Abstract
This paper investigates the cross-country distribution of the relationship between economic conditions and well-being. Using a large sample of individuals from 94 countries worldwide, we find that the effect of income on well-being is larger in countries with lower GDP per capita, while the negative effect of being unemployed is stronger in countries with higher unemployment rate or higher GDP per capita. Interestingly, the effect of being unemployed displays positive spatial dependence across countries that is not accounted for by aggregate socio-economic conditions. Overall, the results indicate that geography, culture and institutions must be explicitly taken into account in order to understand the relationship between economic conditions and well-being.
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Notes
Blanchflower (2008, p. 7) summarizes the evidence as follows: “the main ceteris paribus findings from happiness and life satisfaction equations across countries and time [are that] well-being is higher among those who are women, married, highly educated, actively involved in religion, healthy, with a high income, young or old, self-employed, with low blood pressure, sexually active, without children”.
Clark et al. (2005) find significant country differences in the relationship between income and happiness in 12 European countries. Aslam and Corrado (2007) investigate the effect of social interactions on well-being across European regions. Brereton et al. (2008) use geographical information systems with data at the individual and local level for Ireland to examine the role played by location-specific characteristics, such as climate and environmental conditions, in explaining self-reported well-being. See also Ballas (2007) and White (2007) for recent contributions on the geography of happiness.
Di Tella et al. (2001) use a similar approach to estimate preferences over inflation and unemployment in 12 European countries and the United States.
See Frey and Stutzer (2002a) for a discussion of the use of alternative indicators of subjective well-being as an empirical approximation of individual happiness.
The countries in the sample are: Albania, Algeria, Andorra, Argentina, Armenia, Australia, Austria, Azerbaijan, Bangladesh, Belarus, Belgium, Bosnia and Herzegovina, Brazil, Bulgaria, Burkina Faso, Canada, Chile, China, Colombia, Croatia, Cyprus, Czech Republic, Denmark, Dominican Republic, Egypt, El Salvador, Estonia, Ethiopia, Finland, France, Georgia, Germany, Ghana, Greece, Hong Kong, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Japan, Jordan, Latvia, Lithuania, Luxembourg, Macedonia, Malaysia, Mali, Malta, Mexico, Moldova, Montenegro, Morocco, Netherlands, New Zealand, Nigeria, North Korea, Norway, Pakistan, Peru, Philippines, Poland, Portugal, Puerto Rico, Romania, Russian Federation, Rwanda, Serbia, Singapore, Slovakia, Slovenia, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, Tanzania, Thailand, Trinidad and Tobago, Turkey, Uganda, Ukraine, United Kingdom, United States, Uruguay, Venezuela, Viet Nam, Zambia, Zimbabwe.
The original answers on a scale 1 (dissatisfied) to 10 (satisfied) were multiplied by 10 in order to ease interpretation of regression results.
The map in Fig. 2 reports the effect of unemployment on life satisfaction multiplied by −1, so that a figure of x indicates that, ceteris paribus, being unemployed is associated with a negative x-point differential in life satisfaction.
As observed by Blanchflower (2008, p. 7) “Generally, it makes little or no difference if you use an OLS or an ordered logit. The results are similar—but not identical—for happiness and life satisfaction”.
In a recent cross-country study based on individual data from the Gallup World Poll, Deaton (2008) finds that individuals report higher life-satisfaction in high-income countries, and that this relationship is stronger in poorer countries.
In a recent review of the cross-country evidence on the determinants of well-being, Blanchflower (2008, p. 8) observes that “The structure of a happiness equation has the same general form in each industrialized country (and possibly in developing nations, though only a small amount of evidence has so far been collected)”. A similar point is made by Di Tella and MacCulloch (2008, p. 24): “micro-happiness regressions (where well-being answers are regressed on personal characteristics of respondents) have a similar structure across a number of nations”.
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Stanca, L. The Geography of Economics and Happiness: Spatial Patterns in the Effects of Economic Conditions on Well-Being. Soc Indic Res 99, 115–133 (2010). https://doi.org/10.1007/s11205-009-9571-1
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DOI: https://doi.org/10.1007/s11205-009-9571-1