In the rest of the paper we test how these different factors affect inference about the relation between subjective well-being and income inequality. The list of factors is nonexhaustive and we do not pretend to cover in this paper all possible causes of empirical heterogeneity. However, if the factors listed above contribute to explain such heterogeneity, then any inference from any study on the relation between happiness and inequality is context specific and cannot be generalized to other contexts. On the contrary, if life satisfaction and income inequality are strongly correlated, then the significance of this relation should persist under different specifications of the life satisfaction equation and the sign of this relation should be consistent irrespective of the factors listed.

# **4 Data, model and variables**

The _data set_ adopted has been compiled aggregating all rounds of the European and the World values surveys carried out between 1981 and 2004.<sup>7</sup> These surveys question individuals worldwide on happiness, personal values, social attitudes and individual attributes and include questions on income and inequality. The version of the data set we use is a 2006 version which contains a total of 267,870 individuals, 1,349 regions and 84 countries where each country has been surveyed from a minimum of one to a maximum of four times. Table A2 in the annex provides details on countries, years and number of observations.

We also merged this data set with two other variables: GDP per capita at Purchasing Power Parity (PPP) extracted from the IMF world economic outlook database<sup>8</sup> and the Gini coefficient extracted from the United Nations University, World Institute for Development Economics Research (UNU-WIDER) database on inequality.<sup>9</sup> We use GDP per capita to control for countries wealth and the UNU-WIDER Gini to adopt an alternative measure of income inequality independent of the database we use.

As a benchmark for our analysis, we use what we could call a ‘standard’ model in happiness studies that combines cross-country and longitudinal data (see for example Alesina et al., 2004). Let _H_ = Subjective well-being; _X_ = Income; _I_ = Income inequality; _R_ = Relative income; _W_ = A measure of countries’ wealth; _C_ = A vector of control variables for individual characteristics; _T_ = A vector of country dummies; _Y_ = A vector of case of welfare studies, if information on household welfare is very homogeneous within clusters, this is an essential information to decide on the use of clusters. Therefore, in the absence of complete and reliable data information, the least risky choice would be to use both robust and cluster options while the most transparent choice would be to compare and discuss results with and without robust and cluster options. 7Data can be freely downloaded from: http://www.jdsurvey.net. We are grateful to the Values Study Group and World Values Survey Association for creating and making accessible the EUROPEAN AND WORLD VALUES SURVEYS FOUR-WAVE INTEGRATED DATA FILE, 1981-2004, (v.20060423, 2006). Aggregate File Producers: An ́alisis Sociol ́ogicos Econ ́omicos y Pol ́ıticos (ASEP) and JD Systems (JDS), Madrid, Spain/Tilburg University, Tilburg, The Netherlands. Data Files Suppliers: Analisis Sociologicos Economicos y Politicos (ASEP) and JD Systems (JDS), Madrid, Spain/Tillburg University, Tillburg, The Netherlands/ Zentralarchiv fur Empirische Sozialforschung (ZA), Cologne, Germany:) Aggregate File Distributors: An ́alisis Sociol ́ogicos Econ ́omicos y Pol ́ıticos (ASEP) and JD Systems (JDS), Madrid, Spain/Tillburg University, Tilburg, The Netherlands/Zentralarchiv fur Empirische Sozialforschung (ZA) Cologne, Germany.

> 8Wired at www.imf.org/data.

> 9Version ’WIID2C’ wired at: http://www.wider.unu.edu.

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