about their happiness provide a shorter scale of answers (such as “very happy,” “pretty happy,” and “not so happy”) than
do those asking typical life satisfaction questions (which often use the “ladder” technique described above).
A final measurement issue to consider is the likely functional form of the relationship between subjective well-
being and income. Most early studies considered the relationship between the level of absolute income and the level of
happiness, and thus often found a curvilinear relationship. In some cases the lack of evidence of a clear linear relationship
between GDP per capita and happiness led to theories of a satiation point, beyond which more income would not increase
happiness. A more natural starting point might be to represent well-being as a function of the logarithm of income rather
than absolute income. And indeed, recent research has shown that within countries “the supposed attenuation at higher
income levels of the happiness-income relation does not occur when happiness is regressed on log income, rather than
absolute income” (Easterlin, 2001, p. 468). However, if happiness is linearly related to log income in the within-country
cross section, then cross-country studies should also examine the relationship between average levels of subjective well-
being and average levels of log income. If economic development raises individual incomes equiproportionately, then
average log income will rise or fall in tandem with the log of average income. Thus, most of our analysis assesses the
relationship across countries between well-being and the log of GDP per capita, which is (surprisingly enough) a
departure from much of the literature.6 Throughout our analysis we make heavy use of bivariate scatterplots and
nonparametric regression techniques in order to allow the reader to assess the appropriate functional forms visually.
Finally, as in the existing literature, our analysis of the relationship between happiness and income involves an
assessment of correlations rather than an attempt to establish tight causal links. Thus, our aim is simply to sort out the
stylized facts about the link between income and well-being. Several interesting variants of the question could be asked—
such as whether it is GDP, broader measures of economic development, or alternatively, changes in output or in
productivity that drive happiness. Unfortunately, we lack the statistical power to resolve these questions.
III. Cross-Country Comparisons of Income and Well-Being
In his seminal 1974 paper Easterlin asked whether “richer countries are happier countries” (1974, p. 104).
Examining two international datasets, he found a relationship across countries between aggregate happiness and income
that he described as “ambiguous” and, although perhaps positive, small (ibid., p.108). Subsequent research began to show
a more robust positive relationship between a country’s income and the happiness of its people, leading Easterlin to later
conclude that “a positive happiness-income relationship typically turns up in international comparisons” (1995, p. 42).
However, this relationship has been argued as prevailing only over low levels of GDP per capita; once wealthy countries
have satisfied basic needs, they have been described as on the “‘flat of the curve,’ with additional income buying little if
any extra happiness” (Clark, Frijters, and Shields, 2008, p. 96). Although the literature has largely settled on the view that
6 Previous authors examining the relationship between well-being and log GDP include Easterlin (1995), Leigh and Wolfers (2006),
and Deaton (2008); however, explicit discussion of the appropriate functional form is quite rare.