small for the dependent and control variables. There are instead noticeable differences for the two Gini, GDP per capita and income between sample ‘1’ and sample ‘2’. This is due to the fact that some countries and years are lost with the smaller sample and this has an impact on the mean of key variables, particularly those variables estimated at the country/year level. For the discussion that follows, it is important to keep in mind that sample ‘2’ is a sub-sample of sample ‘1’ and represents a sub-set of countries and years. Table A2 in the annex provides details on the samples considered in this paper.

We start by estimating the full model as described in equation [1] and including robust and regional clusters and country and year fixed effects. A robust estimator allows to relax the assumption that regressors and error term are identically distributed whereas the regional cluster option let us relax the assumption that individual observations within regions are independent. Country and year dummies control for country heterogeneity and time dependence. This is what we could call a standard approach when working with a pooled sample of world citizens. It is also the approach followed by Alesina et al. (2004) that we said we use as a benchmark for our tests. The exercise is repeated for the Gini WVS and Gini WIDER and for the two samples considered.<sup>15</sup> Results are shown in Table 1. The coefficients for both Gini are negative and significant in both samples indicating that higher income inequality is associated with lower life satisfaction. The fact that this result is consistent across different Gini and different samples shows that results are robust. Sign and significance concord whether we use the Gini WVS (constructed with mid-class values from the ten-steps income variable contained in the World and European values surveys) or the Gini WIDER (imported from the UNU-WIDER database). They also concord if we use the Gini WIDER constructed with different income measures or the Gini WIDER constructed only with disposable income and they concord if we use the larger or smaller samples. The standard model provides consistent evidence of a negative association between life satisfaction and income inequality.

It is important to note, however, that the Gini is highly collinear with other independent variables used in the model. This is visible in all four equations considered as indicated by the high levels of the Variance Inflation Factor (VIF) reported on the bottom of Table 1.<sup>16</sup> When we tested for collinearity of the Gini with other variables, we found that this is due to GDP per capita and to most countries and years dummies included into the model. We found a large and significant correlation between the two Gini and GDP per capita (Pearson correlation coefficient of +0.6 for the Gini WVS and +0.5 for the Gini WIDER) and we also found these correlations to be high with most country and

> 15Note that we are not trying to replicate Alesina et al. results, we simply use the same form of equation as also used in other contributions and with different data. Our purpose is to test this general form of equation under different specifications.

> 16The VIF is estimated as 1/(1-R2) from an OLS regression where the dependent variable is the Gini and the independent variables are all other regressors used in the equations. This is perhaps the most popular test for collinearity. A VIF equal to one indicates no collinearity while values higher than one indicate higher degrees of collinearity. Values of five or more are generally considered as indicators of high levels of multicollinearity.

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