However worldwide surveys assessing
someone’s happiness show that within and among countries people have similar concerns regarding what is important for happiness and what influences happiness. For example, self-reported wellbeing tends to be higher in countries that are experiencing less social and
political distress. The relevance of economic concerns can be tested for developed as well as non-developed countries (Easterlin, 1973).
The aim of this thesis is to research the impact of income in the form of GDP per capita for non-developed and developed countries on average self-reported life satisfaction of their residents. It is clear that there is a possibility that happiness not only depends on income. Happiness is also referred to as life satisfaction and wellbeing in the literature. In this thesis life satisfaction will be used as the term of interest. Life satisfaction seems to have a relation with some other personal variables. In this thesis the focus of those personal variables will be on age, education, employment and marital status. Life satisfaction also depends on the macroeconomic variable of interest gross domestic product (Graham, 2007).
My research question is then:
What is the effect of gross domestic product per capita on life satisfaction for non-developed and developed countries?
The countries that are being researched are the 60 countries that where available from the world Values Surveys. The survey researched how satisfied the individuals of a country are with their life. They used a ranking method from 1 to 10 where 10 is the highest satisfaction score subjects can report.
The main focus is to see what the influence is of GDP per capita controlled for demographic variables on life satisfaction. Even though that will answer the part of the effect of everyone’s income, with respect to the chosen countries, on life satisfaction it is also interesting to
research whether the other demographic variables discussed are significant and how they influence life satisfaction of the residents from the non-developed and developed countries. To estimate the effects of all the explanatory variables on the dependent variable life satisfaction ordinary least squares is used. We use data from the World Values Surveys, Trading Economics and the World Bank.
Literature review
A vast amount of related studies has been done about the topic if life satisfaction depends on GDP per capita. These studies aim is to measure whether the effect of GDP per capita on an individual’s self-reported life satisfaction is relatively strong or weak. The available literature about happiness is then also plentiful. In order to make a selection in the vast amount of literature the focus will be on literature that reflects relevant methods that connects to this thesis method to measure the effect on self- reported life satisfaction with regards to the chosen explanatory variables. The literature also will provide a clearer insight on what defines life satisfaction. On the basis of this information the OLS variables are chosen.
Methodology and Empirical findings
The best way in order to get a grip on what defines self-reported life satisfaction is through the use of surveys. The general belief about the relationship between life satisfaction and income is that most people think that if their income increases they will be happier. Survey evidence provides a picture that subjective wellbeing is largely inconsistent with this belief. To measure subjective wellbeing people are asked how satisfied they are with their lives all things considered. Options they can choose range between very happy, pretty happy or not too happy. The results show that on average, even though large increases in real income per capita have occurred during the last four decades, the measured reports of happiness have not changed much. The global effect on happiness has been roughly consistent between the surveyed countries even though real income per capita has changed. (Kahneman et al., 2006)
Easterlin (1974) finds that through the use of surveys, covering 19 developed and less developed countries during the period World War II took place, there exist a relationship between income level and happiness. The survey had one direct question which was: “In general, how happy would you say you are-very happy, fairly happy or not very happy?” Some individuals where asked to respond to a follow up question that states: “in your own words, what the word “happiness” means to you.” An important finding when making a comparison between different societies for self-reported happiness is that living and social norms are found to be roughly the same. The resulting positive correlation between income and happiness is not consistent between countries but appeared to be weakly if noticeable at all. Where a higher level of income increases ones’ self-reported happiness.
A positive relationship between gross domestic product and happiness is also found by Di Tella et al (2003). The data on self-reported happiness and self-reported life satisfaction comes from twelve European countries and the United States. Respondents answers on self-reported happiness can be influenced trough framing, the order questions are asked and available categories used that determines happiness. In order to solve these problems
averages are taken from large numbers of observations. The survey questions that were asked in the European countries are first “Taking all things together, how would you say things are these days-would you say you’re very happy, fairly happy, or not too happy these days? The second question is on life satisfaction and states “On the whole, are you very satisfied, fairly satisfied, not very satisfied, or not at all satisfied with the life you lead?” Life satisfaction is included because the word happy translates differently across languages. The survey question that was asked in the United states “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?”. They gathered the data from the United States General Social Survey (1972-1994). They found a correlation between happiness and life satisfaction with a coefficient of 0.56.
Layard (2005) also concludes something similar that average life satisfaction tends to rise with gross domestic product (GDP) per capita at low levels of income. The question asked to determine self-reported life satisfaction is “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 countries of interest for this research where the United States, Japan and Europe. The interesting and added finding to prior researches is that when GDP per capita for levels of income exceeds a certain amount the increase in average life satisfaction increases no further. Also known as diminishing marginal utility of income.
Oswald (1997) doubts the finding of Easterlin that higher income increases ones’ happiness appeared to be only weakly noticeable. Oswald includes the subjects’ employment status adding information to the first basis of information though surveys that ask individuals about their life satisfaction. The results show that people that are very unhappy also tend to be unemployed and in developed countries an increase in income only provides a slight increase in happiness. Oswald wonders why it is that if money buys so little well-being individuals strife to increase their income. Oswald points out that the answer might be that individuals in rich countries are more interested in his/her relative income. It gives an explanation for why national income only has a slight effect on life satisfaction. Oishi (2011) did a cross-national research about the relationship between income inequality and happiness. The cross-national
results show that Americans were on average happier with less national income inequality than with more income inequality. The difference was explained by perceived fairness and general trust. It shows that if income inequality increases, individuals perceive each other to be less fair and diminished the trust between one another.
Hayo (2004), Borooah (2006) and Easterlin (1995) are mentioning the importance of other variables that have an influence on life satisfaction. The variables found are educational level, country, age, income, religion employment and marital status. The question that was used in these cross-sectional studies to determine life satisfaction is “On the whole, are you very satisfied, not very satisfied, or not at all satisfied with the life you lead?” The main finding of these studies is that income was independent of these variables and has the strongest
relationship with life satisfaction.
Dunn (2011) studies the relationship between income and happiness and finds that the
correlation is surprisingly weak. However, he points out that it is not necessarily the case that income plays no significant role for happiness reports, but rather that income is not
necessarily used in happiness-improving ways. So he argues that income indeed positively influences happiness but has a weaker effect than most people would think due to these confounds. Kahneman & et al (2006) find that most people believe that they would be happier if they were richer, but survey evidence on subjective well-being is largely inconsistent with that belief.
According to OECD (2011) 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 verifiable. Diener (2000) mentions that when measuring happiness, we measure how one feels, how one evaluates his/her life. Veenhoven and Timmermans (1998) finds that despite the fact that different questions or measurement methods are used in multiple researches to find self-reported life satisfaction, 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).
An interesting finding is the one of focusing illusion. It mentions the importance that measurement of self-reported life satisfaction can be prone to exaggeration of individuals.
This could occur when there is only one single factor that supposedly should influence their self-reported life satisfaction in the surveys. This focusing factor can be income but also any other variable that could possible influence one’s life satisfaction. This single factor draws individual’s attention to the subject of their wellbeing with regard to the factor used and distracts them of other factors that could possibly diminish or increase their self-reported life satisfaction. This can induce people to make errors in significant decisions that they make (Schkade, 1998). One way that can solve the problem of focusing illusion is to use large data sets for example country averages in order to wash the bias of exaggeration out of the
regression results.
The takeaway from all these literature studies is that income has an effect on the self-reported life satisfaction of an individual. However, views differ in how significant the impact of income is on life satisfaction, provided that there is any significant impact at all and that there are certain demographic variables that influences life satisfaction.
World Values Survey
For years, studies are conducted to research the self-reported life satisfaction of people. Life satisfaction is used in surveys to determine happiness of people. The economics of happiness is researching the effect on life satisfaction not only looking at the relationship between income and welfare of individuals, but rather looking for complements that could possibly increase welfare to create a broader definition of ones’ life satisfaction. The results of these studies are obtained due to large scale-surveys, across countries and over time. These surveys enlighten the effects of different variables, such as highest level of education, marital and employment status on life satisfaction (Graham, 2005).
The World Values Survey database is used to obtain the information about the self-reported life satisfaction across the residents of different countries. The World Values Survey database is divided in six waves all representing a different time frame for the surveys being held. In this study, we use data from the most recent wave collected between 2010 and 2014. In this wave, the life satisfaction survey covered 60 countries that are divided between developed and non-developed countries according to the statistical index country classification. It should be noted that not every country is represented, but every continent is.
Reviewing the data collected on life satisfaction for developed and non-developed countries it is possible to rank the countries from highest to lowest average life satisfaction through the use of the ranking method discussed earlier (cf. tables 1 and 2).
The developed countries with the highest average life satisfaction are New Zealand and Sweden with an average life satisfaction score of respectively 7.65 and 7.55. The developed countries with the least average life satisfaction are South Korea and Estonia with an average life satisfaction score of respectively 6.51 and 6.25. The non-developed countries with the highest average life satisfaction are Mexico and Colombia with an average life satisfaction score of respectively 8.51 and 8.39. The non-developed countries with the least average life satisfaction are India and Egypt with an average life satisfaction score of respectively 5.08 and 4.85.
Comparing the self-reported average life satisfaction results of the developed countries and the non-developed countries with each other it can be noticed that both the country with the lowest and the one with the highest average life satisfaction score are the non-developed
countries. However, the cross-country average of life satisfaction is higher for developed countries 7.10 than for non-developed countries 6.73 according to WVS.
Table 1: Average life satisfaction in Developed Countries
Ranking Country Life Satisfaction Ranking Country Life Satisfaction
1 New Zealand 7.65 8 Cyprus 7.16
2 Sweden 7.55 9 Poland 7.09
3 Netherlands 7.49 10 Japan 6.91
4 United States 7.37 11 Spain 6.79
5 Germany 7.36 12 Romania 6.69
6 Slovenia 7.35 13 South Korea 6.51
7 Australia 7.20 14 Estonia 6.25
Source: WVS 2010-2014 wave 6
Table 2: Average life satisfaction in Non-Developed Countries
Ranking Country Life Satisfaction Ranking Country Life Satisfaction
1 Mexico 8.51 27 South Africa 6.63
2 Colombia 8.39 28 Jordan 6.61 3 Qatar 8.00 29 Lebanon 6.50 4 Ecuador 7.92 30 Rwanda 6.47 5 Uzbekistan 7.89 31 Algeria 6.30 6 Brazil 7.84 32 Nigeria 6.25 7 Uruguay 7.60 33 Russia 6.17 8 Thailand 7.57 34 Ghana 6.14 9 Pakistan 7.54 35 Morocco 5.94 10 Argentina 7.49 36 Iraq 5.91
11 Trinidad and Tobago 7.47 37 Ukraine 5.90
12 Philippines 7.33
13 Chile 7.27 38 Yemen 5.89
14 Libya 7.25 39 Zimbabwe 5.81
16 Kasachstan 7.23 40 Belarus 5.78 17 Kuwait 7.21 41 Palestine 5.62 18 Peru 7.14 42 Tunisia 5.58 19 Malaysia 7.13 43 Georgia 5.45 20 Singapore 6.96 44 Armenia 5.24 21 Kyrgyzstan 6.96 45 India 5.08 22 Taiwan 6.90 46 Egypt 4.85 23 China 6.85 24 Hong Kong 6.85 25 Bahrain 6.79 26 Azerbaijan 6.66 Source: WVS 2010-2014 wave 6
Source for non-developed developed country classification:
Research Methodology
The purpose of this research is to look at what the influences of GDP per capita (GDP) is on the average self-reported life satisfaction of the residents from the 14 developed and 46 non-developed countries. The question that is asked by World Values Survey is: All things considered, how satisfied are you with your life as a whole these days? The respondents are then asked which number they give their life satisfaction as a whole given the option to rank their satisfaction from 1 to 10. The numbers resemble life satisfaction where 1 stand for completely dissatisfied and is 10 completely satisfied.
Life satisfaction is the satisfaction a random individual chooses for her life at this moment. The data for life satisfaction is retrieved from the World Values Survey (WVS) through cross-sectional surveys. The World Bank and Trading Economics provide information for the GDP per capita of the 60 countries. The timeframe that is used to estimate the data for the used variables ranges from 2010 until 2014.
In order to account for personal characteristics that might be relevant for the life-satisfaction reports of subjects, the following demographic control variables are added: age (AGE), highest educational level attained (HELA), marital and employment status (MS and ES). The data for the explanatory control variables is retrieved from WVS wave 6. WVS provides the information for the country averages for the chosen demographic variables.
To estimate the effect on the dependent variable life satisfaction Ordinary Least Squares (OLS) is used. The explanatory variables, GDP, AGE, HELA, MS and ES are part of the regression. A similar research with usage of OLS is done by others (Agan et al. 2009, Bjornskov 2003).
The OLS regression including control variables will have the following form:
LIFESATISFACTIONc = β0 + β1log(GDPc) + β2AGEc + β3HELAc + β4MSc + β5ESc + ε Where life satisfaction is the country average self-reported satisfaction of a non-developed or developed country. GDP is gross domestic product per capita, Age is the age in years, HELA is the highest education level attained, MS is the marital status and ES is the employment status. The values of AGE, HELA, MS, ES and GDP are the country averages in percentages for the 46 non-developed and 14 developed countries.
The OLS regression will provide the necessary information to answer the main research question: What is the average effect of gross domestic product per capita on life satisfaction for residents of non-developed and developed countries?
Hypothesis
The topic of interest is to study what the effects of GDP per capita of non-developed and developed countries are on self-reported life satisfaction. After finding the resulting effect of the macroeconomic variable GDP per capita is on life satisfaction it’s relevant to research whether it is robust when controlling for personal characteristics which we observe as country averages. In my regression I focus on GDP per capita and use the following characteristics as controls: age, highest educational level attained, marital and employment status.
The first hypothesis that arises from the literature review is about the effect of GDP per capita on life satisfaction. The second hypothesis will then cover all the chosen variables to
determine the effects and significance of these variables. These hypotheses apply to non-developed and non-developed countries.
The two hypotheses are:
Hypothesis 1: An increase in GDP per capita will increase life satisfaction for the 14 developed and 46 non-developed countries.
Hypothesis 2: The chosen personal characteristics (age, highest educational level attained, marital and employment status) and the macroeconomic variable GDP per capita have a significant effect on life satisfaction.
After the OLS regressions provides the necessary information the hypotheses can be answered and a comparison of the possible differences and effects of the explanatory variables can be made between non-developed and developed countries.
Source for GDP per capita: http://www.tradingeconomics.com/country-list/gdp-per-capita and
Results and Discussion
Standard linear regression methods are used to estimate the effects of GDP per capita on average life-satisfaction reports. More specifically, we use the logarithm of the GDP per capita as the explanatory variable and control for country averages of age, highest education level attained, marital and employment status.
The OLS regression is in the form: LIFESATISFACTIONc = β0 + β1log(GDPc) + β2AGEc + β3HELAc + β4MSc + β5ESc + ε
The hypotheses describe what is of interest for this OLS regression to research. The first hypothesis states that an increase in GDP per capita will increase life satisfaction for the 14 developed and 46 non-developed countries. To interpret the variable GDP, it’s important to understand how to convert the log variable to estimate the change of life satisfaction. This is done in the following way: Δ(Life satisfaction) =(β1/100)%ΔGDP. If we increase GDP per capita by one percent, we expect life satisfaction to increase by (β1/100) units of life satisfaction. The result for both the OLS regression table 3(table 4) for non-developed (developed) countries show that GDP per capita indeed has a positive effect on life satisfaction. So when GDP per capita increases with 1% life satisfaction increases with 0.007859144 (0.007574545). Life satisfaction increases more in non-developed countries than in developed countries with an increase in GDP per capita. One of the reasons for this finding could be the diminishing marginal utility of income.
The dependent variable life satisfaction (LS) consist of subcategories ranked from 1 to 10 where 1 is the lowest and 10 is the highest score of self-reported life satisfaction that
residents of the 46 non-developed and 14 developed countries could report. The demographic control variables have subcategories that are ranked in a particular order. The first control variable age (AGE) consist of the following subcategories: age in years up to 29, 30-49 and 50 and more. These subcategories are ranked respectively from 1 to 3. The estimate of age for non-developed (developed) countries is -1.822521 (-.7089421). This implies that age has a negative effect on life satisfaction when jumping from the first age category up to the next on. Life satisfaction decreases when individuals become older. The decrease in life
The second control variable highest education level attained (HELA) consist of the subcategories: no education, primary school completed, secondary school completed and university level education with degree. These subcategories are ranked respectively from 1 to 4. The estimate of HELA for non-developed (developed) countries is 1.621184 (1.259262). This implies that HELA has a positive effect on life satisfaction when a higher education degree is achieved.
The third control variable marital status (MS) consist of the subcategories: married or single. These subcategories are ranked respectively 1 to 2. The estimate of MS for non-developed (developed) is 0.1391053 (-2.045156). The effect of MS is not the same for non-developed and developed countries. The effect of being married is positive for non-developed and negative for developed countries. A possible explanation is that marriage in non-developed countries is important to improve status of individuals while marriage does not have dramatic direct economic benefits in most developed countries.
The final control variable employment status (ES) consist of the subcategories: full
employment, part-time and unemployed. These subcategories are ranked respectively from 1 to 3. The estimate of ES for non-developed (developed) countries is -0.2064498 (0.5717489). The effect of ES is not the same for non-developed and developed countries. The effect of working less than full employment is negative in non-developed and positive in developed countries. A possible explanation for this opposite effect is that developed countries have a good social welfare system. In the worst case scenario when individuals are jobless funds are available for these individuals to provide them in their inherent needs. Also the average income in developed countries is higher so individuals are able to afford their basic needs with less work in comparison with the non-developed countries.
Table 3 non-developed countries OLS regression:
LS Coef. Std. Err. t P>|t| [95% Conf. Interval]
AGE -1.822521 .0279188 -65.28 0.000 -1.877242 -1.767801 HELA 1.621184 .020453 79.26 0.000 1.581096 1.661271 MS .1391053 .0292586 4.75 0.000 .0817585 .1964522 ES -.2064498 .0157358 -13.12 0.000 -.2372919 -.1756076 Log(GDP) .7859144 .0177846 44.19 0.000 .7510566 .8207723 _cons -1.553896 .1757085 -8.84 0.000 -1.898284 -1.209507
Source SS df MS Number of obs = 69,371 Model 39672.4502 5 7934.49003 F(5, 69365) = 1587.32 Residual 346732.167 69,365 4.99866168 Prob > F = 0.0000 Total 386404.618 69,370 5.57019775 R-squared = 0.1027 Adj R-squared = 0.1026 Root MSE = 2.2358
Table 4 developed countries OLS regression:
LS Coef. Std. Err. t P>|t| [95% Conf. Interval]
AGE -.7089421 .0444272 -15.96 0.000 -.796023 -.6218611 HELA 1.259262 .0326244 38.60 0.000 1.195316 1.323209 MS -2.045156 .0606591 -33.72 0.000 -2.164052 -1.926259 ES .5717489 .0385599 14.83 0.000 .4961683 .6473294 Log(GDP) .7574545 .0583488 12.98 0.000 .643086 .8718229 _cons -1.322925 .6172955 -2.14 0.000 -2.532873 -.1129759
Source SS df MS Number of obs = 20,411
Model 9787.21584 5 1957.44317 F(5, 20405) = 575.41 Residual 69414.531 20,405 3.4018393 Prob > F = 0.0000 Total 79201.7468 20,410 3.88053635 R-squared = 0.1236 Adj R-squared = 0.1234 Root MSE = 1.8444
The second hypothesis stated that the chosen personal characteristics (age, highest
educational level attained, marital and employment status) and the macroeconomic variable GDP per capita have a significant effect on life satisfaction. The answer on the second hypothesis can be found in table 3 and 4. The p-values of the control variables and
macroeconomic variable are all 0.000 which implies that these variables are very significant. The quality of the model is represented by the R-squared and the adjusted R-squared which also can be found in table 3 and 4 for respectively non-developed and developed countries. Table 3 and 4 are OLS regression results including all the variables. The R-squared and the adjusted R-squared of the OLS regression with the dependent variable life satisfaction and only the explanatory GDP variable are respectively for the non-developed (developed)
countries 0.0150 (0.0170). Adding the personal control variables improve the model for non-developed (non-developed) countries from 0.0150 (0.0170) to 0.1027 (0.1263).
Table 5 and 6 give descriptive statistics of the dependent variable LS, the control variables AGE, HELA, MS, ES and the variable of interest log(GDP). The means of all the OLS variables are shown and are respectively for non-developed (developed) countries. LS has a mean of 6.729008 (7.095782), AGE 1.94786 (2.278036), MS 1.367185 (1.457351), ES 2.141284 (1.988438) and log(GDP) 9.734919 (10.53207).
The mean represents the average value of the variables and can be used to interpret the results. Comparing respectively non-developed and developed countries which each other, with the information provided, the interpretation of the variables can be completed.
The first finding is that the average LS is higher in developed countries. So overall the people of developed countries are more satisfied with their life. The average mean of AGE is higher in developed countries so people of these countries become overall older. The average age is 39,77 (48,03) in years. The MS value is lower for non-developed countries and that implies that more people are married in these countries. The ES is higher in non-developed countries which implies that the unemployment in these countries is higher. The log(GDP) is higher in developed countries meaning that gross domestic product per capita is higher in developed countries. The average GDP is 16,897.46 (37,499.017).
Table 5 non developed countries
Variable Mean Std. Err. [95% Conf. Interval] Std. Dev. Variance Number of obs LS 6.729008 .0089608 6.711445 6.746571 2.360119 5.570161 69,371 AGE 1.94786 .0029195 1.942138 1.953582 .7689645 .5913064 69,371 HELA 2.735033 .0036695 2.727841 2.742226 .9664944 .9341115 69,371 MS 1.367185 .0018302 1.363598 1.370772 .4820436 .232366 69,371 ES 2.141284 .0035818 2.134263 2.148304 .9433891 .8899831 69,371 Log(GDP) 9.734919 .001836 9.731321 9.738518 .4835725 .2338424 69,371
Table 6 developed countries
Variable Mean Std. Err. [95% Conf. Interval] Std. Dev. Variance Number of obs LS 7.095782 .0137884 7.068755 7.122808 1.969908 3.880536 20,411
AGE 2.278036 .005312 2.267624 2.288448 .7589138 .5759502 20,411 HELA 3.09686 .0056041 3.085875 3.107844 .8006461 .6410342 20,411
MS 1.457351 .0034871 1.450516 1.464186 .49819 .2481933 20,411 ES 1.988438 .0066219 1.975458 2.001417 .9460527 .8950157 20,411 Log(GDP) 10.53207 .0016001 10.52894 10.53521 .2286062 .0522608 20,411
Conclusion
The main question that needed to be answered was: What is the average effect of gross domestic product per capita on life satisfaction for residents of non-developed and developed countries? So the question if GDP per capita influences life satisfaction can be answered with yes. The effect of GDP per capita on the self-reported life satisfaction of the residents from the 46 non-developed and 14 developed countries seems to be positive from the OLS regressions provided. Where the effect of GDP per capita on life satisfaction is stronger for the non-developed countries. One of the reasons for this finding could be the diminishing marginal utility of income. The results of the demographic control variables are that these variables are significant and improve the OLS regression. The demographic control variables provide some interesting different outcomes with respect to life satisfaction for
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