Tanzania and Nigeria, have the two highest levels of average happiness, yet both have much lower average life
satisfaction—indeed, Tanzania reported the lowest average satisfaction of any country.17
This apparent noise in the happiness-GDP link partly explains why earlier analyses of subjective well-being data
have yielded mixed results. We reran both the happiness and life satisfaction regressions with Tanzania and Nigeria
removed, and it turns out that these outliers explain at least part of the puzzle. In the absence of these two countries, the
well-being-GDP gradients, measured using either life satisfaction or happiness, turn out to be very similar. Equally, in
these data the correlation between happiness and GDP per capita remains lower than that between satisfaction and GDP
per capita.
To better understand whether the happiness-GDP gradient systematically differs from the satisfaction-GDP
gradient, we searched for other data collections that asked respondents about both happiness and life satisfaction. Figure 6
brings together two such surveys: the 1975 Gallup-Kettering survey and the First European Quality of Life Survey,
conducted in 2003. In addition, the bottom panels of Figure 6 show data from the 2006 Eurobarometer, which asked about
happiness in its survey 66.3 and life satisfaction in survey 66.1. In each case the happiness-GDP link appears to be
roughly similar to the life satisfaction-GDP link, although perhaps, as with the World Values Survey, slightly weaker.
Table 1 formalizes all of the analysis discussed thus far with a series of regressions of subjective well-being on
log GDP per capita, using data from the Gallup World Poll, all four waves of the World Values Survey, and the Pew
Global Attitudes Survey. The coefficient on log GDP per capita is reported along with its standard error. The first column
reports coefficient estimates from ordered probit regressions of individual well-being on the natural log of real GDP per
capita, with robust standard errors clustered by country; the second column adds controls for gender and a quartic in age
and its interaction with gender. The third column reports the results of a two-stage process: in the first stage we
aggregated the data to the country level by running an ordered probit regression of subjective well-being on country fixed
effects, which we interpret as a measure of average national happiness. In the second stage we estimated an ordinary least
squares regression of these country fixed effects on log GDP per capita. The coefficient from this second regression is
reported in the third column of Table 1. In all the data sets examined, estimates of the relationship obtained from the
respondent-level analysis are similar to that obtained through the two-stage process. Moreover, each of these datasets
yields remarkably similar estimates of the subjective well-being-GDP gradient, typically centered around 0.4.
The regressions reported in the first three columns of Table 1 are performed on the complete sample of countries
for each survey; the samples in the remaining two columns consist only of countries with GDP per capita above or below
$15,000 (in 2000 dollars), using the same two-stage process as in the third column, to allow us to assess whether the well-
17 One might suspect that survey problems are to blame, and indeed, the survey notes for Tanzania suggest (somewhat opaquely) that
“There were some questions that caused problems when the question was translated, especially questions related to... Happiness
because there are different perceptions about it.” We are not aware of any other happiness data for Tanzania, but note that in the 2002
Pew survey Tanzania registered the second-lowest level of average satisfaction among forty-four countries (Figure 3). The high levels
of happiness recorded in Nigeria seem more persistent: Nigeria also reported the eleventh-highest happiness rating in the 1994-99
wave of the World Values Survey, although it was around the mean in the 1989-93 wave.
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