The use of Subjective Wellbeing (SWB) measures in economics research has grown markedly (Kahneman and Krueger 2006). This has come about for at least two reasons. First, the measures have been systematically validated as reliable for examining a range of questions. Second, economists have long relied on income as a proxy for wellbeing. However, research shows that there are potentially large slippages between economic indicators and wellbeing (Diener and Seligman 2004). Thus, SWB measures have become an important alternative proxy for wellbeing. Indeed, SWB measures have also caught the attention of policy makers. The OECD launched the Better Life Index in 2011 as an alternative wellbeing measure; and the former French President Nicolas Sarkozy formed the Stiglitz Commission in 2008 to identify the limits of gross domestic product (GDP) as a measure of wellbeing and to identify alternative measures (Stiglitz, Sen, and Fitoussi 2010).

When studying the distribution of income, economists have long recognized the importance of examining measures of central tendency and dispersion, as the latter are necessary to understand income inequality and poverty (Stiglitz, Sen, and Fitoussi 2010). Thus, there is a vast literature analyzing both the first and second moments of the distribution of income. For example, the Lorenz and Kuznets curves try to model the distribution of income, and the Gini coefficient summarizes the entire distribution in a scalar (see Atkinson 1970; Gastwirth 1972; Gini 1921; Gottschalk and Smeeding 1887; Kuznets 1955; and Lorenz 1905). In contrast, the vast majority of SWB research focuses on mean SWB. Given the current interest in SWB measures, and recognizing that the entire distribution of SWB merits study, we believe it is important to study SWB inequality (dispersion) as well as mean SWB.

In this paper, we contribute to the emerging SWB literature by investigating the relationship between economic growth and SWB inequality using data from the World Values Survey (WVS) and the World Bank’s World Development Indicators (WDI). The results suggest that economic growth is inversely related to SWB inequality in cross-sectional analysis. There is also some evidence from time-series analysis that countries that experience greater economic growth rates also experience the greater decreases in SWB inequality, although this pattern does not hold for two of the fastest growing countries in the dataset. This is important because it indicates that economic growth may reduce SWB inequality over time, even if it does not increase mean SWB. The paper proceeds as follows. Section II reviews the related literature. Section III describes the data. Section IV presents the results. Section V concludes.