Table B1 compares the main coefficient estimates from Tables 1-3 with that which we obtain by including all
country-wave observations. The first panel shows the between-country analysis, with columns 1 and 3 reproducing the
results shown in column 3 of Table 1 for life satisfaction and happiness respectively. Since the excluded observations
typically represent a group with above average income and education (and hence, likely higher happiness), our
expectation is that incorporating these countries will yield lower estimates of the well-being-income gradient. These
estimates are shown in column 2 for life satisfaction, and column 4 for happiness. The first and the last wave show little
impact on the estimated coefficient, as the samples are largely the same (only Argentina was excluded from the first wave
and only Chile and Egypt from the last). The 1989-93 and 1994-99 waves yield larger differences as 6 countries were
excluded from the former and 8 from the latter. As expected, including these biased samples attenuates the estimated
coefficients substantially. Yet in all cases the estimated coefficient remains positive and statistically significant.
The second panel examines the impact of including the unrepresentative national samples on the within-country
cross-sectional estimates. The first and third column reproduces the coefficients from column 2 of Table 2. Despite the
truncation of the poor in these samples, the fact that subjective well-being is linearly related to log income suggests that
excluding a portion of the income distribution will not bias the coefficient estimates. Moreover, as we show in Figure 10,
most countries have a subjective well-being income gradient of around 0.4 and there is little systematic variation in that
gradient. As such we should expect little difference in the Table 2 estimates that are obtained when we include
observations from all of the country waves. Indeed, the estimated coefficients with the excluded samples, again shown in
columns 3 and 4, are little different from those obtained without these countries.
Finally, the last panel reproduces the estimates shown in Table 3, column 2 in which we analyze the World
Values Survey as a country-wave panel dataset using the country aggregated (macro) data. The Table 3 estimates are
shown in columns 1 and 3, while the comparison estimates, in which unrepresentative country-wave observations are
included, are shown in columns 2 and 4. The first row shows the simple bivariate well-being-GDP relationship, and
hence pools both within-country and between country variation. These results are little impact by the inclusion of the
unrepresentative country-wave observations. The second row includes country fixed-effects in these regressions and
therefore isolates the within-country time series variation. The inclusion in columns 2 and 4 of countries whose sample
becomes more representative as GDP grows, reduces the estimated coefficient. The third row adds controls for each wave
of the World Values Survey in addition to the country controls. Again, the inclusion of the non-representative samples
reduces the estimated coefficients. Finally, the last two columns considers first differences of consecutive country-wave
observations and long-differences, including only the first and last observation for each country. Excluding differences
involving countries where the survey frame changed yields positive robust estimates of a positive relationship between
life satisfaction and income and happiness and income over time. Not surprisingly, including countries whose samples are
becoming increasingly representative of the poor over time decreases these estimates substantially. Including the non-
comparable intertemporal variation in well-being also yields less precise estimates. Even when these countries are
included, the results are still roughly consistent with the null that the time series well-being-income gradient is close to the
0.4 range obtained from our between-country and within-country analyses.
Appendix—5