Clark (2003) using the British Household Panel Survey finds a positive correlation between happiness and inequality for the employed population. A study by Alesina et al.(2004) found that individuals tend to be less happy if inequality is high but that this effect is stronger in the EU than in the US. Also, the poor and left-wing people in the EU are less happy if inequality is high while this phenomenon is not visible in the US. Graham and Felton (2006) looked at Latin American countries and found that inequality (measured in terms of relative wealth) made people in upper quintiles happier and those in the poorest quintile less happy but they also find that the Gini coefficient is non significant in a happiness equation. Senik (2004) does not find a significant correlation between happiness and inequality for Russia using the Russian Longitudinal Monitoring Survey. A study by Helliwell (2003) finds no evidence that income inequality is correlated with happiness and, according to Veenhoven (1996) “ _Income inequality in nations appears almost unrelated to final quality of life as measured by average happiness_ (...)” (p. 34).

Table A3 in the annex provides more detailed information on the cited literature in chronological order. Leaving aside the first study by Morawetz et al. (1977), we can observe some similarities and dissimilarities. The data sets used in these studies are all different with the exception of two papers which both use the US-GSS study. Three studies use longitudinal panels of individual observations, four studies use cross-country studies with multiple years and one study uses a cross-country study with one year. The estimation models used can be ordered logit, ordered probit or OLS and this is a normative choice rather than a choice dictated by the data. The measure of inequality is the Gini for all studies except for part of the Hagerty (2000) study. Some papers estimate the Gini from the data set used while others extract the gini from other data sets. The Gini can also be estimated for countries, regions, Primary Sample Units (PSU) or particular reference groups. All studies use, in conjunction with the inequality measure, one or more measures of income such as income (in continuous or categorical form), lagged income, relative income or measures of countries’ wealth. Most studies use country or regional fixed effects but two studies do not while years fixed effects are used by all studies with longitudinal data except one. Finally, some papers report the use of robust standard errors and/or cluster estimations while other papers do not report how the standard errors have been estimated. In the next section, we will put forward some hypotheses on how these diversities in choices may contribute to explain diversity in results.

# **3 Some hypotheses**

There are several factors that may lead to controversial empirical results on the correlation between happiness and income inequality. Some of these factors relate to the specific data available or to the choice of the inequality measure made by the researcher. Other factors relate to econometric choices that may or may not relate to the data at hand. We discuss these two groups of hypotheses in turn.

The _choice of the inequality measure_ is a first critical choice. Some studies use Gini 4