the entire sample.25 Overall, the average well-being-income gradient is 0.38, with the majority of the estimates between
.25 and .45 and 90 percent are between 0.07 and 0.72. In turn, much of the heterogeneity likely reflects simple sampling
variation: the average country-specific standard error is 0.07, and 90 percent of the country-specific regressions have
standard errors between 0.04 and 0.11.
As an alternative representation of these data, Figure 11 directly compares within-country and between-country
estimates of the well-being-income gradient. Each solid circle plots the GDP per capita and average well-being of a single
country (hence the circles suggests the between-country well-being-GDP gradient), and the slope of the arrows, fitted to
each circle, represents the slope of the well-being-income gradient estimated within that country. Not only are the slopes
of the arrows remarkably similar across countries; they are also typically quite close to the between-country well-being-
GDP slope (the thick dashed line). Figure 12 repeats this exercise using data from the 1999-2004 wave of the World
Values Survey. The household income data in that survey are not as uniform as those in the Gallup World Poll, requiring
us to omit several countries.26 However, for the countries with sufficient data, the pattern that emerges is similar to that
seen in the Gallup data. Repeating the same exercise for the Pew data also yields similar findings (not shown).
Table 2 pools the various national surveys so as to arrive at a summary estimate of the within-country well-being-
income gradient. Thus, for each international dataset, we perform an ordered probit of subjective well-being on log
household income, controlling for country (or, for the World Values Survey, country by wave) fixed effects, which serve
to control for not only the between-country variation in GDP per capita, but also variation in measured income due to
differences in exchange rates, purchasing power or other country-specific factors. The first column shows the results from
a simple ordered probit of well-being on log household income, controlling for these fixed effects; the second column
adds controls for gender, a quartic in age, and the interaction of these variables. Comparison of these results with the
corresponding between-country estimates in Table 1 shows them to be roughly similar in magnitude, although as seen in
the figures, in most cases the between-country estimates are larger than the within-country estimates, which are centered
around 0.3.
An important issue in considering the within-country cross-sectional relationship between income and subjective
well-being is the extent to which measured income differences at a point in time reflect differences in permanent income
versus transitory shocks. If people are able to smooth their consumption, then subjective well-being should change little
with transitory income changes, and permanent shocks should have a much larger impact. The variation in GDP per capita
between countries is likely dominated by variation in permanent income, whereas the variation in annual income within a
population likely reflects both permanent and transitory shocks.
25 As with the GSS, our various data sources typically report income in categories, rather than as a continuous variable. We follow the
same method for each of our datasets, fitting interval regressions to these income data on the assumption that income follows a log-
normal distribution. If a dataset contains a bottom income category of zero, we combine it with the succeeding income category. We
perform these regressions separately for each country-wave of each dataset.
26 In many cases, particularly in earlier waves of the World Values Survey, household income is reported only as an ordinal variable
with no information regarding the underlying cardinal measure.
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