Possible effects of national population homogeneity on happiness.

Barrett, J. Douglas ; Van Rensselaer, Kristen N. ; Gordon, Bruce L. 等

ABSTRACT

This cross-country study investigates what influence, if any, different measures of homogeneity have on happiness. Using self-perceived life satisfaction as an indicator of happiness, data from 65 nations are analyzed with regression analysis. The results of the study indicate that income inequality and ethnic homogeneity are related to happiness. Other variables determined to be significant indicators of happiness include income levels (GDP per capita), inflation, and life expectancy.

INTRODUCTION

What leads to happiness? This question is one basis for every philosophical and ethical system. Extended to the political and economic realm, we are reminded of "life, liberty, and the pursuit of happiness" as inalienable rights in the Declaration of Independence. Happiness is the purported goal of most everyone, but finding an efficacious metric has proved elusive for researchers in fields such as psychology, sociology, economics, political science, and evolutionary biology. These past failures have not deterred researchers over the centuries.

Economists refer to "utility" as a happiness measure (See, e.g., Mankiw 2004). Jeremy Bentham and John Stuart Mill developed the concept of "utilitarianism" as aiming to maximize the greatest good for the greatest number (See Ekelund and Hebert 1990). Von Neumann and Morgenstern (1944) formulated game theory based on the premise that individuals and groups reach decisions in an attempt to maximize utility. The issue of systemic maximization of happiness obviously has ramifications in socioeconomic and legal/political realms.

A potentially related (and somewhat more easily measured) issue of interest is cultural homogeneity. How similar (or diverse) are the people in a given geographic area? Many past studies have focused on ethnic and linguistic (or ethnolinguistic), religious, or economic homogeneity (See, e.g., Masters and McMillan 2003). These studies overlap the aforementioned fields. In most cases, the homogeneity dimensions were considered separately. Recent studies have combined them to assess an overall level of homogeneity (See, e.g., Barrett and Couch 2006).

The main issue of interest in the current study is what relationship, if any, exists between cultural homogeneity and happiness for nations of the world. The happiness metric employed is self-perceived life satisfaction and is obtained from studies by Veenhoven (1991, 1996, 2001). A sample of 65 countries is analyzed using regression analysis with a set of control variables to determine whether happiness (life satisfaction) is influenced by income homogeneity, ethnic homogeneity, and religious homogeneity.

LITERATURE REVIEW

The topic of "national happiness" has suddenly become ubiquitous in the sociological and economic literature, due mainly to Ruut Veenhoven. The Dutch sociologist used a survey to obtain happiness indices for 91 countries. (See Veenhoven 1991, 1996, 2001). Following the seminal study, many authors have used the Veenhoven data in other investigations linking happiness to multiple sociological and economic variables. A recent entire issue of the Journal of Socio-Economics (Vol. 35, 2006) was devoted to papers on happiness.

Veenhoven (2000) assessed the relationship between freedom and happiness, and found a significant positive relationship between the two. Such an association is expected, as well as being appreciated for advocates of increased political and economic freedom. Helliwell (2002), Frey and Stutzer (2002 a, b), and Inglehart and Klingemann (2000) also studied freedom-intensive variables and freedom. In each case, the given freedom metric was significantly positively related to national happiness.

Ovaska and Takashima (2006) investigated economic policy and happiness, using a large set of independent variables as controls. Their study yielded significance of health (as measured by life expectancy) and economic freedom as related to happiness. Veenhoven (1991), Easterly (1999), Frey and Stutzer (2000 a, b), and Blanchflower and Oswald (2000) found that income is positively related to happiness. The conclusions tend to suggest that money can't buy happiness, but it surely ameliorates pain.

Di Tella et all (2001) showed that a sudden drop in the unemployment rate coincides with a significant drop in happiness. This result is consistent with Clark and Oswald (1994), Gerlach and Stephan (1996), and Winkelmann and Winkelmann (1998), who each also showed that unemployment is negatively related to happiness. Peiro (2006) specifically addresses overall economic conditions and happiness. His study confirms the significance of the aforementioned economic variables.

Blanchflower and Oswald (2000) and Frey and Stutzer (2002 a, b) looked at educational attainment as a potential predictor for happiness. Each showed a significant result, though Helliwell (2002) found no significance in an analogous test. Blanchflower and Oswald (2004), Argyle and Martin (1991), and Lee et al (1999) assessed social relationships, finding that married people tend to be happier. Political stability was also studied extensively, including Argyle (1987) and Frey and Stutzer (2000 a, b), with a more stable political environment tending to be associated with a happier populace.

With respect to homogeneity, there is a plethora of literature on all dimensions, though they are generally considered separately. Masters and McMillan (2003) used an evolutionary analysis on the effects of ethno-linguistic diversity on economic growth. Borjas (1998) investigated residential segregation in industrialized countries. Easterly and Levine (1996) looked at African countries and the corresponding relationship ethnic divisions and policies. Peterson (1997) discusses homogeneity in general in the tome Ethnicity Counts. Much of the work in the area contains analyses of the relationships between ethno-linguistic homogeneity and measures of economic well-being. A major difficulty exists with regard to establishing the boundaries of "ethnicity."

Religious homogeneity presents an equally vexing concept, as the lines between certain divisions are unclear. Religious deviations differ between nations, as well as within-nation divisions in different areas of the world. Barro and McCleary (2003) studied religion and economic growth in nations, finding that economic growth responds positively to certain (vague) religious beliefs, but negatively to church attendance rates. Guiso, Sapienza, and Zingales (2003) found that economic attitudes differ based on religious background. Perhaps most relevant to the current study, Mookerjee and Beron (2005) found that religious fractionalization is negatively correlated with happiness.

There is hardly a dearth of economic inequality studies. There is also no consensus on whether such disparity is "good," though the prevailing opinion of the relevant author(s) surely impact the nature of given studies. Frank and Freeman (2002) found that American income inequality and economic growth are slightly negatively related. Similar studies on state data were conducted by Kuznets (1955), Kakwani (1980), Galor and Zeira (1993), Meltzer (1998), Partridge (1997), and Forbes (2000). Akhand and Liu (2002), Al-Samarrie and Miller (1967), Cowell (1995), and Piketty and Saez (2003) studied international economic homogeneity data. The common thread in each study is that "freer" countries tend to exhibit more income equality.

Recent studies have combined information from multiple dimensions of homogeneity. Alesina et al (2003) used data from 190 countries to obtain measures of ethnic, linguistic, and religious fractionalization (heterogeneity). They then looked at how the fractionalization measures help determine economic success, finding that economic and linguistic fractionalization tend to be significant positive indicators of GDP growth, literacy rate, health factors, and political freedom. The religious fractionalization was seen to be much more weakly related to the other variables.

Barrett and Couch (2006) created a "homogeneity index" for the fifty states using measures for all four dimensions. The measures were combined to obtain an overall measure of homogeneity as a basis for comparing the states. The indices may be used in future studies on state homogeneity as it relates to other economic and sociological variables. In the same vein as in Alesina et al (2003), one must view more than one dimension to determine a truer "homogeneity" of a given geographic entity.

No study to date has considered all dimensions of homogeneity for international data. Alesina et al (2003) come close, but their study did not take into account economic inequality. The current study seeks to extend the literature by considering all dimensions of homogeneity and how they relate to other measures for different nations of the world.

DATA AND METHODOLOGY

The measure used for happiness is life satisfaction, obtained from the World Database of Happiness (Veenhoven, 2006). While the World Database of Happiness offers several measures of happiness and self-perceived well-being, two of the most commonly used measures are happiness and life satisfaction. The distinction between the two is that the happiness measure is considered to be self-perception of current well-being, whereas life satisfaction measures perception about overall life fulfillment. We chose to use life satisfaction, as it offers the advantage over happiness in that life satisfaction may be less heavily influenced by short-term economic and emotional factors (Helliwell, 2002). The life satisfaction measures were obtained through survey data. The respondents to the survey ranked how much they liked their lives as a whole on a scale of 0 to 10. According to Veenhoven (1996, 2000), life satisfaction or well- being scores are comparable across nations and cultures.

This study uses three measures of homogeneity for income, ethnicity, and religion. The Gini coefficient is a commonly used as a measure of income inequality. A Gini coefficient of zero indicates perfect income equality and, at the opposite extreme, a Gini coefficient of 100 indicates perfect income inequality. The ethnic homogeneity measure is the Vanhanen Ethnic Homogentity Index (Vanhanen, 1991). This is the percentage of the population belonging to the largest homogenous ethnic group in a country. The religious homogeneity is measured in like manner (percentage of the population belonging to the nation's chief religious affiliation). These three homogeneity measures will allow us to study whether life satisfaction across countries is related to income, ethnic, and religious homogeneity as measured by these three variables.

Other economic and social variables are also used in this study. Several of the variables selected for this study are based upon the recent work of Ovaska and Takashima (2006). In their study, variables measuring economic growth, unemployment, inflation, income (GDP per capita), level of foreign trading, income of neighboring countries, economic freedom, political freedom, government size, female labor participation, religion, life expectancy, and aging population were used. While they performed multiple regression analysis in stages, the two variables that had the most consistent impact on life satisfaction were economic freedom and life expectancy. Many of the economic variables turned out to be insignificant statistically or the coefficients were so small that they would be considered economically insignificant (income of neighboring countries, inflation, government spending, unemployment, and foreign trade). Several of the socio-demographic variables were also found to be insignificant (political freedom, female labor participation, education, and aging population).

In addition to the three homogeneity measures, we used foreign aid per capita, arable land, GDP per capita, GDP growth, government spending, inflation, female labor force participation, life expectancy, enrollment in secondary school, and illiteracy rate as additional control variables. We started using the sample of 91 nations for which life satisfaction was available, as we gathered data from the World Development Indicators, Freedom House (Economic Freedom Index), Human Development Report (Gini Coefficients), The World Almanac (ethnic, religious homogeneity, and illiteracy). Data were not available for all 91 countries, and the sample size was subsequently reduced to 65 countries. Table 1 shows the descriptive statistics for the data used in the final statistical model (descriptive statistics for the full data set are available by contacting the authors). Appendix A describes the data used for the study as well as their sources. Appendix B lists the countries used for the study. Since the thrust of this research is to explore the relationships between life satisfaction and homogeneity, multiple regression analysis was used to analyze the data.

RESULTS

Table 2 presents the results of the most parsimonious regression model. This model explains approximately 71% of the variation in life satisfaction across the 65 nations used in this study. The error terms were checked for normality, serial correlation, and hetroskedasticity. The diagnostics for the regression results indicated that the residuals are normally distributed and there was no evidence of serial correlation or hetroskedasticity. Correlation analysis of the independent variables did not reveal any substantive multicollinearity (see Appendix C). The strongest correlation between two independent variables exists between GDP per capita and life expectancy. Also, as different models were explored, the coefficients for the significant variables maintained their signs, significance, and exhibited minimal changes in magnitude.

Of the three homogeneity variables, income inequality (Gini coefficient) and ethnic homogeneity were statistically significant. The income inequality measure shows a positive relationship with life satisfaction. Since lower Gini coefficients indicate greater income equality, a negative relationship is counterintuitive. The results indicate that life satisfaction is improved with greater differences in income. Many believe that having greater income equality would equate to less strife and more life satisfaction in a society. Our results run contrary to that view. The ethnic homogeneity index was negatively related to life satisfaction, implying the higher the ethnic majority, the lower life satisfaction. This suggests that ethnic diversity is a source of life enrichment. The religious homogeneity index was not statistically significant.

Aid per capita was marginally significant (10% level) and the coefficient was negative indicating that more aid relates to lower life satisfaction, but it is hard to define the cause and effect. Countries that receive more aid tend to have lower standards of living, which would be related to lower levels of life satisfaction. GDP per capita is positively related to life satisfaction. Higher incomes are associated with higher levels of life satisfaction. Inflation and life satisfaction are negatively associated. Since inflation creates uncertainty, our results confirm that nations with higher inflation rates have lower levels of life satisfaction. The coefficient for GDP growth, while only marginally significant (10% level), is negative signifying that countries with higher economic growth have lower levels of life satisfaction. This is not surprising since developed nations tend to have low stable growth rates compared to lesser-developed nations. The most important determinant of life satisfaction is life expectancy. Higher life expectancy in a nation is associated with higher life satisfaction. Life expectancy is used as a measure of health. Health has been found to be one of the most important variables explaining self-perceived level of well-being (Helliwell, 2002).

Comparing our major outcomes to the study by Ovaska and Takashima (2006), we too find that life expectancy is a crucial determinate of life satisfaction. Contrary to Ovaska and Takashima (2006), our analysis indicates that greater economic freedom is not a significant indicator of life satisfaction. It is possible that economic freedom is being measured through other variables included in this study such as the Gini Coefficient, but this result warrants additional investigation.

CONCLUSIONS

Based on our multiple regression analysis, we obtained some expected and at least one unexpected relationship(s) with respect to the relationships between homogeneity measures and happiness as measured by life satisfaction. The results suggest that life satisfaction is significantly negatively related to ethnic homogeneity in the presence of our control variables. This is consistent with the "melting pot" concept, as multiple cultural influences create a richer living environment. Religious homogeneity was not significantly related to life satisfaction, and this is due in part to the overwhelming significance of income as an explanatory variable. Also, differences in the tenets of diverse religions may well be expected to lead to different views of what constitutes "happiness."

The most surprising result is the significant positive association between life satisfaction and income inequality in the presence of the other variables. One may defer to the opinion that "wealth" is not a zero-sum game. Perhaps it is overall standard of living, and not the gap between "rich" and "poor" that tends to most influence life satisfaction. However, this does not explain why there is a positive relationship even when holding standard of living constant. It is possible that people in some cultures are inured to the inherent existence of such gaps, and even embrace them for rewarding such traits as talent, hard work, and perseverance. At this point, we can at most note the result and conclude that further tests using other explanatory variables is required before we can offer more definitive interpretations regarding this interesting finding.

Future investigations on homogeneity and happiness are needed to assess the aforementioned relationship between income inequality and life satisfaction. In addition, alternative measures of homogeneity (such as the "fractionalization" metrics of Alesina et al) may be employed. The Vanhanen indices used in the current study are adequate measures of homogeneity, but the fractionalization indices offer a greater degree of variation in heterogeneity. Other studies separately examining national homogeneity/heterogeneity are also warranted, as no study to date has assessed the relationships between the four separate dimensions (income, ethnic, linguistic, and religious) therein. Such inquiry should be of interest to virtually every social science.

APPENDIX A: VARIABLE DEFINITIONS AND SOURCES

Variable Comments Source

Happiness How much people enjoy World Database of
 their life-as-a-whole on Happiness
 a scale of 0 to 10;
 average 1995-2005

Gini Coefficient 0 indicates perfect Human Development
 equality and 100 Report
 indicates perfect (2005)
 inequality; most recent
 index value used

Economic Freedom Average of Economic Freedom House
 Freedom Index over
 2000-2004

Ethnic Homogeneity Percentage of population World Almanac (2006)
 belonging to the major
 ethnic category in nation

Religious Percentage of population World Almanac (2006)
Homogeneity belonging to the major
 religion in nation

Illiteracy Percentage of population World Almanac (2006)
 that is illiterate

GDP per capita Average GDP per capita, World Development
 PPP, in constant 2000 Indicators online
 dollars from 1995-2004 database

GDP Growth Average growth rate in World Development
 GDP per capital from Indicators online
 1995-2004 database

Government Average government World Development
Consumption consumption as a percent Indicators online
 of GDP from 1995-2004 database

Inflation Average annual GDP World Development
 Deflator from 1995-2004 Indicators online
 database

Arable Land Average arable land per World Development
 capita (hectares) from Indicators online
 1995-2004 database

Female Labor Force Female Participation in World Development
 the labor force as Indicators online
 measure as the percent database
 of total labor force;
 average 1995-2004

Life Expectancy Average life expectancy World Development
 at birth from 1995-2004 Indicators online
 database

Secondary School Ratio of total enrollment World Development
Enrollment in secondary school, Indicators online
 regardless of age, to the database
 population of the age
 group that officially
 corresponds to the level
 of education shown. The
 average is taken over
 1995-2004.

APPENDIX B: LIST OF NATIONS

Albania Dom Rep Mali United Kingdom
Algeria Egypt Mexico United States
Argentina El Salvador Moldova Uruguay
Armenia Estonia Morocco Uzbekistan
Australia Finland Netherlands Zimbabwe
Austria Georgia New Zealand
Azerbaijan Germany Peru
Bangladesh Ghana Philippines
Belarus Greece Poland
Belgium Honduras Romania
Bolivia Hungary Senegal
Bosnia India Singapore
Brazil Indonesia Slovenia
Bulgaria Iran South Africa
Canada Israel Spain
Chile Japan Sweden
Columbia Jordon Switzerland
Cote d'Ivoire Kenya Tanzania
Croatia Lithuania Turkey
Czech Rep Macedonia Uganda

APPENDIX C: CORRELATIONS

 Life Gini Aid per GDP
 Satisfaction Cap. Growth

Gini Coefficient 0.03
Aid per Capita -0.37 -0.10
GDP Growth -0.29 -0.38 0.52
GDP per Capita 0.69 -0.36 -0.33 -0.13
Inflation -0.46 0.00 -0.06 0.01
Life Expectancy 0.58 -0.42 -0.10 0.18
Ethnic Hom. 0.03 -0.23 -0.13 0.01

 GDP per Inflation Life Exp.
 Cap.

Gini Coefficient
Aid per Capita
GDP Growth
GDP per Capita
Inflation -0.31
Life Expectancy 0.67 -0.22
Ethnic Hom. 0.21 0.07 0.42

REFERENCES

Akhand, H. & H. Liu (2002). Income inequality in the United States: what the individual tax files say. Applied Economics Letters, 9 (4), 255-59.

Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S., & R. Wacziarg (2003). Fractionalization. Journal of Economic Growth, 8 (2), 155-194.

Al-Samarrie, A. & H. P. Miller (1967). State differentials in income concentration. American Economic Review, 57 (1), 59-72.

American Religious Identification Survey. (n.d.) Retr ieved May, 2006 from http://www.gc.cuny.edu/faculty/research_studies/aris.pdf

Argyle, M. (1987). The Psychology of Happiness, New York: Methuen.

Argyle, M. & M. Martin (1991). The psychological causes of happiness. In F. Strack, M. Argyle, and N. Schwarz Editors, Subjective Well Being, Oxford: Pergamon Press.

Barrett, J. D. & J. F. Couch (2006). An investigation of state homogeneity. Submitted for publication.

Barro, R. J. & R. M. McCleary (2003) Religion and economic growth across countries. American Sociological Review, 68(5), 760-81.

Blanchflower, D. G. & A. J. Oswald (2004). Well-being over time in Britain and the USA. Journal of Public Economics, 88 (7), 1359-1386.

Borjas, G. J. (1998). To ghetto or not to ghetto: ethnicity and residential segregation. Journal of Urban Economics, 44(2), 228-53.

Clark, A. E. & A. J. Oswald (1994). Unhappiness and unemployment. Economic Journal, 104 (424), 648-659.

Cowell, F.A. (1995). Measuring Inequality, London School of Economics Handbooks in Economics Series, No. 2, Harvester Wheatsheaf: Prentice Hall.

Di Tella, R., MacCulloch, R.J., and A.J. Oswald (2001). Preferences over inflation and employment: evidence from surveys of happiness. American Economic Review, 91 (1), 335-341.

Easterly, W. (1999). Life during growth. Journal of Economic Growth, 4(3), 239-276.

Easterly, W. & R. Levine (1996). Africa's growth tragedy: policies and ethnic divisions. Quarterly Journal of Economics, 112 (4), 1203-50.

Ekelund, R.B. & R.F. Hebert (1990). A History of Economic Theory and Method. New York: McGraw-Hill,

Forbes, K. J. (2000). A reassessment of the relationship between inequality and economic growth. American Economic Review, 90 (4), 869-87.

Frank, M. & D. Freeman (2002). Relationship of income inequality to economic growth: evidence from U.S. state-level data. Pennsylvania Economic Review, 11, 24-36.

Frey, B.S. & A. Stutzer (2000 a). Happiness, economy, and institutions. Economic Journal, 110 (446), 918-938.

Frey, B.S. & A. Stutzer (2000 b). Maximizing happiness? German Economic Review, 1(2), 145-167.

Frey, B.S. & A. Stutzer (2002 a). Happiness and Economics: How the Economy and Institutions Affect Well-being. Oxford and Princeton: Princeton University Press.

Frey, B.S. & A. Stutzer (2002 b). What can economists learn from happiness research? Journal of Economic Literature, 40 (2), 402-435.

Galor, O. & J. Zeira (1993). Income distribution and macroeconomics. Review of Economic Studies, 60 (202), 35-52.

Gerlach, K. & G. Stephan (1996). A paper on unhappiness and unemployment in Germany. Economics Letters, 52, 325-330.

Gini Coefficient at Wikipedia. (n.d.) Retrieved in May, 2006 from http://en.wikipedia.org/wiki/Gini_coefficient

Guiso, L., Sapienza, P. & L. Zingales (2003). People's opium? religion and economic attitudes. Journal of Monetary Economics, 50 (1), 225-82.

Helliwell, J.F. (2002). How's life? Combining individual and national variables to explain subjective well-being. Economic Modelling, 20 (2), 331-360.

Human Development Report (2005). Retrieved in May and June, 2006 from http://hdr.undp.org/reports/global/2005.

Inglehart, R. & H.D. Klingemann (2000). Genes, culture, democracy, and happiness. In E. Diener and E.M. Suh, Editors, Subjective Well-Being Across Cultures, Cambridge, MA: MIT Press.

Kakwani, N.C. (1980). Income Inequality and Poverty: Methods of Estimation and Policy Applications. New York: Oxford University Press for the World Bank.

Kuznets, S. (1955). Economic growth and income inequality. American Economic Review, 45 (1), 1-28.

Lee, D.Y., Park, S.H., Uhlemann, M.R., & P. Patsula (1999). What makes you happy? A comparison of self-reported criteria of happiness between two cultures. Social Indicators Research, 50, 351-362.

Mankiw, N.G. (2004). Principles of Economics. Mason, OH: Thomson.

Masters, W. & M. McMillan (2003). Ethnolinguistic diversity, government and growth. In Frank Salter Ed., Welfare, Ethnicity and Altruism: New Findings and Evolutionary Theory, London: Frank Cass.

Meltzer, A. (1998) Comment on 'economic consequences of income inequality.' Economic Inequality: Issues and Policy Options, Kansas City, KS: Federal Reserve of Kansas City.

Mookerjee, R. & K. Beron (2005). Gender, religion, and happiness. Journal of Socio-Economics, 34 (5), 674-685.

Ovaska, T. & R. Takashima (2006). Economic policy and the level of self-perceived well-being: an international comparison. Journal of Socio-Economics, 35 (2), 308-325.

Partridge, M. (1997). Is inequality harmful for growth? Comment. American Economic Review, 87, 1019-32.

Peterssen, W. (1997). Ethnicity Counts, New Brunswick: Transaction.

Piketty, T. & E. Saez (2003). Income inequality in the United States. Quarterly Journal of Economics, 118 (1), 1-39.

Vanhanen, T. (1991). Politics of Ethnic Nepotism: India as an Example, New Delhi: Sterling.

Veenhoven, R. (1991). Is happiness relative? Social Indicators Research, 24 (1), 1-34.

Veenhoven, R. (1996). The study of life satisfaction. In Saris, W., Veenhoven, R., Scherpenzeel, A.C., and B. Bunting Eds., A Comparative Study of Satisfaction with Life in Europe (pp.11-48), Eotvos, Budapest, Hungary: University Press, 11-48.

Veenhoven, R. (2001). Are the Russians as unhappy as they say they are? Journal of Happiness Studies, 2, 111-136.

Veenhoven, R. (2006). Average happiness in 91 nations 1995-2005, World Database of Happiness, RankReport 2006-1, Retrieved in May and June, 2006 from worlddatabaseofhappiness.eur.nl

Von Neumann, J. & O. Morgenstern (1944). Theory of Games and economic Behavior. Princeton, NJ: Princeton University Press.

Winkelmann, L. & R. Winkelmann (1998). Why are the unemployed so unhappy? Evidence from panel data. Economica, 65 (257), 1-15.

World Bank (2005). World Development Indicators. Online Database.

The World Almanac and Book of Facts (2006). New York: World Almanac Books.

J. Douglas Barrett, University of North Alabama Kristen N. Van Rensselaer, University of North Alabama Bruce L. Gordon, University of North Alabama

Table 1: Descriptive Statistics for Variables Used in Final Multiple
Regression Model

Variable Number of Mean Standard
 Obs. Deviation

Life Satisfaction 65 5.99 1.25
Gini Coefficient 65 38.14 9.55
Aid per Capita 65 27.16 38.49
GDP Growth 65 2.82 2.72
GDP per Capita 65 10490.76 9521.94
Inflation 65 16.38 28.88
Life Expectancy 65 69.41 9.59
Ethnic Homogeneity 65 75.40 21.39

Variable Minimum Maximum

Life Satisfaction 3.2 8.1
Gini Coefficient 24.9 59.3
Aid per Capita 0 210.35
GDP Growth -2.79 18.38
GDP per Capita 532.09 33316.37
Inflation -1.00 154.24
Life Expectancy 40.70 80.88
Ethnic Homogeneity 17 100

Table 2: Regression Analysis Results Dependent Variable:
Life Satisfaction Included observations: 65

Variable Coefficient Std. Error t-Statistic

Constant 1.053190 0.998550 1.054719
Gini Coefficient 0.032855 0.011049 2.973616
Aid per Capita -0.004658 0.002779 -1.676134
GDP Growth -0.074820 0.041372 -1.808462
GDP per Capita 4.46E-05 1.44E-05 3.101399
Inflation -0.010257 0.003229 -3.176481
Life Expectancy 0.066353 0.013981 4.745980
Ethnic Homogeneity -0.011722 0.004532 -2.586403

R-squared 0.739524
Adjusted R-squared 0.707536
S.E. of regression 0.678141
F-statistic 23.11860
Prob(F-statistic) 0.000000
Durbin-Watson stat 2.291274

Variable Prob.

Constant 0.2960
Gini Coefficient 0.0043
Aid per Capita 0.0992
GDP Growth 0.0758
GDP per Capita 0.0030
Inflation 0.0024
Life Expectancy 0.0000
Ethnic Homogeneity 0.0123

R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
Durbin-Watson stat