The final model with level 2 explanatory variables

We have to deal with two problems here: the low number of countries, which limits the number of variables that can added to the model, and the problem that many variables are rather strongly correlated. It is therefore advisable to base the choice on theory and test the model with one or a few country level variables at a time. For happiness, we could reason that people in rich countries or countries with good living conditions will be happier than people in poor countries. This point at Gross Domestic Product (GDP) per capita and the Human Development Index (HDI). We can only use one of them at a time, since GDP is a component of HDI. We could also examine the hypothesis inspired by Wilkinson and Pickett (2010) that inequality lowers levels of happiness and include the Gini coefficient as a measure of inequality. We could also argue that gender equality promotes happiness and include the gender empowerment measure from the Human Development Report. Life expectancy at birth can be seen as an objective quality-of-life measure. Does it also lead to happiness?

Country classifications can also be added as contextual variables. They can build upon a complex set of variables and be an alternative to the more specific continuous variables reviewed above. We can also test the welfare state classification that is already in the data file. Looking at the categories, we see that the classification is correlated to a country’s wealth, especially the difference between the Nordic, Continental and Liberal regimes, on the one hand, and the Southern and Eastern European regimes, on the other.

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