exogenous to the survey used for the life satisfaction estimations, others use Gini calculated from within the surveys used. For example, Alesina at al. (2004) use the Gini taken from the Deninger and Squire database<sup>5</sup> and Helliwell (2003) uses the Gini taken from a World Bank database whereas Senik (2004) and Clark (2003) calculate the Gini from within their own surveys.

This choice is mostly dictated by the data. The first two studies are cross-country studies that make use of values surveys. Values surveys such as the World Values Surveys, the European Values Surveys and the US Social Survey do not hold information on individual incomes in continuous form. Income is typically reported in terms of income classes. When these surveys are used, researchers either transform income classes into comparable monetary values or they draw on external sources for measures of inequality. This explains the choice of ‘exogenous’ inequality variables. The second set of studies uses instead longitudinal data on single countries such as Russia, the UK or Germany where individual income is typically available in continuous form. The shortcoming here is that only a few panel surveys have questions on life satisfaction and one also needs many years or split the sample into sub-groups to make some inference on the role of inequality.

Combining longitudinal and cross-country data can also lead to different conclusions. Suppose that we could use an ‘endogenous’ and an ‘exogenous’ income Gini simultaneously. Suppose also that both samples on which the Ginis are estimated are representative of the population under study. The two Gini may, in fact, be different in value either because the income distribution cannot be identical in the two samples or because the welfare measure is different (such as income as opposed to consumption). Moreover, when the two Gini are compared across countries and time, the cross-section and longitudinal distributions of such Gini may also be very different affecting the covariance between income inequality and subjective well-being.

Another factor may relate to _different tastes for inequality_ across different population groups. This may relate to different income groups, to different groups partitioned on other criteria such as region, gender, ethnic group or others or to different groups of countries. Some population groups are more sensitive to or have opposite tastes for inequality than other population groups and it may be difficult to isolate which groups behave homogeneously. When studies do not disaggregate by relevant group the net effect may be non significant. Moreover, people in different countries may have very different tastes for inequality due to cultural and other factors and this effect may overlap with the effect due to the different wealth of countries. Poor and rich countries may have different tastes for inequality.

A different set of explanations for the empirical heterogeneity relates to econometric factors. The _choice of key regressors_ is a first critical choice. Combining different sets of regressors can lead to different results especially if these regressors include other measures of income or relative income which are likely to be correlated with the inequality measure under study. For example, the Gini index can be expressed as a function of income,

> 5For the Europeans countries considered.

5