necessary for the transformations.<sup>11</sup> In order to check on the results of this work, we compared the resulting values from our database with the IMF GDP per capita PPP data and we also verified the consistency of results across countries and years.

We use two different Gini as alternative measures of _income inequality_ ( _I_ ) _._ The first is calculated by country and year using the income variable already described present in the database we use (Gini WVS for short). The second is the Gini coefficient taken from the UNU-WIDER database on inequality (Gini WIDER for short).

The Gini WIDER puts together country estimates of the Gini coefficient calculated from a variety of income and consumption measures. For this reason, we opted to use two forms of the Gini WIDER. The first form is constructed with different types of income or consumption measures giving priority to disposable income, other forms of income and consumption in this order.<sup>12</sup> This allows us to cover all country/year data points available in the World and European Values surveys. The second form is the Gini WIDER estimated using disposable income only. This restricts the usable sample to two-thirds of the original size but provides more precise estimates for income inequality. Given that we use two different samples for the Gini WIDER we also present results for the Gini WVS for both samples. Thus, all tables will report four sets of results (two Gini for each of the two samples).

_Relative income_ ( _R_ ) is measured as income divided by mean income within each country and year. In happiness research this variable is often used in conjunction with income and/or income inequality to test the importance of the relative income position as opposed to absolute income in explaining satisfaction. Relative income has been found to have a significant and positive effect on satisfaction but the sign and significance of this variable may be affected by collinearity with other variables such as income or income inequality. It is important therefore to test how the inclusion and exclusion of this variable may alter results for income inequality.

Countries’ wealth ( _W_ ) is measured with GDP per capita for each country and year. As already mentioned, this variable has been extracted from the IMF database and is used in real terms, USD and PPP values.

We also use a number of control variables ( _C_ ) as follows. A first set of variables measures _individual and family attributes_ which are possible predictors of life-satisfaction. These are being unemployed (dummy), sex (female), age (continuous with the addition of age squared) and a dummy for tertiary education and marriage status (dummy where one includes: “ _married_ ” and “ _living together as married_ ”). These are all variables which have

> 11Note that for countries that changed currency during the period considered (adoption of the EURO or USD or introduction of a new local currency) the IMF data use only the latest currency. This meant that we had to transform first the income values from our database into the same currencies used by the IMF using the appropriate exchange rates for each currency and each year and only then apply the constant, USD and PPP transformations.

> 12For the selection of the most appropriate Gini, we followed indications provided by Gruen and Klasen (2008). According to tests conducted by these authors on the Gini WIDER database: “ _Gini coefficients based on expenditures or consumption are significantly lower than based on incomes, and those based on disposable incomes are also significantly lower than those based on gross incomes, particularly in OECD countries._ ” (p. 219).

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