We present our analysis chronologically, so that the reader may see how the literature has progressed. To allow
easy visual comparisons, we use a similar scale when graphing happiness and GDP and try to keep this scale consistent
throughout the paper.
The top row of graphs in Figure 1 shows the three earliest cross-country comparisons of subjective well-being of
which we are aware. Each of these comparisons is based on only four to nine countries, which were similar in terms of
economic development. As a consequence, these comparisons yield quite imprecise estimates of the link between
happiness and GDP. We have provided two useful visual devices to aid in interpretation: a dashed line showing the
ordinary least squares regression line (our focus), and a shaded area that shows a central part of the happiness distribution,
with a width equal to the cross-sectional standard deviation.
The graphs in the second row of Figure 1 show the cross-country comparisons presented by Easterlin (1974).10
Analyzing the 1960 data, Easterlin argues that “the association between wealth and happiness indicated by Cantril’s
international data is not so clear-cut.... The inference about a positive association relies heavily on the observations for
India and the United States” (ibid., p. 108). 11 Turning to the 1965 World Survey III data, Easterlin argues that “The results
are ambiguous.. If there is a positive association between income and happiness, it is certainly not a strong one” (ibid.).
Rather than highlighting the positive association suggested by the regression line, he argues that “what is perhaps most
striking is that the personal happiness ratings for 10 of the 14 countries lie virtually within a half a point of the midpoint
rating of 5 [on the raw 0-10 scale].. The closeness of the happiness ratings implies also that a similar lack of association
would be found between happiness and other economic magnitudes” (ibid., p. 106). The clustering of countries within the
shaded area on the chart gives a sense of this argument. However, the ordered probit index is quite useful here in
quantifying the differences in average levels of happiness across countries relative to the within-country variation. Unlike
the raw data, the ordered probit suggests quite large differences in well-being relative to the cross-sectional standard
deviation. Similarly, the use of log income rather than absolute income highlights the linear-log relationship. Finally,
Easterlin mentions briefly the 1946 and 1949 data shown in the top row of Figure 1, noting that “the results are similar.
if there is a positive association among countries between income and happiness it is not very clear” (ibid., p. 108).
Although the correlation between income and happiness in these early surveys is not especially convincing, this
does not imply that income has only a minor influence on happiness, but rather that other factors (possibly including
measurement error) also affect the national happiness aggregates. Even so, three of these five datasets suggest a
statistically significant relationship between happiness and the natural logarithm of GDP per capita. More important, the
point estimates reveal a positive relationship between well-being and income, and a precision-weighted average of these
five regression coefficients is 0.45, which is comparable to the sort of well-being-GDP gradient suggested in cross-
sectional comparisons of rich and poor people within a society (a theme we explore further below).
10 We plot the ordered probit index, whereas Easterlin graphs the mean response..
11 Following Cantril (1965), Easterlin also notes that “the values for Cuba and the Dominican Republic reflect unusual political
circumstances—the immediate aftermath of a successful revolution in Cuba and prolonged political turmoil in the Dominican
Republic.”