The robust and cluster choices can make a difference to inference about the Gini, especially if the sample is small and the number of countries and years is reduced. Different choices do not have an impact on the sign or size of the coefficients but can have an impact on standard errors and significance levels. In Table 3 we see that when the robust and cluster estimations are introduced (eq. 2 and 3) the z-statistics can visibly change. This is particularly true for the Gini WIDER and for the smaller sample that considers a restricted number of countries and years (sample ‘2’).

More importantly, introducing or removing country and year fixed effects can alter inference on inequality remarkably. When country fixed effects or both country and year fixed effects are introduced, all Gini are negative and significant (eq. 4, 7, 8, 10, 12, 13, 15, 16). This is what we found in Tables 1 and 2 where we used country and year fixed effects for all equations. When country and year fixed effects are removed or year fixed effects are used alone, the Gini turns positive and significant or non-significant (eq. 1, 2, 3, 5, 6, 9, 11 and 14). In particular, it would seem that country fixed effects have an important influence on multicollinearity and significance levels while year fixed effects have a relevant role in changing the sign of the Gini. Indeed, the VIF values for the Gini are small only when country fixed effects are removed and the Gini turns positive only when year fixed effects are used alone. This phenomenon applies equally to both Gini and both samples considered indicating that this is not a phenomenon dependent on the choice of these factors.

The sensitivity of the Gini coefficient to country and year fixed effects may relate to various factors. It may be for example that there is moderate within countries variation of the Gini over time or, if there is variation, time trends are similar across countries. The World Values Survey is characterized by many countries, few years for each country (from one to four years depending on the country) and several years in between any two consecutive observations within countries. Changes in the Gini can be significant but, among the countries with more than one observation, about half have decreasing Ginis, one has a Gini that goes up and down and the rest of the countries have increasing Ginis. Therefore, the Ginis do not move together over time across countries whereas the within countries variation is limited by the number of years available for each country.

Having more data points for the Gini may help to better capture the relation between happiness and inequality but this is not always the case. For example, Senik (2004) finds non significant coefficients for the Gini when she estimates the Gini at the national, regional or PSU levels. Also, in a previous version of the paper, we estimated the Gini WVS at the regional level and compared the coefficient of this variable with that of the Gini estimated at the country level. We found that the Gini region was even more sensitive than the Gini country to the use of country and year fixed effects.

In substance, there is a trade-off between the inclusion of country and year dummies, which allows to control for unobserved factors but generates collinearity, and the exclusion of these dummies, which fixes collinearity but increases the problem of unobserved heterogeneity. Moreover, with smaller samples, increased standard errors can be generated by 14