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Unlike other studies that focus solely on income, ÌìÃÀ´«Ã½'s Franz H. Heukamp and Miguel A. Ariño examine the role that social and cultural variables also play in life satisfaction.

The considerable body of research on happiness often uses economics as its starting point: GDP and inflation are the variables used to explain differences in life satisfaction between countries. Recessions and booms, and different unemployment benefits, are some of the factors that have been shown to influence happiness — itself an elusive concept, often understood as living the good life.

ÌìÃÀ´«Ã½ professors Franz H. Heukamp and Miguel A. Ariño wanted to go one step further. Instead of happiness, they dealt with subjective well-being (SWB) and life satisfaction, analyzed on a country rather than individual level.

They also expanded the phenomena used to explain differences in SWB between countries, taking into account social and cultural factors, not just economic ones.

Using data from the World Values Survey between 1981 and 2004, .

To find the country characteristics that make people feel more or less satisfied, they ran the data through two levels of analysis — first, to take all of the individual characteristics of people into account, and based on that, to identify national variables that make people in a country feel satisfied with their lives.

Five variables that influence well-being

The authors' findings, published in Social Indicators Research of Springer Science+Business Media, reveal five main variables that would seem to explain SWB at a country level.

1. Life Expectancy. The higher the life expectancy, the higher the chance of well-being in a country. An additional 10 years of life expectancy, for example, increases life satisfaction by almost one standard deviation point.

Life expectancy is reflected through income distribution, literacy, health care, sanitation and nutrition. In this regard, SWB is indirectly linked to economics, as it is affected by a country's level of economic development. These are the positive effects of income, rather than income itself.

2. Natality. Generally, as the birthrate goes up, so does a country's SWB. An increase in 10 births per 1,000 people translates into a 0.58 increase in life satisfaction. Interestingly, in some poorer countries where people may have more children to ensure future economic security, there appear to be higher levels of life satisfaction.

3. Religion. The authors tested for religious beliefs, categorizing countries according to the highest percentage of adherents of a particular faith, even though not everyone professed that faith. These included Christians (Roman Catholics, Protestants, Orthodox), Muslims and some other major faiths typically associated with Asian countries.

According to the data, being in a country with a dominant Muslim culture lowered the chance of well-being. The authors were not sure what to make of this finding. Could it be that many of these Muslim countries are also emerging economies, and therefore the economic development variables hold sway? Could it be related to the lack of democracy in many of these countries, which would help explain current popular uprisings across North Africa and the Middle East?

The authors substituted "quality of political governance" and "democracy" for "Muslim" variables, but the outcomes were inconclusive.

4. Corruption. This factor significantly affects results. The lower the corruption, the higher the chance of well-being, and vice versa. Factors related to corruption include trust in others and the acceptability of cheating. Corruption leads to inefficiencies that undermine economic development. But more than that, it alters the social fabric and leads to high levels of frustration.

5. Latitude. Finally, the authors considered a country's geographic or latitudinal position. The closer one lives to the equator, they find, the higher the levels of life satisfaction. Moving from the equator to Frankfurt, for example, decreases your life satisfaction by 1.3, which corresponds to almost two standard deviations of the dependent variable.

Surprises and recommendations

Based on these variables, the authors then grouped the countries into six clusters to show how they stacked up against each other.

At the lower end of the scale is a cluster comprising Iraq, Albania, Bulgaria, Egypt and Zimbabwe — though it should be noted that this study was conducted prior to the latest sociopolitical developments in Egypt, for example.

At the top of the scale are the countries with the highest SWB. This cluster counts Denmark, Switzerland and Sweden, though what surprised the authors is the equal presence of Colombia and Puerto Rico in this group.

Given that corruption in these Latin American countries is comparatively high, the authors believe that there must be some other variables at work to account for this, which lie beyond the scope of their model.

This study ends with some indicators for policy makers. The authors recommend the following to boost the well-being of nations.

• Adopt measures to reduce corruption and foster honesty.
• Support family-friendly policies that favor the well-being of children.
• Invest in health care and encourage healthy lifestyles and other activities that boost life expectancy.